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J MCP ■ Journal of Mana g ed Care & Specialty Pharmac y® ■ March 2015 Journal of Managed Care & specialty Pharmacy Di ab et es Iss ue ® Volume 21 ■ Number 3 ■ March 2015 ■ Vol. 21, No. 3 ■■ SPECIALTY PHARMACY Assessment of Pharmacists’ Views on Biosimilar Naming Conventions Sara Fernandez-Lopez, PhD, MBA; Denise Kazzaz, BA; Mohamed Bashir, MHA; and Trent McLaughlin, BSc, PhD ■ Pa g es 179-260 Patterns of Medication Utilization and Costs Associated with the Use of Etanercept, Adalimumab, and Ustekinumab in the Management of Moderate-to-Severe Psoriasis Steven R. Feldman, MD; Yang Zhao, PhD; Prakash Navaratnam, RPh, MPH, PhD; Howard S. Friedman, PhD; Jackie Lu, PharmD, MS; and Mary Helen Tran, PharmD, MBA Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study R. Brett McQueen, PhD; Terrie Livingston, PharmD; Timothy Vollmer, MD; John Corboy, MD; Brieana Buckley, PharmD; Richard Read Allen, MS; Kavita Nair, PhD; and Jonathan D. Campbell, PhD ■■ DIABETES MANAGEMENT Resource Utilization and Costs Associated with Using Insulin Therapy Within a Newly Diagnosed Type 2 Diabetes Mellitus Population Kelly Bell, PharmD, MSPhr; Shreekant Parasuraman, PhD; Aditya Raju, BPharm, MS; Manan Shah, PharmD, PhD; John Graham, PharmD; and Melissa Denno, PharmD, MS Association of Visit-to-Visit Variability of Hemoglobin A1c and Medication Adherence Ambili Ramachandran, MD, MS; Michael Winter, MPH; and Devin M. Mann, MD, MS Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database Sumesh Kachroo, PhD; Hugh Kawabata, MS; Susan Colilla, PhD, MPH; Lizheng Shi, PhD; Yingnan Zhao, PhD; Jayanti Mukherjee, PhD; Uchenna Iloeje, MD, MPH; and Vivian Fonseca, MD ■■ TRANSITIONS OF CARE Pharmacist-Coordinated Multidisciplinary Hospital Follow-up Visits Improve Patient Outcomes Jamie J. Cavanaugh, PharmD, CPP, BCPS; Kimberly N. Lindsey, PharmD; Betsy B. Shilliday, PharmD, CDE, CPP, BCACP; and Shana P. Ratner, MD Journal of Managed Care & Specialty Pharmacy® Previously published as JMCP, the Journal of Managed Care Pharmacy® A Peer-Reviewed Journal of the Academy of Managed Care Pharmacy ■ www.jmcp.org ■ www.amcp.org AMCP — the place for specialty pharmacy! SPECIALTY PHARMACY AC A D E M Y O F M A N AG E D C A R E P H A R M AC Y ’ S CONFERENCE A P R I L 7 – 8 | S A N D I E G O, C A Get real-world insights. Learn from leading experts leaders. S A N Dand I E G O, industry CA A P R 7 – 8 , 2015 Gain knowledge, strategies and connections to thrive in this challenging arena. AMCP’s 2nd annual Specialty Pharmacy Conference kicks off AMCP’s 27th Annual Meeting & Expo with a global overview of the fastest-growing sector in pharmaceuticals. The Specialty Pharmacy Conference begins Tuesday, April 7, and ends Wednesday, April 8. o EDUCATIONAL SESSIONS FEATURED SPEAKERS AND MUCH MORE! Topics include: F. Randy Vogenberg, PhD, RPh Networking lunch and access to the specialty pharmaceuticals pipeline session during AMCP’s Annual Meeting & Expo included. For full details, visit www.amcpmeetings.org and click on the Specialty Pharmacy Conference tab. n Specialty Drug Trend Reports and Benefit Design n Oncology Specialty Drug Management n Emerging Sites of Care and Specialty Networks n The Employer’s Perspective Principal, Institute for Integrated Healthcare; Partner, Access Market Intelligence Joel Owerbach, PharmD Consultant, Pharmaceutical Strategy-Solutions Kevin Colgan Associate Vice President, Specialty Pharmacy, University Health System Consortium Cheryl Larson 3 CPE CREDIT The Academy of Managed Care Pharmacy (AMCP) is accredited by the Accreditation Council for Pharmacy Education (ACPE) as a provider of continuing pharmacy education. Pharmacists can earn a maximum of 3.75 contact hours of CPE credit for AMCP’s Specialty Pharmacy Conference (not including satellite symposia held in conjunction with the meeting). Please visit www.amcpmeetings.org for specific session details. / Vice President, Midwest Business Group on Health Register n o w! This is the perfect complement to the Annual Meeting’s specialty pharmacy programming— save when you register for both events! www.amcpmeetings.org A new option for type 2 diabetes therapy starts here Trulicity™ is a glucagon-like peptide-1 receptor agonist (GLP-1 RA) that is indicated as an adjunct to diet and exercise to improve glycemic control in adults with type 2 diabetes mellitus. Select Important Safety Information Limitations of Use: Not recommended as first-line therapy for patients inadequately controlled on diet and exercise. Has not been studied in patients with a history of pancreatitis; consider another antidiabetic therapy. Not for the treatment of type 1 diabetes mellitus or diabetic ketoacidosis. Not a substitute for insulin. Has not been studied in patients with severe gastrointestinal disease, including severe gastroparesis. Not for patients with pre-existing severe gastrointestinal disease. Has not been studied in combination with basal insulin. Please see Important Safety Information including Boxed Warning about possible thyroid tumors including thyroid cancer and Brief Summary of Prescribing Information on following pages. WARNING: RISK OF THYROID C-CELL TUMORS In male and female rats, dulaglutide causes a dose-related and treatment-duration-dependent increase in the incidence of thyroid C-cell tumors (adenomas and carcinomas) after lifetime exposure. It is unknown whether Trulicity causes thyroid C-cell tumors, including medullary thyroid carcinoma (MTC), in humans as human relevance could not be determined from clinical or nonclinical studies. Trulicity is contraindicated in patients with a personal or family history of MTC and in patients with Multiple Endocrine Neoplasia syndrome type 2 (MEN 2). Routine serum calcitonin or thyroid ultrasound monitoring is of uncertain value in patients treated with Trulicity. Counsel regarding the risk factors and symptoms of thyroid tumors. A new once-weekly GLP-1 RA therapy is now approved 1 Trulicity™ offers proven A1C reduction and once-weekly dosing in the Trulicity pen.1 Trulicity is a new option for adult patients with type 2 diabetes who need more control than oral medications are providing.1 To learn more about Trulicity, visit www.trulicity.com or contact your Lilly Account Manager. Important Safety Information WARNING: RISK OF THYROID C-CELL TUMORS In male and female rats, dulaglutide causes dose-related and treatment-duration-dependent increase in the incidence of thyroid C-cell tumors (adenomas and carcinomas) after lifetime exposure. It is unknown whether Trulicity causes thyroid C-cell tumors, including medullary thyroid carcinoma (MTC), in humans as human relevance could not be determined from clinical or nonclinical studies. Trulicity is contraindicated in patients with a personal or family history of MTC and in patients with Multiple Endocrine Neoplasia syndrome type 2 (MEN 2). Routine serum calcitonin or thyroid ultrasound monitoring is of uncertain value in patients treated with Trulicity. Counsel regarding the risk factors and symptoms of thyroid tumors. Trulicity is contraindicated in patients with a prior serious hypersensitivity reaction to dulaglutide or any of the product components. Risk of Thyroid C-cell Tumors: Counsel patients regarding the risk of medullary thyroid carcinoma and the symptoms of thyroid tumors (eg, a mass in the neck, dysphasia, dyspnea, persistent hoarseness). Patients with elevated serum calcitonin (if measured) and patients with thyroid nodules noted on physical examination or neck imaging should be referred to an endocrinologist for further evaluation. Pancreatitis: Has been reported in clinical trials. Observe patients for signs and symptoms including persistent severe abdominal pain. If pancreatitis is suspected discontinue Trulicity promptly. Do not restart if pancreatitis is confirmed. Consider other antidiabetic therapy. Please see Important Safety Information continued on following page. DG92549 09/2014 PRINTED IN USA ©Lilly USA, LLC 2014. All rights reserved. Important Safety Information, continued Once-weekly Trulicity showed significant A1C reduction1 A1C reduction from baseline1-3 A1C reduction from baseline Hypoglycemia: The risk of hypoglycemia is increased when Trulicity is used in combination with insulin secretagogues (eg, sulfonylureas) or insulin. Patients may require a lower dose of the sulfonylurea or insulin to reduce the risk of hypoglycemia. 8.0 7.6 7.4 Pregnancy: There are no adequate and well-controlled studies of Trulicity in pregnant women. Use only if potential benefit outweighs potential risk to fetus. Nursing Mothers: It is not known whether Trulicity is excreted in human milk. A decision should be made whether to discontinue nursing or to discontinue Trulicity taking into account the importance of the drug to the mother. Pediatric Use: Safety and effectiveness of Trulicity have not been established and use is not recommended in patients less than 18 years of age. Please see Brief Summary of Full Prescribing Information including Boxed Warning about possible thyroid tumors including thyroid cancer on following pages. Please see Instructions for Use included with the pen. DG HCP ISI 18SEP2014 Trulicity™ is a trademark of Eli Lilly and Company and is available by prescription only. Other product/company names mentioned herein are the trademarks of their respective owners. -1.0 Trulicity™ (0.75 m (n=280; Baseline Injections: ~52 7.2 7.0 ‡ 6.8 -1.3* † -1.5* Trulicity™ (1.5 m (n=279; Baseline Injections: ~52 † 6.6 6.4 93% fewer injections 6.2 Baseline Week 13 Week 26 Placebo (n=141; Baseline A1C: 8.1%) Byetta® (10 mcg BID) (n=276; Baseline A1C: 8.1%) Injections: ~730/year Macrovascular Outcomes: There have been no clinical studies establishing conclusive evidence of macrovascular risk reduction with Trulicity or any other antidiabetic drug. Gastric emptying is slowed by Trulicity, which may impact absorption of concomitantly administered oral medications. Use caution when oral medications are used with Trulicity. Drug levels of oral medications with a narrow therapeutic index should be adequately monitored when concomitantly administered with Trulicity. In clinical pharmacology studies, Trulicity did not affect the absorption of the tested, orally administered medications to a clinically relevant degree. -0.5 Byetta® (10 mcg (n=276; Baseline Injections: ~73 7.8 Severe Gastrointestinal Disease: Use of Trulicity may be associated with gastrointestinal adverse reactions, sometimes severe. Trulicity has not been studied in patients with severe gastrointestinal disease, including severe gastroparesis, and is therefore not recommended in these patients. The most common adverse reactions reported in ≥5% of Trulicity-treated patients in placebo-controlled trials (placebo, Trulicity 0.75 mg and 1.5 mg) were nausea (5.3%, 12.4%, 21.1%), diarrhea (6.7%, 8.9%, 12.6%), vomiting (2.3%, 6.0%, 12.7%), abdominal pain (4.9%, 6.5%, 9.4%), decreased appetite (1.6%, 4.9%, 8.6%), dyspepsia (2.3%, 4.1%, 5.8%), and fatigue (2.6%, 4.2%, 5.6%). Placebo (n=141; Baseline 8.2 LS mean A1C (%) Renal Impairment: In patients treated with GLP-1 RAs there have been postmarketing reports of acute renal failure and worsening of chronic renal failure, sometimes requiring hemodialysis. A majority of reported events occurred in patients who had experienced nausea, vomiting, diarrhea, or dehydration. In patients with renal impairment, use caution when initiating or escalating doses of Trulicity and monitor renal function in patients experiencing severe adverse gastrointestinal reactions. 8.4 LS mean A1C (%) Hypersensitivity Reactions: Systemic reactions were observed in clinical trials in patients receiving Trulicity. Instruct patients who experience symptoms to discontinue Trulicity and promptly seek medical advice. 1-3 Trulicity™ (0.75 mg) (n=280; Baseline A1C: 8.1%) Injections: ~52/year Trulicity™ (1.5 mg) (n=279; Baseline A1C: 8.1%) Injections: ~52/year Data represent least-squares mean ± standard error. * Multiplicity-adjusted 1-sided P value <.025 for superiority of Trulicity vs Byetta for A1C. † Multiplicity-adjusted 1-sided P value <.001 for superiority of Trulicity vs placebo for A1C. Mixed model repeated measures analysis. After 26 weeks, placebo-treated patients were switched in a blinded fashion to Trulicity 1.5 mg or Trulicity 0.75 mg. Data represent least-squares mean ± standard error. ‡ American Diabetes Association recommended target goal. Treatment should be individualized.4 * Multiplicity-adjusted 1-sided P value <.025 for superiority of Trulicity vs Byetta for A1C. • • † 52-week, randomized,1-sided placebo-controlled 3 study of Trulicity vs placebo for A1C. Multiplicity-adjusted P value <.001phase for superiority (open-label assignment to Byetta or blinded assignment to Mixed model repeated measures analysis. Trulicity or placebo) of adult patients with type 2 diabetes treated with maximally tolerated metformin (≥1500 mg/day) After 26 weeks, placebo-treated and Actos® (up to 45 mg/day) patients were switched in a blinded fashion to Trulicity 1.5 mg or Trulicity 0.75 mg. American Diabeteswas Association recommended targetof goal. Treatment should be individualized. Primary objective to demonstrate superiority Trulicity 1.5 mg vs placebo on change in A1C from baseline at 26 weeks (-1.5% vs -0.5%, respectively; • difference of -1.1%; 95% CI [-1.2, -0.9]; multiplicity-adjusted 1-sided P value <.001; analysis of covariance using last observation carried forward); primary objective met ‡ 4 52-week, randomized, placebo-controlled phase 3 study (open-label assign assignment to Trulicity or placebo) of adult patients with type 2 diabetes tre tolerated metformin (≥1500 mg/day) and Actos (up to 45 mg/day) References Primary objective was to demonstrate superiority of Trulicity 1.5 mg vs plac from baseline at 26 weeks (-1.5% vs -0.5%, respectively; difference of -1.1%; 95 adjusted 1-sided P value <.001; analysis of covariance using last observatio objective met 1. •Trulicity [Prescribing Information]. Indianapolis, IN: Lilly USA, LLC; 2014. 2. Data on file, Lilly USA, LLC. TRU20140910A. 3. Data on file, Lilly USA, LLC. TRU20140919C. 4. American Diabetes Association. Standards of medical care in diabetes—2014. Diabetes Care. 2014;37(Suppl 1):S14-S80. References 1. Trulicity [Prescribing Information]. Indianapolis, IN: Lilly USA, LLC; 2014. 2. Data on file, Lilly USA, LLC. TRU20140910A. 3. Data on file, Lilly USA, LLC. TRU20140919C. 4. American Diabetes Association. Standards of medical care in diabetes—2014. Diabetes Care. 2014;37(Suppl 1):S14-S80. TrulicityTM INDICATIONS AND USAGE Trulicity™ is indicated as an adjunct to diet and exercise to improve glycemic control in adults with type 2 diabetes mellitus. Limitations of Use: Not recommended as a first-line therapy for patients who have inadequate glycemic control on diet and exercise. Has not been studied in patients with a history of pancreatitis. Consider other antidiabetic therapies in patients with a history of pancreatitis. Should not be used in patients with type 1 diabetes mellitus or for the treatment of diabetic ketoacidosis. It is not a substitute for insulin. Has not been studied in patients with severe gastrointestinal disease, including severe gastroparesis. Not recommended in patients with pre-existing severe gastrointestinal disease. The concurrent use of Trulicity and basal insulin has not been studied. CONTRAINDICATIONS Do not use in patients with a personal or family history of MTC or in patients with MEN 2. Do not use in patients with a prior serious hypersensitivity reaction to dulaglutide or to any of the product components. WARNINGS AND PRECAUTIONS Risk of Thyroid C-cell Tumors: In male and female rats, dulaglutide causes a dose-related and treatment-duration-dependent increase in the incidence of thyroid C-cell tumors (adenomas and carcinomas) after lifetime exposure. Glucagon-like peptide (GLP-1) receptor agonists have induced thyroid C-cell adenomas and carcinomas in mice and rats at clinically relevant exposures. It is unknown whether Trulicity will cause thyroid C-cell tumors, including medullary thyroid carcinoma (MTC), in humans, as the human relevance of this signal could not be determined from the clinical or nonclinical studies. One case of MTC was reported in a patient treated with Trulicity. This patient had pretreatment calcitonin levels approximately 8 times the upper limit of normal (ULN). Trulicity is contraindicated in patients with a personal or family history of MTC or in patients with MEN 2. Counsel patients regarding the risk for MTC with the use of Trulicity and inform them of symptoms of thyroid tumors (eg, a mass in the neck, dysphagia, dyspnea, persistent hoarseness). The role of serum calcitonin monitoring or thyroid ultrasound monitoring for the purpose of early detection of MTC in patients treated with Trulicity is unknown. Such monitoring may increase the risk of unnecessary procedures, due to the low specificity of serum calcitonin as a screening test for MTC and a high background incidence of thyroid disease. Very elevated serum calcitonin value may indicate MTC and patients with MTC usually have calcitonin values >50 ng/L. If serum calcitonin is measured and found to be elevated, the patient should be referred to an endocrinologist for further evaluation. Patients with thyroid nodules noted on physical examination or neck imaging should also be referred to an endocrinologist for further evaluation. Pancreatitis: In Phase 2 and Phase 3 clinical studies, 12 (3.4 cases per 1000 patient years) pancreatitis-related adverse reactions were reported in patients exposed to Trulicity versus 3 in non-incretin comparators (2.7 cases per 1000 patient years). An analysis of adjudicated events revealed 5 cases of confirmed pancreatitis in patients exposed to Trulicity (1.4 cases per 1000 patient years) versus 1 case in non-incretin comparators (0.88 cases per 1000 patient years). After initiation of Trulicity, observe patients carefully for signs and symptoms of pancreatitis, including persistent severe abdominal pain. If pancreatitis is suspected, promptly discontinue Trulicity. If pancreatitis is confirmed, Trulicity should not be restarted. Trulicity has not been evaluated in patients with a prior history of pancreatitis. Consider other antidiabetic therapies in patients with a history of pancreatitis. Hypoglycemia with Concomitant Use of Insulin Secretagogues or Insulin: The risk of hypoglycemia is increased when Trulicity is used in combination with insulin secretagogues (eg, sulfonylureas) or insulin. Patients may require a lower dose of sulfonylurea or insulin to reduce the risk of hypoglycemia. Hypersensitivity Reactions: Systemic hypersensitivity reactions were observed in patients receiving Trulicity in clinical trials. If a hypersensitivity reaction occurs, the patient should discontinue Trulicity and promptly seek medical advice. Renal Impairment: In patients treated with GLP-1 receptor agonists, there have been postmarketing reports of acute renal failure and worsening of chronic renal failure, which may sometimes require hemodialysis. Some of these events were reported in patients without known underlying renal disease. A majority of reported events occurred in patients who had experienced nausea, vomiting, diarrhea, or dehydration. Because these reactions may worsen renal failure, use caution when initiating or escalating doses of Trulicity in patients with renal impairment. Monitor renal function in patients with renal impairment reporting severe adverse gastrointestinal reactions. Severe Gastrointestinal Disease: Use of Trulicity may be associated with gastrointestinal adverse reactions, sometimes severe. Trulicity has not been studied in patients with severe gastrointestinal disease, including severe gastroparesis, and is therefore not recommended in these patients. Macrovascular Outcomes: There have been no clinical studies establishing conclusive evidence of macrovascular risk reduction with Trulicity or any other antidiabetic drug. ADVERSE REACTIONS Clinical Studies Experience: Because clinical studies are conducted under widely varying conditions, adverse reaction rates observed in the clinical studies of a drug cannot be directly compared to rates in the clinical studies of another drug and may not reflect the rates observed in practice. Pool of Placebo-controlled Trials: These data reflect exposure of 1670 patients to Trulicity and a mean duration of exposure to Trulicity of 23.8 weeks. Across the treatment arms, the mean age of patients was 56 years, 1% were 75 years or older and 53% were male. The population in these studies was 69% White, 7% Black or African American, 13% Asian; 30% were of Hispanic or Latino ethnicity. At baseline, the population had diabetes for an average of 8.0 years and had a mean HbA1c of 8.0%. At baseline, 2.5% of the population reported retinopathy. Baseline estimated renal function was normal or mildly impaired (eGFR ≥60mL/min/1.73 m2) in 96.0% of the pooled study populations. Adverse Reactions in Placebo-Controlled Trials Reported in ≥5% of Trulicity-Treated Patients: Placebo (N=568), Trulicity 0.75mg (N=836), Trulicity 1.5 mg (N=834) (listed as placebo, 0.75 mg, 1.5 mg) nausea (5.3%, 12.4%, 21.1%), diarrheaa (6.7%, 8.9%, 12.6%), vomitingb (2.3%, 6.0%, 12.7%), abdominal painc (4.9%, 6.5%, 9.4%), decreased appetite (1.6%, 4.9%, 8.6%), dyspepsia (2.3%, 4.1%, 5.8%), fatigued (2.6%, 4.2%, 5.6%). (a Includes diarrhea, fecal volume increased, frequent bowel movements. b Includes retching, vomiting, vomiting projectile. c Includes abdominal discomfort, abdominal pain, abdominal pain lower, abdominal pain upper, abdominal tenderness, gastrointestinal pain. d Includes fatigue, asthenia, malaise.) Note: Percentages reflect the number of patients that reported at least 1 treatment-emergent occurrence of the adverse reaction. Gastrointestinal Adverse Reactions: In the pool of placebo-controlled trials, gastrointestinal adverse reactions occurred more frequently among patients receiving Trulicity than placebo (placebo 21.3%, 0.75 mg 31.6%,1.5 mg 41.0%). More patients receiving Trulicity 0.75 mg (1.3%) and Trulicity 1.5 mg (3.5%) discontinued treatment due to gastrointestinal adverse reactions than patients receiving placebo (0.2%). Investigators graded the severity of gastrointestinal adverse reactions occurring on 0.75 mg and 1.5 mg of Trulicity as “mild” in 58% and 48% of cases, respectively, “moderate” in 35% and 43% of cases, respectively, or “severe” in 7% and 11% of cases, respectively. In addition to the adverse reactions ≥5% listed above, the following adverse reactions were reported more frequently in Trulicity-treated patients than placebo (frequencies listed, respectively, as: placebo; 0.75 mg; 1.5 mg): constipation (0.7%; 3.9%; 3.7%), flatulence (1.4%; 1.4%; 3.4%), abdominal distension (0.7%; 2.9%; 2.3%), gastroesophageal reflux disease (0.5%; 1.7%; 2.0%), and eructation (0.2%; 0.6%; 1.6%). Pool of Placebo- and Active-Controlled Trials: The occurrence of adverse reactions was also evaluated in a larger pool of patients with type 2 diabetes participating in 6 placebo- and active-controlled trials evaluating the use of Trulicity as monotherapy and add-on therapy to oral medications or insulin. In this pool, a total of 3342 patients with type 2 diabetes were treated with Trulicity for a mean duration 52 weeks. The mean age of patients was 56 years, 2% were 75 years or older and 51% were male. The population in these studies was 71% White, 7% Black or African American, 11% Asian; 32% were of Hispanic or Latino ethnicity. At baseline, the population had diabetes for an average of 8.2 years and had a mean HbA1c of 7.6-8.5%. At baseline, 5.2% of the population reported retinopathy. Baseline estimated renal function was normal or mildly impaired (eGFR ≥60 ml/min/1.73 m2) in 95.7% of the Trulicity population. In the pool of placebo- and active-controlled trials, the types and frequency of common adverse reactions, excluding hypoglycemia, were similar to those listed as ≥5% above. Other Adverse Reactions: Hypoglycemia: Incidence (%) of Documented Symptomatic (≤70 mg/dL Glucose Threshold) and Severe Hypoglycemia in Placebo-Controlled Trials: Add-on to Metformin at 26 weeks, Placebo (N=177), Trulicity 0.75 mg (N=302), Trulicity 1.5 mg (N=304), Documented symptomatic: Placebo: 1.1%, 0.75 mg: 2.6%, 1.5 mg: 5.6%; Severe: all 0. Add-on to Metformin + Pioglitazone at 26 weeks, Placebo (N=141), TRULICITY 0.75 mg (N=280), Trulicity 1.5 mg (N=279), Documented symptomatic: Placebo: 1.4%, 0.75 mg: 4.6%, 1.5 mg: 5.0%; Severe: all 0. Hypoglycemia was more frequent when Trulicity was used in combination with a sulfonylurea or insulin. Documented symptomatic hypoglycemia occurred in 39% and 40% of patients when Trulicity 0.75 mg and 1.5 mg, respectively, was co-administered with a sulfonylurea. Severe hypoglycemia occurred in 0% and 0.7% of patients when Trulicity 0.75 mg and 1.5 mg, respectively, was co-administered with a sulfonylurea. Documented symptomatic hypoglycemia occurred in 85% and 80% of patients when Trulicity 0.75 mg and 1.5 mg, respectively, was co-administered with prandial insulin. Severe hypoglycemia occurred in 2.4% and 3.4% of patients when Trulicity 0.75 mg and 1.5 mg, respectively, was co-administered with prandial insulin. Heart Rate Increase and Tachycardia Related Adverse Reactions: Trulicity 0.75 mg and 1.5 mg resulted in a mean increase in heart rate (HR) of 2-4 beats per minute (bpm). The long-term clinical effects of the increase in HR have not been established. Adverse reactions of sinus tachycardia were reported more frequently in patients exposed to Trulicity. Sinus tachycardia was reported in 3.0%, 2.8%, and 5.6% of patient treated with placebo, Trulicity 0.75 mg and Trulicity 1.5 mg, respectively. Persistence of sinus tachycardia (reported at more than 2 visits) was reported in 0.2%, 0.4% and 1.6% of patients treated with placebo, Trulicity 0.75 mg and Trulicity 1.5 mg, respectively. Episodes of sinus tachycardia, associated with a concomitant increase from baseline in heart rate of ≥15 beats per minute, were reported in 0.7%, 1.3% and 2.2% of patient treated with placebo, Trulicity 0.75 mg and Trulicity 1.5 mg, respectively. Immunogenicity: Across four Phase 2 and five Phase 3 clinical studies, 64 (1.6%) TRULICITY-treated patients developed anti-drug antibodies (ADAs) to the active ingredient in Trulicity (ie, dulaglutide). Of the 64 dulaglutide-treated patients that developed dulaglutide ADAs, 34 patients (0.9% of the overall population) had dulaglutide-neutralizing antibodies, and 36 patients (0.9% of the overall population) developed antibodies against native GLP-1. The detection of antibody formation is highly dependent on the sensitivity and specificity of the assay. Additionally, the observed incidence of antibody (including neutralizing antibody) positivity in an TrulicityTM (dulaglutide) TrulicityTM (dulaglutide) (dulaglutide) Brief Summary: Consult the package insert for complete prescribing information. WARNING: RISK OF THYROID C-CELL TUMORS • In male and female rats, dulaglutide causes a dose-related and treatment-durationdependent increase in the incidence of thyroid C-cell tumors (adenomas and carcinomas) after lifetime exposure. It is unknown whether Trulicity causes thyroid C-cell tumors, including medullary thyroid carcinoma (MTC), in humans as human relevance could not be determined from clinical or nonclinical studies. • Trulicity is contraindicated in patients with a personal or family history of MTC and in patients with Multiple Endocrine Neoplasia syndrome type 2 (MEN 2). Routine serum calcitonin or thyroid ultrasound monitoring is of uncertain value in patients treated with Trulicity. Counsel regarding the risk factors and symptoms of thyroid tumors. HCP BS 18SEP2014 HCP BS 18SEP2014 assay may be influenced by several factors including assay methodology, sample handling, timing of sample collection, concomitant medications, and underlying disease. For these reasons, the incidence of antibodies to dulaglutide cannot be directly compared with the incidence of antibodies of other products. Hypersensitivity: Systemic hypersensitivity adverse reactions sometimes severe (eg, severe urticaria, systemic rash, facial edema, lip swelling) occurred in 0.5% of patients on Trulicity in the four Phase 2 and Phase 3 studies. Injection-site Reactions: In the placebo-controlled studies, injection-site reactions (eg, injection-site rash, erythema) were reported in 0.5% of Trulicitytreated patients and in 0.0% of placebo-treated patients. PR Interval Prolongation and Adverse Reactions of First Degree Atrioventricular (AV) Block: A mean increase from baseline in PR interval of 2-3 milliseconds was observed in Trulicity-treated patients in contrast to a mean decrease of 0.9 millisecond in placebo-treated patients. The adverse reaction of first degree AV block occurred more frequently in patients treated with Trulicity than placebo (0.9%, 1.7% and 2.3% for placebo, Trulicity 0.75 mg and Trulicity 1.5 mg, respectively). On electrocardiograms, a PR interval increase to at least 220 milliseconds was observed in 0.7%, 2.5% and 3.2% of patients treated with placebo, Trulicity 0.75 mg and Trulicity 1.5 mg, respectively. Amylase and Lipase Increase: Patients exposed to Trulicity had mean increases from baseline in lipase and/or pancreatic amylase of 14% to 20%, while placebo-treated patients had mean increases of up to 3%. DRUG INTERACTIONS Trulicity slows gastric emptying and thus has the potential to reduce the rate of absorption of concomitantly administered oral medications. Caution should be exercised when oral medications are concomitantly administered with Trulicity. Drug levels of oral medications with a narrow therapeutic index should be adequately monitored when concomitantly administered with Trulicity. In clinical pharmacology studies, Trulicity did not affect the absorption of the tested, orally administered medications to any clinically relevant degree. USE IN SPECIFIC POPULATIONS Pregnancy - Pregnancy Category C: There are no adequate and well-controlled studies of Trulicity in pregnant women. The risk of birth defects, loss, or other adverse outcomes is increased in pregnancies complicated by hyperglycemia and may be decreased with good metabolic control. It is essential for patients with diabetes to maintain good metabolic control before conception and throughout pregnancy. Trulicity should be used during pregnancy only if the potential benefit justifies the potential risk to the fetus. In rats and rabbits, dulaglutide administered during the major period of organogenesis produced fetal growth reductions and/or skeletal anomalies and ossification deficits in association with decreased maternal weight and food consumption attributed to the pharmacology of dulaglutide. Nursing Mothers: It is not known whether Trulicity is excreted in human milk. Because many drugs are excreted in human milk and because of the potential for clinical adverse reactions from Trulicity in nursing infants, a decision should be made whether to discontinue nursing or to discontinue Trulicity, taking into account the importance of the drug to the mother. Pediatric Use: Safety and effectiveness of Trulicity have not been established in pediatric patients. Trulicity is not recommended for use in pediatric patients younger than 18 years. Geriatric Use: In the pool of placebo- and active-controlled trials, 620 (18.6%) Trulicity-treated patients were 65 years of age and over and 65 Trulicity-treated patients (1.9%) were 75 years of age and over. No overall differences in safety or efficacy were detected between these patients and younger patients, but greater sensitivity of some older individuals cannot be ruled out. Hepatic Impairment: There is limited clinical experience in patients with mild, moderate, or severe hepatic impairment. Therefore, Trulicity should be used with caution in these patient populations. In a clinical pharmacology study in subjects with varying degrees of hepatic impairment, no clinically relevant change in dulaglutide pharmacokinetics (PK) was observed. Renal Impairment: In the four Phase 2 and five Phase 3 randomized clinical studies, at baseline, 50 (1.2%) Trulicity-treated patients had mild renal impairment (eGFR ≥60 but <90 mL/min/1.73 m2), 171 (4.3%) Trulicity-treated patients had moderate renal impairment (eGFR ≥30 but <60 mL/min/1.73 m2) and no Trulicity-treated patients had severe renal impairment (eGFR <30 mL/min/1.73 m2). No overall differences in safety or effectiveness were observed relative to patients with normal renal function, though conclusions are limited due to small numbers. In a clinical pharmacology study in subjects with renal impairment including end-stage renal disease (ESRD), no clinically relevant change in dulaglutide PK was observed. There is limited clinical experience in patients with severe renal impairment or ESRD. Trulicity should be used with caution, and if these patients experience adverse gastrointestinal side effects, renal function should be closely monitored. Gastroparesis: Dulaglutide slows gastric emptying. Trulicity has not been studied in patients with pre-existing gastroparesis. OVERDOSAGE Overdoses have been reported in clinical studies. Effects associated with these overdoses were primarily mild or moderate gastrointestinal events (eg, nausea, vomiting) and non-severe hypoglycemia. In the event of overdose, appropriate supportive care (including frequent plasma glucose monitoring) should be initiated according to the patient’s clinical signs and symptoms. PATIENT COUNSELING INFORMATION See FDA-approved Medication Guide • Inform patients that Trulicity causes benign and malignant thyroid C-cell tumors in rats and that the human relevance of this finding is unknown. Counsel patients to report symptoms of thyroid tumors (e.g., a lump in the neck, persistent hoarseness, dysphagia, or dyspnea) to their physician. • Inform patients that persistent severe abdominal pain, that may radiate to the back and which may (or may not) be accompanied by vomiting, is the hallmark symptom of acute pancreatitis. Instruct patients to discontinue Trulicity promptly, and to contact their physician, if persistent severe abdominal pain occurs. • The risk of hypoglycemia may be increased when Trulicity is used in combination with a medicine that can cause hypoglycemia, such as a sulfonylurea or insulin. Review and reinforce instructions for hypoglycemia management when initiating Trulicity therapy, particularly when concomitantly administered with a sulfonylurea or insulin. • Patients treated with Trulicity should be advised of the potential risk of dehydration due to gastrointestinal adverse reactions and take precautions to avoid fluid depletion. Inform patients treated with Trulicity of the potential risk for worsening renal function and explain the associated signs and symptoms of renal impairment, as well as the possibility of dialysis as a medical intervention if renal failure occurs. • Inform patients that serious hypersensitivity reactions have been reported during postmarketing use of GLP-1 receptor agonists. If symptoms of hypersensitivity reactions occur, patients must stop taking Trulicity and seek medical advice promptly. • Advise patients to inform their healthcare provider if they are pregnant or intend to become pregnant. • Prior to initiation of Trulicity, train patients on proper injection technique to ensure a full dose is delivered. Refer to the accompanying Instructions for Use for complete administration instructions with illustrations. • Inform patients of the potential risks and benefits of Trulicity and of alternative modes of therapy. Inform patients about the importance of adherence to dietary instructions, regular physical activity, periodic blood glucose monitoring and HbA1c testing, recognition and management of hypoglycemia and hyperglycemia, and assessment for diabetes complications. During periods of stress such as fever, trauma, infection, or surgery, medication requirements may change and advise patients to seek medical advice promptly. • Each weekly dose of Trulicity can be administered at any time of day, with or without food. The day of once weekly administration can be changed if necessary, as long as the last dose was administered 3 or more days before. If a dose is missed and there are at least 3 days (72 hours) until the next scheduled dose, it should be administered as soon as possible. Thereafter, patients can resume their usual once weekly dosing schedule. If a dose is missed and the next regularly scheduled dose is due in 1 or 2 days, the patient should not administer the missed dose and instead resume Trulicity with the next regularly scheduled dose. • Advise patients treated with Trulicity of the potential risk of gastrointestinal side effects. • Instruct patients to read the Medication Guide and the Instructions for Use before starting Trulicity therapy and review them each time the prescription is refilled. • Instruct patients to inform their doctor or pharmacist if they develop any unusual symptom, or if any known symptom persists or worsens. • Inform patients that response to all diabetic therapies should be monitored by periodic measurements of blood glucose and HbA1c levels, with a goal of decreasing these levels towards the normal range. HbA1c is especially useful for evaluating long-term glycemic control. TrulicityTM (dulaglutide) TrulicityTM (dulaglutide) HCP BS 18SEP2014 Eli Lilly and Company, Indianapolis, IN 46285, USA US License Number 1891 Copyright © 2014, Eli Lilly and Company. All rights reserved. Additional information can be found at www.trulicity.com DG HCP BS 18SEP2014 HCP BS 18SEP2014 AMCP Headquarters 100 North Pitt St., Suite 400, Alexandria, VA 22314 Tel.: 703.683.8416 • Fax: 703.683.8417 Editorial staff Volume 21, No. 3 Editor-in-Chief John Mackowiak, PhD 919.942.9903, [email protected] C ONTENTS Publisher Edith A. Rosato, RPh, IOM, Chief Executive Officer Academy of Managed Care Pharmacy Assistant Editor Laura E. Happe, PharmD, MPH 864.938.3837, [email protected] ■ RESEARCH 188 Assessment of Pharmacists’ Views on Biosimilar Naming Conventions Sara Fernandez-Lopez, PhD, MBA; Denise Kazzaz, BA; Mohamed Bashir, MHA; and Trent McLaughlin, BSc, PhD 201 Patterns of Medication Utilization and Costs Associated with the Use of Etanercept, Adalimumab, and Ustekinumab in the Management of Moderate-to-Severe Psoriasis Steven R. Feldman, MD; Yang Zhao, PhD; Prakash Navaratnam, RPh, MPH, PhD; Howard S. Friedman, PhD; Jackie Lu, PharmD, MS; and Mary Helen Tran, PharmD, MBA 210 220 Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study R. Brett McQueen, PhD; Terrie Livingston, PharmD; Timothy Vollmer, MD; John Corboy, MD; Brieana Buckley, PharmD; Richard Read Allen, MS; Kavita Nair, PhD; and Jonathan D. Campbell, PhD Resource Utilization and Costs Associated with Using Insulin Therapy Within a Newly Diagnosed Type 2 Diabetes Mellitus Population Kelly Bell, PharmD, MSPhr; Shreekant Parasuraman, PhD; Aditya Raju, BPharm, MS; Manan Shah, PharmD, PhD; John Graham, PharmD; and Melissa Denno, PharmD, MS Assistant Editor Eleanor M. Perfetto, MS, PhD 410.706.6989, [email protected] Assistant Editor Karen L. Rascati, PhD 512.471.1637, [email protected] Managing Editor Jennifer A. Booker 703.317.0725, [email protected] Copy Editor Carol Blumentritt 602.616.7249, [email protected] Graphic Designer Margie C. Hunter 703.297.9319, [email protected] Advertising Advertising for the Journal of Managed Care & Specialty Pharmacy is accepted in accordance with the advertising policy of the Academy of Managed Care Pharmacy. For advertising information, contact: Derek Lundsten, VP, Business Development American Medical Communications, Inc. 630 Madison Avenue, Manalapan, NJ 07726 Tel.: 973.713.2650 E-mail: [email protected] editorial Questions related to editorial content and submission should be directed to JMCP Managing Editor Jennifer Booker: [email protected]; 703.317.0725. Manuscripts should be submitted electronically at jmcp.msubmit.net. subscriptions Annual subscription rates: USA, individuals, institutions–$90; other countries–$120. Single copies cost $15. Missing issues are replaced free of charge up to 6 months after date of issue. Send requests to AMCP headquarters. reprints (continued on page 186) Journal of Managed Care & Specialty Pharmacy® (ISSN 2376-1032) is published 12 times per year and is the official publication of the Academy of Managed Care Pharmacy (AMCP), 100 North Pitt St., Suite 400, Alexandria, VA 22314; 703.683.8416; 800.TAP.AMCP; 703.683.8417 (fax). The paper used by the Journal of Managed Care & Specialty Pharmacy meets the requirements of ANSI/NISO Z39.48-1992 (Permanence of Paper) effective with Volume 7, Issue 5, 2001; prior to that issue, all paper was acid-free. Annual membership dues for AMCP include $90 allocated for the Journal of Managed Care & Specialty Pharmacy. Send address changes to JMCP, 100 North Pitt St., Suite 400, Alexandria, VA 22314. 184 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Vol. 21, No. 3 www.amcp.org Information about commercial reprints and permission to reuse material from JMCP may be found at amcp.org/ JMCP_Reprints_and_Permissions. Authors may order reprints from the Sheridan Press; contact contact Tamara Smith, [email protected], 800.352.2210 All articles published represent the opinions of the authors and do not reflect the official policy or views of the Academy of Managed Care Pharmacy or the authors’ institutions unless so specified. Copyright © 2015, Academy of Managed Care Pharmacy. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, without written permission from the Academy of Managed Care Pharmacy. EX APRIL 7–10 SAN DIEGO PO AMCP’S 2 7 TH AL MEETIN G & A U NN 2015 Get Informed. Get Connected. Get Involved. Come to AMCP’s 27th Annual Meeting & Expo, April 7–10, in San Diego! It’s the one meeting that combines continuing pharmacy education, networking opportunities, and special events, all as part of the year’s largest gathering of managed care pharmacists, health plan administrators, medical and pharmacy directors, formulary decision-makers, doctors, nurses and other health care professionals. The education sessions are designed by and for managed care pharmacy professionals and experts in six Topic Tracks. Business Trends in Managed Care The Landscape of Contemporary Managed Care Pharmacy Hear how industry and marketplace developments are affecting the business and operation of managed care pharmacy. Controversies, new ideas, and niche issues—these sessions examine a wide variety of topics currently impacting managed care pharmacy. Staying On Course With Legislative and Regulatory Issues New federal and state laws and regulations are changing the way we operate. These sessions will keep you current on what’s happening and how it will affect you. Global Perspectives on Formulary Management Formulary management practices can vary both locally and globally. Learn innovative formulary decision-making strategies from experts across the country and beyond. Spotlight on Medication Therapy Management (MTM) Beginning with AMCP’s 27th Annual Meeting & Expo, one track will focus in-depth on a featured managed care topic. This year, the spotlight will be on MTM strategies and best practices. Research and its Practical Application These sessions will provide the latest in managed care pharmacy research and explain how it is being used in the profession. Additional professional opportunities can be found in poster and pipeline sessions, satellite symposia presented by participating partners, and so much more! Go to www.amcpmeetings.org for updated session descriptions, AMCP Foundation events, pre-meeting programs and to register. Board of Directors Volume 21, No. 3 C ONTENTS (continued) President: Dana Davis McCormick, RPh President-Elect: Raulo S. Frear, PharmD Past President: Kim A. Caldwell, RPh Treasurer: H. Eric Cannon, PharmD, FAMCP Chief Executive Officer: Edith A. Rosato, RPh, IOM Director: Stanley E. Ferrell, RPh, MBA Director: James T. Kenney, Jr., RPh, MBA Director: Janeen McBride, RPh Director: Lynn Nishida, RPh Director: Gary M. Owens, MD Editorial advisory board ■ RESEARCH 229 Association of Visit-to-Visit Variability of Hemoglobin A1c and Medication Adherence Ambili Ramachandran, MD, MS; Michael Winter, MPH; and Devin M. Mann, MD, MS 243 Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database Sumesh Kachroo, PhD; Hugh Kawabata, MS; Susan Colilla, PhD, MPH; Lizheng Shi, PhD; Yingnan Zhao, PhD; Jayanti Mukherjee, PhD; Uchenna Iloeje, MD, MPH; and Vivian Fonseca, MD 256 Pharmacist-Coordinated Multidisciplinary Hospital Follow-up Visits Improve Patient Outcomes Jamie J. Cavanaugh, PharmD, CPP, BCPS; Kimberly N. Lindsey, PharmD; Betsy B. Shilliday, PharmD, CDE, CPP, BCACP; and Shana P. Ratner, MD Editorial Mission JMCP publishes peer-reviewed original research manuscripts, subject reviews, and other content intended to advance the use of the scientific method, including the interpretation of research findings in managed care pharmacy. JMCP is dedicated to improving the quality of care delivered to patients served by managed care and specialty pharmacy by providing its readers with the results of scientific investigation and evaluation of clinical, health, service, and economic outcomes of pharmacy services and pharmaceutical interventions, including formulary management. JMCP strives to engage and serve professionals in pharmacy, medicine, nursing, and related fields to optimize the value of pharmaceutical products and pharmacy services delivered to patients. JMCP employs extensive biasmanagement procedures intended to ensure the integrity and reliability of published work. 186 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Vol. 21, No. 3 www.amcp.org Committee Purpose: To advise and assist the editors and staff in the solicitation and development of JMCP content. For more information see: www.amcp.org/ eab. To volunteer to be a committee member, watch for the call for AMCP volunteers every September. Trent McLaughlin, BSPharm, Xcenda, Scottsdale, AZ (chair) Karen Worley, PhD, Humana, Inc., Cincinnati, OH (chair) Christopher Bell, MS, GlaxoSmithKline, Research Triangle Park, NC Gary Besinque, PharmD, FCSHP, Kaiser Permanente, Downey, CA Norman V. Carroll, BSPharm, PhD, Virginia Commonwealth University, Richmond, VA Mark Conklin, MS, PharmD, Pharmacy Quality Solutions, Sewickley, PA Bridget Flavin, PharmD, Regence, Portland, OR Renee Rizzo Fleming, BSPharm, MBA, PRN Managed Care Consulting Services, LLC, East Amherst, NY Patrick Gleason, PharmD, BCPS, FCCP, Prime Therapeutics LLC, Minneapolis, MN Todd A. Hood, MHA, PharmD, Celgene Corporation, Cumming, GA Mark Jackson, BSPharm, PIVINA Consulting, Inc., Windsor, ON Donald Klepser, MBA, PhD, University of Nebraska Medical Center, Omaha, NE Stephen J. Kogut, BSPharm, MBA, PhD, University of Rhode Island, Kingston, RI Bradley C. Martin, PharmD, PhD, University of Arkansas for Medical Sciences, Little Rock, AR Uche Anadu Ndefo, BCPS, PharmD, Texas Southern University, Houston, TX Robert L. Ohsfeldt, PhD, Texas A&M Health Science Center, College Station, TX Gary M. Owens, MD, Gary Owens Associates, Ocean View, DE Cathlene Richmond, BCPS, BS, PharmD, Kaiser Permanente, Oakland, CA Mark Christopher Roebuck, MBA, PhD, RxEconomics, Hunt Valley, MD Jordana Schmier, MA, Exponent, Alexandria, VA Marv Shepherd, BSPharm, MS, PhD, University of Texas at Austin, Austin, TX Jennifer Booker, Academy of Managed Care Pharmacy, Alexandria, VA John Mackowiak, PhD, Academy of Managed Care Pharmacy, Alexandria, VA Author Guidelines: http://amcp.org/JMCP_AuthorGuidelines Mission Statement: http://amcp.org/JMCP_MissionStatement AMCP Foundation Fun In San Diego — UAL MEETING EX APRIL 7–10 SAN DIEGO PO the “fun” in Foundation with two very special N AN & The AMCP Foundation will be putting AMCP’S 2 7 TH Two Ways to Give and Enjoy! 2015 events held at AMCP’s 27th Annual Meeting & Expo in San Diego, California, April 7–10, 2015. AMCP Foundation’s second annual 5K to the Future Revel in Casino Night Fun-Run and 1K Walk on Thursday, April 9. Start your aboard the historic morning off right with a scenic jaunt in competition with aircraft carrier USS fellow runners/walkers. Your entry fee of $40 per person Midway, where you can try your luck at games of chance includes a commemorative t-shirt. New this year: team or simply enjoy the waterfront beauty of San Diego. Your competition and awards in 4 categories! donation of $75 per person supports the Foundation’s research and education goals. The event will be held Supported by Thursday, April 9, from 8:30 pm–10:30 pm. You’ll even have a chance to view 19 different aircraft and try out the flight simulator! See you in San Diego! Both events are dedicated to good times and supporting a great cause! To register, go to www.amcpmeetings.org. RESEARCH Assessment of Pharmacists’ Views on Biosimilar Naming Conventions Sara Fernandez-Lopez, PhD, MBA; Denise Kazzaz, BA; Mohamed Bashir, MHA; and Trent McLaughlin, BSc, PhD ABSTRACT BACKGROUND: As the date for the introduction of biosimilars in the United States approaches, questions remain regarding the naming, coding, and approval process for these agents that will need to be carefully considered. OBJECTIVES: To (a) ascertain pharmacists’ awareness of and comfort level with biosimilars and (b) determine the impact of identical or different nonproprietary names on pharmacists’ confidence in substituting interchangeable biologics. METHODS: The Academy of Managed Care Pharmacy, the American Pharmacists Association, and the American Society of Health-System Pharmacists fielded a survey to their membership or a partial segment of their membership. The survey consisted of 2 sections: (1) current processes for reporting biologics being dispensed and (2) familiarity and preferences regarding biosimilars. RESULTS: A substantial majority (70.1%) of respondents reported regularly using National Drug Code numbers as the identifier for biological products dispensed to patients; however, 10.4% of respondents reported using either the nonproprietary name or the Healthcare Common Procedure Coding System code as the identifier. When presented with 3 scenarios for naming conventions of interchangeable biosimilars and asked to rate their level of confidence (1 = not confident, 5 = very confident) to substitute, 74.6% of pharmacists indicated that they would be confident or very confident in substituting an interchangeable biosimilar with the reference product if both shared the same active ingredient or nonproprietary name of the reference biologic; 25.3% of pharmacists were confident in substituting when the nonproprietary name is not shared with the biologic; and 37.3% of pharmacists expressed confidence in substituting when the biologic and biosimilar product did not share the same nonproprietary name because of a prefix or suffix. CONCLUSIONS: The imminent entry of biosimilars into the U.S. market highlights the need to carefully evaluate current processes of identification, reporting, and recording of the biological products dispensed. The results of this survey indicate that the ultimate decision on the naming convention for biosimilars may influence dispensing pharmacists, with the majority of respondents being most comfortable with biosimilars having the same nonproprietary name as the reference biologic. J Manag Care Spec Pharm. 2015;21(3):188-95 Copyright © 2015, Academy of Managed Care Pharmacy. All rights reserved. What is already known about this subject •A new approval pathway for biosimilars has been established, and applications have been submitted to the FDA. One or more biosimilar agents may launch in the United States in 2015. 188 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 •Products will be similar to reference biologics, not exact replicas, given the intricacy of their molecular structure and the complexity of the production methods. •Potential skepticism from the public, including patients and health care professionals, remains regarding safety and efficacy of these new products. What this study adds •Assessment of the impact of identical or different nonproprietary names on pharmacists’ confidence in substituting interchangeable biologics. •Evaluation of whether current processes for information sharing and data recording are enough to differentiate the biologic used without the need for different nonproprietary names. I n July 2014, the U.S. Food and Drug Administration (FDA) accepted the first application for approval of a biologic under the new biosimilar pathway created in the Biologics Price Competition and Innovation Act, which was introduced as part of the Affordable Care Act. Because of the complexity of biologic products and the complexity of their manufacturing processes, biosimilars are never expected to be exact replicas of the reference product but are “highly similar to the reference product notwithstanding minor differences in clinically inactive components.”1 Thus, the naming and identification rules commonly applied to generics of small molecules have to be evaluated, considering the complexities of these biological products. In Europe, Japan, Australia, and other markets, biosimilars have been in the market for over 7 years (Table 1). In the United States, the FDA has received at least 4 applications under the biosimilar pathway (section 351[k] of the Public Health Service Act). On July 24, 2014, Sandoz announced that the FDA had accepted its application for its biosimilar to Neupogen (filgrastim)2; on January 7, 2015, the FDA Oncologic Drugs Advisory Committee unanimously recommended its approval.3 In August 2014, Celltrion announced the filing of its application for FDA approval of its biosimilar to Remicade (infliximab), the first biosimilar monoclonal antibody to be filed through the new biosimilar pathway,4 and in December 2014, Hospira and Apotex filed applications for biosimilars to Epogen/Procrit (epoetin alfa) and Neulasta (pegfilgrastim), respectively.5,6 In the United States, the FDA has released draft guidance documents on the approval process and exclusivity requirements7; however, a key question still remaining is how these Vol. 21, No. 3 www.amcp.org Assessment of Pharmacists’ Views on Biosimilar Naming Conventions TABLE 1 List of Biosimilar Products Approved by the EMA18 Nonproprietary Name Substance Medicine Name Epoetin alfa Abseamed Epoetin alfa Binocrit Epoetin alfa Epoetin Alfa Hexal Epoetin zeta Retacrit Epoetin zeta Silapo Filgrastim Accofil Filgrastim Biograstim Filgrastim Filgrastim Hexal Filgrastim Filgrastim ratiopharm a Filgrastim Grastofil Filgrastim Nivestim Filgrastim Ratiograstim Filgrastim Tevagrastim Filgrastim Zarzio Follitropin alfa Bemfola Follitropin alfa Ovaleap Infliximab Inflectra Iinfliximab Remsima Insulin glargine Abasria Somatropin Omnitrope Somatropin Valtropin a aWithdrawn after approval. EMA = European Medicine Agency. Marketing Authorization Holder MEDICE Arzneimittel Pütter GmbH & Co. KG Sandoz GmbH Hexal AG Hospira UK Limited STADA Arzneimittel AG Accord Healthcare Ltd AbZ-Pharma GmbH Hexal AG Ratiopharm GmbH Apotex Europe BV Hospira UK Ltd. Ratiopharm GmbH Teva GmbH Sandoz GmbH Finox Biotech AG Teva Pharma BV Hospira UK Limited Celltrion Healthcare Hungary Kft. Eli Lilly Regional Operations GmbH Sandoz GmbH Biopartners GmbH biosimilar products will be named. Proponents of using the same international nonproprietary name (INN), or generic name, highlight the fact that having different INNs would cause confusion among prescribers, possibly creating an artificial barrier in the adoption of biosimilars, and could affect the substitution of interchangeable biosimilars.8,9 Proponents suggest that the use of National Drug Code (NDC) numbers and other product identifiers are sufficient for postmarketing surveillance. However, opponents to a single INN for reference and biosimilar products point to the need to reduce the likelihood of inadvertent and inappropriate product switching and of the ability to clearly identify which product is used for postmarketing safety and effectiveness monitoring.10,11 Given the importance of this issue, we conducted a survey among the Academy of Managed Care Pharmacy (AMCP), the American Pharmacists Association (APhA), and the American Society of Health-System Pharmacists (ASHP) members to understand the following: (a) the current processes to report dispensing information to other stakeholders (prescribers, payers, patients); (b) the dispensing information recorded in the patient record for biologics; and (c) how different naming options for biosimilars may influence pharmacists’ likelihood of product substitution for interchangeable biosimilars. In this article, we describe different options proposed in the United States and adopted in other parts of the world for www.amcp.org Vol. 21, No. 3 Year of Approval 2007 2007 2007 2007 2007 2014 2008 2009 2008 2013 2010 2008 2008 2009 2014 2013 2013 2013 2014 2006 2006 Reference Product (Innovator) Eprex/Erypo (Janssen-Cilag GmbH) Neupogen (Amgen) Gonal-F (Merck Serono Europe Ltd) Remicade (Janssen Biologics BV) Lantus (Sanofi) Genotropin (Pfizer) Humatrope (Eli Lilly) biosimilar naming, analyze the value of each option, and report our survey findings on pharmacists’ views on biosimilars naming conventions. Naming of Pharmaceuticals The World Health Organization (WHO) determines INNs for all marketed therapeutic products. INNs provide global names to drugs to prevent confusion with the use of multiple nonproprietary names in different countries. An INN is specific to a given defined substance regardless of the manufacturer. In the case of small molecules, the active substance in the original product and all subsequent generics share the same INN (e.g., acetaminophen), although nonactive ingredients may vary. As of now, biological substances are assigned INNs following the same general principles that apply to all INNs, while accounting for the specific complexities of biologics. For nonglycosylated proteins that share the same protein sequence as the originator, the same INN has been assigned so far (e.g., filgrastim). For more complex glycosylated proteins, the INN program introduced a second word representing a Greek letter to differentiate between different glycoform profiles (e.g., epoetin alfa and epoetin zeta). Even though the WHO determines INNs, each regulatory authority decides whether to adopt the INN in their specific market. In the United States, the United States Adopted Name (USAN) council proposes and selects most generic names, March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 189 Assessment of Pharmacists’ Views on Biosimilar Naming Conventions called USAN in the United States, after consultation with the WHO-INN program.12 Since the FDA has representation in the USAN, there is a strong collaboration among both groups, USAN council and FDA, when selecting a nonproprietary name.13 Trade names in the United States are proposed by manufacturers and ultimately approved or rejected by the FDA.14 In the case of biosimilars, different regulatory authorities have followed different approaches in naming these new biologics. For example, an epoetin alfa registered in Europe with the INN “epoetin alfa” was later introduced in Australia with the generic name “epoetin lambda” to differentiate it from the original biologic.15 To avoid confusion and allow a more comprehensive harmonized approach, different regulatory authorities requested that the WHO develop a global naming scheme for biosimilars. Under this voluntary scheme, the WHO INN Expert Group would develop a biologic qualifier (BQ) for all biological substances. The BQ, a 4-letter code assigned at random, would identify the manufacturer, as well as the manufacturing site, so pharmacovigilance can be guaranteed, as it would allow a global framework, while still differentiating production at 2 different sites.15 In the United States, the FDA has not released any guidance on biosimilar naming, despite previous requests.16 Different groups have laid out their reasons supporting their different positions. Through our survey, we aimed to understand pharmacists’ perspectives on the issue, specifically as it relates to their current processes of data sharing with other stakeholders (providers, payers, patients) and how naming of biosimilars may influence their likelihood of substitution for interchangeable biologics. ■■ Methods To obtain input from pharmacists on their level of awareness and preferences concerning naming conventions of biosimilars, an online survey was fielded in November and December 2014 to the membership, or a cross-section of the membership, of 3 associations that represent pharmacists across the United States: AMCP, APhA, and ASHP. The online survey was sent to all members of the AMCP and ASHP, as well as members of the Government Affairs Committee and Biosimilar Taskforce (n = 21) of the APhA. Participants were not excluded or terminated from the survey based on any of their responses. A combined total of 93 respondents participated in the survey. Pharmacists were asked to identify the type of organization in which they are currently employed (Table 2). Based on their answers, participants were categorized into 3 main categories: (1) dispensing organizations; (2) managed care organizations, pharmacy benefit managers (PBMs), and consultants; and (3) manufacturers. If respondents indicated “other” as their classification, their open-ended responses were used to categorize them into 1 of the 3 organization types. 190 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 TABLE 2 Characteristics of Survey Participants Type of Pharmacy or Organization % (n) 45.2(42) Managed care New Classification Managed care/PBM/ consultant Dispensing organizations Manufacturer Dispensing organizations Dispensing organizations Dispensing organizations Dispensing organizations Dispensing organizations Dispensing organizations 14.0(13) Hospital Manufacturer 12.9 (12) Specialty 3.2(3) Clinic 1.0(1) Independent 1.0(1) 1.0(1) Pharmacy small chain Pharmacy large chain 1.0(1) Other: retail and hospital (1), VA (1), 7.5(7) federal facility (1), IDN (1), ACO (1), LTC (1), home infusion (1) Other: consultant/vendor (9), 11.8(11) Managed care/PBM/ PBM (2) consultant Other: pharmaceuticals 1.0(1) Manufacturer ACO = accountable care organization; IDN = integrated delivery network; LTC = long-term care; PBM = pharmacy benefit manager; VA = Veterans Administration. The survey instrument was developed based on a previous assessment of pharmacists’ views on generic medications. That instrument was modified to incorporate the particular issues related to receipt, storage, and dispensing of biosimilar agents. To assist in the aggregation of results but still allow for additional detail on specific issues, the survey included a mix of close-ended and open-ended questions. Sample questions included the following: “Assuming it would be permissible to do so, what would be your level of confidence to substitute an interchangeable biosimilar for a reference biologic in the following circumstances? Rank your level of confidence on a scale of 1 to 5, with 1 being not confident and 5 being very confident,” and “If a prescription was written ambiguously with a common root non-proprietary name (such as filgrastim), and the existing biosimilar and interchangeable products include that root name along with an additional unique suffix or prefix, how would you fill the prescription?”. Association members were given approximately 3 weeks to respond to the survey. Survey responses were aggregated and analyzed using SAS 9.4 (SAS Institute, Inc., Carey, NC). Since this is a descriptive analysis, aggregate results are presented as a percentage endorsing a particular response category; no comparisons were made to assess statistical significance. No patient information was included in this study nor were survey respondents identifiable at any point; therefore, institutional review board approval was not necessary. ■■ Results A total of 93 pharmacists submitted a response to the survey. Not all questions required responses, and some pharmacists elected to not answer every question. The demographics of survey respondents are summarized in Table 2. The majority of Vol. 21, No. 3 www.amcp.org Assessment of Pharmacists’ Views on Biosimilar Naming Conventions FIGURE 1 Current Practices for Sharing Dispensing Information A. Stakeholders with whom general dispensing information is shared (multiple selection allowed) (Percentage of respondents that shared dispensing information with these stakeholders) Other 4.3% Patient 44.1% Prescriber 66.7% Payer/PBM 0% 78.5% 20% 40% N = 93 60% 80% 100% B. Methods of sharing dispensing information (multiple selection allowed) (Percentage of respondents that used these methods of communication to share dispensing information) 11.8% Other 25.8% E-mail 31.2% Paper copy 35.5% Fax or telephone 46.2% E-prescribing software Interoperable health information technology, fully intergrated electronic health record 51.6% 0% 20% N = 93 40% 60% PBM = pharmacy benefit manager. respondents represented managed care organizations (45.2%), hospitals (14.0%), or manufacturers (12.9%). When asked about current practices, pharmacists reported sharing information regarding dispensed products mainly with payers and PBMs (78.5%) and prescribers (66.7%). The methods used to share information included interoperable health information technology (51.6%), e-prescribing software (46.2%), fax or telephone (35.5%), paper copy (31.2%), or e-mail (25.8%; Figure 1). www.amcp.org Vol. 21, No. 3 When asked about methods typically used to record what biologics were dispensed, pharmacists selected scanning a barcode that links to and populates a patient health record (24.7%), typing the information into an electronic patient record (23.4%), and selecting the product from a drop-down menu (23.4%; Figure 2). In most cases (76.6%), the information recorded allowed for identification of which biologic was dispensed, either by the use of NDC numbers (70.1%) or a combination of the March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 191 Assessment of Pharmacists’ Views on Biosimilar Naming Conventions FIGURE 2 Methods to Record Which Biologic Product Was Dispensed to Patient (N = 77) 24.7% 28.6% 23.4% 23.4% Scan a barcode that links to and populates a patient health record Select product from drop-down menu that has been prepopulated with information supplied by patient health system Type information into electronic patient health record system Othera aOther responses (22) included the following: Not applicable/not dispensing (13), pharmacy system (3), National Drug Codes (2), proprietary software (1), articles (1), formulary selection (1), scan bar code for prescription products, manual entry for injectables (1). nonproprietary name or Healthcare Common Procedure Coding System (HCPCS) code and the manufacturer or brand name (6.5%; Table 3); however, in 10.4% of cases the information recorded did not include specific brand or manufacturer of the product, since only the HCPCS or nonproprietary name was recorded. This is in line with what has been documented before.17 TABLE 3 Pharmacists were asked to rate their familiarity with biosimilars on a level of 1 to 5, with 1 being the least familiar and 5 being the most familiar. Over half of the respondents (66.2%) identified a familiarity level of 4 or 5 with biosimilars. The percentage of respondents indicating the same level of familiarity with interchangeable biosimilars fell to 50.6%. Finally, 72.7% of respondents indicated an awareness level of 4 or 5 regarding whether biosimilars were being sold in other countries. Respondents representing managed care, PBMs and consultants, and dispensing organizations were fairly familiar with biosimilars (69.0% and 68.0%, respectively, indicated level 4 or 5) and fairly aware of biosimilars being sold in other countries (76.2% and 76.0%, respectively), while they were less familiar with interchangeable biologics (52.4% and 60%, respectively). Respondents from manufacturers were the least familiar of all, with 50.0% indicating a familiarity level of 4 or 5 with biosimilars and awareness of biosimilars being sold in other countries. Only 20.0% of respondents indicated familiarity with interchangeable biologics (Table 4). When asked about their confidence in substituting interchangeable biologics under different naming scenarios, pharmacists felt most comfortable with a scenario in which the reference product and the biosimilar shared the same nonproprietary name, with 56 respondents (74.6%) being confident or very confident. In a scenario with different nonproprietary names, only 19 (25.3%) indicated a confidence level of 4 or 5. Finally, in a third scenario in which reference products and biosimilars would not share a nonproprietary name because of a prefix or suffix, 28 (37.3%) indicated a confidence level of 4 or 5 (Figure 3). Lastly, when asked whether physician postdispensing notification requirements would affect their willingness to dispense an interchangeable biosimilar, 52.7% of respondents reported that such a notification requirement would not affect their likelihood to substitute; 19.4% of respondents indicated that it would make them less likely to substitute; and 23.7% were not sure how this would affect their substitution practices. Only 4.3% of pharmacists felt that a notification requirement would make them more likely to substitute (Figure 4). Information Recorded When a Biologic Product Is Dispensed NDC Not Recorded NDC Recorded Nonproprietary Name or HCPCS Code with Manufacturer or Brand Name Nonproprietary Name or HCPCS Code with No Manufacturer or Brand Name Type of Respondent %(n) %(n) %(n) All respondents (N = 77) 70.1(54) 6.5(5) 10.4(8) Dispensing organizations (n = 25) 72.0 (18) 16.0(4) 5.5(3) Managed care/PBM/consultant (n = 42) 69.0 (29) 2.4(1) 9.5(4) Manufacturers (n = 10) 70.0 (7) 0 10.0(1) HCPCS = Healthcare Common Procedure Coding System; NDC = National Drug Code; PBM = pharmacy benefit manager. 192 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Vol. 21, No. 3 www.amcp.org Not Dispensing %(n) 13.0 (10) 0 19.0 (8) 20.0 (2) Assessment of Pharmacists’ Views on Biosimilar Naming Conventions TABLE 4 Familiarity of Survey Respondents with Biosimilars Familiarity with Biosimilars (Level 4 or 5) Respondent Type All respondents (N = 77) Dispensing organizations (n = 25) Managed care/PBM/consultant (n = 42) Manufacturers (n = 10) PBM = pharmacy benefit manager. FIGURE 3 Familiarity with Interchangeable Biosimilars (Level 4 or 5) %(n) 66.2(51) 68.0(17) 69.0(29) 50.0 (5) %(n) 50.6(39) 60.0(15) 52.4(22) 20.0 (2) Awareness of Biosimilars Being Sold Outside United States (Level 4 or 5) %(n) 72.7(56) 76.0(19) 76.2(32) 50.0 (5) Confidence of Survey Respondents in Substituting Interchangeable Biosimilars Number of Respondents (N = 75) 50.0 44.0 45.0 Percentage (%) 40.0 35.0 29.3 29.3 30.0 24.0 25.0 15.0 20.0 21.3 14.7 13.3 10.7 10.0 5.0 0 If both products did NOT share the same active ingredient or nonproprietary name? 25.3 20.0 20.0 If both products share the same active ingredient or nonproprietary name? 30.7 If both products did not share the same active ingredient or nonproprietary name because of a prefix or suffix? 12.0 4.0 1.3 1 1 = Not confident 2 3 4 Level of Confidence ■■ Discussion As the United States prepares for the introduction of the first biosimilars and interchangeable biologics, the debate on the appropriate naming conventions for biosimilars continues. Both sides of the debate have compelling arguments for their positions. On one hand, there is the need for adequate identification of product dispensed for pharmacovigilance efforts; on the other hand, there needs to be assurance that biosimilars do not encounter artificial barriers to their adoption. The WHO proposal for development of a BQ that identifies the manufacturer and site of production, but still preserves the same INN for biosimilars and reference biologics, could be a potential solution for the naming of biosimilars. www.amcp.org Vol. 21, No. 3 15 5 = Very confident While 66.2% of respondents indicated a high level of familiarity (level 4 or 5, with 5 being very familiar) with biosimilars, only 50.6% of pharmacists reported the same level of familiarity with interchangeable biosimilars. The naming convention selected for biosimilars will play a pivotal role in the substitution practices of interchangeable biosimilars, given that most pharmacists have the highest level of confidence of substitution only when the interchangeable biosimilar and reference product share the same active ingredient or nonproprietary name. Limitations Limitations to this analysis include the lack of temporal data to determine if views have changed over time given the increased March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 193 Assessment of Pharmacists’ Views on Biosimilar Naming Conventions FIGURE 4 Influence of Postdispensing Notification Requirements on Pharmacists’ Likelihood of Substitution I would be MORE likely to substitute 4.3% I would be LESS likely to substitute 19.4% Not sure 23.7% It would not affect me 52.7% 0 exposure to these topics in health-related publications and the lack of generalizability given the low overall response rate. ■■ Conclusions The results of the survey used in this analysis highlight the importance of the upcoming FDA decision on how biosimilars will be named. On one hand, results indicate that current processes for identifying biologics may not be sufficient if biosimilars and reference products share the same INN and HCPCS codes. On the other hand, different INNs may influence pharmacists’ likelihood to substitute interchangeable biologics and prevent full adoption of biosimilars in the market, since most pharmacists indicated feeling confident or very confident with biosimilar substitution only when the interchangeable biologic and the reference product shared a generic or nonproprietary name. In addition, based on the lower levels of familiarity with interchangeable biologics and how naming of biosimilars may influence their behavior, this survey also indicates that pharmacists, who will be on the front lines when it comes to dispensing biosimilars, will require substantial education on biosimilars and interchangeable biosimilars prior to the launch of the first agent in the United States. This education should focus on 3 areas: (1) instances where substitution is allowed according to FDA approval (as a biosimilar or interchangeable biologic); (2) appropriate recording of a biologic dispensed for pharmacovigilance efforts; and (3) notification requirements driven by specific state laws. 194 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 0.1 0.2 0.3 N = 93 0.4 0.5 0.6 Authors SARA FERNANDEZ-LOPEZ, PhD, MBA, is Director, Reimbursement Strategy, Xcenda, San Bruno, California, and MOHAMED BASHIR, MHA, is Manager, Market Insights, Xcenda, Charlotte, North Carolina. DENISE KAZZAZ, BA, is Director, Scientific Client Strategies, Xcenda, and TRENT MCLAUGHLIN, BSc, PhD, is Vice President, Scientific Client Strategies, Xcenda, Palm Harbor, Florida. AUTHOR CORRESPONDENCE: Sara Fernandez-Lopez, PhD, MBA, Director, Reimbursement Strategy, Xcenda, AmerisourceBergen Specialty Group, 999 Bayhill Dr., San Bruno, CA 94066. E-mail: [email protected]. DISCLOSURES The study instrument was developed in collaboration with AMCP leadership. The authors report no financial conflicts of interest related to the subject or products mentioned in this article. Study concept and design were contributed by all the authors. Data collection was performed by Kazzaz, Fernandez-Lopez, and Bashir, and data were analysed by Bashir, Fernandez-Lopez, and Kazzaz. The manuscript was written by Fernandez-Lopez, Kazzaz, and Bashir and revised by McLaughlin, Fernandez-Lopez, Kazzaz, and Bashir. REFERENCES 1. Patient Protection and Affordable Care Act. Public Law 111-148: 124 Statute 119. 111th Congress. March 23, 2010. Available at: http://beta.congress. gov/111/plaws/publ148/PLAW-111publ148.pdf. Accessed February 5, 2015. 2. Novartis Global. Media releases. FDA accepts Sandoz application for biosimilar filgrastim. July 24, 2014. Available at: http://www.novartis.com/newsroom/media-releases/en/2014/1835571.shtml. Accessed February 5, 2015. Vol. 21, No. 3 www.amcp.org Assessment of Pharmacists’ Views on Biosimilar Naming Conventions 3. Novartis Global. Media releases. Sandoz biosimilar filgrastim recommended for approval by FDA Oncologic Drugs Advisory Committee. January 7, 2015. Available at: http://www.novartis.com/newsroom/media-releases/ en/2015/1885139.shtml. Accessed February 5, 2015. 4. Celltrion. What’s New? Celltrion files for U.S. FDA approval of Remsima. August 11, 2014. Available at: http://www.celltrion.com/en/COMPANY/ notice_view.asp?idx=456&code=ennews&intNowPage=1&menu_ num=&align_year=all. Accessed February 5, 2015. 5. Apotex. Apotex announces FDA has accepted for filing its biosimilar application for pegfilgrastim. December 17, 2014. Available at: http://www. apotex.com/global/about/press/20141217.asp. February 5, 2015. 6. Hospira. Press release. Hospira submits new biologics license application to U.S. FDA for proposed epoetin alfa biosimilar. January 12, 2015. Available at: http://phx.corporate-ir.net/phoenix.zhtml?c=175550&p=irolnewsArticle&ID=2006860. Accessed February 5, 2015. 7. U.S. Food and Drug Administration. Biosimilars guidances. [A complete listing of the biosimilars guidances published between March 2013 and August 2014]. Available at: http://www.fda.gov/Drugs/ GuidanceComplianceRegulatoryInformation/Guidances/ucm290967.htm. Accessed February 5, 2015. 12. American Medical Association. How to apply for a name. Available at: http://www.ama-assn.org/ama/pub/physician-resources/medical-science/ united-states-adopted-names-council/how-to-apply-for-usan.page. Accessed February 5, 2015. 13. American Medical Association. United States adopted names: latest news. Available at: http://www.ama-assn.org/ama/pub/physician-resources/ medical-science/united-states-adopted-names-council.page. Accessed February 5, 2015. 14. U.S. Food and Drug Administration. 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Available at: http:// www.help.senate.gov/newsroom/press/release/?id=d3a2624c-2410-4d08b76e-883592c3885a. Accessed on February 5, 2015. 9. Letter to Commissioner Magaret A. Hamburg from multiple stakeholder organizations re consumers to the FDA on biosimilars INN. July 1, 2014. Available at: http://www.gphaonline.org/media/cms/Lttr_to_FDA_on_biosimilars_INN_ June_2014.FINAL.pdf. Accessed February 5, 2015. 17. AMCP Task Force on Biosimilar Collective Intelligence Systems. Utilizing data consortia to monitor safety and effectiveness of biosimilars and their innovator products. J Manag Care Spec Pharm. 2015;21(1):23-34. Available at: http://www.amcp.org/WorkArea/DownloadAsset.aspx?id=18903. 10. Dolinar R. Letter to Commissioner Margaret A. Hamburg re the Alliance for Safe Biologic Medicines to the Food and Drug Administration on naming of biologics. August 30, 2012. Available at: http://safebiologics.org/pdf/asbmnaming.pdf. Accessed February 5, 2015. 18. European Medicine Agency. European public assessment reports. Available at: http://www.ema.europa.eu/ema/index.jsp?curl=pages%2Fmedi cines%2Flanding%2Fepar_search.jsp&mid=WC0b01ac058001d124&searc hTab=searchByAuthType&alreadyLoaded=true&isNewQuery=true&status= Authorised&status=Withdrawn&status=Suspended&status=Refused&keyw ord=Enter+keywords&searchType=name&taxonomyPath=&treeNumber=& searchGenericType=biosimilars&genericsKeywordSearch=Submit. Accessed February 5, 2015. 11. Saltonstall PL. Letter to Commissioner Margaret A. Hamburg re National Organization for Rare Disorder (NORD) and adoption of distinguishable names for biologics. June 3, 2014. Available at: http://rarediseases.org/advocacy/policy-statements/NORDSupportsSeparateNamingforBiosimilars.pdf. Accessed February 5, 2015. www.amcp.org Vol. 21, No. 3 March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 195 NOW APPROVED Indication OPDIVO® (nivolumab) is indicated for the treatment of patients with unresectable or metastatic melanoma and disease progression following ipilimumab and, if BRAF V600 mutation positive, a BRAF inhibitor. This indication is approved under accelerated approval based on tumor response rate and durability of response. Continued approval for this indication may be contingent upon verification and description of clinical benefit in the confirmatory trials. Select Important Safety Information OPDIVO is associated with the following Warnings and Precautions including immune-mediated: pneumonitis, colitis, hepatitis, nephritis and renal dysfunction, hypothyroidism, hyperthyroidism, other adverse reactions; and embryofetal toxicity. Please see additional Important Safety Information on adjacent page. IMPORTANT SAFETY INFORMATION Immune-Mediated Pneumonitis Severe pneumonitis or interstitial lung disease, including fatal cases, occurred with OPDIVO® (nivolumab) treatment. Across the clinical trial experience in 574 patients with solid tumors, fatal immune-mediated pneumonitis occurred in 0.9% (5/574) of patients receiving OPDIVO; no cases occurred in Trial 1. In Trial 1, pneumonitis, including interstitial lung disease, occurred in 3.4% (9/268) of patients receiving OPDIVO and none of the 102 patients receiving chemotherapy. Immunemediated pneumonitis occurred in 2.2% (6/268) of patients receiving OPDIVO; one with Grade 3 and five with Grade 2. Monitor patients for signs and symptoms of pneumonitis. Administer corticosteroids for Grade 2 or greater pneumonitis. Permanently discontinue OPDIVO for Grade 3 or 4 and withhold OPDIVO until resolution for Grade 2. Immune-Mediated Colitis In Trial 1, diarrhea or colitis occurred in 21% (57/268) of patients receiving OPDIVO and 18% (18/102) of patients receiving chemotherapy. Immune-mediated colitis occurred in 2.2% (6/268) of patients receiving OPDIVO; five with Grade 3 and one with Grade 2. Monitor patients for immune-mediated colitis. Administer corticosteroids for Grade 2 (of more than 5 days duration), 3, or 4 colitis. Withhold OPDIVO for Grade 2 or 3. Permanently discontinue OPDIVO for Grade 4 colitis or recurrent colitis upon restarting OPDIVO. Immune-Mediated Hepatitis In Trial 1, there was an increased incidence of liver test abnormalities in the OPDIVO-treated group as compared to the chemotherapy-treated group, with increases in AST (28% vs 12%), alkaline phosphatase (22% vs 13%), ALT (16% vs 5%), and total bilirubin (9% vs 0). Immunemediated hepatitis occurred in 1.1% (3/268) of patients receiving OPDIVO; two with Grade 3 and one with Grade 2. Monitor patients for abnormal liver tests prior to and periodically during treatment. Administer corticosteroids for Grade 2 or greater transaminase elevations. Withhold OPDIVO for Grade 2 and permanently discontinue OPDIVO for Grade 3 or 4 immune-mediated hepatitis. Immune-Mediated Nephritis and Renal Dysfunction In Trial 1, there was an increased incidence of elevated creatinine in the OPDIVO-treated group as compared to the chemotherapy-treated group (13% vs 9%). Grade 2 or 3 immune-mediated nephritis or renal dysfunction occurred in 0.7% (2/268) of patients. Monitor patients for elevated serum creatinine prior to and periodically during treatment. For Grade 2 or 3 serum creatinine elevation, withhold OPDIVO and administer corticosteroids; if worsening or no improvement occurs, permanently discontinue OPDIVO. Administer corticosteroids for Grade 4 serum creatinine elevation and permanently discontinue OPDIVO. Immune-Mediated Hypothyroidism and Hyperthyroidism In Trial 1, Grade 1 or 2 hypothyroidism occurred in 8% (21/268) of patients receiving OPDIVO and none of the 102 patients receiving chemotherapy. Grade 1 or 2 hyperthyroidism occurred in 3% (8/268) of patients receiving OPDIVO and 1% (1/102) of patients receiving chemotherapy. Monitor thyroid function prior to and periodically during treatment. Administer hormone replacement therapy for hypothyroidism. Initiate medical management for control of hyperthyroidism. Other Immune-Mediated Adverse Reactions In Trial 1, the following clinically significant, immunemediated adverse reactions occurred in less than 1% of OPDIVO-treated patients: pancreatitis, uveitis, demyelination, autoimmune neuropathy, adrenal insufficiency, and facial and abducens nerve paresis. Across clinical trials of OPDIVO administered at doses 3 mg/kg and 10 mg/kg, additional clinically significant, immune-mediated adverse reactions were identified: hypophysitis, diabetic ketoacidosis, hypopituitarism, Guillian-Barré syndrome, and myasthenic syndrome. Based on the severity of adverse reaction, withhold OPDIVO, administer high-dose corticosteroids, and, if appropriate, initiate hormone-replacement therapy. Embryofetal Toxicity Based on its mechanism of action, OPDIVO can cause fetal harm when administered to a pregnant woman. Advise pregnant women of the potential risk to a fetus. Advise females of reproductive potential to use effective contraception during treatment with OPDIVO and for at least 5 months after the last dose of OPDIVO. Lactation It is not known whether OPDIVO is present in human milk. Because many drugs, including antibodies, are excreted in human milk and because of the potential for serious adverse reactions in nursing infants from OPDIVO, advise women to discontinue breastfeeding during treatment. Serious Adverse Reactions Serious adverse reactions occurred in 41% of patients receiving OPDIVO. Grade 3 and 4 adverse reactions occurred in 42% of patients receiving OPDIVO. The most frequent Grade 3 and 4 adverse drug reactions reported in 2% to <5% of patients receiving OPDIVO were abdominal pain, hyponatremia, increased aspartate aminotransferase, and increased lipase. Common Adverse Reactions The most common adverse reaction (≥20%) reported with OPDIVO was rash (21%). Please see brief summary of Full Prescribing Information on the following pages. References: 1 OPDIVO [package insert]. Princeton, NJ: Bristol-Myers Squibb Company; 2014. OPDIVO® and the related logo are trademarks of Bristol-Myers Squibb Company. ©2014 Bristol-Myers Squibb Company. All rights reserved. Printed in USA. 1506US14BRO1009-01-01 08/14 OPDIVO® (nivolumab) injection, for intravenous use Brief Summary of Prescribing Information. For complete prescribing information consult official package insert. INDICATIONS AND USAGE OPDIVO® (nivolumab) is indicated for the treatment of patients with unresectable or metastatic melanoma and disease progression following ipilimumab and, if BRAF V600 mutation positive, a BRAF inhibitor [see Clinical Studies (14) in full Prescribing Information]. This indication is approved under accelerated approval based on tumor response rate and durability of response. Continued approval for this indication may be contingent upon verification and description of clinical benefit in the confirmatory trials. CONTRAINDICATIONS None. WARNINGS AND PRECAUTIONS Immune-Mediated Pneumonitis Severe pneumonitis or interstitial lung disease, including fatal cases, occurred with OPDIVO treatment. Across the clinical trial experience in 574 patients with solid tumors, fatal immune-mediated pneumonitis occurred in 0.9% (5/574) of patients receiving OPDIVO. No cases of fatal pneumonitis occurred in Trial 1; all five fatal cases occurred in a dose-finding study with OPDIVO doses of 1 mg/kg (two patients), 3 mg/kg (two patients), and 10 mg/kg (one patient). In Trial 1, pneumonitis, including interstitial lung disease, occurred in 3.4% (9/268) of patients receiving OPDIVO and none of the 102 patients receiving chemotherapy. Immune-mediated pneumonitis, defined as requiring use of corticosteroids and no clear alternate etiology, occurred in 2.2% (6/268) of patients receiving OPDIVO: one with Grade 3 and five with Grade 2 pneumonitis. The median time to onset for the six cases was 2.2 months (range: 25 days-3.5 months). In two patients, pneumonitis was diagnosed after discontinuation of OPDIVO for other reasons, and Grade 2 pneumonitis led to interruption or permanent discontinuation of OPDIVO in the remaining four patients. All six patients received high-dose corticosteroids (at least 40 mg prednisone equivalents per day); immune-mediated pneumonitis improved to Grade 0 or 1 with corticosteroids in all six patients. There were two patients with Grade 2 pneumonitis that completely resolved (defined as improved to Grade 0 with completion of corticosteroids) and OPDIVO was restarted without recurrence of pneumonitis. Monitor patients for signs and symptoms of pneumonitis. Administer corticosteroids at a dose of 1 to 2 mg/kg/day prednisone equivalents for Grade 2 or greater pneumonitis, followed by corticosteroid taper. Permanently discontinue OPDIVO for severe (Grade 3) or life-threatening (Grade 4) pneumonitis and withhold OPDIVO until resolution for moderate (Grade 2) pneumonitis [see Dosage and Administration (2.2) in full Prescribing Information]. Immune-Mediated Colitis In Trial 1, diarrhea or colitis occurred in 21% (57/268) of patients receiving OPDIVO and 18% (18/102) of patients receiving chemotherapy. Immune-mediated colitis, defined as requiring use of corticosteroids with no clear alternate etiology, occurred in 2.2% (6/268) of patients receiving OPDIVO: five patients with Grade 3 and one patient with Grade 2 colitis. The median time to onset of immunemediated colitis from initiation of OPDIVO was 2.5 months (range: 1-6 months). In three patients, colitis was diagnosed after discontinuation of OPDIVO for other reasons, and Grade 2 or 3 colitis led to interruption or permanent discontinuation of OPDIVO in the remaining three patients. Five of these six patients received high-dose corticosteroids (at least 40 mg prednisone equivalents) for a median duration of 1.4 months (range: 3 days-2.4 months) preceding corticosteroid taper. The sixth patient continued on low-dose corticosteroids started for another immune-mediated adverse reaction. Immune-mediated colitis improved to Grade 0 with corticosteroids in five patients, including one patient with Grade 3 colitis retreated after complete resolution (defined as improved to Grade 0 with completion of corticosteroids) without additional events of colitis. Grade 2 colitis was ongoing in one patient. Monitor patients for immune-mediated colitis. Administer corticosteroids at a dose of 1 to 2 mg/kg/day prednisone equivalents followed by corticosteroid taper for severe (Grade 3) or life-threatening (Grade 4) colitis. Administer corticosteroids at a dose of 0.5 to 1 mg/kg/day prednisone equivalents followed by corticosteroid taper for moderate (Grade 2) colitis of more than 5 days duration; if worsening or no improvement occurs despite initiation of corticosteroids, increase dose to 1 to 2 mg/kg/day prednisone equivalents. Withhold OPDIVO for Grade 2 or 3 immune-mediated colitis. Permanently discontinue OPDIVO for Grade 4 colitis or for recurrent colitis upon restarting OPDIVO [see Dosage and Administration (2.2) in full Prescribing Information]. Immune-Mediated Hepatitis In Trial 1, there was an increased incidence of liver test abnormalities in the OPDIVO (nivolumab)-treated group as compared to the chemotherapy-treated group, with increases in AST (28% vs. 12%), alkaline phosphatase (22% vs. 13%), ALT (16% vs. 5%), and total bilirubin (9% vs. 0). Immune-mediated hepatitis, defined as requirement for corticosteroids and no clear alternate etiology, occurred in 1.1% (3/268) of patients receiving OPDIVO: two patients with Grade 3 and one patient with Grade 2 hepatitis. The time to onset was 97, 113, and 86 days after initiation of OPDIVO. In one patient, hepatitis was diagnosed after discontinuation of OPDIVO for other reasons. In two patients, OPDIVO was withheld. All three patients received high-dose corticosteroids (at least 40 mg prednisone equivalents). Liver tests improved to Grade 1 within 4-15 days of initiation of corticosteroids. Immune-mediated hepatitis resolved and did not recur with continuation of corticosteroids in two patients; the third patient died of disease progression with persistent hepatitis. The two patients with Grade 3 hepatitis that resolved restarted OPDIVO and, in one patient, Grade 3 immunemediated hepatitis recurred resulting in permanent discontinuation of OPDIVO. Monitor patients for abnormal liver tests prior to and periodically during treatment. Administer corticosteroids at a dose of 1 to 2 mg/kg/day prednisone equivalents for Grade 2 or greater transaminase elevations, with or without concomitant elevation in total bilirubin. Withhold OPDIVO for moderate (Grade 2) and permanently discontinue OPDIVO for severe (Grade 3) or life-threatening (Grade 4) immune-mediated hepatitis [see Dosage and Administration (2.2) in full Prescribing Information and Adverse Reactions]. Immune-Mediated Nephritis and Renal Dysfunction In Trial 1, there was an increased incidence of elevated creatinine in the OPDIVOtreated group as compared to the chemotherapy-treated group (13% vs. 9%). Grade 2 or 3 immune-mediated nephritis or renal dysfunction (defined as ≥ Grade 2 increased creatinine, requirement for corticosteroids, and no clear alternate etiology) occurred in 0.7% (2/268) of patients at 3.5 and 6 months after OPDIVO initiation, respectively. OPDIVO was permanently discontinued in both patients; both received high-dose corticosteroids (at least 40 mg prednisone equivalents). Immune-mediated nephritis resolved and did not recur with continuation of corticosteroids in one patient. Renal dysfunction was ongoing in one patient. Monitor patients for elevated serum creatinine prior to and periodically during treatment. Administer corticosteroids at a dose of 1 to 2 mg/kg/day prednisone equivalents followed by corticosteroid taper for life-threatening (Grade 4) serum creatinine elevation and permanently discontinue OPDIVO. For severe (Grade 3) or moderate (Grade 2) serum creatinine elevation, withhold OPDIVO and administer corticosteroids at a dose of 0.5 to 1 mg/kg/day prednisone equivalents followed by corticosteroid taper; if worsening or no improvement occurs, increase dose of corticosteroids to 1 to 2 mg/kg/day prednisone equivalents and permanently discontinue OPDIVO [see Dosage and Administration (2.2) in full Prescribing Information and Adverse Reactions]. Immune-Mediated Hypothyroidism and Hyperthyroidism In Trial 1, where patients were evaluated at baseline and during the trial for thyroid function, Grade 1 or 2 hypothyroidism occurred in 8% (21/268) of patients receiving OPDIVO and none of the 102 patients receiving chemotherapy. The median time to onset was 2.5 months (range: 24 days-11.7 months). Seventeen of the 21 patients with hypothyroidism received levothyroxine. Fifteen of 17 patients received subsequent OPDIVO dosing while continuing to receive levothyroxine. Grade 1 or 2 hyperthyroidism occurred in 3% (8/268) of patients receiving OPDIVO and 1% (1/102) of patients receiving chemotherapy. The median time to onset in OPDIVO-treated patients was 1.6 months (range: 0-3.3 months). Four of five patients with Grade 1 hyperthyroidism and two of three patients with Grade 2 hyperthyroidism had documented resolution of hyperthyroidism; all three patients received medical management for Grade 2 hyperthyroidism. Monitor thyroid function prior to and periodically during treatment. Administer hormone replacement therapy for hypothyroidism. Initiate medical management for control of hyperthyroidism. There are no recommended dose adjustments of OPDIVO for hypothyroidism or hyperthyroidism. Other Immune-Mediated Adverse Reactions Other clinically significant immune-mediated adverse reactions can occur. Immunemediated adverse reactions may occur after discontinuation of OPDIVO therapy. The following clinically significant, immune-mediated adverse reactions occurred in less than 1% of OPDIVO-treated patients in Trial 1: pancreatitis, uveitis, demyelination, autoimmune neuropathy, adrenal insufficiency, and facial and abducens nerve paresis. Across clinical trials of OPDIVO administered at doses of 3 mg/kg and 10 mg/kg the following additional clinically significant, immune-mediated adverse reactions were identified: hypophysitis, diabetic ketoacidosis, hypopituitarism, Guillain-Barré syndrome, and myasthenic syndrome. For any suspected immune-mediated adverse reactions, exclude other causes. Based on the severity of the adverse reaction, withhold OPDIVO (nivolumab), administer high-dose corticosteroids, and if appropriate, initiate hormonereplacement therapy. Upon improvement to Grade 1 or less, initiate corticosteroid taper and continue to taper over at least 1 month. Consider restarting OPDIVO after completion of corticosteroid taper based on the severity of the event [see Dosage and Administration (2.2) in full Prescribing Information]. Embryofetal Toxicity Based on its mechanism of action and data from animal studies, OPDIVO can cause fetal harm when administered to a pregnant woman. In animal reproduction studies, administration of nivolumab to cynomolgus monkeys from the onset of organogenesis through delivery resulted in increased abortion and premature infant death. Advise pregnant women of the potential risk to a fetus. Advise females of reproductive potential to use effective contraception during treatment with OPDIVO and for at least 5 months after the last dose of OPDIVO [see Use in Specific Populations]. ADVERSE REACTIONS The following adverse reactions are discussed in greater detail in other sections of the labeling. • Immune-Mediated Pneumonitis [see Warnings and Precautions] • Immune-Mediated Colitis [see Warnings and Precautions] • Immune-Mediated Hepatitis [see Warnings and Precautions] • Immune-Mediated Nephritis and Renal Dysfunction [see Warnings and Precautions] • Immune-Mediated Hypothyroidism and Hyperthyroidism [see Warnings and Precautions] • Other Immune-Mediated Adverse Reactions [see Warnings and Precautions] Clinical Trials Experience Because clinical trials are conducted under widely varying conditions, the adverse reaction rates observed in the clinical trials of a drug cannot be directly compared to rates in the clinical trials of another drug and may not reflect the rates observed in clinical practice. The data described in the WARNINGS and PRECAUTIONS section and below reflect exposure to OPDIVO in Trial 1, a randomized, open-label trial in which 370 patients with unresectable or metastatic melanoma received OPDIVO 3 mg/kg every 2 weeks (n=268) or investigator’s choice of chemotherapy (n=102), either dacarbazine 1000 mg/m2 every 3 weeks or the combination of carboplatin AUC 6 every 3 weeks plus paclitaxel 175 mg/m2 every 3 weeks [see Clinical Studies (14) in full Prescribing Information]. The median duration of exposure was 5.3 months (range: 1 day-13.8+ months) with a median of eight doses (range: 1 to 31) in OPDIVO-treated patients and was 2 months (range: 1 day-9.6+ months) in chemotherapy treated patients. In this ongoing trial, 24% of patients received OPDIVO for greater than 6 months and 3% of patients received OPDIVO for greater than 1 year. Clinically significant adverse reactions were also evaluated in 574 patients with solid tumors enrolled in two clinical trials receiving OPDIVO at doses of 0.1 to 10 mg/kg every 2 weeks, supplemented by immune-mediated adverse reaction reports across ongoing clinical trials [see Warnings and Precautions]. In Trial 1, patients had documented disease progression following treatment with ipilimumab and, if BRAF V600 mutation positive, a BRAF inhibitor. The trial excluded patients with autoimmune disease, prior ipilimumabrelated Grade 4 adverse reactions (except for endocrinopathies) or Grade 3 ipilimumab-related adverse reactions that had not resolved or were inadequately controlled within 12 weeks of the initiating event, patients with a condition requiring chronic systemic treatment with corticosteroids (>10 mg daily prednisone equivalent) or other immunosuppressive medications, a positive test for hepatitis B or C, and a history of HIV. The study population characteristics in the OPDIVO group and the chemotherapy group were similar: 66% male, median age 59.5 years, 98% white, baseline ECOG performance status 0 (59%) or 1 (41%), 74% with M1c stage disease, 73% with cutaneous melanoma, 11% with mucosal melanoma, 73% received two or more prior therapies for advanced or metastatic disease, and 18% had brain metastasis. There were more patients in the OPDIVO group with elevated LDH at baseline (51% vs. 38%). OPDIVO was discontinued for adverse reactions in 9% of patients. Twenty-six percent of patients receiving OPDIVO had a drug delay for an adverse reaction. Serious adverse reactions occurred in 41% of patients receiving OPDIVO. Grade 3 and 4 adverse reactions occurred in 42% of patients receiving OPDIVO. The most frequent Grade 3 and 4 adverse reactions reported in 2% to less than 5% of patients receiving OPDIVO were abdominal pain, hyponatremia, increased aspartate aminotransferase, and increased lipase. Table 1 summarizes the adverse reactions that occurred in at least 10% of OPDIVO-treated patients. The most common adverse reaction (reported in at least 20% of patients) was rash. Table 1: Selected Adverse Reactions Occurring in ≥10% of OPDIVO (nivolumab)-Treated Patients and at a Higher Incidence than in the Chemotherapy Arm (Between Arm Difference of ≥5% [All Grades] or ≥2% [Grades 3-4]) (Trial 1) OPDIVO (n=268) All Grades All Grades Grades 3-4 Percentage (%) of Patients Adverse Reaction Skin and Subcutaneous Tissue Disorders Rasha Pruritus Respiratory, Thoracic, and Mediastinal Disorders Cough Infections and Infestations Upper respiratory tract infectionb General Disorders and Administration Site Conditions Peripheral edema a Grades 3-4 Chemotherapy (n=102) 21 19 0.4 0 7 3.9 0 0 17 0 6 0 11 0 2.0 0 10 0 5 0 Rash is a composite term which includes maculopapular rash, rash erythematous, rash pruritic, rash follicular, rash macular, rash papular, rash pustular, rash vesicular, and dermatitis acneiform. b Upper respiratory tract infection is a composite term which includes rhinitis, pharyngitis, and nasopharyngitis. Other clinically important adverse reactions in less than 10% of patients treated with OPDIVO were: Cardiac Disorders: ventricular arrhythmia Eye Disorders: iridocyclitis General Disorders and Administration Site Conditions: infusion-related reactions Investigations: increased amylase, increased lipase Nervous System Disorders: dizziness, peripheral and sensory neuropathy Skin and Subcutaneous Tissue Disorders: exfoliative dermatitis, erythema multiforme, vitiligo, psoriasis. Table 2: Selected Laboratory Abnormalities Increased from Baseline Occurring in ≥10% of OPDIVO-Treated Patients and at a Higher Incidence than in the Chemotherapy Arm (Between Arm Difference of ≥5% [All Grades] or ≥2% [Grades 3-4]) (Trial 1) Percentage of Patients with Worsening Laboratory Test from Baselinea OPDIVO Test Increased AST Increased alkaline phosphatase Hyponatremia Increased ALT Hyperkalemia a Chemotherapy All Grades Grades 3-4 All Grades Grades 3-4 28 22 2.4 2.4 12 13 1.0 1.1 25 16 15 5 1.6 2.0 18 5 6 1.1 0 0 Each test incidence is based on the number of patients who had both baseline and at least one on-study laboratory measurement available: OPDIVO group (range 252 to 256 patients) and chemotherapy group (range 94 to 96 patients). Immunogenicity As with all therapeutic proteins, there is a potential for immunogenicity. Of 281 patients who were treated with OPDIVO 3 mg/kg every 2 weeks and evaluable for the presence of anti-product antibodies, 24 patients (8.5%) tested positive for treatment-emergent anti-product antibodies by an electrochemiluminescent (ECL) assay. Neutralizing antibodies were detected in two patients (0.7%). There was no evidence of altered pharmacokinetic profile or toxicity profile with anti-product binding antibody development based on the population pharmacokinetic and exposure-response analyses. The detection of antibody formation is highly dependent on the sensitivity and specificity of the assay. Additionally, the observed incidence of antibody (including neutralizing antibody) positivity in an assay may be influenced by several factors including assay methodology, sample handling, timing of sample collection, concomitant medications, and underlying disease. For these reasons, comparison of incidence of antibodies to OPDIVO (nivolumab) with the incidences of antibodies to other products may be misleading. DRUG INTERACTIONS No formal pharmacokinetic drug-drug interaction studies have been conducted with OPDIVO. USE IN SPECIFIC POPULATIONS Pregnancy Risk Summary Based on its mechanism of action [see Clinical Pharmacology (12.1) in full Prescribing Information] and data from animal studies, OPDIVO can cause fetal harm when administered to a pregnant woman [see Clinical Pharmacology (12.1) in full Prescribing Information]. In animal reproduction studies, administration of nivolumab to cynomolgus monkeys from the onset of organogenesis through delivery resulted in increased abortion and premature infant death [see Data]. Human IgG4 is known to cross the placental barrier and nivolumab is an immunoglobulin G4 (IgG4); therefore, nivolumab has the potential to be transmitted from the mother to the developing fetus. The effects of OPDIVO are likely to be greater during the second and third trimesters of pregnancy. There are no available human data informing the drug-associated risk. Advise pregnant women of the potential risk to a fetus. The background risk of major birth defects and miscarriage for the indicated population is unknown; however, the background risk in the U.S. general population of major birth defects is 2-4% and of miscarriage is 15-20% of clinically recognized pregnancies. Data Animal Data A central function of the PD-1/PD-L1 pathway is to preserve pregnancy by maintaining maternal immune tolerance to the fetus. Blockade of PD-L1 signaling has been shown in murine models of pregnancy to disrupt tolerance to the fetus and to increase fetal loss. The effects of nivolumab on prenatal and postnatal development were evaluated in monkeys that received nivolumab twice weekly from the onset of organogenesis through delivery, at exposure levels of between 9 and 42 times higher than those observed at the clinical dose of 3 mg/kg of nivolumab (based on AUC). Nivolumab administration resulted in a non-doserelated increase in spontaneous abortion and increased neonatal death. Based on its mechanism of action, fetal exposure to nivolumab may increase the risk of developing immune-mediated disorders or altering the normal immune response and immune-mediated disorders have been reported in PD-1 knockout mice. In surviving infants (18 of 32 compared to 11 of 16 vehicle-exposed infants) of cynomolgus monkeys treated with nivolumab, there were no apparent malformations and no effects on neurobehavioral, immunological, or clinical pathology parameters throughout the 6-month postnatal period. Lactation Risk Summary It is not known whether OPDIVO is present in human milk. Because many drugs, including antibodies are excreted in human milk and because of the potential for serious adverse reactions in nursing infants from OPDIVO, advise women to discontinue breastfeeding during treatment with OPDIVO. Females and Males of Reproductive Potential Contraception Based on its mechanism of action, OPDIVO can cause fetal harm when administered to a pregnant woman [see Use in Specific Populations]. Advise females of reproductive potential to use effective contraception during treatment with OPDIVO and for at least 5 months following the last dose of OPDIVO. Pediatric Use The safety and effectiveness of OPDIVO have not been established in pediatric patients. Geriatric Use Clinical studies of OPDIVO did not include sufficient numbers of patients aged 65 years and older to determine whether they respond differently from younger patients. Of the 272 patients randomized to OPDIVO in Trial 1, 35% of patients were 65 years or older and 15% were 75 years or older. Renal Impairment Based on a population pharmacokinetic analysis, no dose adjustment is recommended in patients with renal impairment [see Clinical Pharmacology (12.3) in full Prescribing Information]. Hepatic Impairment Based on a population pharmacokinetic analysis, no dose adjustment is recommended for patients with mild hepatic impairment. OPDIVO (nivolumab) has not been studied in patients with moderate or severe hepatic impairment [see Clinical Pharmacology (12.3) in full Prescribing Information]. OVERDOSAGE There is no information on overdosage with OPDIVO. PATIENT COUNSELING INFORMATION Advise the patient to read the FDA-approved patient labeling (Medication Guide). Inform patients of the risk of immune-mediated adverse reactions that may require corticosteroid treatment and interruption or discontinuation of OPDIVO, including: • Pneumonitis: Advise patients to contact their healthcare provider immediately for any new or worsening cough, chest pain, or shortness of breath [see Warnings and Precautions]. • Colitis: Advise patients to contact their healthcare provider immediately for diarrhea or severe abdominal pain [see Warnings and Precautions]. • Hepatitis: Advise patients to contact their healthcare provider immediately for jaundice, severe nausea or vomiting, pain on the right side of abdomen, lethargy, or easy bruising or bleeding [see Warnings and Precautions]. • Nephritis and Renal Dysfunction: Advise patients to contact their healthcare provider immediately for signs or symptoms of nephritis including decreased urine output, blood in urine, swelling in ankles, loss of appetite, and any other symptoms of renal dysfunction [see Warnings and Precautions]. • Hypothyroidism and Hyperthyroidism: Advise patients to contact their healthcare provider immediately for signs or symptoms of hypothyroidism and hyperthyroidism [see Warnings and Precautions]. Advise patients of the importance of keeping scheduled appointments for blood work or other laboratory tests [see Warnings and Precautions]. Advise females of reproductive potential of the potential risk to a fetus and to inform their healthcare provider of a known or suspected pregnancy [see Warnings and Precautions, Use in Specific Populations]. Advise females of reproductive potential to use effective contraception during treatment with OPDIVO and for at least 5 months following the last dose of OPDIVO [see Use in Specific Populations]. Advise women not to breastfeed while taking OPDIVO [see Use in Specific Populations]. Manufactured by: Bristol-Myers Squibb Company Princeton, NJ 08543 USA U.S. License No. 1713 1321663A0 Issued December 2014 1506US14BR02624-01-01 RESEARCH Patterns of Medication Utilization and Costs Associated with the Use of Etanercept, Adalimumab, and Ustekinumab in the Management of Moderate-to-Severe Psoriasis Steven R. Feldman, MD; Yang Zhao, PhD; Prakash Navaratnam, RPh, MPH, PhD; Howard S. Friedman, PhD; Jackie Lu, PharmD, MS; and Mary Helen Tran, PharmD, MBA ABSTRACT BACKGROUND: Dose escalations of biologic agents may be attempted in the management of moderate-to-severe psoriasis. This has implications for the real-world cost of treatment. OBJECTIVE: To examine the utilization patterns and costs associated with the use of etanercept, adalimumab, and ustekinumab among patients with moderate-to-severe psoriasis. METHODS: This was a retrospective cross-sectional study. Patients with 2 or more medical claims with a diagnosis of psoriasis (excluding psoriatic arthritis) who were enrolled in large employer-sponsored health plans (including a pharmacy benefit) in the United States from January 2007 to March 2012 were identified and extracted from the MarketScan Commercial Encounters Database. Patients aged at least 18 years were required to have 2 or more pharmacy claims for etanercept, adalimumab, or ustekinumab; the index date was the first biologic fill date. Demographics and comorbidities were identified during the 1-year pre-index period, and medication utilization and costs were evaluated in the 1-year post-index period after a titration period according to the product prescribing information (2 weeks to 12 weeks). Medication utilization parameters such as dose escalation, dose reduction, persistence, switching, discontinuation, and restarts were assessed at 6, 9, and 12 months from the end of the dose titration window. RESULTS: A total of 4,309 patients were included with a mean average age of 46 years, and 55% were male. Fifty-seven percent of the patients were started on etanercept, 39% on adalimumab, and 5% on ustekinumab. Patients had substantial dose escalation rates (etanercept: 41%; adalimumab: 37%; ustekinumab: 36%, P < 0.05) and discontinuation rates (etanercept: 35%; adalimumab: 27%; ustekinumab: 16%, P < 0.05) over the 12-month post-titration period. Many patients also restarted the same biologic (etanercept: 37%; adalimumab: 10%; ustekinumab: 6%, P < 0.05) or switched to another biologic (etanercept: 15%; adalimumab: 10%; ustekinumab: 5%, P < 0.05) over the 12-month post-titration period. The persistence rates over 12 months were 19%, 53%, and 71% for etanercept, adalimumab, and ustekinumab, respectively (P < 0.05). Close to one-third of the patients at 6 months and 39% at 12 months postdose titration experienced a dose escalation. Approximately half of the patients who experienced a dose escalation also had a discontinuation or a dose reduction over the 12-month post-titration period. CONCLUSIONS: Over one-third of psoriasis patients experienced a dose escalation of their biologic agents, and most of the dose escalation occurred during the first 6 months. Restarting, switching, and discontinuing index biologics were also common. J Manag Care Spec Pharm. 2015;21(3):201-09 Copyright © 2015, Academy of Managed Care Pharmacy. All rights reserved. www.amcp.org Vol. 21, No. 3 What is already known about this subject •Psoriasis is a chronic and painful skin disease that is associated with increased risk of comorbid medical conditions and reduced health-related quality of life. •The cost of treating psoriasis is substantial, owing to the chronic nature of the disease, the need for health care utilization, the presence of comorbidities that also require treatment, and lost work productivity. •Biologic agents represent an increasingly widely used treatment option for patients with moderate-to-severe psoriasis. What this study adds •This retrospective health care claims database analysis provides a real-world perspective on the dosing patterns associated with biologic therapies (etanercept, adalimumab, and ustekinumab) for the management of moderate-to-severe psoriasis. •Dose escalation, dose reduction, restarting, switching, and discontinuation of biologics were common. •Many patients who initiated biologic therapy with etanercept, adalimumab, or ustekinumab experienced medication disruptions in the first year after initiating the therapy. P soriasis is a chronic and painful condition of the skin, most often appearing as red and scaly patches accompanied by itching and sometimes bleeding.1 It is the most prevalent immune-mediated disease in the United States, affecting as many as 7.5 million people.1 The impact of psoriasis goes beyond its dermatologic manifestations. Psoriasis is associated with an elevated risk of serious and chronic conditions, including diabetes mellitus, stroke, hypertension, cardiovascular disease, and cancer.2-5 Furthermore, lower quality of life and higher rates of anxiety, depression, and psychosocial impairment are observed in psoriasis patients when compared with the general population.6,7 Psychological interpersonal difficulties impact all aspects of daily life for psoriasis patients; anxiety and pathological worry occur in at least one-third of these patients.7 Given that psoriasis most often strikes between the ages of 15 and 35, patients will require lifelong attention and March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 201 Patterns of Medication Utilization and Costs Associated with the Use of Etanercept, Adalimumab, and Ustekinumab in the Management of Moderate-to-Severe Psoriasis treatment.1 Consequently, the economic burden of psoriasis is considerable for patients and the health care system. In 2008, the U.S. total direct medical costs and indirect costs (i.e., lost productivity) of psoriasis were estimated at $11.25 billion annually, with lost time at work accounting for 40% of the economic burden.8 People with psoriasis have significantly higher health care resource utilization and costs than the general population.9 Additionally, the comorbid conditions in patients with psoriasis represent a substantial economic burden to society.10 According to American Academy of Dermatology psoriasis treatment guidelines, several treatment options are available for psoriasis patients depending on the severity of the disease.11 Patients with mild psoriasis are often treated with prescription and over-the-counter topical moisturizers, creams, and ointments. Moderate and severe psoriasis is often treated with topical medications as well as other treatments, including phototherapy, systemic pharmaceuticals (i.e., acitretin, cyclosporine, and methotrexate), and biologic agents.1 Since their introduction more than a decade ago, biologic agents have represented an increasingly widely used treatment option for patients with moderate-to-severe psoriasis who have not responded to or have experienced side effects from other treatments.12 These agents act by blocking the action of specific types of immune cells, such as the T cell, or by blocking proteins in the immune system, such as tumor necrosis factor-alpha (TNF-alpha) or interleukins 12 and 23 (IL-12 and IL-23).1 Etanercept and adalimumab are 2 of the most commonly used TNF-alpha blockers, but both agents may increase risk of serious infections, and malignancies have been reported with both.13,14 A recent survey by the National Psoriasis Foundation reported that, in 2011, approximately 12% of psoriasis patients were using either etanercept or adalimumab.12 This survey also found that 50% of patients were dissatisfied with their psoriasis treatment. The primary reasons for discontinuation of biologic agents were lack of efficacy (25%) and adverse effects (16%).12 Ustekinumab is a commonly used IL-12 and IL-23 antagonist. Although randomized trials have demonstrated the efficacy of ustekinumab in psoriasis, there has been concern that ustekinumab may increase the risk of infections or malignancy15 or increase the risk of cardiovascular events.16 A recent review of pooled data from phase 2 and phase 3 trials of ustekinumab, however, did not reveal an increased risk of these events.17 Although the standard approved dosing regimens of these biologic agents have been established in large, randomized, double-blind, placebo-controlled clinical trials, clinical experience in real-world practice settings suggests that off-label dosing regimens are frequently used and clinically relevant.18-26 Off-label dosing regimens can be broadly categorized as dose escalations, dose reductions, intermittent therapy, or interruptions in therapy followed by retreatment.18 The reasons for these dose adjustments may be to improve efficacy in very 202 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 severe cases, address increasing dose tolerance over time, manage patients with obesity for whom standard dosing may not be adequate, or prepare for surgeries with significant infectious risks.18 A recent systematic review by Brezinski et al. (2012) identified 23 studies in which off-label dosing regimens of etanercept, adalimumab, or ustekinumab were being used in clinical practice to address issues with nonresponders or gaps in efficacy.18 The authors found that, among nonresponders, the use of off-label dosing regimens (i.e., dose escalations) resulted in greater efficacy than standard dosing regimens.18 Given the high costs associated with biologic agents, the real-world dosing of these agents may have significant cost implications for clinicians and formulary decision makers within managed care organizations. The variations in dosing schedules combined with the influence of gaps in therapy, discontinuation, or switching between agents within a given class make it challenging for clinicians and other providers to determine the true economic impact of these agents. The primary objective of this study was to examine the medication utilization patterns (dosing, persistence, switching, discontinuation, restarts), patient demographics, and clinical characteristics associated with the use of etanercept, adalimumab, and ustekinumab among patients with moderate-tosevere psoriasis in a real-world setting. A secondary objective of the study was to examine the time to dose escalation for each of the study biologics. ■■ Methods Study Design Retrospective analyses were performed using health care claims data from a large U.S. employer-based claims database, the MarketScan Commercial Encounters Database, between January 2007 and March 2012. Nearly 29 million individuals are included in the database, which encompasses employees, their spouses, and dependents who are covered by employersponsored private health insurance, including a pharmacy benefit. The MarketScan Commercial Encounters Database provides data on hospitalizations, inpatient and outpatient services, emergency room visits, inpatient and outpatient diagnoses (using International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes), outpatient prescription drugs, and costs. This database fully integrates the pharmacy and medical claims records at the patient level and allows for the tracking of patient information across sites, across providers, and over time. Additionally, the MarketScan Commercial Encounters Database has broad geographic coverage at the national and state levels. This database is compliant with the Health Insurance Portability and Accountability Act and contains synthetic identifiers to protect the privacy of individual patients and data contributors. Vol. 21, No. 3 www.amcp.org Patterns of Medication Utilization and Costs Associated with the Use of Etanercept, Adalimumab, and Ustekinumab in the Management of Moderate-to-Severe Psoriasis TABLE 1 Dosage Strengths and Dose Titration Schedules for Etanercept, Adalimumab, and Ustekinumab Induction Etanercept 50 mg twice weekly for 3 months followed by 50 mg per week Adalimumab 80 mg initial dose followed by 40 mg 1 week after initial dose Ustekinumab < 100 kg (220 lb): 45 mg initially and 4 weeks later; then 45 mg every 12 weeks kg = kilogram; lb = pounds; mg = milligram. Time to Maintenance Dose 12 weeks from initiation 2 weeks from initiation 4 weeks after initiation Patient Selection Adults who were at least aged 18 years with moderate-tosevere psoriasis as determined by treatment with etanercept, adalimumab, or ustekinumab (which are only indicated for treatment of moderate-to-severe disease) were included in the study if they had 2 or more pharmacy claims for these agents between January 1, 2007, and March 20, 2012. Ustekinumab received U.S. Food and Drug Administration (FDA) approval in September 2009, and data for the period from January 2007 through August 2009 were used to capture etanercept and adalimumab patients. The first fill date was defined as the index date, and the biologic received on this date was referred to as the index biologic. Patients were required to have 2 or more distinct medical claims with a diagnosis of psoriasis (ICD-9-CM code 696.1) on or within 1 year prior to the index date. Patients with a diagnosis of psoriatic arthritis (ICD-9-CM code 696.0) were excluded from the analysis. Patients were required to have been continuously enrolled in the health plan for at least 1 year before the index date and 1 year plus the titration window after the index date. The titration window was defined as the period from the initiation of therapy to the end of the recommended induction period according to the product label. Dosage strengths and titration schedules for these agents can be found in Table 1. Patients were excluded for the following reasons: (a) if they received any biologic agent within 1 year before the index date; (b) if they had a concomitant diagnosis of human immunodeficiency virus infection (ICD-9-CM code 042.x) or cancer (ICD9-CM code 140.X-239.x) in the 1-year pre-index period; (c) if they had records with missing dosing information (i.e., missing quantity dispensed or days’ supply information) during the study period; or (d) if they had fewer than 2 medical claims with a diagnosis of psoriasis in the pre-index period. Patients with the following diagnoses in the 1-year pre-index period were also excluded to ensure that the biologic agents examined here were used to treat psoriasis: rheumatoid arthritis (ICD9-CM code 714.2), juvenile idiopathic arthritis (ICD-9-CM www.amcp.org Vol. 21, No. 3 code 714.3), ankylosing spondylitis (ICD-9-CM code 720.0), Crohn’s disease (ICD-9-CM code 555.2), or ulcerative colitis (ICD-9-CM code 556.8). Outcomes Assessments The primary outcomes for this analysis were medication utilization patterns for etanercept, adalimumab, and ustekinumab among patients with moderate-to-severe psoriasis. These outcomes were evaluated in the 1-year post-index period after the dose titration window. Other outcomes included demographics, comorbidities as measured by the Charlson Comorbidity Index (CCI), and all-cause total health care costs (adjusted to year 2012 U.S. dollars based on the Medical Care component of the Consumer Price Index). These outcomes were analyzed during the 1-year pre-index period. The medication utilization patterns examined included dose escalation, dose reduction, persistence, switching, discontinuation, and restarting therapy, as well as the time to dose escalation. Dosing for each biologic was measured in average weekly dose, which was a function of the prescribed strength (based on National Drug Code and J-code descriptors) and the interval between the preceding dose and the next refill. Dose escalation (or reduction) was defined as the patient’s experiencing a dose increase (or decrease) of at least 25% following the titration window. The 25% threshold was chosen based on clinical expert opinion and a literature review of the various methodologies used to define a minimal clinically important difference in dose change associated with biologic agents.27-29 The review revealed that the range for a minimal clinically important difference was between 15% and 50%.27-29 Patients who had an average weekly dose in the post-titration window that exceeded this threshold when compared with their baseline dose (dose at end of titration window) were deemed to have had a dose escalation. In addition, the time to dose escalation (in days) was examined for each biologic over the follow-up period (12 months after the end of the titration window). Medication persistence outcomes were determined based on the permissible treatment gaps for each study biologic. A treatment gap was defined as the time between the preceding fill and the time to the next refill. Permissible treatment gaps were defined as 4 weeks for etanercept, 8 weeks for adalimumab, and 18 weeks for ustekinumab, based on clinical expert opinion. Patients were considered to be persistent if they had no treatment gaps that exceeded the specified time thresholds. Patients were deemed to have switched therapy if they had a treatment gap exceeding the specified time threshold for the index biologic and had initiated a new biologic agent after the preceding fill. If patients were not persistent on the index biologic and there were no biologics identified following the preceding fill, patients were deemed to have discontinued the therapy. If a patient had a treatment gap that exceeded the specified time thresholds and subsequently re-initiated the March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 203 Patterns of Medication Utilization and Costs Associated with the Use of Etanercept, Adalimumab, and Ustekinumab in the Management of Moderate-to-Severe Psoriasis FIGURE 1 Patient Selection Unique Patients Inclusion Criteria 155,982 1. Two or more medication fills for etanercept, adalimumab, or ustekinumab â 22,517 2. Two or more distinct medical claims with a diagnosis of psoriasis within 1 year before index datea â 22,165 3. Aged ≥ 18 years â 11,100 4. Continuously enrolled at least 1 year before the index date and 1 year plus the titration window after the index dateb â Exclusion Criteria 7,321 1. Receiving any biologic within 1 year before index date â 2. Diagnosis of other biologic agent prescribing condition within 1 year before index date 7,274 c â 5,746 3. Diagnosis of HIV or cancer within 1 year before index date â 5,563 4. Missing records for quantity dispensed or days’ supply â 4,309 (final sample size) 5. Only 1 pre-index day with a diagnosis of psoriasis â â â Etanercept Adalimumab Ustekinumab n = 2,452 n = 1,662 n = 195 Data Source: MarketScan Commercial Encounters Database. Study period: January 2007 to March 2012. aThe first fill date was defined as the index date, and the biologic received on this date was referred to as the index biologic. bThe titration window was defined as the period from the initiation of therapy to the end of the induction period as stated in the product label. cPatients with a record of the following diagnoses where biologic agents may be utilized in the 1-year pre-index period were excluded: rheumatoid arthritis, juvenile idiopathic arthritis, ankylosing spondylitis, Crohn’s disease, and ulcerative colitis. HIV = human immunodeficiency virus. same index biologic agent, the patient was deemed to have restarted therapy. Data Analyses Patients were assigned to 1 of 3 unique biologic cohorts based on their index biologic agents. Demographics and comorbidities were analyzed during the 1-year pre-index period to examine differences in patient characteristics between the biologic cohorts. Medication utilization parameters were evaluated in the 1-year post-index period after the titration period (2 to 12 weeks, depending on the prescribing information); chi-square testing was used to examine between-group differences for dose escalation, dose reduction, persistence, restart, switching, and discontinuation. Dose escalation was also assessed at 6 and 9 months from the end of the dose titration window. Total health care costs were annualized and examined during the 1-year pre-index period. Sensitivity analyses were performed to determine the consequences of alternative assumptions about the threshold definition (i.e., change from 25% dose increase to other threshold) of dose escalation. 204 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 ■■ Results Patient Characteristics A total of 4,309 patients met the study criteria and were included in the analysis (Figure 1). The selection process identified 2,452 (56.9%) receiving etanercept, 1,662 (38.6%) receiving adalimumab, and 195 (4.5%) receiving ustekinumab. The majority of patients were male (55.5%), and the average age of patients was 45 years (Table 2). Among all 3 patient groups, ustekinumab patients were older, more likely to have seen a dermatologist, and more likely to have used mail-order prescriptions. In general, all patients had similar comorbidity scores as measured by the CCI. Most patients resided in the South (43%) and North Central (28.5%) regions of the United States. Mean total costs for all patients were $30,568. Dosing Patterns Dose escalation rates over the 12-month follow-up period were substantial (Table 3). Among all study patients, 33.0% experienced a dose escalation at 6 months, 37.2% at 9 months, and 39.1% at 12 months postdose titration. Dose escalation rates at Vol. 21, No. 3 www.amcp.org Patterns of Medication Utilization and Costs Associated with the Use of Etanercept, Adalimumab, and Ustekinumab in the Management of Moderate-to-Severe Psoriasis TABLE 2 Patient Characteristics Characteristic All Etanercept Adalimumab 4,309(100.0) 2,452(56.9) 1,662(38.6) Patients, n (%) 45.7(12.9) 45.5(12.9) 45.5(12.7) Age, years, mean (SD) Male, % 55.5 55.1 56.0 0.4(0.8) 0.4(0.8) 0.4(0.8) CCI, mean (SD)a Region, % Northeast 11.7 11.6 11.4 North Central 28.5 29.0 27.9 South 43.0 41.0 46.2 West 15.9 17.5 13.5 Unknown 1.0 0.9 1.0 Index year, % 2007 16.5 24.9 6.1 2008 28.0 31.9 25.6 2009 24.5 23.0 28.6 2010 23.4 15.9 30.6 2011 7.6 4.3 9.0 Percentage with pre-index mail-order records, % 21.8 20.7 22.1 Percentage with dermatologist as physician specialty, % 82.2 81.4 82.3 30,568 28,018 31,369 Mean total costs,b $ aThe CCI was used to define the comorbid status of each patient. A score of zero represents no serious existing comorbid conditions. bMean total costs were adjusted to year 2012 U.S. dollars based on the Medical Care component of the Consumer Price Index. CCI = Charlson Comorbidity Index; SD = standard deviation. 12 months were 41.0% for etanercept, 36.6% for adalimumab, and 35.9% for ustekinumab (P < 0.05). The median number of days to dose escalation was lowest in the adalimumab cohort (55 days) and highest in the ustekinumab cohort (108 days). Dose reduction rates were substantial, with 50.1% of patients experiencing a dose reduction over the 12-month follow-up period in the full population and 48.7%, 53.7%, and 37.4% in the etanercept, adalimumab, and ustekinumab cohorts, respectively (P < 0.05). Of the 1,355 nonswitch patients who experienced a dose escalation over the 12-month follow-up period, 51% of these patients also experienced a discontinuation or dose reduction (Figure 2). Persistence Patterns Overall, the persistence rate for the 12-month follow-up period for all biologic cohorts was 34.6% (Figure 3). The persistence rate was lowest for etanercept (19.0%) and highest for ustekinumab (70.8%; P < 0.05). Among patients who experienced a treatment gap, many restarted the same biologic (25.3%) or switched to another biologic therapy (12.5%). Approximately 30.8% of patients discontinued their index biologic therapies over the 12-month follow-up period: 34.5% in the etanercept cohort, 27.2% in the adalimumab cohort, and 15.9% in the ustekinumab cohort (P < 0.05). Among all the cohorts, more etanercept patients experienced dose reductions, switches, discontinuations, and restarts, followed by adalimumab and ustekinumab (P < 0.05). www.amcp.org Vol. 21, No. 3 TABLE 3 Ustekinumab 195(4.5) 49.9(13.5) 56.1 0.6(1.3) 14.4 27.2 41.5 15.9 1.0 0.0 0.0 8.2 54.9 36.9 32.8 89.7 55,794 Dose Escalation and Reduction Patterns by Index Biologic Cohort All N = 4,309 Etanercept n = 2,452 Patients with dose Escalation, % at 6 months 33.0 34.3 37.2 39.3 at 9 monthsa 39.1 41.0 at 12 monthsa Patients with dose Reduction, % 50.1 48.7 at 12 monthsa Median days to doseb Escalation 61 a P < 0.05 for between-group comparison. bSignificance test not performed. Adalimumab Ustekinumab n = 1,662 n = 195 31.5 34.5 36.6 28.2 33.3 35.9 53.7 37.4 55 108 Sensitivity analyses completed for nonswitch patients only revealed that the results were sensitive to assumptions regarding the threshold definition of dose escalation, with a higher threshold definition resulting in fewer patients experiencing a dose escalation, as expected (Figure 4). For example, at the 25% threshold used in this study to define dose escalation, the rate of dose escalations was approximately 37%. When the threshold definition was varied to 50%, 23% of patients were identified as having experienced a dose escalation. March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 205 Patterns of Medication Utilization and Costs Associated with the Use of Etanercept, Adalimumab, and Ustekinumab in the Management of Moderate-to-Severe Psoriasis FIGURE 2 Dosing Patterns Among Nonswitch Patients Experiencing Dose Escalation Over 12-Month Post-Titration Period N = 1,355 n = 361 (27%) n = 664 (49%) n = 330 (24%) Escalation only Escalation + Discontinuation Escalation + Reduction (No Discontinuation) ■■ Discussion Analyses of the real-world medication utilization and costs of biologic agents have revealed that managing moderate-tosevere psoriasis is costly and complex. This study found that disruptions in therapy, defined as dose escalations, dose reductions, switches, discontinuations, and restarts, were common, since half of the patients experienced some form of disruption during the 12 months after starting a biologic. One-third of the patients in our analysis experienced a dose escalation within 6 months of therapy initiation, suggesting that inadequate response to or dissatisfaction with standard dosing of biologic agents is common among moderate-to-severe psoriasis patients. Although dose escalation rates were similar for all of the biologics examined, the time to dose escalation varied substantially across biologics. The decision to increase dose might be influenced by many factors, including efficacy, safety, and dosing frequency. Since the reason for dose escalation was not documented in the database examined here, future studies might be needed to focus on this area. Furthermore, dose escalations were commonly followed by dose reductions or discontinuation, implying that higher-than-standard doses of the biologics studied might not be sustainable. 206 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 The overall low persistence rates highlight an unmet need in existing biologic treatment for psoriasis. Only about onethird of patients were persistent with their biologic therapies by the end of the 12-month follow-up period, and one-third of patients ceased treatment with biologics. The remaining patients were either restarted on the same biologic or switched to another agent. Interestingly, restart was more common than switching, which suggests that physicians and patients were inclined to modify treatment using the same biologic rather than try a different biologic agent. The results of our study are consistent with those of other recent claims analyses evaluating the annualized costs and medication outcomes of TNF-alpha inhibitors for managing psoriasis in commercially insured patient populations.30-33 Bonafede et al. (2012) examined the real-world medication utilization patterns (persistence, switching, restart, and discontinuation) from 2005 to 2009 for etanercept and adalimumab in patients with psoriasis, using the same MarketScan Commercial Encounters database.33 Their results demonstrated that disruptions in biologic therapy were quite common (persistence rate of 22% for etanercept and 33% for adalimumab).33 However, an important distinction of our study is that it took a comprehensive approach to analyzing medication utilization outcomes, including dose escalations and dose reductions as well as persistence, switching, restart, and discontinuation. Other claims analyses also have taken a less extensive approach than ours to the assessment of medication utilization outcomes. Recent biologic utilization studies from Europe also examined discontinuation of these agents.34-36 Using a large prospective registry in Denmark, Gniadecki et al. (2015) found that 41% of patients taking either adalimumab, etanercept, infliximab, or ustekinumab had discontinued treatment over 10 years of follow-up.34 Van den Reek et al. (2014)35 and LópezFerrer et al. (2013),36 examining adalimumab utilization at single centers, found discontinuation rates of 39% over 4.5 years of follow-up in the Netherlands and 41% over 5 years in Spain, respectively. Although these rates of discontinuation are higher compared with our findings, the follow-up periods in these studies were 4.5 to 10 years, which were longer than our 1-year post-titration follow-up period.34-36 Overall, our study showed that the management of moderate-to-severe psoriasis requires intensive health care resources. The frequency of dosing practices that deviate from standard regimens suggests that there is a large unmet need for effective and sustainable treatment despite the already substantial economic burden placed on patients with psoriasis and on the health care system. Limitations This study was subject to several limitations. First, information that may affect study outcomes, such as measures of disease Vol. 21, No. 3 www.amcp.org Patterns of Medication Utilization and Costs Associated with the Use of Etanercept, Adalimumab, and Ustekinumab in the Management of Moderate-to-Severe Psoriasis FIGURE 3 Persistence, Restart, Switching, and Discontinuation Rates Over 12-Month Post-Titration Perioda 80 70.8 Patients (%) 70 60 30 20 37.2 34.6 19.0 10 0 All Etanercept Adalimumab Ustekinumab 53.4 50 40 N = 4,309 Persistence 30.8 25.3 9.9 6.2 15.1 12.5 Restart 34.5 27.2 15.9 9.7 5.1 Switching Discontinuation P < 0.05 for all statistical comparisons. a Patients may experience multiple utilization patterns and thus can be counted more than once. www.amcp.org Vol. 21, No. 3 FIGURE 4 Patients with Dose Escalation (%) severity, socioeconomic status, concurrent immunosuppressants or other agents used to treat psoriasis, and allergy profiles were either not readily available in the claims database or not included as part of the data capture. Second, the claims database did not report patients’ body weight, which is an important criterion in selecting the ustekinumab dose. As a consequence, dose escalations associated with ustekinumab were inferred based on observations of dose strength and the interval of ustekinumab dosing. Third, the benefit designs of patients’ health plans are unknown; data regarding preferred specialty pharmacy, prior authorization requirements, and preferred product tiers were unavailable. Fourth, as with most retrospective claims analyses, it is possible that there may have been miscoding of diagnoses, resource use services, or procedures, leading to potential errors in estimation. Independent confirmation of coding or chart review of individual patient records was not possible. Fifth, the patient cohorts were determined from a database encompassing employees, their spouses, and dependents who were covered by large employersponsored private health insurance plans. As a result, the findings of this study may not be generalizable to the entire U.S. population, particularly individuals who are covered under Medicaid or Medicare. Sixth, we retrospectively examined biologic usage from January 2007 through March 2012, but ustekinumab received FDA approval in September 2009. It is possible, therefore, that its use was underrepresented in the patients included in our sample. More up-to-date studies may be needed to confirm our findings with this agent. Most of these limitations are inherent to any claims database analysis and do not preclude the development of clinically relevant conclusions about the real-world utilization of biologic agents in the management of patients with moderateto-severe psoriasis. Dose Escalation Rates in Psoriasis Patients by Various Dose Escalation Definitionsa 60 50 40 30 20 10 0 0 5 10 15 20 25 30 35 40 45 50 Dose Escalation Threshold Definition (% aThe sensitivity analysis was conducted on nonswitch patients only. ■■ Conclusions Using a large and recent U.S. administrative claims database, this study examined health care costs and dosing patterns associated with etanercept, adalimumab, and ustekinumab among moderate-to-severe psoriasis patients. Many patients who initiated biologic therapy with etanercept, adalimumab, or ustekinumab experienced disruptions in therapy over the course of 1 year after starting the treatment. Not only did a high proportion of patients undergo dose escalations, but these escalations also occurred soon after treatment initiation, suggesting an inadequacy of standard dosing regimens or disease exacerbation of increased severity. Dose reductions, restarts, switches, and discontinuations of biologics were also common among these patients. March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 207 Patterns of Medication Utilization and Costs Associated with the Use of Etanercept, Adalimumab, and Ustekinumab in the Management of Moderate-to-Severe Psoriasis Authors STEVEN R. FELDMAN, MD, is Professor of Dermatology, Wake Forest University, Winston-Salem, North Carolina; YANG ZHAO, PhD, is Director, Health Economics and Outcomes Research, Novartis Pharmaceuticals, East Hanover, New Jersey; JACKIE LU, PharmD, MS, is Regional Clinical Account Director, AstraZeneca, Wilmington, Delaware; and MARY HELEN TRAN, PharmD, MBA, is Associate Vice President, Global Value & Access, Sanofi, Bridgewater, New Jersey. PRAKASH NAVARATNAM, RPh, MPH, PhD, is Senior Partner and Director, Business Development, and HOWARD S. FRIEDMAN, PhD, is Senior Partner, DataMed Solutions, New York, New York. AUTHOR CORRESPONDENCE: Yang Zhao, PhD, Director, HEOR, U.S. Medical and Drug Regulatory Affairs, Novartis Pharmaceuticals, One Health Plaza, East Hanover, NJ 07936-1080. Tel.: 862.778.3662; Cell: 201.396.2910; Fax: 973.781.2390; E-mail: [email protected]. 4. Prodanovich S, Kirsner RS, Kravetz JD, Ma F, Martinez L, Federman DG. Association of psoriasis with coronary artery, cerebrovascular, and peripheral vascular diseases and mortality. Arch Dermatol. 2009;145(6):700-03. 5. Solomon DH, Love TJ, Canning C, Schneeweiss S. Risk of diabetes among patients with rheumatoid arthritis, psoriatic arthritis and psoriasis. Ann Rheum Dis. 2010;69(12):2114-17. 6. Kurd SK, Troxel AB, Crits-Christop P, Gelfand JM. The risk of depression, anxiety and suicidality in patients with psoriasis: a population-based cohort study. Arch Dermatol. 2010:146(8):891-95. 7. Langley RG, Krueger GG, Griffiths CE. Psoriasis: epidemiology, clinical features, and quality of life. Ann Rheum Dis. 2005;64(Suppl 2):ii18-23. 8. Fowler JF, Duh MS, Rovba L, et al. The impact of psoriasis on health care costs and patient work loss. J Am Acad Dermatol. 2008;59(5):772-80. 9. Yu AP, Tang J, Xie J, et al. Economic burden of psoriasis compared to the general population and stratified by disease severity. Curr Med Res Opin. 2009;25(10):2429-38. 10. Kimball AB, Guérin A, Tsaneva M, et al. Economic burden of comorbidities in patients with psoriasis is substantial. J Eur Acad Dermatol Venereol. 2011;25(2):157-63. 11. American Academy of Dermatology Work Group, Menter A, Korman NJ, et al. Guidelines of care for the management of psoriasis and psoriatic arthritis: section 6. Guidelines of care for the treatment of psoriasis and psoriatic arthritis: case-based presentations and evidence-based conclusions. J Am Acad Dermatol. 2011;65(1):137-74. DISCLOSURES Support for this research was provided by Novartis Pharmaceuticals, East Hanover, New Jersey. Navaratnam and Friedman are paid consultants for Novartis Pharmaceuticals and are employees of DataMed Solutions. Feldman is a board certified dermatologist on the faculty of the Wake Forest School of Medicine and was engaged by Novartis Pharmaceuticals as a paid clinical expert and scientific advisor for this study. Zhao is an employee of Novartis Pharmaceuticals. Tran and Lu were employees of Novartis Pharmaceuticals Corporation when the study was conducted. Portions of this work were presented at the 2014 Annual Meeting of the Academy of Managed Care Pharmacy, April 1-4, 2014, Tampa, Florida. Study concept and design were contributed by Feldman, Zhao, Friedman, Navaratnam, and Tran. Navaratnam and Friedman were responsible for data collection. Data were interpreted by Zhao, Lu, Tran, Navaratnam, and Friedman, with assistance from Feldman. The manuscript was written primarily by Feldman, Zhao, and Navaratnam, with assistance from Lu, Tran, and Friedman. The manuscript was revised by Zhao, Lu, and Tran, assisted by Navaratnam, Friedman, and Feldman. ACKNOWLEDGMENTS Medical writing assistance was provided by Michael Mafilios at Health Economics Associates, and editorial assistance was provided by BioScience Communications. Medical writing and editorial assistance were funded by Novartis Pharmaceuticals. REFERENCES 1. National Psoriasis Foundation. Psoriasis overview; Psoriasis statistics. Available at: http://www.psoriasis.org/document.doc?id=215 and http:// www.psoriasis.org/research/science-of-psoriasis/statistics. Accessed February 3, 2015. 12. Armstrong AW, Robertson AD, Wu J, Schupp C, Lebwohl MG. Undertreatment, treatment trends, and treatment dissatisfaction among patients with psoriasis and psoriatic arthritis in the United States: findings from the National Psoriasis Foundation surveys, 2003-2011. JAMA Dermatol. 2013;149(10):1180-85. 13. Enbrel (etanercept) solution for subcutaneous use. Amgen Inc. Revised November 2013. Available at: http://pi.amgen.com/united_states/enbrel/ derm/enbrel_pi.pdf. Accessed January 13, 2015. 14. Humira (adalimumab) injection, subcutaneous use. AbbVie Inc. Revised December 2014. Available at: http://www.rxabbvie.com/pdf/humira.pdf. Accessed January 13, 2015. 15. Stelara (ustekinumab) injection, for subcutaneous use. Janssen Biotech, Inc. Revised March 2014. Available at: http://www.stelarainfo.com/pdf/ PrescribingInformation.pdf. Accessed January 13, 2015. 16. Ryan C, Leonardi CL, Krueger JG, et al. Association between biologic therapies for chronic plaque psoriasis and cardiovascular events: a metaanalysis of randomized controlled trials. JAMA. 2011;306(8):864-71. 17. Reich K, Papp KA, Griffiths CE, et al; ACCEPT investigators. An update on the long-term safety experience of ustekinumab: results from the psoriasis clinical development program with up to four years of follow-up. J Drugs Dermatol. 2012;11(3):300-12. 18. Brezinski EA, Armstrong AW. Off-label biologic regimens in psoriasis: a systematic review of efficacy and safety of dose escalation, reduction, and interrupted biologic therapy. PLoS One. 2012;7(4):e33486. 19. Papp KA, Tyring S, Lahfa M, et al; Etanercept Psoriasis Study Group. A global phase III randomized controlled trial of etanercept in psoriasis: safety, efficacy, and effect of dose reduction. Br J Dermatol. 2005;152(6):1304-12. 20. Leonardi CL, Powers JL, Matheson RT, et al; Etanercept Psoriasis Study Group. Etanercept as monotherapy in patients with psoriasis. N Engl J Med. 2003;349(21):2014-22. 2. Kaye JA, Li L, Jick SS. Incidence of risk factors for myocardial infarction and other vascular diseases in patients with psoriasis. Br J Dermatol. 2008;159(4):895-902. 21. Menter A, Tyring SK, Gordon K, et al. Adalimumab therapy for moderate to severe psoriasis: a randomized, controlled phase III trial. J Am Acad Dermatol. 2008;58(1):106-15. 3. Neimann AL, Shin DB, Wang X, Margolis DJ, Troxel AB, Gelfand JM. Prevalence of cardiovascular risk factors in patients with psoriasis. J Am Acad Dermatol. 2006;55(5):829-35. 22. Menter A, Gordon KB, Leonardi CL, Gu Y, Goldblum OM. Efficacy and safety of adalimumab across subgroups of patients with moderate to severe psoriasis. J Am Acad Dermatol. 2010;63(3):448-56. 208 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Vol. 21, No. 3 www.amcp.org Patterns of Medication Utilization and Costs Associated with the Use of Etanercept, Adalimumab, and Ustekinumab in the Management of Moderate-to-Severe Psoriasis 23. Reich K, Nestle FO, Papp K, et al.; EXPRESS study investigators. Infliximab induction and maintenance therapy for moderate-to-severe psoriasis: a phase III, multicentre, double-blind trial. Lancet. 2005;366 (9494):1367-74. 24. Leonardi CL, Kimball AB, Papp KA, et al; PHOENIX 1 Study Investigators. Efficacy and safety of ustekinumab, a human interleukin-12/23 monoclonal antibody, in patients with psoriasis: 76-week results from a randomised, double-blind, placebo-controlled trial (PHOENIX 1). Lancet. 2008;371(9625):1665-74. 25. Papp KA, Langley RG, Lebwohl M, et al; PHOENIX 2 Study Investigators. Efficacy and safety of ustekinumab, a human interleukin-12/23 monoclonal antibody, in patients with psoriasis: 52-week results from a randomised, double-blind, placebo-controlled trial (PHOENIX 2). Lancet. 2008;371(9625):1675-84. 26. Krueger GG, Papp KA, Stough DB, Loven KH, Gulliver WP, Ellis CN; Alefacept Clinical Study Group. A randomized, double-blind, placebocontrolled phase III study evaluating efficacy and tolerability of 2 courses of alefacept in patients with chronic plaque psoriasis. J Am Acad Dermatol. 2002;47(6):821-33. 27. Wu E, Chen L, Birnbaum H, Yang E, Cifaldi M. Retrospective claims data analysis of dosage adjustment patterns of TNF antagonists among patients with rheumatoid arthritis. Curr Med Res Opin. 2008;24(8):2229-40. 28. Huang X, Gu NY, Fox KM, Harrison DJ, Globe D. Comparison of methods for measuring dose escalation of the subcutaneous TNF antagonists for rheumatoid arthritis patients treated in routine clinical practice. Curr Med Res Opin. 2010;26(7):1637-45. www.amcp.org Vol. 21, No. 3 29. Ollendorf DA, Klingman D, Hazard E, Ray S. Differences in annual medication costs and rates of dosage increase between tumor necrosis factorantagonist therapies for rheumatoid arthritis in a managed care population. Clin Ther. 2009;31(4):825-35. 30. Bonafede M, Gandra SR, Watson C, Princic N, Fox KM. Cost per treated patient for etanercept, adalimumab, and infliximab across adult indications: a claims analysis. Adv Ther. 2012;29(3):234-48. 31. Schabert VF, Watson C, Gandra SR, Goodman S, Fox KM, Harrison DJ. Annual costs of tumor necrosis factor inhibitors using real-world data in a commercially insured population in the United States. J Med Econ. 2012;15(2):264-75. 32. Schabert VF, Watson C, Joseph GJ, Iversen P, Burudpakdee C, Harrison DJ. Costs of tumor necrosis factor blockers per treated patient using real-world drug data in a managed care population. J Manag Care Spec Pharm. 2013;19(8):621-30. Available at: http://www.amcp.org/WorkArea/ DownloadAsset.aspx?id=17205. 33. Bonafede M, Fox KM, Watson C, Princic N, Gandra SR. Treatment patterns in the first year after initiating tumor necrosis factor blockers in realworld settings. Adv Ther. 2012;29(8):664-74. 34. Gniadecki R, Bang B, Bryld LE, Iversen L, Lasthein S, Skov L. Comparison of long-term drug survival and safety of biologic agents in patients with psoriasis vulgaris. Br J Dermatol. 2015;172(1):244-252. 35. van den Reek JM, Tummers M, Zweegers J, et al. Predictors of adalimumab drug survival in psoriasis differ by reason for discontinuation: long-term results from the Bio-CAPTURE registry. J Eur Acad Dermatol Venereol. 2014. August 4. [Epub ahead of print]. 36. López-Ferrer A, Vilarrasa E, Gich IJ, Puig L. Adalimumab for the treatment of psoriasis in real life: a retrospective cohort of 119 patients at a single Spanish centre. Br J Dermatol. 2013;169(5):1141-47. March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 209 RESEARCH Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study R. Brett McQueen, PhD; Terrie Livingston, PharmD; Timothy Vollmer, MD; John Corboy, MD; Brieana Buckley, PharmD; Richard Read Allen, MS; Kavita Nair, PhD; and Jonathan D. Campbell, PhD ABSTRACT BACKGROUND: Natalizumab disease-modifying therapy for relapsingremitting multiple sclerosis (MS) is efficacious in randomized controlled trials. Few studies have estimated the association between real-world natalizumab persistence behavior and relapse-related outcomes. OBJECTIVES: To (a) examine the impact of using natalizumab consistently (i.e., persistent) on relapse-related outcomes as compared with transitioning to inconsistent natalizumab use (i.e., nonpersistent) and (b) examine the impact of other treatment patterns on relapse-related outcomes for those who initiated natalizumab. METHODS: Using the IMS PharMetrics Plus claims database (years 20062012), we identified MS subjects who initiated natalizumab (no natalizumab claims in year prior) and had at least 2 years of follow-up. Persistence in annual follow-up periods was defined as no 90-day or greater gap in natalizumab therapy. Relapse was an MS-related hospitalization or outpatient visit with intravenous or oral steroid burst claim within 7 days. Analyses compared observations based on changes in natalizumab persistence and natalizumab nonpersistence status from 1 year to the next (e.g., transitioning from persistent to nonpersistent), estimating differences in mean annual relapses and mean annual relapse-related costs. RESULTS: A total of 2,407 natalizumab initiators had at least 2 years of follow-up, yielding 4,770 year-to-year natalizumab treatment patterns where each subject contributed 1, 2, or 3 year-to-year treatment patterns. In the year prior, 3,187 treatment patterns were persistent; 731 (22.9%) of these transitioned to nonpersistence. The remaining 1,583 treatment patterns were nonpersistent in the year prior; 132 (8.3%) of these transitioned to persistence. Persistent to nonpersistent treatment patterns were associated with a mean relapse-rate increase of 0.23 (95% CI = 0.12, 0.35), and a mean increase in relapse-related costs of $1,346 (95% CI = $97, $2,595). Nonpersistent to persistent treatment patterns were associated with a mean relapse-rate decrease of -0.15 (95% CI = -0.32, 0.017) and a mean decrease in relapse-related costs of -$1,369 (95% CI = -$2,761, $23). CONCLUSIONS: Findings suggest that real-world persistent natalizumab users who become nonpersistent have statistically significant increases in annual relapses and relapse-related costs. Those who transition from nonpersistent to persistent have nonsignificant reductions in relapses and their associated costs. J Manag Care Spec Pharm. 2015;21(3):210-18 Copyright © 2015, Academy of Managed Care Pharmacy. All rights reserved. 210 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 What is already known about this subject •Previous clinical trial studies on natalizumab use in multiple sclerosis (MS) patients have provided evidence that nonpersistence, through switching or discontinuation from natalizumab, has been associated with a return of disease activity. •The use of natalizumab as a second-line agent (i.e., switching to natalizumab from a first-line agent) has been effective in improving outcomes for MS patients. What this study adds •Findings suggest subjects who were persistent on natalizumab prior to a change in treatment without natalizumab (e.g., switch or discontinue) had significant increases in relapses and relapserelated costs. •Subjects who were not persistent on natalizumab prior to a change in treatment with persistent use of natalizumab had nonsignificant reductions in relapses and relapse-related costs. M ultiple sclerosis (MS) is a chronic and debilitating disorder of the central nervous system. Recent research has estimated MS prevalence in the United States at approximately 570,000.1 MS imposes an economic burden on patients with annual medical direct costs of over $20,000 higher than individuals without MS.1 MS is characterized by acute and unpredictable attacks known as relapses, followed by a period of partial or full recovery of symptoms known as remission.2 Disease symptoms such as difficulty with balance, memory problems, poor vision, and speech problems may become progressively worse over time. Approximately 85% of individuals with MS are initially diagnosed with the relapsingremitting (RRMS) form of the disease.3 Most individuals with RRMS will then transition to secondary progressive MS with a median time to transition of 15 years postdiagnosis.4 A small percentage of people are diagnosed with primary progressive MS, characterized by steadily worsening neurologic function from their initial diagnosis. The goal of disease-modifying therapies (DMTs) for the treatment of MS is to prevent or reduce the risk of relapses and slow disease progression. DMTs such as interferons (IFNs) and glatiramer acetate (GA) have been used as first-line agents for the treatment of RRMS.5 However, some patients do not Vol. 21, No. 3 www.amcp.org Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study FIGURE 1 Sample Selection Flowchart n = 207,120 At least 1 diagnosis of MS (ICD-9-CM 340.xx) between January 1, 2006-December 31, 2012 n = 199,503 No natalizumab claims n = 7,617 First initiation of natalizumab between January 1, 2007 - December 31, 2011 (index date) n = 3,047 Not continuously enrolled for 1 year prior to index date n = 4,570 First initiation of natalizumab and continuously enrolled 1 year prior to index date n = 2,163 Not continuously enrolled for 2 years post-index date N = 2,407 New natalizumab users who have continuous enrollment for 1 year prior to index date through at least 2 years of follow-up ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification; MS = multiple sclerosis. respond to treatment. Reasons for switching or discontinuing include tolerability (e.g., skin reaction, injection site reactions, and flu-like symptoms); safety (e.g., allergic reaction, elevations in liver enzymes, depression, and immediate postinjection reaction); lack of efficacy (e.g., no perceived benefit); and burden (e.g., injection frequency).6-8 To maximize the benefits of DMTs, MS patients must be persistent and adherent to their prescribed therapies. Persistence is a measurement of medication behavior and has been defined as “the duration of time from initiation to discontinuation of a therapy.”9 Natalizumab (Tysabri, Biogen Idec) is approved as monotherapy for the treatment of patients with relapsing forms of MS to delay physical disability and reduce relapse activity.10 Data from clinical studies have provided evidence that nonpersistence, through switching or discontinuation from natalizumab, has been associated with a return of disease activity.11,12 Additionally, studies have shown that the use of natalizumab as a second-line agent (i.e., switching to natalizumab from a first-line agent) has been effective in improving outcomes for MS patients.13-15 www.amcp.org Vol. 21, No. 3 Previous studies have measured persistence but have not linked persistence behavior to outcomes such as relapses or relapse-related costs.16-20 Some studies have measured persistence and linked behavior to relapse outcomes;21-28 however, few have focused specifically on natalizumab,13-15,24,29 and of those studies, none have focused on natalizumab treatment patterns and the resulting impact on relapse-related outcomes in a large U.S. commercially insured population. Our objectives in this study were to use a large longitudinal claims database to (a) estimate the change in treatment patterns based on persistence status, with a primary treatment pattern identified as the transition from natalizumab persistent to natalizumab nonpersistent, and (b) estimate the change in relapses and relapse-related costs associated with the change in treatment patterns. ■■ Methods Data Source This retrospective study utilized the IMS PharMetrics Plus claims database, a nationwide U.S. database of longitudinal subject-level health care utilization and expenditure data for commercially insured enrollees. PharMetrics Plus combines data from Blue Health Intelligence for a total of 150 million covered lives; approximately 87 million of those covered lives have both pharmacy and medical coverage across all years. The database includes prescribed medications, diagnoses and procedures, medical and pharmacy costs, demographics, and payer and provider information. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) diagnosis codes were used to identify individuals with MS (ICD9-CM 340.xx). Medical claims were identified using Current Procedural Terminology, Version 4 (CPT-4) procedure codes, Healthcare Common Procedure Coding System (HCPCS) procedure codes, and (ICD-9-CM) procedure codes. National Drug Code numbers 59075-730-15 and 64406-008-01 and HCPCS code J2323 were used to identify natalizumab claims. Sample Selection Figure 1 displays the sample selection process. The study subjects were selected from commercially insured members who initiated natalizumab and had at least 1 previous MS diagnosis. Specifically, inclusion criteria included (a) first initiation of natalizumab between January 1, 2006, and December 31, 2012 (defined as the index date); (b) at least 1 MS diagnosis (ICD-9-CM 340.xx) during the 12-month baseline period prior to index date; (c) no natalizumab claims during the 12-month baseline period prior to index date; and (d) at least 2 years of continuous enrollment post-index date. Each subject provided at least 3 years of observations (baseline or year 0, follow-up year 1, follow-up year 2) and up to 5 years of observations (baseline or year 0, follow-up year 1, follow-up year 2, followup year 3, follow-up year 4). March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 211 Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study Measures Demographics. Subject demographics included age, gender, region, health plan type, and baseline health care utilization. Proxies for baseline MS disease severity were estimated using subject-level counts of MS-related symptoms, mobility status, and the Charlson Comorbidity Index (CCI) score adapted for claims-based analyses.30-32 Baseline MS-related symptoms were captured using ICD-9-CM codes and coded as binary indicator variables. These symptoms were categorized based on previous work by Curkendall et al. (2011)33 and included visual symptoms, movement disorders, facial neuralgia, dizziness, chronic fatigue, paralysis, headache, muscle symptoms, speech symptoms, and disturbance of skin sensation. Mobility status was captured using CPT-4 and HCPCS codes for wheelchairs and walking aids. Treatment Patterns. The primary explanatory variable for relapses and relapse-related costs was the change from natalizumab persistence to nonpersistence over the same 2-year period. Persistence in annual follow-up periods was defined as no gap in natalizumab claims greater than 90 days, which is similar to other claims-based studies.16,23 Nonpersistence was defined as switched to another DMT (i.e., use of 2 or more DMTs within a year and no 90-day or greater gap on the final DMT used at the end of the year); discontinued natalizumab (i.e., no natalizumab or other DMT claim for at least the last 90-day period in the year following natalizumab initiation); or had at least one 90-day or greater gap in natalizumab claims during a calendar year after natalizumab initiation with subsequent natalizumab or other DMT use that did not fall into the switch definition. Previous claims analyses have estimated outcomes separately for those who switch or discontinue, rather than merging these groups into a larger category of nonpersistence.16,23 There were 2 main reasons that we chose to merge switching and discontinuation into the category of nonpersistence: (1) there was little statistical difference in baseline characteristics between the subcategories switch, discontinue, and a 90-day or greater gap following natalizumab initiation (Appendix A, available in online article); and (2) the goal of this analysis was to examine the global impact of using natalizumab consistently as compared with inconsistent natalizumab use, even if that involved switching to another therapy or discontinuing. Merging these groups expanded the traditional definition of nonpersistence to include not only gaps in therapy, but also the clinical failure or intolerability of natalizumab. Those who may have switched to another therapy would be grouped into natalizumab nonpersistence. While the primary analysis focused on the pattern of natalizumab persistence to nonpersistence, we also grouped and estimated additional patterns of persistence and split these observations into 4 mutually exclusive and exhaustive groups for each 2-year period post-index date. Similar to annual 212 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 change in outcomes, subjects contributed a minimum of 1 persistence treatment pattern over 2 years and a maximum of 3 persistence treatment patterns over 4 years after natalizumab initiation. During each year prior to and after observation, a subject could be persistent for 2 years (i.e., persistent to persistent); persistent for the year prior, then nonpersistent during the year after (i.e., persistent to nonpersistent); nonpersistent during the year prior, then persistent during the year after (i.e., nonpersistent to persistent); and nonpersistent during both years prior and after (i.e., nonpersistent to nonpersistent). Relapse Outcomes. Proxies were used to define relapse outcomes. Relapse was defined as an MS-related hospitalization or MS-related emergency or outpatient visit with intravenous or oral steroid burst claim (methylprednisolone, prednisolone, prednisone, or adrenocorticotropic hormone) within 7 days.34,35 Relapse-related cost was defined as the payer and subject paid claims for the associated relapse-related events. All costs were inflated to 2012 U.S. dollars using an average of the medical inflation rate over the study period. The unit of analysis was a within person year-to-year pair of observations from each contributing plan member. The outcomes of interest were the time-varying change in relapses and relapse-related costs following initiation of natalizumab. Change in relapse variables were defined the following: ∆ Annual relapse outcomes = year post relapse outcomes – year prior relapse outcomes Annual relapse outcomes were evaluated at years 1, 2, 3, and 4 post-index date. Subjects contributed a minimum of 1 change in relapse-outcome observation (2 years of follow-up) and a maximum of 3 changes in relapse-outcome observations (4 years of follow-up). Each subject had at least 1 “year prior” outcome and 1 “year post” outcome. For example, a subject with 2 follow-up years post-index date would have 1 change in relapse outcome (change from year 1 post-index date to year 2). Similarly, a subject with 3 follow-up years would have 2 changes in relapse outcomes (change from year 1 post-index date to year 2, and change from year 2 post-index date to year 3). Statistical Analysis. Univariate analysis was used to compare baseline demographics and MS-related measures of disease severity between those who were persistent on natalizumab during the first year post-index date and those who were not persistent in the first year post-index date. We used chisquared distributional tests for categorical variables and independent sample t-tests for continuous variables. The change in relapses and relapse-related costs were estimated within each treatment pattern group using linear regression with cluster-robust standard errors to account for the within subject correlation over time.36,37 This method utilizes the longitudinal nature of the data by analyzing within patient changes in treatment patterns on changes in relapse outcomes. Cluster-robust standard errors allow for intrasubject correlation Vol. 21, No. 3 www.amcp.org Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study by relaxing the assumption of independent observations. That is, the individual subjects themselves are independent but the observations within subjects are not independent. In the multivariable model, we adjusted for prior DMT use (any claim for interferon beta 1a [intramuscular], interferon beta 1a [subcutaneous], interferon beta 1b [subcutaneous], or glatiramer acetate during baseline period), baseline CCI, gender, baseline age, and region of the United States. The estimates are presented as baseline outcomes with the change and 95% confidence interval [CI] in annualized relapse rates and relapse-related costs within each persistent group. We also tested whether the changes in relapse outcomes for each persistent group were different from the reference group persistent to persistent. Relapse counts and relapse-related cost data can often be non-normal and skewed. However, after differencing both variables, histograms and detailed summary statistics showed both variables were normally distributed, indicating the appropriate use of linear regression. Analyses were performed using STATA version 12 (StataCorp, College Station, TX). Sensitivity Analyses. We performed separate subgroup analyses for those subjects contributing at least 2 years, at least 3 years, and at least 4 years of observations to analyze if differences in the number of contributing years from subjects altered the findings. Additionally, the base analysis was performed with inclusion of all baseline MS-related symptom variables regardless of significance level. Concern over inclusion of these variables in the main analysis was due to collinearity between the MS-related symptom variables and CCI, since both are calculated from individual ICD-9-CM coding. Residual plots were used to analyze normality of residuals, assumption of constant variance, and any outliers. We removed any extreme outliers and examined the impact on the results. ■■ Results Table 1 presents the demographic and clinical characteristics before natalizumab initiation for the overall population, for those who were persistent in the initiation year, and for those that were not persistent in the initiation year. A total of 2,407 natalizumab initiators had at least 2 years of follow-up data. The majority of the subjects were females (74%), and the sample was largely insured in preferred provider networks (76%). Seventy-five percent of patients were persistent during the first year post-index date, with prior DMT use significantly different between those persistent during the initiation year and those nonpersistent during the initiation year (P < 0.001). Sixty-two percent of patients attempted at least 1 DMT prior to initiating natalizumab. Treatment Patterns Among the 4 persistence pattern groups identified, there were 4,770 treatment patterns where each subject contributed 1, 2, or 3 year-to-year natalizumab transitions (Table 2). From the 3,187 treatment patterns that were persistent in the year prior, www.amcp.org Vol. 21, No. 3 731 (22.9%) of those transitioned to nonpersistence in the year after, and 2,456 stayed persistent in the year after. The remaining 1,583 treatment patterns were nonpersistent in the year prior; 132 (8.3%) of these transitioned to persistence; and 1,451 (92%) stayed nonpersistent in the year after. Relapse Outcomes Table 3 presents the regression-adjusted relapse rate and relapse-related costs in the year prior to persistence transition for each 1 of the 4 persistence transition groups. On average, relapse rate and relapse-related costs in the year prior to treatment transition were highest for those who were nonpersistent in the year prior and remained nonpersistent (annualized relapse rate = 0.67, 95% CI = 0.52, 0.81; relapse-related cost = $3,816, 95% CI = $3,029, $4,602). The lowest relapse rate and relapse-related cost in the year prior to treatment transition were for those patients who were persistent and remained persistent (annualized relapse rate = 0.28, 95% CI = 0.24, 0.32; relapse-related cost = $1,289, 95% CI = $988, $1,590). Figure 2 and Figure 3 display the respective adjusted longitudinal linear regression results for relapse rates and relapserelated costs within each of the 4 possible persistence transition patterns. There was a statistically significant (P < 0.001) within-group increase in relapse rates of 0.23 (95% CI = 0.12, 0.35) for transitions from persistent to nonpersistent. In percentage terms, transitioning from persistent to nonpersistent was associated with a 52% increase in relapse rates. There was a decrease in relapse rates of -0.15 (95% CI = -0.32, 0.017) for those who transitioned from nonpersistent to persistent, but this change was significant at P = 0.08. Transitioning from nonpersistent to persistent was associated with a 32% decrease in relapse rates. There was also a small decrease in relapse rates of 12% for those transitioning from nonpersistent to nonpersistent (-0.08, 95% CI = -0.16, -0.01). Relapse-related costs in Figure 3 showed a similar pattern. Within-group increases in relapse-related costs were significant for transitions from persistent to nonpersistent ($1,346, 95% CI = $97, $2,595). In percentage terms, transitioning from persistent to nonpersistent was associated with a 58% increase in relapse-related costs. There was a decrease in relapse-related costs for those who transitioned from nonpersistent to persistent (-$1,369, 95% CI = -$2,761, $23), but this within-group change was also only significant at P = 0.054. Transitioning from nonpersistent to persistent was associated with a 56% decrease in relapse-related costs. Differences between groups were also tested for changes in relapses and relapse-related costs. Compared with the persistent to persistent reference group, the relapse rate (0.23 vs. -0.01) and relapse-related cost changes ($1,346 vs. -$130) for persistent to nonpersistent were significantly different at P = 0.045; the relapse-related cost change for nonpersistent to persistent was also significantly different at P = 0.039 (-$1,369 vs. -$130). March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 213 Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study TABLE 1 Demographics Pre-DMT Initiation Persistent in Initiation Year Overall N 2,407 % — N 1,800 % 74.78 Nonpersistent in Initiation Year N 607 % 25.22 P Valuea Total Gender 0.259 Male 621 25.80 450 25.00 171 28.17 Female 1,785 74.16 1,349 74.94 436 71.83 Age group 0.923 0-35 404 16.78 297 16.50 107 17.63 36-44 745 30.95 558 31.00 187 30.81 45-54 843 35.02 635 35.28 208 34.27 55+ 415 17.24 310 17.22 105 17.30 Payer type 0.239 Commercial 1,504 62.48 1,135 63.06 369 60.79 Medicaid or Medicare 42 1.74 29 1.61 13 2.14 Self-insured 861 35.77 636 35.33 225 37.07 Insurance type 0.498 Health maintenance organization 288 11.97 213 11.83 75 12.36 Indemnity 100 4.15 75 4.17 25 4.12 Preferred provider network 1,831 76.07 1,382 76.78 449 73.97 Point of service 154 6.40 105 5.83 49 8.07 Consumer driven 13 0.54 9 0.50 4 0.66 Unknown 21 0.87 16 0.89 5 0.82 Region 0.216 East 743 30.87 557 30.94 186 30.64 Midwest 716 29.75 551 30.61 165 27.18 South 696 28.92 502 27.89 194 31.96 West 252 10.47 190 10.56 62 10.21 MS-related symptomsb Visual 454 18.86 326 18.11 128 21.09 0.105 Movement disorders 662 27.50 507 28.17 155 25.54 0.209 Facial neuralgia 52 2.16 35 1.94 17 2.80 0.210 Dizziness 220 9.14 157 8.72 63 10.38 0.221 Fatigue 52 2.16 38 2.11 14 2.31 0.775 Paralysis 111 4.61 84 4.67 27 4.45 0.824 Headache 33 1.37 25 1.39 8 1.32 0.897 Muscle 245 10.18 178 9.89 67 11.04 0.418 Speech 42 1.74 31 1.72 11 1.81 0.884 Skin disturbance 432 17.95 319 17.72 113 18.62 0.620 Movement aidsc Walker 80 3.32 64 3.56 16 2.64 0.274 Wheelchair 77 3.20 21 1.17 56 9.23 0.673 1,426 59.24 1,111 61.72 315 51.89 < 0.001 Prior DMT use (yes)d Charlson Comorbidity Index (mean, SD) 3.43 1.85 3.41 1.84 3.48 1.91 0.467 Relapse count 0.57 1.14 0.55 1.09 0.62 1.27 0.239 a P value difference between persistent and nonpersistent natalizumab initiators within initiation year; chi-squared distributional test for categorical variables; t-test used for continuous variables. bSymptom codes identified in Table 1 published in Curkendall et al.33 c Movement aid codes identified in Appendix B (available in online article). d Prior DMT use was defined as any claim for interferon beta 1a IM, interferon beta 1a SC, interferon beta 1b SC, or glatiramer acetate during the 12-month period prior to index date. DMT = disease-modifying therapy; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification; IM = intramuscular; SC = subcutaneous; SD = standard deviation. 214 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Vol. 21, No. 3 www.amcp.org Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study TABLE 2 Frequency and Percentage of Treatment Patterns for Natalizumab Initiators with at Least 2 Years of Follow-up Data (N = 2,407) Nonpersistent Persistent in in Year Post Year Post Total Nonpersistent in Year Prior n 1,451 132 1,583 % 91.66 8.34 100.0 Persistent in Year Prior n 731 2,456 3,187 % 22.94 77.06 100.0 Total n 2,182 2,588 4,770 % 45.74 54.26 100.0 Note: n equals the number of year-to-year treatment patterns; % equals the percentage of total treatment patterns based on prior year. Sensitivity Analyses Similar results were found when stratifying the population by contribution of observations. Specifically, the persistent to nonpersistent change in mean relapse (0.23) was positive and significant for all analyses including those subjects contributing at least 2 years, at least 3 years, and at least 4 years. Additionally, findings were consistent with those presented in figures 2 and 3 when including all baseline MS-related symptom variables. The main results presented do not include 1 extreme outlier found in the data. ■■ Discussion In this observational study, we found significant and clinically meaningful increases in relapse counts and relapse-related costs for natalizumab transitions from persistent to nonpersistent. Downward nonsignificant changes in relapse outcomes for natalizumab transitions from nonpersistent to persistent were also observed. Findings suggest subjects who were persistent on natalizumab prior to a change in treatment without natalizumab (e.g., switch or discontinue) had significant increases in relapses and relapse-related costs. Alternatively, subjects who were not persistent on natalizumab prior to a change in treatment with persistent use of natalizumab had nonsignificant reductions in relapses and relapse-related costs. This study adds to previous research by linking persistence behavior over time to relapse-related outcomes for subjects prescribed natalizumab in a large commercially insured MS population in the United States.24,29 An interesting finding from this study comes from those that stayed in the same persistent group from the year prior to the year after treatment transitions. For example, for those who remained nonpersistent in the year prior and after transition, there was a small but significant downward trend in mean relapses. In contrast, for those who remained persistent in the www.amcp.org Vol. 21, No. 3 TABLE 3 Mean Relapse Outcomes in Year Prior to Treatment Transition Nonpersistent in Persistent in Year Post Year Post Nonpersistent in Year Prior n = 1,451 n = 132 0.47 Annual number of relapses 0.67a (0.52, 0.81) (0.27, 0.67) in year prior Annual relapse cost in 3,816 a 2,062 year prior ($) (3,029, 4,602) (855, 3,269) Persistent in Year Prior n = 731 n = 2,456 0.28 Annual number of relapses 0.45a (0.37, 0.54) (0.24, 0.32) in year prior 1,289 Annual relapse cost in 2,645a (1,753, 3,537) (988, 1,590) year prior ($) Note: Data are means (95% CI) in prior year. Costs in 2012 U.S. dollars. a Significant at P < 0.05 when compared with reference group persistent in year prior and year post. CI = confidence interval. year prior and after transition, there was a small nonsignificant downward change in relapses. These changes must be taken in context with the relapse rates in the year prior to treatment transition. That is, the nonpersistent subjects began at a much higher mean annualized relapse rate compared with the persistent subjects in the year prior to treatment transition (0.67 vs. 0.28). This suggests that those subjects who did switch or discontinue natalizumab were on some other treatment or best supportive care to reduce an already clinically high annualized relapse rate. Furthermore, some of these subjects did have natalizumab claims but were not persistent as defined in this study. The treatment pattern results presented in this study are similar to previous research on persistence and discontinuation related to natalizumab.18 Bonafede et al. (2013) also used a large administrative claims database to analyze treatment patterns for commercially insured MS subjects prescribed platform therapies (e.g., IFNs) or natalizumab.18 The authors found that 13.9% of natalizumab users had a first switch and 9% discontinued during the 2 years after natalizumab initiation. This is similar to what is observed in our study where 22.9% who were persistent in the year prior to treatment transition were nonpersistent in the year after treatment transition. Our study adds to the previous work done by Bonafede et al.18 by linking treatment patterns to relapse outcomes. Bergvall et al. (2014) used the PharMetrics Plus database to estimate persistence and differences in health care resource utilization after a switch to natalizumab or fingolimod.24 We found similar persistence percentages after initiation of natalizumab. In our study, we found that approximately 75% of subjects were persistent in the year after initiation of natalizumab; Bergvall et al. found persistence on natalizumab was approximately 76% the year after initiation.24 The authors also found significant reductions in relapses in the 12 months postinitiation of natalizumab compared with a pre-initiation March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 215 Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study 0.8 0.7 0.23b (0.12, 0.34) 0.5 0.3 0.2 -0.02 (-0.07, 0.03) Changes in Mean Relapse Cost by Persistence Status from Prior Year to Post Year 4,500 -0.07a (-0.13, -0.003) 0.6 0.4 FIGURE 3 Changes in Mean Relapse by Persistence Status from Prior Year to Post Year -0.15a (-0.32, 0.013) Prior Year Post Year Change in Relapse and 95% CI Nonpersistent to Nonpersistent (n = 1,451) Persistent to Nonpersistent (n = 731) Nonpersistent to Persistent (n = 132) Persistent to Persistent (n = 2,456) Mean Annual Relapse-Related Cost ($) Mean Annual Relapse Count FIGURE 2 3,500 216 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 1,346a (97, 2,595) 3,000 2,500 -1,369a (-2,761, 23) 2,000 1,500 -130 (-775, 514) 1,000 500 0 Note: 95% CI refers to within-group change in relapse uncertainty. aChange significantly different at P < 0.10 from reference group Persistent to Persistent. b Change significantly different at P < 0.05 from reference group Persistent to Persistent. CI = confidence interval. period using alternative DMTs. While there were similarities in treatment pattern results and relapse outcomes, our study design approach differs from Bergvall et al.24 We focused on a within-natalizumab group analysis with a longer follow-up period after initiation of natalizumab. Our findings are consistent with previous observational studies suggesting a switch or discontinuation from other DMTs (e.g., IFNs or GA) is associated with a return in relapse activity.23,38 Reynolds et al. (2010) used PharMetrics PatientCentric Database to analyze health care resource utilization during an 18-month period after discontinuation or switch from an IFN or GA.23 The authors found that about half of patients remained on their index drugs. Of those that switched or discontinued, significantly higher health care utilization occurred during the 18-month follow-up period compared with those who were persistent on their original index drugs. Raimundo et al. (2013) conducted a subgroup analysis on switchers and discontinuers and found that high relapse activity predicts switching or discontinuing from DMTs among MS patients who had used a DMT previously.38 Our study adds to this research by correlating changes in persistence status with relapse outcomes for natalizumab specifically. Moreover, the results are similar to previous studies suggesting a switch to natalizumab is associated with improvements in health for MS patients.13-15 Recently, Castillo-Trivino et al. (2011) conducted a retrospective cohort study at the University of California San Francisco MS Center to compare clinical -255 (-1,060, 549) 4,000 Prior Year Slope and 95% CI Post Year Nonpersistent to Nonpersistent (n = 1,451) Persistent to Nonpersistent (n = 731) Nonpersistent to Persistent (n = 132) Persistent to Persistent (n = 2,456) Note: 95% CI refers to within-group change in relapse uncertainty. Costs in 2012 U.S. dollars. aChange significantly different at P < 0.05 from reference group Persistent to Persistent. CI = confidence interval. effects of switching to natalizumab from first-line therapies.13 The authors found a significant and clinically meaningful 70% (95% CI = 50%, 82%) reduction in the relapse rate for those switching from first-line DMTs to natalizumab. The study population was focused on MS patients experiencing breakthrough relapsing MS, even with prior DMT treatment. The patient selection in the present study may differ from Castillo-Trivino et al., since nearly 40% of the subjects were DMT treatment naïve as of 1 year prior to natalizumab initiation. Lanzillo et al. (2013) estimated annualized relapse rates (ARR) in a group of MS patients who had been treated with natalizumab for at least 1 year after switching from a first-line DMT.14 They found a significant reduction in ARR within the natalizumab group and a statistically significant ARR between the group that stayed on first-line DMTs. Our results suggest that those who were persistent on natalizumab longer had fewer relapses and relapse-related costs than those who were not persistent. Our relapse-related cost results differ from other studies because of our definition of relapse-related resource utilization. For example, O’Brien et al. (2003) split relapse care and Vol. 21, No. 3 www.amcp.org Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study subsequent health care utilization into 3 levels: high intensity management including hospitalization and follow-up care; medium level of intervention defined as a mix of emergency room visits, administration of acute treatments, or steroid burst in an outpatient setting; and lowest intensity of care including an outpatient visit with symptom-related medication prescriptions.39 Our estimates are in between the highest intensity treatment from O’Brien et al. (mean of $12,870 in 2002 U.S. dollars) and closer to the medium level of care (mean of $1,847). O’Brien et al. included additional resources such as skilled nursing facility use, home health care services, and rehabilitation services. We did not include these additional services in our definition of relapse-related costs. Our relapserelated cost estimates are closer to recent studies on natalizumab such as by Bonafede et al. (2013)29 that estimate relapse costs based on MS-specific resource utilization, along with the use of corticosteroids. Limitations The limitations of this analysis are similar to other claimsbased studies. ICD-9-CM diagnosis codes may have been miscoded. Misclassification bias was likely mitigated by our inclusion criteria (i.e., requiring an MS diagnosis prior to index date and initiation of natalizumab). The ICD-9-CM codes do not provide enough information to distinguish between relapsing-remitting and progressive forms of MS. Natalizumab is indicated for relapsing forms of MS; therefore, patients with progressive forms of MS may have a higher likelihood of nonpersistence with natalizumab.40 Patients with progressive forms of MS are known to have lower relapse rates on average than their RRMS counterparts.41 This limitation would potentially bias our results toward zero. Generalizability of this study limits the interpretation of the results to MS patients enrolled in commercial health plans in the United States. Unfortunately, reasons for a subject being persistent or nonpersistent cannot be fully determined from administrative claims data. We were unable to observe important factors such as JC virus status, duration of disease, Expanded Disability Status Scale, and clinical markers, which could have provided key variables for adjustment at the analysis phase. In an attempt to address these limitations, we extracted measures of severity using ICD-9-CM diagnosis codes for MS-related symptoms and CPT-4 codes for movement aids. Despite these limitations, there were few significant differences between persistent and nonpersistent individuals in the first year after initiation of natalizumab. One difference, prior DMT use, was included in the multivariable analysis as a control variable. Since relapse rates were annualized, we were not able to capture the distribution of relapses during each year. Previous studies have suggested a return in disease activity in subsets of patients specific to the first 3-7 months postdiscontinuation from natalizumab.11,42,43 The timing effect for the return in disease activity after discontinuing natalizumab should be further evaluated. Additionally, the timing of a relapse could impact www.amcp.org Vol. 21, No. 3 the persistence status of subjects in this study; therefore, a causal relationship between persistence and relapse activity cannot be established. Switching and discontinuing therapies are not necessarily considered nonpersistent behavior. Rather, switching is often a physician and patient choice due to a number of reasons. Similarly, discontinuation could happen because of adverse events. For the reasons stated previously, this limitation is somewhat mitigated by the objective of the analysis to compare the impact of using natalizumab consistently with inconsistent natalizumab use. It will be important for future studies to address this limitation with larger sample sizes if multiple treatment options are included to estimate the impact of treatment patterns on relapse-related outcomes. ■■ Conclusions The results of this study suggest that real-world persistent natalizumab users who become nonpersistent have meaningful and statistically significant increases in annual relapses and relapse-related costs. Those who transition from nonpersistent to persistent have nonsignificant reductions in relapses and their associated costs. Authors R. BRETT MCQUEEN, PhD, is Postdoctoral Fellow; KAVITA NAIR, PhD, is Professor; and JONATHAN D. CAMPBELL, PhD, is Assistant Professor, Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado Anschutz Medical Campus, Aurora. TERRIE LIVINGSTON, PharmD, is Senior Director, US Medical, and BRIEANA BUCKLEY, PharmD, is Associate Director, Value Based Outcomes, Biogen Idec, Weston, Massachusetts. TIMOTHY VOLLMER, MD, is Professor, and JOHN CORBOY, MD, is Professor, Department of Neurology, University of Colorado School of Medicine, Aurora. RICHARD READ ALLEN, MS, is Owner and CEO, Peak Statistical Services, Evergreen, Colorado. AUTHOR CORRESPONDENCE: R. Brett McQueen, PhD, Postdoctoral Fellow, Department of Clinical Pharmacy, University of Colorado Skaggs School of Pharmacy and Pharmaceutical Sciences, Anschutz Medical Campus, Mail Stop C238, 12850 E. Montview Blvd., Aurora, CO 80045. Tel.: 303.709.9264; FAX: 303.724.0979; E-mail: [email protected]. DISCLOSURES This study was supported by an investigator-initiated grant from Biogen Idec to the University of Colorado. The content is solely the responsibility of the authors. McQueen was supported by a postdoctoral grant from the PhRMA Foundation. Study concept and design were conducted primarily by McQueen and Campbell with assistance from Livingston, Vollmer, Corboy, Buckley, Allen, and Nair. Programming of the data was done primarily by Allen with assistance from McQueen and Campbell. Data analysis was performed by McQueen, Campbell, and Allen. The manuscript was written by McQueen and Campbell and revised by Livingston, Vollmer, Corboy, Buckley, and Nair. March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 217 Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study References 1. Campbell JD, Ghushchyan V, Brett McQueen R, et al. Burden of multiple sclerosis on direct, indirect costs and quality of life: national U.S. estimates. Mult Scler Relat Disord. 2014;3(2):227-36. 2. Compston A, Coles A. Multiple sclerosis. Lancet. 2002;359(9313):1221-31. 3. National Multiple Sclerosis Society. Types of MS. Available at: http://www. nationalmssociety.org/What-is-MS/Types-of-MS. Accessed January 15, 2015. 4. Scalfari A, Neuhaus A, Daumer M, Muraro PA, Ebers GC. Onset of secondary progressive phase and long-term evolution of multiple sclerosis. J Neurol Neurosurg Psychiatry. 2014;85(1):67-75. 5. Weinstock-Guttman B. An update on new and emerging therapies for relapsing-remitting multiple sclerosis. Am J Manag Care. 2013;19(17 Suppl): S343-54. 6. Devonshire V, Lapierre Y, Macdonell R, et al. The Global Adherence Project (GAP): a multicenter observational study on adherence to diseasemodifying therapies in patients with relapsing-remitting multiple sclerosis. Eur J Neurol. 2011;18(1):69-77. 7. Beer K, Müller M, Hew-Winzeler AM, et al. The prevalence of injectionsite reactions with disease-modifying therapies and their effect on adherence in patients with multiple sclerosis: an observational study. BMC Neurol. 2011;11:144. 8. Fox RJ, Salter AR, Tyry T, et al. Treatment discontinuation and disease progression with injectable disease-modifying therapies: findings from the North American research committee on multiple sclerosis database. Int J MS Care. 2013;15(4):194-201. 9. Cramer JA, Roy A, Burrell A, et al. Medication compliance and persistence: terminology and definitions. Value Health. 2008;11(1):44-47. 10. Tysabri (natalizumab) injection, for intravenous use. Biogen Idec, Inc. Revised January 2012. Available at: http://www.accessdata.fda.gov/drugsatfda_docs/label/2012/125104s0576lbl.pdf. Accessed January 15, 2015. 11. O’Connor PW, Goodman A, Kappos L, et al. Disease activity return during natalizumab treatment interruption in patients with multiple sclerosis. Neurology. 2011;76(22):1858-65. 12. Havla J, Gerdes LA, Meinl I, et al. De-escalation from natalizumab in multiple sclerosis: recurrence of disease activity despite switching to glatiramer acetate. J Neurol. 2011;258(9):1665-69. 13. Castillo-Trivino T, Mowry EM, Gajofatto A, et al. Switching multiple sclerosis patients with breakthrough disease to second-line therapy. PloS One. 2011;6(2):e16664. 14. Lanzillo R, Bonavita S, Quarantelli M, et al. Natalizumab is effective in multiple sclerosis patients switching from other disease modifying therapies in clinical practice. Neurol Sci. 2013;34(4):521-28. 15. Prosperini L, Gianni C, Leonardi L, et al. Escalation to natalizumab or switching among immunomodulators in relapsing multiple sclerosis. Mult Scler. 2012;18(1):64-71. 16. Reynolds MW, Stephen R, Seaman C, Rajagopalan K. Persistence and adherence to disease modifying drugs among patients with multiple sclerosis. Curr Med Res Opin. 2010;26(3):663-74. 17. Oleen-Burkey M, Cyhaniuk A, Swallow E. Treatment patterns in multiple sclerosis: administrative claims analysis over 10 years. J Med Econ. 2013;16(3):397-406. 18. Bonafede MM, Johnson BH, Wenten M, Watson C. Treatment patterns in disease-modifying therapy for patients with multiple sclerosis in the United States. Clin Ther. 2013;35(10):1501-12. 19. Kleinman NL, Beren IA, Rajagopalan K, Brook RA. Medication adherence with disease modifying treatments for multiple sclerosis among U.S. employees. J Med Econ. 2010;13(4):633-40. 20. Agashivala N, Wu N, Abouzaid S, et al. Compliance to fingolimod and other disease modifying treatments in multiple sclerosis patients, a retrospective cohort study. BMC Neurol. 2013;13:138. 21. Bergvall N, Makin C, Lahoz R, et al. Relapse rates in patients with multiple sclerosis switching from interferon to fingolimod or glatiramer acetate: a US claims database study. PloS One. 2014;9(2):e88472. 22. Gajofatto A, Bacchetti P, Grimes B, High A, Waubant E. Switching firstline disease-modifying therapy after failure: impact on the course of relapsing-remitting multiple sclerosis. Mult Scler. 2009;15(1):50-58. 23. Reynolds MW, Stephen R, Seaman C, Rajagopalan K. Healthcare resource utilization following switch or discontinuation in multiple sclerosis patients on disease modifying drugs. J Med Econ. 2010;13(1):90-98. 218 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 24. Bergvall N, Lahoz R, Reynolds T, Korn JR. Healthcare resource use and relapses with fingolimod versus natalizumab for treating multiple sclerosis: a retrospective US claims database analysis. Curr Med Res Opin. 2014;30(8):1461-71. 25. Bergvall N, Petrilla AA, Karkare SU, et al. Persistence with and adherence to fingolimod compared with other disease-modifying therapies for the treatment of multiple sclerosis: a retrospective U.S. claims database analysis. J Med Econ. 2014;17(10):696-707. 26. Ivanova JI, Bergman RE, Birnbaum HG, Phillips AL, Stewart M, Meletiche DM. Impact of medication adherence to disease-modifying drugs on severe relapse, and direct and indirect costs among employees with multiple sclerosis in the U.S. J Med Econ. 2012;15(3):601-09. 27. Kozma CM, Phillips AL, Meletiche DM. Use of an early disease-modifying drug adherence measure to predict future adherence in patients with multiple sclerosis. J Manag Care Spec Pharm. 2014;20(8):800-07. Available at: http://www.amcp.org/WorkArea/DownloadAsset.aspx?id=18369. 28. Steinberg SC, Faris RJ, Chang CF, Chan A, Tankersley MA. Impact of adherence to interferons in the treatment of multiple sclerosis: a non-experimental, retrospective, cohort study. Clin Drug Investig. 2010;30(2):89-100. 29. Bonafede MM, Johnson BH, Watson C. Health care-resource utilization before and after natalizumab initiation in multiple sclerosis patients in the U.S. Clinicoecon Outcomes Res. 2013;6:11-20. 30. Charlson ME, Pompei P, Ales KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-83. 31. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613-19. 32. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care. 2005;43(11):1130-39. 33. Curkendall SM, Wang C, Johnson BH, et al. Potential health care cost savings associated with early treatment of multiple sclerosis using diseasemodifying therapy. Clin Ther. 2011;33(7):914-25. 34. Chastek BJ, Oleen-Burkey M, Lopez-Bresnahan MV. Medical chart validation of an algorithm for identifying multiple sclerosis relapse in healthcare claims. J Med Econ. 2010;13(4):618-25. 35. Ollendorf DA, Jilinskaia E, Oleen-Burkey M. Clinical and economic impact of glatiramer acetate versus beta interferon therapy among patients with multiple sclerosis in a managed care population. J Manag Care Spec Pharm. 2002;8(6):469-76. Available at: http://www.amcp.org/data/jmcp/ Research-469-476.pdf. 36. Cameron AC, Trivedi PK. Microeconometrics Using Stata. College Station, TX: Stata Press; 2009. 37. Cameron AC, Trivedi PK. Microeconometrics: Methods and Applications. Cambridge University Press; 2005. 38. Raimundo K, Tian H, Zhou H, et al. Resource utilization, costs and treatment patterns of switching and discontinuing treatment of MS patients with high relapse activity. BMC Health Serv Res. 2013;13:131. 39. O’Brien JA, Ward AJ, Patrick AR, Caro J. Cost of managing an episode of relapse in multiple sclerosis in the United States. BMC Health Serv Res. 2003;3(1):17. 40. Rudick R, Polman C, Clifford D, Miller D, Steinman L. Natalizumab: bench to bedside and beyond. JAMA Neurol. 2013;70(2):172-82. 41. Ebers GC. Natural history of multiple sclerosis. J Neurol Neurosurg Psychiatry. 2001;71(Suppl 2):ii16-19. 42. Sorensen PS, Koch-Henriksen N, Petersen T, Ravnborg M, Oturai A, Sellebjerg F. Recurrence or rebound of clinical relapses after discontinuation of natalizumab therapy in highly active MS patients. J Neurol. 2014;261(6):1170-77. 43. Jokubaitis VG, Li V, Kalincik T, et al. Fingolimod after natalizumab and the risk of short-term relapse. Neurology. 2014;82(14):1204-11. Vol. 21, No. 3 www.amcp.org Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study Appendix A Demographics Pre-DMT Initiation All Nonpersistent in Initiation Year Switch in Initiation Year Discontinue in Initiation Year Did Not Switch or Discontinue but Nonpersistent in Initiation Year N % P Valuea N % P Valuea N % N % P Valuea Total 607 174 29.00 347 57.00 86 14.00 Gender 0.552 0.820 0.274 Male 171 28.17 52 29.89 99 28.53 20 23.26 Female 436 71.83 122 70.11 248 71.47 66 76.74 Age group 0.920 0.692 0.585 0-35 107 17.63 29 16.67 65 18.73 13 15.12 36-44 187 30.81 57 32.76 105 30.26 25 29.07 45-54 208 34.27 59 33.91 114 32.85 35 40.70 55+ 105 17.30 29 16.67 63 18.16 13 15.12 Payer type 0.184 0.290 0.767 Commercial 369 60.79 116 66.67 203 58.50 50 58.14 Medicaid or Medicare 13 2.14 0 0.00 11 3.17 2 2.33 Self-insured 225 37.07 58 33.33 133 38.33 34 39.53 Insurance type 0.137 0.594 0.155 Health maintenance organization 75 12.36 26 14.94 40 11.53 9 10.47 Indemnity 25 4.12 11 6.32 11 3.17 3 3.49 Preferred provider network 449 73.97 125 71.84 259 74.64 65 75.58 Point of service 49 8.07 12 6.90 32 9.22 5 5.81 Consumer driven 4 0.66 0 0.00 2 0.58 2 2.33 Unknown 5 0.82 0 0.00 3 0.86 2 2.33 Region 0.055 0.212 0.859 East 186 30.64 63 36.21 97 27.95 26 30.23 Midwest 165 27.18 44 25.29 99 28.53 22 25.58 South 194 31.96 57 32.76 110 31.70 27 31.40 West 62 10.21 10 5.75 41 11.82 11 12.79 MS-related symptomsb Visual 128 21.09 39 22.41 0.612 74 21.33 0.868 15 17.44 0.371 Movement disorders 155 25.54 45 25.86 0.907 90 25.94 0.793 20 23.26 0.601 Facial neuralgia 17 2.80 10 5.75 0.005 5 1.44 0.019 2 2.33 0.773 Dizziness 63 10.38 22 12.64 0.246 30 8.65 0.106 11 12.79 0.429 Fatigue 14 2.31 3 1.72 0.545 10 2.88 0.275 1 1.16 0.446 Paralysis 27 4.45 7 4.02 0.747 18 5.19 0.307 2 2.33 0.303 Headache 8 1.32 2 1.15 0.817 4 1.15 0.680 2 2.33 0.376 Muscle 67 11.04 22 12.64 0.424 41 11.82 0.480 4 4.65 0.041 Speech 11 1.81 4 2.30 0.569 7 2.02 0.662 0 0.00 0.174 60 17.29 0.333 19 22.09 0.371 Skin disturbance 113 18.62 34 19.54 0.711 Movement aidsc Walker 16 2.64 7 4.02 0.176 9 2.59 0.940 0 0.00 0.100 Wheelchair 21 3.46 8 4.60 0.331 13 3.75 0.655 0 0.00 0.058 315 51.89 136 78.16 < 0.001 148 42.65 < 0.001 31 36.05 < 0.001 Prior DMT use (yes)d Charlson Comorbidity Index (mean, SD) 3.48 1.91 3.51 1.94 0.810 3.53 1.86 0.445 3.21 2.01 0.162 Relapse count 0.62 1.27 0.76 1.16 0.079 0.54 1.12 0.108 0.62 1.88 0.999 a P value difference between each nonpersistent category (i.e., switch vs. combination of discontinue and did not switch or continue but nonpersistent in initiation year); chisquared distributional test for categorical variables; t-test used for continuous variables. bSymptom codes identified in Table 1 published in Curkendall et al.33 c Movement aid codes identified in Appendix B (available in online article). d Prior DMT use was defined as any claim for interferon beta 1a IM, interferon beta 1a SC, interferon beta 1b SC, or glatiramer acetate during the 12-month period prior to index date. DMT = disease-modifying therapy; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification; IM = intramuscular; SC = subcutaneous; SD = standard deviation. 218a Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Vol. 21, No. 3 www.amcp.org Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study Appendix B CPT and HCPCS Codes Used for Walking Aids Category Code Type Code CPT 97542 HCPCS E1140 HCPCS E1150 HCPCS E1260 HCPCS K0001 HCPCS E1088 HCPCS K0003 HCPCS K0004 Wheelchairs HCPCS K0005 HCPCS K0006 HCPCS K0007 HCPCS K0008 HCPCS E1211 HCPCS E1212 HCPCS K0011 HCPCS K0014 Scooter HCPCS E1230 HCPCS E0130 HCPCS E0135 HCPCS E0140 HCPCS E0141 Walkers HCPCS E0143 HCPCS E0147 HCPCS E0148 HCPCS E0149 CPT = Current Procedural Terminology; HCPCS = Healthcare Common Procedure Coding System. 218b Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Vol. 21, No. 3 www.amcp.org and safety of SAVAYSA in elderly (65 years or older) and younger patients were similar [see Adverse Reactions (6.1), Clinical Pharmacology (12.3), and Clinical Studies (14) in the full prescribing information]. 8.6 Renal Impairment Renal clearance accounts for approximately 50% of the total clearance of edoxaban. Consequently, edoxaban blood levels are increased in patients with poor renal function compared to those with higher renal function. Reduce SAVAYSA dose to 30 mg once daily in patients with CrCL 15-50 mL/min. There are limited clinical data with SAVAYSA in patients with CrCL < 15 mL/min; SAVAYSA is therefore not recommended in these patients. Hemodialysis does not significantly contribute to SAVAYSA clearance [see Dosage and Administration (2.1, 2.2) and Clinical Pharmacology (12.3) in the full prescribing information]. As renal function improves and edoxaban blood levels decrease, the risk for ischemic stroke increases in patients with NVAF [see Indications and Usage (1.1), Dosage and Administration (2.1), and Clinical Studies (14.1) in the full prescribing information]. 8.7 Hepatic Impairment The use of SAVAYSA in patients with moderate or severe hepatic impairment (Child-Pugh B and C) is not recommended as these patients may have intrinsic coagulation abnormalities. No dose reduction is required in patients with mild hepatic impairment (Child-Pugh A) [see Clinical Pharmacology (12.3) in the full prescribing information]. 8.8 Low Body Weight Consideration for Patients treated for DVT and/or PE Based on the clinical experience from the Hokusai VTE study, reduce SAVAYSA dose to 30 mg in patients with body weight less than or equal to 60 kg [see Dosage and Administration (2.2) and Clinical Studies (14.2) in the full prescribing information]. 10 OVERDOSAGE A specific reversal agent for edoxaban is not available. Overdose of SAVAYSA increases the risk of bleeding. The following are not expected to reverse the anticoagulant effects of edoxaban: protamine sulfate, vitamin K, and tranexamic acid. Hemodialysis does not significantly contribute to edoxaban clearance [see Pharmacokinetics (12.3) in the full prescribing information]. 17 PATIENT COUNSELING INFORMATION Advise the patient to read the FDA-approved patient labeling (Medication Guide). Advise patients of the following: • they may bleed more easily, may bleed longer, or bruise more easily when treated with SAVAYSA • to report any unusual bleeding immediately to their healthcare provider • to take SAVAYSA exactly as prescribed • to not discontinue SAVAYSA without talking to the healthcare provider who prescribed it • to inform their healthcare providers that they are taking SAVAYSA before any surgery, medical, or dental procedure is scheduled • to inform their healthcare providers and dentists if they plan to take, or are taking any prescription medications, over-the-counter drugs or herbal products • to inform their healthcare provider immediately if they become pregnant or intend to become pregnant or are breastfeeding or intend to breastfeed during treatment with SAVAYSA • that if a dose is missed, take SAVAYSA as soon as possible the same day, and resume the normal dosing schedule the following day. The dose should not be doubled to make up for a missing dose • that if they are having neuraxial anesthesia or spinal puncture, advise patients to watch for signs and symptoms of spinal or epidural hematoma, such as back pain, tingling, numbness (especially in the lower limbs), muscle weakness, and stool or urine incontinence. If any of these symptoms occur, advise the patient to contact his or her physician immediately [see Boxed Warning]. SAVAYSA™ is a trademark of Daiichi Sankyo Co., LTD. Manufactured by: Daiichi Sankyo Co., LTD. Tokyo 103-8426 Japan Distributed by: Daiichi Sankyo, Inc. Parsippany, NJ 07054 USA Copyright© 2015, Daiichi Sankyo, Inc. PRINTED IN USA. P1805212-BRIEF/DSSV15000114 RESEARCH Resource Utilization and Costs Associated with Using Insulin Therapy Within a Newly Diagnosed Type 2 Diabetes Mellitus Population Kelly Bell, PharmD, MSPhr; Shreekant Parasuraman, PhD; Aditya Raju, BPharm, MS; Manan Shah, PharmD, PhD; John Graham, PharmD; and Melissa Denno, PharmD, MS ABSTRACT BACKGROUND: Although oral antidiabetic medications are the mainstay for managing type 2 diabetes mellitus (T2DM), patients often require insulin therapy to achieve optimal glycemic control. Given the prevalence of insulin use among patients with T2DM, this study evaluated the economic impact of this treatment modality in patients treated in a managed care setting. OBJECTIVE: To estimate costs and resource utilization associated with using insulin therapy among patients with newly diagnosed T2DM who were initially treated with other noninsulin antidiabetic (NIAD) medications. METHODS: An observational, retrospective study design was implemented using integrated medical and pharmacy claims data. Adults with a diagnosis of T2DM from July 1, 2003, through March 31, 2008, were identified. The date of first diagnosis was deemed the index date. The 24-month period after the index date was used to assess treatment patterns. Based on the treatment patterns, the following 2 cohorts were selected: NIAD-only cohort, users who received >1 NIAD class medication but never received insulin, and insulin-use cohort, NIAD users who switched to/added on insulin therapy (duration ≥ 60 days). Patients were matched in a 1:3 (insulin-use:NIADonly) ratio based on propensity scores and other key covariates of interest. Hypoglycemia rates, monthly costs, and resource use during the outcome assessment period were compared between cohorts. RESULTS: After matching, 1,400 patients (350 insulin users and 1,050 NIADonly users) were included in the analysis (42% women; mean age, 56 years). After controlling for covariates, the insulin-use cohort incurred $71 per patient per month higher total T2DM-specific costs than the NIAD-only cohort ($241/month vs. $170/month, P = 0.0003). Pharmacy costs and utilization of physician visits were drivers of cost differences between cohorts. The rate of hypoglycemic events was 10.2 per 100 person-years for the insulin-use cohort versus 2.9 per 100 person-years in the NIAD-only cohort (P < 0.0001). CONCLUSIONS: Use of insulin therapy is associated with increased hypoglycemic events, increased pharmacy and medical costs, and greater utilization of T2DM-specific health care services. J Manag Care Spec Pharm. 2015;21(3):220-28 Copyright © 2015, Academy of Managed Care Pharmacy. All rights reserved. What is already known about this subject •Insulin is often prescribed to patients with type 2 diabetes (T2DM) when glycemic control is inadequate with noninsulin antidiabetic (NIAD) therapy. •Several studies have demonstrated the comparable efficacy of insulin and oral antidiabetic agents in achieving target glycemic levels. However, there is limited evidence quantifying the economic impact of insulin use in patients with T2DM. 220 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 What this study adds •The introduction of insulin therapy early in the treatment pathway was associated with increased T2DM-specific costs of $71 per month per patient. These increased costs were driven mainly by higher pharmacy costs and a higher number of physician visits. •Inclusion of insulin therapy in the treatment regimens of patients with T2DM was associated with an approximately 4-fold increase in rates of hypoglycemic events compared with treatment regimens that include NIAD therapies only. T ype 2 diabetes mellitus (T2DM) is a chronic disease commonly associated with serious microvascular and macrovascular complications. It is associated with high morbidity and mortality, resulting in increased health care resource utilization and costs. The American Diabetes Association (ADA) estimated that the total costs (direct and indirect) associated with managing diabetes were $245 billion in 2012.1 Furthermore, in 2012, although people with diabetes accounted for only 7% of the U.S. population, more than 1 in 5 health care dollars were spent caring for diabetic patients.1 Using age- and sex-adjusted data, the ADA also reported that people with diabetes had annual health care expenditures of approximately $13,741, whereas the same population without diabetes had annual health care expenditures of approximately $5,853. Therefore, annual excess health care expenditures of approximately $7,888 per patient could be attributed to diabetes.1 Diabetes care is complex and requires that many issues be addressed. Achieving long-term glycemic control is key in the management of diabetes and in the reduction and prevention of complications.2 Treatment guidelines include lifestyle modifications, noninsulin oral antidiabetic (NIAD) therapy, and/or insulin therapy for achieving target fasting plasma glucose and glycated hemoglobin (A1c) levels. However, use of metformin, together with lifestyle modifications, is recommended as firstline treatment before the addition of another NIAD or insulin for most patients.3,4 Insulin use has also been shown to have rates of glycemic control similar to oral alternatives.5-7 Lingvay et al. (2009) found that patients taking an oral regimen (metformin, pioglitazone, and glyburide) had A1c levels similar to those of patients who were taking insulin and metformin.5 Likewise, findings from 2 meta-analyses suggest that add-on Vol. 21, No. 3 www.amcp.org Resource Utilization and Costs Associated with Using Insulin Therapy Within a Newly Diagnosed Type 2 Diabetes Mellitus Population insulin therapy or oral agents produce comparable results with respect to A1c reduction.6,7 Furthermore, patients who use insulin or medication combinations that include insulin are at higher risk for hypoglycemic events (plasma glucose < 70 milligrams per deciliter) than patients who do not use insulin.8-11 Henderson et al. (2003) reported that 73% of patients had experienced hypoglycemia since starting insulin treatment and that the risk of hypoglycemia increased with increasing duration of diabetes and insulin therapy.8 Hypoglycemia is associated with adverse short- and long-term outcomes, such as increased mortality, seizures, and coma.12-16 In addition, fear of hypoglycemia can lead to medication noncompliance and failure to achieve glycemic control.12,15 Given the similarities between the effects of oral and insulin-containing antidiabetic therapies on glycemic control and the risk of hypoglycemia with insulin, there is a need to assess the impact of adding insulin to a T2DM regimen on outcomes of health care costs and resource use. Limited evidence exists for these outcomes,17 so the purpose of this study was to assess health care resource utilization and costs associated with the use of insulin therapy for the treatment of T2DM. Specifically, the aim was to evaluate the costs, resource utilization, and hypoglycemia rates associated with the use of insulin therapy among newly diagnosed patients with T2DM receiving NIAD therapy in a managed care setting. TABLE 1 Antidiabetic Therapies Antidiabetic Therapy Insulin Drug Name Insulin aspart, insulin aspart protamine and aspart (human), insulin detemir, insulin glargine, insulin glulisine, insulin isophane, insulin isophane and regular (human), insulin isophane (human), insulin isophane (pork), insulin lispro (human), insulin lispro protamine and lispro (human), insulin regular (human) buffered, insulin regular Noninsulin medications Sulfonylureas Chlorpropamide, glipizide, glimepiride, glyburide Meglitinides Repaglinide, nateglinide Biguanides Metformin DPP-4 inhibitors Sitagliptin, saxagliptin Thiazolidinediones Rosiglitazone, pioglitazone Alpha-glucosidase inhibitors Acarbose, miglitol GLP-1 receptor agonists Exenatide, liraglutide Amylin analogues Pramlintide Combination drug products Metaglip (glipizide/metformin); Glucovance (glyburide/metformin); Avandamet (rosiglitazone/metformin); Actoplus Met (pioglitazone/metformin); Avandaryl (rosiglitazone/glimepiride); Duetact (pioglitazone/glimepiride); Janumet (sitagliptin/metformin) DDP-4 = dipeptidyl peptidase-4; GLP-1 = glucagon-like peptide-1. ■■ Methods Data Source An integrated source of medical and pharmacy claims from the IMS LifeLink Health Plans Claims Database was used for this study. This database included longitudinal, integrated, and patient-level medical and pharmaceutical claims comprising 5 billion patient observations from across the United States for more than 70 million patients from more than 100 health plans, including medical services and prescription drug information across the entire continuum of care. Specifically, inpatient and outpatient diagnoses (by International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] diagnosis codes) and procedures (in Current Procedural Terminology, Version 4, and Healthcare Common Procedural Coding System formats) and retail and mail-order prescription records, which include National Drug Code numbers and the quantity dispensed, were available for all patients. Cost/charge information and dates of service were available for all services rendered. The data were fully de-identified and compliant with the Health Insurance Portability and Accountability Act of 1996. (ICD-9-CM codes 250.x0 or 250.x2) and any evidence of diabetes drug therapy (including all types of insulin and NIAD drugs; Table 1) were the target population. The date of first T2DM diagnosis during the period from July 1, 2003, through March 31, 2008 (deemed the enrollment period) was defined as the index date. The 6-month period before the index date was defined as the pre-index period and was used to ensure that patients were (a) newly diagnosed and (b) new to antidiabetic medication. Receipt of antidiabetic therapy was assessed during the 24-month period after the index date and was defined as the post-index period. The date of the first antidiabetic medication during the post-index period was defined as the index prescription date (Figure 1). Patients were required to be continuously eligible to receive medical and pharmacy services during the pre-index through the post-index periods. Patients were excluded if they had a diagnosis for type 1 diabetes mellitus (ICD-9-CM codes 250. x1 or 250.x3) or gestational diabetes (ICD-9-CM code 648.8x) during the analysis time frame or evidence of pregnancy (ICD9-CM codes 650.xx to 659xx or V22.2) during the pre-index period. Study Design and Patient Population The analysis time frame for this observational, retrospective cohort study ranged from January 1, 2003, through March 31, 2010. Patients aged at least 18 years with a diagnosis of T2DM Treatment Patterns and Group Definitions Antidiabetic therapy patterns of use were analyzed after the index prescription date, and patients were categorized into 7 types based on the following observed patterns: www.amcp.org Vol. 21, No. 3 March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 221 Resource Utilization and Costs Associated with Using Insulin Therapy Within a Newly Diagnosed Type 2 Diabetes Mellitus Population FIGURE 1 Study Design Study Period Enrollment Period January 1, 2003 July 1, 2003 March 31, 2008 March 31, 2010 Index Date Pre-index Period Post-index Period Peri-period Index Prescription 1. Noninsulin user: Patients who received NIAD therapy without any evidence of insulin therapy. 2. Insulin-only user: Patients using insulin without any evidence of NIAD therapy. 3. Early insulin user: Patients using insulin before starting NIAD therapy. 4. Same-day insulin user: Patients starting insulin and NIAD therapy on the same date. 5. Short-term insulin user: Patients who started insulin after NIAD therapy and whose total time on insulin was < 60 days. 6. Intermittent insulin user: Patients who started insulin after NIAD therapy whose total time on insulin was ≥ 60 days, but their medication possession ratio (MPR) was < 25%. 7. Long-term insulin user: Patients who started insulin after NIAD therapy whose total time on insulin was ≥ 60 days, but their MPR was ≥ 25%. As noted in this list, patients starting insulin after NIAD therapy were further classified as short-term, intermittent, and long-term. This classification was based on a combination of insulin duration and MPR because it is common for patients to discontinue insulin therapy due to various clinical issues. 222 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Date of Switch/Add-on Outcomes Assessment Period Insulin duration was defined as the time between the first and last insulin prescription, and MPR was calculated as the ratio of days’ supply of insulin to the total number of days between the first and last insulin prescriptions. Study Cohorts and Assessment of Outcomes To understand the specific impact of using insulin in patients with T2DM who initiated treatment with NIAD, the study cohorts were obtained from the noninsulin users and longterm insulin users described in the previous list, and this constituted the final population. Specifically, noninsulin users who first switched to/added on a second class of NIAD therapy were placed in the NIAD-only cohort, and long-term insulin users who first switched to/added on insulin after the initial NIAD therapy were placed in the insulin-use cohort. For this analysis, the 24-month post-index period was split into 2 periods: (1) the peri-period, defined as the time between the index date and date of first therapeutic class switch/ add-on, was used to capture disease severity proxy measures (T2DM-specific hospital/emergency room [ER] and T2DM-specific costs) and (2) the outcomes assessment period, defined as the time between the date of first therapeutic class Vol. 21, No. 3 www.amcp.org Resource Utilization and Costs Associated with Using Insulin Therapy Within a Newly Diagnosed Type 2 Diabetes Mellitus Population TABLE 2 Sample Attrition and Treatment Patterns Total Patients with Index T2DM Claim During Enrollment Period (July 1, 2003-March 31, 2008) N = 1,599,128 n Exclusion criteria a Aged ≤ 18 years 23,171 Not eligible for medical and pharmacy services during 6-month pre-index period through 2-year post-index period 994,027 Receipt of antidiabetic medication during pre-index period 348,166 No evidence of receipt of antidiabetic medication during post-index period 828,337 Incomplete ICD-9-CM codes without evidence of NIAD medication 16,912 Diagnosis of type I diabetes mellitus anytime during the study period 191,110 Diagnosis of gestational diabetes anytime during the study period 11,753 Diagnosis of pregnancy anytime during the study period 22,008 Target sample 110,358 Treatment patterns Noninsulin users 103,138 Insulin-only users 1,712 Early insulin users 1,186 Same-day insulin users 1,049 Short-term insulin users 1,242 Intermittent insulin users 296 Long-term insulin users 1,735 aCriteria not mutually exclusive. ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification; NIAD=noninsulin antidiabetic; T2DM = type 2 diabetes mellitus. switch/add-on and end of the 24-month period, was used to calculate outcomes (Figure 1). The study cohorts were then matched in a ratio of 1:3 (insulin-use:NIAD-only). Matching was done using the technique of nearest available match on peri-period T2DM-specific costs (± $100), time to treatment switch/add-on (± 1 day), presence of T2DM-specific hospital/ER visit during the peri-period (exact), and estimated propensity score (caliper width of ± 0.001 unit). The greedy nearest-neighbor matching technique was employed to form pairs of insulin-use:NIAD-only cohorts matched on the logit of the propensity score. The propensity score for a patient was defined as the probability of being in the insulin-use cohort conditional on the following covariates measured during the pre-index period: patient age; gender; geographic region (categorized as Northeast, Midwest, South, and West); number of prescriptions; number of unique prescription classes; number of unique diagnoses; presence of 10 comorbidities (which included depression, anxiety, hypertension, obesity, cardiovascular disease [CVD], cerebrovascular disease, neuropathy, nephropathy, retinopathy, and peripheral vascular disease); and Charlson Comorbidity Index (CCI) score.18-20 The primary outcomes of interest were T2DM-specific resource utilization and costs, computed on a per-month basis, and hypoglycemia rates, which were captured during the outcomes assessment period. T2DM-specific resource utilization was identified by counting unique dates of service for T2DM (selected if diagnosis of T2DM was coded in the first or second www.amcp.org Vol. 21, No. 3 % 1.5 62.2 21.8 51.8 1.1 12.0 0.7 1.4 6.9 93.5 1.6 1.1 1.0 1.1 0.3 1.6 position of the medical claim). Resource utilization was classified according to the place of service and included the proportion of patients with hospitalizations, ER visits, physician office visits, or other visits. T2DM-specific cost components included pharmacy and medical costs. Pharmacy costs included allowed amounts for all antidiabetic prescription claims and claims for insulin supplies, such as needles, strips, and lancets. Medical costs included allowed amounts for all T2DM-specific resource utilization. Similarly, all-cause medical and pharmacy costs were also calculated by summing allowed amounts for all medical and pharmacy claims, respectively. All costs were adjusted to 2010 U.S. dollars using the medical component of the Consumer Price Index. Hypoglycemia rates were captured by identifying medical claims with an ICD-9-CM primary or secondary diagnosis code for hypoglycemia based on an algorithm proposed in a previous study.21 The hypoglycemia incidence rate, captured during the outcomes assessment period, was calculated as the total number of hypoglycemic events divided by the total number of person-years (PYs) of follow-up and expressed as events per 100 PYs. Statistical Analysis Before matching, baseline characteristics were compared using t-tests for continuous variables and chi-square tests for categorical variables. After matching, baseline characteristics were compared using paired t-tests for continuous variables, and categorical variables were compared using McNemar’s test. The March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 223 Resource Utilization and Costs Associated with Using Insulin Therapy Within a Newly Diagnosed Type 2 Diabetes Mellitus Population TABLE 3 Baseline Description of Study Sample After Matching Insulin Use NIAD-Onlya Standardized Characteristics (n = 350) (n = 1,050) P Valueb Differencec Demographic characteristics Age, years, mean (SD) 56.33 (12.8) 55.66 (11.6) 0.1986 5.5 Women, n (%) 133 (38.0) 449 (42.8) 0.0316 9.7 Geographic region, n (%) East 67 (19.1) 188 (17.9) 3.2 Midwest 169 (48.3) 484 (46.1) 4.4 0.1464 South 63 (18.0) 208 (19.8) 4.6 West 51 (14.6) 170 (16.2) 4.5 Index year (2003-2008), n (%) 2003 26 (7.4) 96 (9.1) 6.2 2004 59 (16.9) 166 (15.8) 2.8 2005 64 (18.3) 187 (17.8) 1.2 0.1785 2006 74 (21.1) 187 (17.8) 8.4 2007 104 (29.7) 344 (32.8) 6.6 2008 23 (6.6) 70 (6.7) 0.4 Comorbidity in pre-index period Number of unique Rx classes, mean (SD) 2.97 (4.3) 2.88 (3.8) 0.6093 2.2 Number of Rx, mean (SD) 3.13 (4.6) 3.04 (4.1) 0.6537 1.9 Number of unique Dx, mean (SD) 4.87 (5.9) 4.64 (5.3) 0.3586 3.9 CCI, mean (SD) 0.30 (0.7) 0.23 (0.7) 0.0216 10.1 Other complications, n (%) Depression 12 (3.4) 48 (4.6) 0.2242 5.8 Anxiety 7 (2.0) 21 (2.0) 1.0000 0.0 Hypertension 84 (24.0) 252 (24.0) 1.0000 0.0 Obesity 10 (2.9) 35 (3.3) 0.6147 2.7 Cardiovascular disease 39 (11.1) 76 (7.2) 0.0030 13.5 Nephropathy 2 (0.6) 5 (0.5) 1.0000 1.3 Time to first treatment (in days), mean (SD) 146 (175.0) 153 (195.0) 0.2755 3.6 Time to treatment switch/add on (in days), mean (SD) 115 (145.0) 115 (144.0) 0.0600 0.0 Peri-period T2DM-specific costs, mean (SD), $ 152 (245.0) 150 (243.0) 0.2040 0.5 T2DM-specific hospital/ER visits in peri-period, n (%) 0 (0.0) 0 (0.0) Note: Bold P values indicate significance (P < 0.05). a Patients receiving 2 or more classes of NIADs. bMcNemar’s test for dichotomous variables and paired t-test for continuous variables. c Standardized difference = 100 × (x1-x2) / √{(s12 + s2 2)/2}, where x1 = mean of group 1, x2 = mean of group 2, s1 = standard deviation of group 1, and s2 = standard deviation of group 2. CCI = Charlson Comorbidity Index; Dx = diagnoses; ER = emergency room; NIAD = noninsulin antidiabetic; Rx = prescription; SD = standard deviation; T2DM = type 2 diabetes mellitus. success of the matching process was also evaluated by assessing standardized differences in the covariates between the 2 cohorts. The standardized difference is the absolute difference in the means of a covariate across the groups divided by an estimate of the pooled standard deviation (SD) of the covariate. It is expressed as a percentage of pooled SDs. A standardized difference of < 10% was considered an acceptable level of difference.22 Differences in resource utilization and costs between the cohorts of interest were evaluated using multivariable logistic and gamma regressions, respectively. Only variables that showed significant between-group differences after the matching procedure were included in the multivariate models. 224 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Negative binomial regression models were used to investigate differences in hypoglycemia rates between the cohorts, and the corresponding incident rate ratios (IRRs) and 95% confidence intervals (CIs) were reported. All statistical analyses were conducted using SAS version 9.2 (SAS Institute, Cary, NC), with an a priori significance level of α = 0.05. ■■ Results Demographics and Treatment Patterns A total of 110,358 patients (7%) of the initially identified target population met all study criteria (Table 2). Most patients were noninsulin users (94%; n = 103,138); approximately 2% of Vol. 21, No. 3 www.amcp.org Resource Utilization and Costs Associated with Using Insulin Therapy Within a Newly Diagnosed Type 2 Diabetes Mellitus Population FIGURE 2 Monthly T2DM-Specific Costs (2010 US$) 300 Monthly Adjusted T2DM-Specific Costs by Study Cohorta P < 0.05 241 250 200 P < 0.05 170 161 150 NIAD-only P < 0.05 100 49 50 0 Total Insulin-use 113 69 Medical Type of Cost Pharmacy a Statistical model: generalized linear model with gamma distribution and log-link function controlling for gender, presence of cardiovascular disease, peripheral vascular disease, and Charlson Comorbidity Index scores. NIAD = noninsulin antidiabetic; T2DM = type 2 diabetes mellitus. patients received only insulin; and another approximately 2% were long-term insulin users (n = 1,735). Among the noninsulin users, the most commonly used NIAD class was biguanides (44%), followed by sulfonylureas (9%). In patients classified as long-term insulin users, the most common treatment regimen was initiation with biguanides and then switching to/adding on insulin (16.6%). To achieve the study objectives, patients on NIAD therapy who switched to/added a second NIAD therapy (n = 35,833) were selected from the noninsulin therapy users (n = 103,138), and patients on NIAD therapy who switched to/added on insulin (n = 758) were selected from the long-term insulin users (n = 1,735). This group constituted the final population. Accordingly, a total of 35,833 patients were categorized into the NIAD-only cohort, and 758 were categorized into the insulinuse cohort. Between the 2 cohorts of interest (insulin-use cohort and NIAD-only cohort), pre-match demographics were similar with respect to age and proportion of women; however, they were dissimilarly distributed with respect to geographic region, index year of T2DM diagnosis, CCI score, and diseaseseverity proxy measures (see Appendix A, available in online article). On average, both cohorts initiated treatment with NIAD within 4 months of index diagnosis; however, patients in the insulin-use cohort switched/added on therapy sooner than patients in the NIAD-only cohort (146 days vs. 167 days, respectively). After matching patients from the 2 study cohorts www.amcp.org Vol. 21, No. 3 using propensity scores, 1,400 patients remained. Based on the 1:3 matching ratio, the insulin-use cohort included 350 patients, and the NIAD-only cohort included 1,050 patients. Table 3 describes the demographic and clinical characteristics of the study cohorts. The cohorts had similar demographic and clinical characteristics with SD values of < 10% for all baseline characteristics, except CCI scores and the proportion of patients with CVD (SD: CCI = 10.1, CVD = 13.5). After matching, the adjusted T2DM-specific costs per month incurred during the outcomes assessment period were compared (Figure 2). The insulin-use cohort had significantly higher total costs (medical + pharmacy) of $241 per month compared with those of the NIAD-only cohort of $170 per month (P = 0.0003). The mean monthly T2DM-specific medical costs for the insulin-use cohort were $69 (Figure 2). The higher medical costs were driven mainly by higher monthly physician visits (91% vs. 86%, P = 0.0311; Table 4). The mean monthly T2DM-specific pharmacy costs were also higher for the insulin-use cohort compared with the NIAD-only cohort ($161/month vs. $113/month). Similar trends were observed for all-cause costs (see Appendix B, available in online article). Furthermore, among matched patients in the insulin-use cohort, the rate of hypoglycemic events was 10.2 per 100 PYs versus 2.9 per 100 PYs in patients belonging to the NIAD-only cohort (IRR = 3.85, 95% CI = 0.03-0.31). March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 225 Resource Utilization and Costs Associated with Using Insulin Therapy Within a Newly Diagnosed Type 2 Diabetes Mellitus Population TABLE 4 T2DM-Specific Resource Utilization Type of Resource NIAD-Onlya Insulin Use P Utilization, n (%) (n = 1,050) (n = 350) Valueb Inpatient hospital visits 3 (0.3) 2 (0.6) 0.9183 ER visits 47 (4.5) 25 (7.1) 0.0849 Physician visits 907 (86.4) 317 (90.6) 0.0311 Outpatient visits 501 (47.7) 175 (50.0) 0.4986 165 (15.7) 56 (16.0) 0.6733 Otherc Note: Bold P values indicate significance (P < 0.05). a Patients receiving 2 or more classes of NIADs. bLogistic regression controlling for female gender, presence of cardiovascular disease, peripheral vascular disease, and Charlson Comorbidity Index score. cHome health care, durable medical equipment, and other inpatient stays (skilled nursing facility and rehabilitation stays). ER = emergency room; NIAD = noninsulin antidiabetic; T2DM = type 2 diabetes mellitus. ■■ Discussion This study assessed health care utilization and costs associated with the use of insulin therapy among newly diagnosed patients with T2DM in a managed care setting. When compared with patients who did not switch to/add on insulin (NIAD-only cohort), patients who switched to/added on insulin (insulin-use cohort) had a higher number of hypoglycemic events and were more likely to utilize health care resources due to T2DM and, therefore, had higher T2DM-specific cost outcomes. Specifically, the insulin-use cohort utilized a significantly higher number of health care resources (physician visits) compared with the NIAD-only cohort. Furthermore, the insulin-use cohort incurred approximately $70 per month more in T2DM-specific costs compared with the NIAD-only cohort. Previous studies have shown equivalence in clinical outcomes between patients using insulin versus NIAD therapy.5-7,23-25 Schwartz et al. (2003) found similar A1c and fasting plasma glucose levels in patients using triple oral antidiabetic therapy versus a combination of metformin and insulin (70/30) after 24 weeks of therapy.23 Another study found that triple oral antidiabetic therapy versus insulin plus metformin resulted in clinically equivalent outcomes and did not affect compliance, treatment satisfaction, or quality of life.5 Data from other studies also showed comparable results in terms of glycemic control when adding insulin versus another oral antidiabetic drug.6 Additionally, 2 studies showed that the mean effect of insulin on A1c does not seem to be greater than that of other medications, such as sulfonylureas, when combined with metformin.24,25 Real-world studies, on the other hand, showed divergent results, wherein use of insulin in T2DM was associated with poor clinical outcomes compared with use of NIAD therapy only.26,27 A study evaluating a cohort from the Veterans Health 226 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Administration, Medicare, and National Death Index databases, retrospectively, found that patients with T2DM who added insulin to metformin had an increased risk of nonfatal cardiovascular outcomes and all-cause mortality compared with patients who added a sulfonylurea to metformin.26 Another retrospective cohort study conducted using the United Kingdom General Practice Research Database from 2000 to 2010 found that insulin use was associated with an increased risk of diabetes-related complications and all-cause mortality in patients with T2DM versus other regimens that did not contain insulin (e.g., metformin monotherapy, sulfonylurea monotherapy, or metformin + sulfonylurea combination therapy).27 Given the divergent results when comparing insulin regimens and oral antidiabetic therapy regimens in terms of clinical outcomes, it is prudent to evaluate costs and health care resource utilization in order to quantify the economic burden of these treatment alternatives in patients with T2DM. A study by Waugh et al. (2010) found that regimens containing insulin were more expensive in terms of direct costs than such thiazolidinediones as pioglitazone and rosiglitazone but had similar effects in controlling plasma glucose.17 This study similarly found significantly higher T2DM-specific costs for patients using insulin compared with oral antidiabetic drugs but was limited by the nature of the data source in evaluating glycemic control. However, this study was able to assess the impact on hypoglycemic events and found the use of oral antidiabetic medications to be associated with a lower risk of hypoglycemia compared with insulin use, similar to findings from another study.28 Curkendall et al. (2011) showed lower hypoglycemic event rates in patients receiving NIAD regimens (sulfonylurea: 4/100 PYs; nonsulfonylurea NIAD: 1.9/100 PYs) compared with patients receiving insulin plus NIAD regimens (insulin + sulfonylurea: 9.2/100 PYs; insulin + nonsulfonylurea NIAD: 6.5/100 PYs).28 Thus, this analysis demonstrates that using noninsulin antidiabetic medications result in lower costs and resource use compared with using insulin and avoids outcomes such as hypoglycemia that could substantially affect the health care system and patient quality of life. Limitations The following limitations must be considered when interpreting these results. Patients who had incomplete ICD-9-CM codes of 250, which makes it difficult to determine whether they have type 1 diabetes mellitus or T2DM, were not included in the study. In addition, there may be errors in the coding of claims or diagnoses. This may attenuate the generalizability of the study; however, results indicate that only approximately 1% of the patients had incomplete ICD-9-CM codes. Hence, it is unlikely that the effects of exclusion would be substantial. Vol. 21, No. 3 www.amcp.org Resource Utilization and Costs Associated with Using Insulin Therapy Within a Newly Diagnosed Type 2 Diabetes Mellitus Population There are also inherent limitations to a database study that may lead to gaps in information gathered, limit evaluation to claims within the study period, and do not allow evaluation of patient compliance with medications. Although this study evaluated patients based on a date of first diagnosis and subsequent receipt of NIAD therapy, it is possible that patients enrolled in the study may have been treated with other/additional NIADs or insulin therapy before the start of the study period. Limitations in the ability to accurately assess patient compliance may result in attributing certain health care utilization or medical costs to therapy. From a data capability perspective, this dataset represents predominantly a managed care population that may be different from other populations, and because of the time lag associated with this dataset, the study period may not reflect current practices. The analysis controlled for the majority of differences in baseline characteristics using propensity score matching, with the exception of baseline CCI scores and CVD. In addition, residual confounding may also exist due to lack of information on clinical measures such as A1c and body mass index, which may differ between cohorts. A1c, in particular, is an important factor, as it characterizes disease severity, which, in turn, may have driven a particular treatment regimen or may have accounted for the differences in hypoglycemic events. Therefore, differences among cohorts in baseline CCI scores, CVD, and residual confounders may have affected health care resource utilization and/or costs. To account for some of the residual confounding due to differences in disease severities between the study cohorts, the analysis controlled for disease-severity proxy measures such as peri-period T2DMspecific costs and rates of hospital/ER visits. As a result of this approach, only patients without T2DM-specific hospital/ER visits in the peri-period were included in the analysis after the match—this aspect may affect the generalizability of the findings. Furthermore, these data also pose a potential limitation of misclassification of patient types, particularly the insulin-use cohort, based on variables such as days’ supply. This limitation was addressed by testing the cohort definition using other available data elements, such as number of prescriptions filled. ■■ Conclusions Use of insulin therapy within a population newly diagnosed with T2DM was associated with increased hypoglycemic events, T2DM-specific pharmacy and medical costs, and utilization of health care services. Future research should focus on the impact of clinical parameters (e.g., A1c) on outcomes associated with patients on NIAD therapies with or without the addition of insulin. Additionally, further investigation on how hypoglycemic events (particularly severe events) affect glucose control and patient compliance is warranted. www.amcp.org Vol. 21, No. 3 Authors KELLY BELL, PharmD, MSPhr, is HEOR Director, U.S. Medical Affairs, AstraZeneca, Wilmington, Delaware, and SHREEKANT PARASURAMAN, PhD, is Senior Director, Incyte Corporation, Wilmington, Delaware. ADITYA RAJU, BPharm, MS, is Manager, Applied Data Analytics, and MELISSA DENNO, PharmD, MS, is Assistant Director, Global Health Economics & Outcomes Research, Xcenda, Palm Harbor, Florida. MANAN SHAH, PharmD, PhD, is Director, Health Services & Outcomes Research, Bristol-Myers Squibb, Plainsboro, New Jersey, and JOHN GRAHAM, PharmD, is Vice President, Global VEO, GlaxoSmithKline Pharmaceuticals, Collegeville, Pennsylvania. AUTHOR CORRESPONDENCE: Melissa Denno, PharmD, MS, Xcenda, LLC, 4114 Woodlands Pkwy., Ste. 500, Palm Harbor, FL 34685. Tel.: 727.771.4167; E-mail: [email protected]. DISCLOSURES This work was supported by Bristol-Myers Squibb and AstraZeneca. Denno and Raju are employees of Xcenda, which has received research funding from Bristol-Myers Squibb. At the time of this research, Bell and Graham were employees of Bristol-Myers Squibb; Bell has stock in BristolMyers Squibb. Parasuraman was an employee of AstraZeneca, and Shah was an employee of Xcenda. Study concept and design were contributed by Bell, Shah, Raju, Graham, and Parasuraman. Data collection was performed by Raju, Shah, Parasuraman, and Bell, and analysis was carried out by Parasuraman, Graham, Shah, Raju, and Bell. The manuscript was written by Denno, Bell, Parasuraman, Raju, and Shah, assisted by Graham, and revised by Denno, Raju, Bell, Parasuraman, and Shah. REFERENCES 1. American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care. 2013;36(4):1033-46. 2. Asche C, LaFleur J, Conner C. A review of diabetes treatment adherence and the association with clinical and economic outcomes. Clin Ther. 2011;33(1):74-109. 3. American Diabetes Association. Standards of medical care in diabetes—2012. Diabetes Care. 2012;35(Suppl 1):S11-S63. 4. Handelsman Y, Mechanick J, Blonde L, et al. American Association of Clinical Endocrinologists medical guidelines for clinical practice for developing a diabetes mellitus comprehensive care plan. 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March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 227 Resource Utilization and Costs Associated with Using Insulin Therapy Within a Newly Diagnosed Type 2 Diabetes Mellitus Population 9. Sarkar U, Karter AJ, Liu JY, et al. Hypoglycemia is more common among type 2 diabetes patients with limited health literacy: the Diabetes Study of Northern California (DISTANCE). J Gen Intern Med. 2010;25(9):962-68. 19. D’Hoore W, Bouckaert A, Tilquin C. Practical considerations on the use of the Charlson comorbidity index with administrative databases. J Clin Epidemiol. 1996;49(12):1429-33. 10. Bennett WL, Wilson LM, Bolen S, et al. Oral diabetes medications for adults with type 2 diabetes: an update. Comparative Effectiveness Review No. 27. (Prepared by Johns Hopkins University Evidence-based Practice Center under Contract No. 290-02-0018.) AHRQ Publication No. 11-EHC038-EF. Rockville, MD: Agency for Healthcare Research and Quality. March 2011. Available at: http://effectivehealthcare.ahrq.gov/ehc/ products/155/644/type-2-diabetes-medications-report-130911.pdf. Accessed February 1, 2015. 20. Romano PS, Roos LL, Jollis JG. Adapting a clinical comorbidity index for use with ICD-9-CM administrative data: differing perspectives. J Clin Epidemiol. 1993;46(10):1075-79. 11. Budnitz DS, Lovegrove MC, Shehab N, et al. Emergency hospitalizations for adverse drug events in older Americans. N Engl J Med. 2011;365(21):2002-12. 12. Moghissi ES, Korytkowski MT, DiNardo M, et al. American Association of Clinical Endocrinologists and American Diabetes Association consensus statement on inpatient glycemic control. Diabetes Care. 2009;32(6):1119-31. 13. Turchin A, Matheny ME, Shubina M, Scanlon JV, Greenwood B, Pendergrass ML. Hypoglycemia and clinical outcomes in patients with diabetes hospitalized in the general ward. Diabetes Care. 2009;32(7):1153-57. 14. Krinsley JS, Grover A. Severe hypoglycemia in critically ill patients: risk factors and outcomes. Crit Care Med. 2007;35(10):2262-67. 15. Cook CB, Jameson KA, Hartsell ZC, et al. Beliefs about hospital diabetes and perceived barriers to glucose management among inpatient midlevel practitioners. Diabetes Educ. 2008;34(1):75-83. 16. American Diabetes Association. Standards of medical care in diabetes— 2011. Diabetes Care. 2011;34(Suppl 1):S11-S61. 17. Waugh N, Cummins E, Royle P, et al. Newer agents for blood glucose control in type 2 diabetes: systematic review and economic evaluation. Health Technol Assess. 2010;14(36):1-248. 18. Deyo RA, Cherkin DC, Ciol MA. Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol. 1992;45(6):613-19. 228 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 21. Ginde AA, Blanc PG, Lieberman RM, et al. Validation of ICD-9-CM coding algorithm for improved identification of hypoglycemia visits. BMC Endocr Disord. 2008;8:4. 22. Austin PC. A critical appraisal of propensity-score matching in the medical literature between 1996 and 2003. Statist Med. 2008;27(12):2037-49. 23. Schwartz S, Sievers R, Strange P, Lyness WH, Hollander P; INS-2061 Study Team. Insulin 70/30 mix plus metformin versus triple oral therapy in the treatment of type 2 diabetes after failure of two oral drugs. Diabetes Care. 2003;26(8):2238-43. 24. Monami M, Lamanna C, Marchionni N, Mannucci E. Comparison of different drugs as add-on treatments to metformin in type 2 diabetes: a metaanalysis. Diabetes Res Clin Pract. 2008;79(2):196-203. 25. Malone JK, Beattie SD, Campaigne BN, Johnson PA, Howard AS, Milicevic Z. Therapy after single oral agent failure: adding a second oral agent or an insulin mixture? Diabetes Res Clin Pract. 2003;62(3):187-95. 26. Roumie CL, Greevy RA, Grijalva CG, et al. Association between intensification of metformin treatment with insulin vs sulfonylureas and cardiovascular events and all-cause mortality among patients with diabetes. JAMA. 2014;311(22):2288-96. 27. Currie CJ, Poole CD, Evans M, Peters JR, Morgan CL. Mortality and other important diabetes-related outcomes with insulin vs other antihyperglycemic therapies in type 2 diabetes. J Clin Endocrinol Metab. 2013;98(2):668-77. 28. Curkendall SM, Zhang B, Oh KS, Williams SA, Pollack MF, Graham J. Incidence and cost of hypoglycemia among patients with type 2 diabetes in the United States: analysis of a health insurance database. J Clin Outcomes Manage. 2011;18(10):455-62. Vol. 21, No. 3 www.amcp.org Resource Utilization and Costs Associated with Using Insulin Therapy Within a Newly Diagnosed Type 2 Diabetes Mellitus Population Appendix A Baseline Description of Study Sample Before Match Insulin Use NIAD-Onlya Standardized Characteristics (N = 758) (N = 35,833) P Valueb Differencec Demographic characteristics Age, years, mean (SD) 55.14 (12.4) 54.54 (11.3) 0.1836 5.1 Female, n (%) 332 (43.8) 15,918 (44.4) 0.7325 1.3 Geographic region, n (%) East 110 (14.5) 9,826 (27.4) 32.1 Midwest 371 (48.9) 14,613 (40.8) 16.5 < 0.0001 South 146 (19.3) 6,261 (17.5) 4.6 West 131 (17.3) 5,133 (14.3) 8.1 Index year (2003-2008), n (%) 2003 48 (6.3) 2,990 (8.3) 7.7 2004 102 (13.5) 6,076 (17.0) 9.8 2005 127 (16.8) 6,582 (18.4) 4.2 < 0.0001 2006 134 (17.7) 7,698 (21.5) 9.6 2007 286 (37.7) 10,335 (28.8) 18.9 2008 61 (8.1) 2,152 (6.0) 8.0 Comorbidity in pre-index period, mean (SD) Number of unique Rx classes 2.96 (4.2) 3.13 (3.7) 0.2777 4.2 Number of Rxs 3.15 (4.6) 3.29 (4.0) 0.4062 3.3 Number of unique diagnoses 4.87 (5.8) 4.96 (5.3) 0.6632 1.7 CCI 0.41 (1.0) 0.28 (0.8) 0.0003 14.8 Time to first treatment (in days), mean (SD) 108 (154) 110 (170) 0.6524 1.6 Time to treatment switch/add on (in days), mean (SD) 146 (172) 167 (195) 0.0010 11.3 Peri-period T2DM-specific costs, mean (SD), $ 1,223 (5,432) 628 (5,036) 0.0029 11.4 29.1 Disease-specific hospital/ER visits in peri-period, n (%) 120 (15.8) 2,410 (6.7) < 0.0001 Note: Bold P values indicate significance (P<0.05). a Patients receiving 2 or more classes of NIADs. b Chi-square test for dichotomous variables and t-test for continuous variables. c Standardized difference = 100 × (x1-x2) / √{(s12 + s2 2)/2}, where x1= mean of group 1, x2 = mean of group 2, s1=standard deviation of group 1, and s2 = standard deviation of group 2. CCI=Charlson Comorbidity Index; ER=emergency room; NIAD=noninsulin antidiabetic; Rx=prescription; SD=standard deviation; T2DM=type 2 diabetes mellitus. Appendix B Monthly Adjusted All-Cause Costs by Study Cohort All-Cause Costs Mean NIAD-Onlya Insulin Use (95% CI) (N = 1,050) (N = 350) P Valueb Total ($) 887 (640-1,353) 1,283 (916-2,003) < 0.0001 Medical ($) 548 (355-1,029) 795 (508-1,555) 0.0002 Pharmacy ($) 338 (253-483) 486 (362-705) 0.0005 Note: Bold P values indicate significance (P < 0.05). a Patients receiving 2 or more classes of NIADs. b Generalized linear model with gamma distribution and log-link function controlling for gender, presence of cardiovascular disease, peripheral vascular disease, and Charlson Comorbidity Index scores. CI = confidence interval; NIAD = noninsulin antidiabetic. 228a Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Vol. 21, No. 3 www.amcp.org RESEARCH Association of Visit-to-Visit Variability of Hemoglobin A1c and Medication Adherence Ambili Ramachandran, MD, MS; Michael Winter, MPH; and Devin M. Mann, MD, MS ABSTRACT BACKGROUND: Medication nonadherence is widespread, but there are few efficient means of detecting medication nonadherence at the point of care. Visit-to-visit variability in clinical biomarkers has shown inconsistent efficiency to predict medication adherence. OBJECTIVE: To examine the performance of visit-to-visit variability (VVV) of hemoglobin A1c to predict nonadherence to antidiabetic medications. METHODS: In this cross-sectional study using a clinical and administrative database, adult members of a managed care plan at a safety-net medical center from 2008 to 2012 were included if they had ≥ 3 noninsulin antidiabetic prescription fills within the same class and ≥ 3 A1c measurements between the first and last prescription fills. The independent variable was VVV of A1c (within-subject standard deviation of A1c), and the dependent variable was medication adherence (defined by medication possession ratio) determined from pharmacy claims. Unadjusted and adjusted multivariate logistic regression models were created to examine the relationship between VVV of A1c and medication nonadherence. Receiver-operating characteristic (ROC) curves assessed the performance of the adjusted model at discriminating adherence from nonadherence. RESULTS: Among 632 eligible subjects, mean A1c was 7.7% ± 1.3%, and 83% of the sample was nonadherent to antidiabetic medications. Increasing quintiles of VVV of A1c and medication nonadherence were both associated with increased within-subject mean A1c and younger subject age. The logistic regression model (adjusted for age, sex, race/ethnicity, within-subject mean A1c, number of A1c measurements, number of days between the first and last antidiabetic medication prescription fills, and rate of primary care visits during the study period) showed a nonsignificant association of VVV of A1c and medication nonadherence (OR = 1.19, 95% CI = 0.42-3.38 for the highest quintile of VVV). Adding VVV of A1c to a model including age, sex, and race only modestly improved the C-statistic of the ROC curve from 0.6786 to 0.7064. CONCLUSIONS: VVV of A1c is not a robust predictor of antidiabetic medication nonadherence. Further innovation is needed to develop novel methods of detecting nonadherence. J Manag Care Spec Pharm. 2015;21(3):229-37 Copyright © 2015, Academy of Managed Care Pharmacy. All rights reserved. What is already known about this subject •Nonadherence to antidiabetic medications is common; however, existing methods of detecting nonadherence are time consuming, inefficient, or reliant on integrated pharmacy data not available to all providers. •Visit-to-visit variability (VVV) in clinical biomarkers has been shown to predict medication adherence in some medical conditions but has not been examined using biomarkers for diabetes such as hemoglobin A1c. www.amcp.org Vol. 21, No. 3 What this study adds •Using information from a clinical and administrative database, 83% of patients at an urban health center were found to be nonadherent to noninsulin antidiabetic medications, with adherence defined by a medication possession ratio of 80% or greater. •The proportion of subjects who were nonadherent to antidiabetic medications increased with increasing quintile of VVV of A1c, yet in the adjusted logistic regression model, there was no significant association between VVV of A1c and medication nonadherence. •When added to other clinical variables, VVV of hemoglobin A1c is not a robust predictor of nonadherence to antidiabetic medications. N onadherence to diabetic medications is a prevalent and costly problem. Patients take only an estimated 65%85% of prescribed doses of oral hypoglycemic agents.1,2 Lower rates of adherence are observed in racial minorities,3 Medicaid enrollees,1 and younger patients.4-6 Nonadherence to diabetic medications, antihypertensives, and cholesterollowering medications is associated with an increased risk of hospitalization and all-cause mortality,7 while satisfactory adherence is associated with decreased likelihood of microvascular complications.8 For every 10% increase in adherence to antidiabetic medications, total annual health care costs are projected to decrease by approximately 8.6%.9,10 In order to achieve the benefits of adequate glycemic control, providers and health systems must be able to accurately detect nonadherence. Yet clinicians are insensitive appraisers of adherence, usually overestimating current and future adherence.11 Existing methods of assessing adherence, such as pill counts or validated questionnaires, are vulnerable to social desirability influences and consume time to complete.12,13 Unless they are working within an integrated pharmacy system, such as the Veterans Health Administration or health maintenance organizations, providers rarely have access to pharmacy refill information. Sophisticated techniques such as medication event monitoring systems (MEMS) are not widely available outside research studies and require integration of electronic information into the patient record during the relevant clinical visit. Thus, there is a need for efficient, reliable methods to diagnose nonadherence at the point of care.14 One candidate indicator of low medication adherence is variability in clinical biomarkers that are directly affected by medications. For example, a patient with hypertension March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 229 Association of Visit-to-Visit Variability of Hemoglobin A1c and Medication Adherence FIGURE 1 Study Flow Diagram Members of BMC Health Plan seen in 2008-2012 Prescribed any noninsulin antidiabetic medications n = 2,004 Inadequate prescriptions (n = 569) At least 3 prescription fills in 2008-2012 n = 1,475 • Aged <18 years (n = 11) • Pregnancy (n = 30) • No outpatient or primary care visits in study period (n = 55) Eligible nonpregnant adults n = 1,379 Inadequate A1c measurements in study period (n=488) At least 3 A1c measurements in study period n = 891 Prescribed insulin in study period (n = 259) Analytic sample N = 632 A1c = hemoglobin A1c; BMC = Boston Medical Center. who is regularly taking antihypertensive medications would be expected to have relatively consistent blood pressure measurements over time. In contrast, inconsistent intake of antihypertensives would be associated with variable blood pressure recordings. In practice, such erratic readings often alert clinicians to possible poor adherence. Rather than rely on clinical intuition alone, this visit-to-visit variability (VVV) in the clinical biomarker could be quantified using electronic health record algorithms and used objectively as a predictor of medication nonadherence during the clinical visit. 230 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Using pharmacy claims data, our group has previously demonstrated that VVV of low-density lipoprotein cholesterol (LDL-C) is associated with nonadherence to statin medications.15 A similar analysis among patients with hypertension also showed a relationship between increasing VVV of systolic blood pressure and degree of antihypertensive medication nonadherence; however, medication adherence explained only a small fraction of VVV of blood pressure.16 There remains uncertainty about the ability of VVV of other biomarkers to predict nonadherence to medications. Given the strong effect that antidiabetic medications have on glycosylated hemoglobin (A1c), we hypothesized that VVV of A1c would be a reliable predictor of nonadherence to antidiabetic medications. VVV of A1c could then be used in the clinical setting to identify patients with low adherence for targeted interventions to improve medication compliance. ■■ Methods Data Source Our study sample consisted of patients enrolled in the Boston Medical Center (BMC) Health Plan who received care at BMC or 1 of 8 affiliated community health centers (CHCs) from 2008 to 2012. BMC is the largest safety-net hospital in New England, and the BMC Health Plan is a managed care organization that offers mostly Medicaid and free or heavily subsidized care. BMC and its affiliated CHCs participate in the Massachusetts Healthcare Disparities Repository (MHDR), a collaborative program that promotes investigations in disparities in access to care and health outcomes by using existing clinical data.17 The MHDR employs the Informatics for Integrating Biology and the Bedside (I2B2) platform to aggregate de-identified clinical data for research purposes. Data available in MHDR and coordinated in I2B2 include visit dates, diagnoses, laboratory results, and medications, which originate from the electronic health record. Claims data from the BMC Health Plan, including filled prescriptions, are also available through I2B2. This study was approved by the Boston University Institutional Review Board. Inclusion Criteria Our sample of interest included adults aged 18 years or older who were taking at least 1 medication for diabetes and who had received at least 1 clinical service in 2008-2012 at BMC (Figure 1). All but 1 subject had a diagnosis of diabetes recorded during the study period (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] code 250.x). Eligible medication classes during this time period were biguanides (metformin); sulfonylureas (glipizide, glimepiride, glyburide); thiazolidinediones (pioglitazone, rosiglitazone); meglitinides (nateglinide, repaglinide); alpha-glucosidase inhibitors (acarbose, miglitol); dipeptidyl peptidase-4 (DPP-4) inhibitors (sitagliptin, linagliptin); incretin mimetics (exenatide, liraglutide); Vol. 21, No. 3 www.amcp.org Association of Visit-to-Visit Variability of Hemoglobin A1c and Medication Adherence or combination products (e.g., glipizide/metformin). Subjects taking insulin were excluded given the complexity of measuring adherence to insulin using claims data alone and since diabetic patients requiring insulin represent a different clinical population from patients on noninsulin regimens. Subjects were required to have at least 3 prescription fills within a given class of diabetic medications during the study period and at least 3 A1c measurements between the first and last prescription fill dates. We further required that subjects have at least 1 visit within a primary care specialty (general medicine, internal medicine, family medicine, women’s health, or geriatrics) to reflect subjects whose diabetes is partly managed in a primary care setting rather than exclusively by specialists. Pregnant subjects were also excluded (ICD-9-CM codes 630 to 679.99 and 760 to 779.99) given different glycemic goals and permitted therapies during pregnancy. Independent Variable The independent variable was the VVV of A1c between the first and last medication fulfillment dates during the 4-year study period. The VVV of A1c was defined as the within-subject standard deviation (SD) of A1c during the study period and was divided into quintiles for the purposes of analysis. A1c measurements that were outside the 0.1 and 99.9 percentiles were top and bottom coded to those values. Dependent Variable The dependent variable was adherence to diabetic medication, defined by the medication possession ratio (MPR). The MPR is calculated as the sum of total days’ supply of the medication from the first to the last prescription fill, divided by the total number of days in this period.18 The MPR was calculated separately for each drug class for all diabetic medications prescribed for a subject using prescription medication claims data from I2B2. Then, an average MPR was calculated with equal weighting of each drug class. The average antidiabetic medication MPR was dichotomized as nonadherent and adherent according to the traditional standard of < 80% and ≥ 80%, respectively.12 Covariates Potential covariates were chosen based on previously reported associations with medication adherence4-6,9,10,12,15,16,19 and availability in the MHDR I2B2 system: age at first medication fill during the study period; sex; race/ethnicity (White, Black, Hispanic, or other); total number of ambulatory visits during the study period; total number of primary care visits during the study period; rate of primary care visits during the study period (calculated as number of primary care visits per 6 months); within-subject mean A1c during the study period; www.amcp.org Vol. 21, No. 3 number of A1c measurements during the study period; number of days between the first and last diabetic medication prescription fills; medication drug class; number of drug classes; and the diagnoses of hypertension (ICD-9-CM, codes 401.0x to 405.0x), ischemic heart disease (codes 410.0x to 414.9x), cerebrovascular disease (codes 430.0x to 438.9x), and chronic kidney disease (codes 585.0x to 585.9). Statistical Analyses Descriptive data are reported as percentages for categorical variables and mean ± SD for continuous variables. Bivariate associations between covariates and quintiles of VVV of A1c, and between covariates and diabetic medication adherence, were tested using the chi-square test for categorical variables and the Wilcoxon rank-sum test for continuous variables. Unadjusted and adjusted multivariate logistic regression models were created to examine the relationship between VVV of A1c and diabetic medication nonadherence. The adjusted model included covariates that were statistically significantly associated with nonadherence based on bivariate associations and variables deemed important to predicting nonadherence according to literature review. Collinearity diagnostics indicated possible collinearity among 3 variables: rate of primary care visits during the study period, number of days between the first and last diabetic medication prescription fills, and number of A1c measurements. Separate adjusted models were thus created using only 2 of these 3 variables. A fully adjusted model including all important covariates (age, sex, race/ethnicity, within-subject mean A1c, number of A1c measurements, number of days between the first and last diabetic medication prescription fills, and rate of primary care visits during the study period) was then created. Parameter estimates were similar across all adjusted models; therefore, the fully adjusted model was accepted as the final model. Using the average MPR across drug classes, a patient could be considered adherent if highly adherent to 1 drug class and less adherent to another drug class (e.g., MPR 88% for metformin but MPR 74% to sulfonylureas, yielding an average MPR of 82%). A stricter definition of adherence would require that the MPR be ≥ 80% for all drug classes. A sensitivity analysis of the fully adjusted model using this latter definition of adherence was performed. The Hosmer-Lemeshow test for goodness-of-fit was used to evaluate the models. For all logistic regression models, the odds ratio (OR) and 95% confidence intervals (CIs) were calculated, and P < 0.05 was used for all significance levels. The performance of each model at discriminating diabetic medication adherence from nonadherence was assessed by plotting the receiver operator characteristic (ROC) curve and calculating the C-statistic. All analyses were conducted using SAS version 9.2 (SAS Institute, Cary, NC). March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 231 Association of Visit-to-Visit Variability of Hemoglobin A1c and Medication Adherence TABLE 1 Association of VVV of A1c and Sociodemographic and Clinical Variables, 2008-2012 VVV of A1c (Quintiles)a Q1: ≤ 0.31 Q2: > 0.31-≤ 0.49 Q3: > 0.49-≤ 0.75 Q4: > 0.75-≤ 1.15 Q5: > 1.15 N = 127, N (%) N = 125, N (%) N = 126, N (%) N = 128, N (%) N = 126, N (%) Overall N = 632, N (%) Sex Female 80.0(63.0) 73.0(58.4) 65.0(51.6) 69.0(53.9) 58.0(46.0) 345.0(54.6) Male 47.0(37.0) 52.0(41.6) 61.0(48.4) 59.0(46.1) 68.0(54.0) 287.0(45.4) Age at first Rx fill (years) 53.8(8.3) 52.1(8.0) 52.8(7.9) 50.5(7.7) 49.1(9.3) 51.7(8.4) Race White 14.0(11.3) 18.0(14.8) 16.0(12.7) 17.0(13.4) 16.0(12.9) 81.0(13.0) Black 66.0(53.2) 61.0(50.0) 60.0(47.6) 61.0(48.0) 75.0(60.5) 323.0(51.9) Hispanic 24.0(19.4) 16.0(13.1) 19.0(15.1) 22.0(17.3) 16.0(12.9) 97.0(15.5) Other 20.0(16.1) 27.0(22.1) 31.0(24.6) 27.0(21.3) 17.0(13.7) 122.0(19.6) Outpatient visits 37.2(29.9) 49.4(40.4) 37.4(26.5) 45.4(35.6) 36.6(25.8) 41.2(32.5) Primary care visitsb 12.0(9.0) 16.0(14.2) 13.7(8.6) 16.6(10.9) 15.0(8.6) 14.7(10.6) Rate of primary care visits/6 months 3.0(3.0) 3.1(4.9) 2.7(1.8) 2.6(1.4) 2.5(1.5) 2.8(2.8) Diagnoses 126.0(99.2) 125.0(100.0) 126.0(100.0) 128.0(100.0) 126.0(100.0) 631.0(99.8) Diabetes mellitus Hypertension 110.0(86.6) 111.0(88.8) 110.0(87.3) 110.0(85.9) 106.0(84.1) 547.0(86.6) Ischemic heart disease 13.0(10.2) 14.0(11.2) 12.0(9.5) 12.0(9.4) 16.0(12.7) 67.0(10.6) Cerebrovascular disease 6.0(4.7) 9.0(7.2) 2.0(1.6) 7.0(5.5) 5.0(4.0) 29.0(4.6) Chronic kidney disease 13.0(10.2) 9.0(7.2) 10.0(7.9) 12.0(9.4) 10.0(7.9) 54.0(8.5) A1c measurements 4.9(2.5) 6.7(3.3) 6.1(3.4) 7.2(3.7) 6.6(2.9) 6.3(3.3) Within-subject mean A1c 6.7(0.9) 6.9(0.8) 7.7(0.9) 8.1(1.2) 8.9(1.2) 7.7(1.3) Days between first and last fill dates 850.2(465.3) 1,107.0(496.9) 1,040.5(506.1) 1,155.4(494.4) 1,181.3(492.0) 1,066.8(503.7) Number of drug classes 1 88.0(69.3) 64.0(51.2) 36.0(28.6) 40.0(31.3) 38.0(30.2) 266.0(42.1) 2 29.0(22.8) 47.0(37.6) 65.0(51.6) 54.0(42.2) 66.0(52.4) 261.0(41.3) 3 10.0(7.9) 14.0(11.2) 23.0(18.3) 32.0(25.0) 22.0(17.5) 101.0(16.0) 2.0(1.6) 2.0(1.6) 0 4.0(0.6) 4 0 0 Antidiabetic MPRc < 80% 101.0(79.5) 96.0(76.8) 101.0(80.2) 110.0(85.9) 118.0(93.7) 526.0(83.2) ≥ 80% 26.0(20.5) 29.0(23.2) 25.0(19.8) 18.0(14.1) 8.0(6.4) 106.0(16.8) Note: Data are presented as mean ± SD or n (%). aVVV of A1c was defined by within-subject standard deviation of A1c. bPrimary care visits were defined as visits to Family Medicine, General Medicine, Women’s Health, Primary Care, and Geriatrics. c MPR was averaged across medication classes for subjects taking medications from more than 1 drug class. Nonadherence was defined as MPR < 80%. A1c = hemoglobin A1c; MPR = medication possession ratio; Q = quintiles; Rx = prescription; SD = standard deviation; VVV = visit-to-visit variability. ■■ Results A total of 632 adults with at least 3 noninsulin diabetic medication fills and at least 3 A1c measurements between the first and last medication fills were identified between 2008 and 2012 (Figure 1). The majority of subjects were female (54.6%) and Black (51.9%), and the mean age in the sample was 52 years (Table 1). Subjects contributed an average of almost 3 years of pharmacy prescription information (mean 1,067 days [SD 504] between first and last prescription fills). Almost 58% of the sample was taking more than 1 drug for diabetes, most commonly metformin (89.9%) and sulfonylureas (57.9%; data not shown). The average within-subject mean A1c level was 7.7% ± 1.3%. Table 1 shows the association between VVV of A1c and sociodemographic and clinical covariates. As the VVV of A1c 232 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 P Value 0.0733 < 0.0001 0.5383 0.0143 0.0003 0.8597 0.7975 0.8644 0.9087 0.2996 0.9118 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.0027 increased, mean age decreased significantly. The number of A1c measurements, the within-subject mean A1c, and the number of days between the first and last medication fills increased significantly with increasing quintile of VVV of A1c. There was no significant association between VVV of A1c and race, sex, or proportion with comorbidities of interest. Approximately 83% of the sample met criteria for nonadherence, and nonadherence was significantly associated with younger age and Black race (Table 2). A greater number of A1c measurements, higher within-subject mean A1c, and a longer interval between first and last fill dates were significantly associated with medication nonadherence. Subjects with hypertension were more likely to be adherent than those without hypertension; no significant associations were found for other diagnoses. Vol. 21, No. 3 www.amcp.org Association of Visit-to-Visit Variability of Hemoglobin A1c and Medication Adherence TABLE 2 Association of Medication Adherence and Sociodemographic and Clinical Variables, 2008-2012 Antidiabetic MPR a MPR < 80% N = 526, N (%) MPR ≥ 80% N = 106, N (%) Overall N = 632, N (%) Sex Female 287.0(54.6) 58.0(54.7) 345.0(54.6) 239.0(45.4) 48.0(45.3) 287.0(45.4) Male 51.0(8.5) 55.2(6.5) 51.7(8.4) Age at first Rx fill (years) Race 63.0(12.1) 18.0(17.3) 81.0(13.0) White Black 284.0(54.7) 39.0(37.5) 323.0(51.9) 79.0(15.2) 18.0(17.3) 97.0(15.5) Hispanic 93.0(17.9) 29.0(27.9) 122.0(19.6) Other Outpatient visits 42.0(32.2) 37.5(33.8) 41.2(32.5) Primary care visitsb 15.1(9.7) 12.6(14.1) 14.7(10.6) Rate of primary care visits/6 months 2.7(1.9) 3.2(5.5) 2.8(2.8) Diagnoses Diabetes mellitus 525.0(99.8) 106.0(100.0) 631.0(99.8) Hypertension 448.0(85.2) 99.0(93.4) 547.0(86.6) Ischemic heart disease 53.0(10.1) 14.0(13.2) 67.0(10.6) Cerebrovascular disease 24.0(4.6) 5.0(4.7) 29.0(4.6) Chronic kidney disease 43.0(8.2) 11.0(10.4) 54.0(8.5) Number of A1c measurements 6.4(3.2) 5.8(3.3) 6.3(3.3) Within-subject mean A1c 7.8(1.3) 7.2(0.9) 7.7(1.3) Days between first and last fill dates 1,109.4(496.8) 855.1(486.3) 1,066.8(503.7) Number of drug classes 1 216.0(41.1) 50.0(47.2) 266.0(42.1) 2 219.0(41.6) 42.0(39.6) 261.0(41.3) 3 87.0(16.5) 14.0(13.2) 101.0(16.0) 0 4 4.0(0.8) 4.0(0.6) Note: Data are presented as mean ± SD or n (%). a MPR was averaged across medication classes for subjects taking medications from more than 1 drug class. Nonadherence was defined as MPR < 80%. bPrimary care visits were defined as visits to Family Medicine, General Medicine, Women’s Health, Primary Care, and Geriatrics. A1c = hemoglobin A1c; MPR = medication possession ratio; Rx = prescription; SD = standard deviation. The proportion of subjects who were nonadherent increased overall with increasing quintiles of VVV of A1c (79.5%, 76.8%, 80.2%, 85.9%, 93.7%, P = 0.0027 for association; Table 1). In the unadjusted logistic regression model, there was a moderately strong association between VVV of A1c and nonadherence to diabetic medication that was statistically significant for the highest quintile (OR = 3.80, 95% CI = 1.65-8.76; Table 3). However, the fully adjusted model showed no association between VVV of A1c and nonadherence. In a sensitivity analysis using the stricter definition of adherence (i.e., MPR ≥ 80% for all drug classes), there remained no significant association. ROC curves for the models demonstrate that addition of VVV of A1c does not add substantially to the discrimination between adherence and nonadherence to diabetic medications (Figure 2). The C-statistic for the model when only age, sex, and race are included is 0.6786; adding VVV of A1c modestly improves the C-statistic to 0.7064. Similarly, adding VVV of A1c to a model that includes age, sex, race, within- www.amcp.org Vol. 21, No. 3 P Value 0.9768 < 0.0001 0.0099 0.0490 <0.0001 0.7451 1.0000 0.0277 0.3860 1.0000 0.4593 0.0116 0.0003 < 0.0001 0.5150 subject mean A1c, number of A1c measurements, number of days between first and last prescription fill dates, and rate of primary care visits improves the C-statistic marginally from 0.7460 to 0.7525. ■■ Discussion Unlike previous studies examining the ability of VVV to predict medication nonadherence,15,16 we did not find that VVV of A1c was a significant predictor of nonadherence to noninsulin diabetic medications when added to other clinical information. However, consistent with prior research,20,21 younger age, Black race, and higher mean A1c levels were statistically significantly associated with medication nonadherence. Given that nearly 60% of subjects were on more than 1 antidiabetic agent, polypharmacy may have complicated the relationship between medication adherence and VVV of A1c. Full therapeutic doses of metformin and thiazolidinediones can lower A1c by 1%-2% and 0.5%-1.4%, respectively.22 In March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 233 Association of Visit-to-Visit Variability of Hemoglobin A1c and Medication Adherence TABLE 3 Logistic Regression Models Predicting Odds of Antidiabetic Nonadherence According to Quintile of VVV of A1c, 2008-2012 Unadjusted Modela Final Adjusted Modelb OR (95% CI) P Value OR (95% CI) VVV of A1cc 1st quintile (reference) 1.00 (-) 1.00 (-) 2nd quintile 0.85(0.47-1.55) 0.62(0.32-1.21) 3rd quintile 0.0052 1.04(0.56-1.92) 0.65(0.32-1.33) 4th quintile 1.57(0.81-3.04) 0.69(0.31-1.57) 5th quintile 3.80(1.65-8.76) 1.19(0.42-3.38) Sex Female (reference) 1.00 (-) Male 1.03(0.65-1.63) Age at first antidiabetic Rx fill 1-year increase 0.94(0.91-0.97) Race White 0.49(0.25-0.95) Black (reference) 1.00 (-) Hispanic 0.59(0.31-1.14) Other 0.47(0.26-0.84) Rate of primary care visits/6 months d 1.01(0.95-1.08) 1.00(1.00-1.00) Days between first and last fill dates 0.87(0.78-0.98) Number of A1c measurements Within-subject mean A1c 1.42(1.06-1.89) Note: Nonadherence was defined as MPR < 80% averaged across antidiabetic medication classes. a Hosmer-Lemeshow goodness-of-fit test, P = 1.000. bHosmer-Lemeshow goodness-of-fit test, P = 0.1734. cVVV of A1c was defined as within-subject standard deviation of A1c during the study period. d Primary care visits were defined as visits to Family Medicine, General Medicine, Women’s Health, Primary Care, and Geriatrics. A1c = hemoglobin A1c; CI = confidence interval; MPR = medication possession ratio; OR = odds ratio; Rx = prescription; VVV = visit-to-visit variability. P Value 0.4070 0.9133 0.0004 0.0371 0.6937 < 0.0001 0.0181 0.0181 comparison, the starting dose of simvastatin lowers LDL-C by 30%.23 The stronger relationship between VVV of LDL-C and statin nonadherence15 may be due to use of a single agent to affect LDL-C and the powerful impact that statins have on cholesterol metabolism. The degree to which a medication can change a biomarker may affect the degree to which variability in the biomarker might be explained by medication adherence. By reducing absolute A1c and fluctuations in glucose levels, improvements in diet, exercise, and weight may be expected to reduce A1c variability as well.24 Unfortunately, in this study, weight data were available within 30 days of the first and last prescription fills for less than one-third of subjects. In the case of LDL-C, statin medications are even more potent than diet and exercise in altering cholesterol levels; therefore, adherence to statins may account for more variability in LDL than adherence to diabetic medications accounts for variability in A1c. Finally, the natural history of diabetes is one of progressive insulin resistance and loss of endogenous insulin production leading to worsening glycemic control. Increase in A1c, and perhaps A1c variability as well, may be expected even with 234 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 ideal medication adherence, although the rapidity may be altered by medication compliance and intensification.24,25 Many studies have shown lower rates of adherence among Black patients with diabetes,5,6,19 although this association is not consistent across diseases.12 Over half of our sample was Black, and while race was not associated with quintile of VVV of A1c, it was significantly associated with nonadherence. A1c levels are higher in Black individuals compared with White individuals across the glycemic spectrum,26 even when controlling for other predictors of A1c, and this discrepancy is most pronounced among diabetics.27 These racial differences should not affect VVV, which is a measure of within-subject variability, but it may explain differences in mean A1c by race in our sample. Our findings of lower adherence among younger subjects is consistent with other studies of medication adherence among diabetic patients, both young adults and seniors.4-6 This association may be related to duration of disease, severity of illness, or management of multiple chronic diseases. These demographic findings support interventions to improve adherence to diabetic medications among young patients and those from minority communities. Vol. 21, No. 3 www.amcp.org Association of Visit-to-Visit Variability of Hemoglobin A1c and Medication Adherence FIGURE 2 ROC Curves for Identification of Antidiabetic Medication Nonadherence 1.00 Sensitivity 0.75 0.50 0.25 0.00 0.00 0.25 0.50 Specificity 0.75 1.00 C-statistic ROC Model (Area Under Curve) Age, sex, race 0.6786 Age, sex, race, VVV of A1c 0.7064 Age, sex, race, within-subject mean 0.7460 A1c, number of A1c measurements, number of days between first and last prescription fill dates, rate of primary care visits Age, sex, race, within-subject mean 0.7525 A1c, number of A1c measurements, number of days between first and last prescription fill dates, rate of primary care visits, VVV of A1c Note: Sequential ROC curves for identification of antidiabetic nonadherence (defined as antidiabetic MPR < 80%) using sociodemographic and clinical variables. Addition of VVV of A1c to the final model improves the C-statistic from 0.7460 to 0.7525 (95% CI = 0.7021-0.8029). A1c = hemoglobin A1c; CI = confidence interval; MPR = medication possession ratio; ROC = receiver operator characteristic; VVV = visit-to-visit variability. Legend While we are the first to explore an association between VVV of A1c and medication adherence, increased variability in A1c is already recognized as a marker of elevated risk for diabetic complications. In the Finnish Diabetes Nephropathy longitudinal cohort study of patients with type 1 diabetes, investigators found that variability in A1c predicted development and progression of renal disease as well as cardiovascular events such as myocardial infarction and stroke.28 Similarly, among subjects with type 2 diabetes, researchers have found that variability in A1c predicted microalbuminuria and progression of nephropathy, independent of mean A1c.29,30 In a study using a clinical database, type 2 diabetics with high www.amcp.org Vol. 21, No. 3 variability in A1c had nearly twice the risk of all-cause mortality compared with subjects with low variability.31 These findings parallel studies of variability in systolic blood pressure, which has been associated with increased risk for stroke and all-cause mortality, independent of mean blood pressure.32,33 More research is needed on the biological basis for this variability in clinical biomarkers and the extent to which specific medication classes and medication adherence may account for this variability. Limitations Our sample consists of insured but low-income patients who are largely of minority race/ethnicity at a single urban health care system; thus, the findings may not apply to other settings. Still, the challenge of medication nonadherence may be greatest in such a population and merits particular attention. Indeed, the MHDR database used in this study was intended to promote research in health care disparities according to race and socioeconomic status. As a younger, non-Medicare sample, this population is also where early, aggressive management of diabetes is essential. Adherence to oral diabetic medications in most studies has ranged from 65%-85% yet is often lower (36%-53%) in populations that are similar to ours.1 The average MPR in our sample was 56.2%, and the rate of medication nonadherence was 83.2%, again limiting generalizability but arguing for the disproportionate burden of the problem of adherence in this community. Over half of our subjects had nearly 3 years of pharmacy prescription information, but we did not assess for change in adherence over that time period, which may occur as patients live with and adapt to their medical conditions. All subjects included in the sample had at least 1 visit with a primary care specialty, which enhances the utility of these results to primary care practitioners, although prescriptions and some management may have been shared with specialists. Although the I2B2 system is a rich collection of important variables, some covariates, such as duration of diabetes, are not available. Future research should consider longitudinal studies to assess temporal trends in adherence and A1c variability. We used pharmacy claims to measure medication adherence, which may overestimate adherence, since it cannot account for pill storing or pill dumping of acquired medications but has been shown to be a reliable and practical method for health services research.34 Future studies should consider replicating analyses using a more rigorous measure of adherence, such as pill counting or MEMS, to test the validity of these findings. We excluded subjects using insulin due to the complexities of measuring dose and compliance, the markedly increased A1c variability that insulin induces, and to achieve a more clinically homogenous sample of diabetic patients. If adherence to insulin could be efficiently measured, it would be important to test the utility of VVV of A1c in predicting adherence in that patient population. We did include subjects March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 235 Association of Visit-to-Visit Variability of Hemoglobin A1c and Medication Adherence on relatively newer diabetic medications, such as incretin mimetics and DPP-4 inhibitors, while most previous studies on medication adherence among diabetics have been restricted to biguanides, sulfonylureas, or other oral agents. We could not account for medications that patients may have obtained without submitting pharmacy prescription claims data, such as free antidiabetic medications available at some large retailers. There is a risk of misclassification bias if patients were not continuously enrolled in the health plan during the study period. If patients temporarily disenrolled and received prescriptions through another plan and then re-enrolled in BMC Health Plan, their MPR would appear falsely low and may have biased our results. Additional factors associated with poor medication adherence, including polypharmacy,12 dose frequency,35 burden of comorbid diseases,21 cost of copayments for medications,36 and psychosocial factors such as personal health beliefs,37 health literacy,38 and depression,39 were not measured in this study. We did include specific diagnoses that often co-occur with diabetes, such as hypertension, or that might affect diabetic medication prescribing practices, such as chronic kidney disease. With the exception of hypertension, these diseases were not associated with nonadherence. ■■ Conclusions We found that VVV of A1c is not a robust indicator of nonadherence to noninsulin diabetic medications. Efficient, reliable means of detecting nonadherence at the bedside are still needed in order to diagnose at-risk patients and deliver intervention to appropriate patients. Researchers and health care systems should explore innovative approaches that leverage health information technology to measure adherence, such as integrating patient information (e.g., MEMS data) or pharmacy information directly into electronic health records to create useful alerts for physicians at the point of care. This study was funded by Boston University’s Clinical and Translational Institute (UL1-TR000157); American Cancer Society Physician in Training Award in Cancer Prevention (PTAPM-97-185-16); and Patient-Oriented Mentored Scientist Award through the National Institute of Diabetes, Digestive, and Kidney Diseases (K23DK081665). The funders had no involvement in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript. The authors declare no conflicts of interest. Study concept and design were contributed by Mann, Winter, and Ramachandran. Winter collected data, assisted by Mann, and all authors contributed equally to data analysis. The writing and revision of the manuscript were carried out primarily by Ramachandran, assisted by Winter and Mann. REFERENCES 1. Rubin RR. Adherence to pharmacologic therapy in patients with type 2 diabetes mellitus. Am J Med. 2005;118(Suppl 5A):S27-34. 2. Cramer JA. A systematic review of adherence with medications for diabetes. Diabetes Care. 2004;27(5):1218-24. 3. Adams AS, Trinacty CM, Zhang F, et al. Medication adherence and racial differences in A1c control. Diabetes Care. 2008;31(5):916-21. 4. Hertz RP, Unger A N, Lustik MB. Adherence with pharmacotherapy for type 2 diabetes: a retrospective cohort study of adults with employer-sponsored health insurance. Clin Ther. 2005;27(7):1064-73. 5. Yang Y, Thumula V, Pace PF, Banahan BF 3rd, Wilkin NE, Lobb WB. Predictors of medication nonadherence among patients with diabetes in Medicare Part D programs: a retrospective cohort study. Clin Ther. 2009;31(10):2178-88. 6. Zhu VJ, Tu W, Marrero DG, Rosenman MB, Overhage JM. Race and medication adherence and glycemic control: findings from an operational health information exchange. AMIA Annu Symp Proc. 2011;2011:1649-57. 7. Ho PM, Rumsfeld JS, Masoudi FA, et al. Effect of medication nonadherence on hospitalization and mortality among patients with diabetes mellitus. Arch Intern Med. 2006;166(17):1836-41. 8. Yu AP, Yu YF, Nichol MB. Estimating the effect of medication adherence on health outcomes among patients with type 2 diabetes—an application of marginal structural models. Value Health. 2010;13(8):1038-45. 9. Balkrishnan R, Rajagopalan R, Camacho FT, Huston SA, Murray FT, Anderson RT. Predictors of medication adherence and associated health care costs in an older population with type 2 diabetes mellitus: a longitudinal cohort study. Clin Ther. 2003;25(11):2958-71. 10. Egede LE, Gebregziabher M, Dismuke CE, et al. Medication nonadherence in diabetes: longitudinal effects on costs and potential cost savings from improvement. Diabetes Care. 2012;35(12):2533-39. Authors AMBILI RAMACHANDRAN, MD, MS, is Assistant Professor of Medicine, Section of General Internal Medicine, and DEVIN M. MANN, MD, MS, is Assistant Professor of Medicine, Section of General Internal Medicine and Section of Preventive Medicine and Epidemiology, Department of Medicine, Boston University School of Medicine, Boston, Massachusetts. MICHAEL WINTER, MPH, is Associate Director, Data Coordinating Center, Boston University School of Public Health, Boston, Massachusetts. AUTHOR CORRESPONDENCE: Ambili Ramachandran, MD, MS, Assistant Professor of Medicine, General Internal Medicine, Boston University School of Medicine, 801 Massachusetts Ave., Crosstown Center, 2nd Fl., Boston, MA 02118. Tel.: 617.414.6955; Fax: 617.414.4676; E-mail: [email protected]. 236 Journal of Managed Care & Specialty Pharmacy DISCLOSURES JMCP March 2015 11. Miller LG, Liu H, Hays RD, et al. How well do clinicians estimate patients’ adherence to combination antiretroviral therapy? J Gen Intern Med. 2002;17(1):1-11. 12. Osterberg L, Blaschke T. Adherence to medication. N Engl J Med. 2005;353(5):487-97. 13. Cramer JA, Mattson RH, Prevey ML, Scheyer RD, Ouellette VL. How often is medication taken as prescribed? A novel assessment technique. JAMA. 1989;261(22):3273-77. 14. Marcum ZA, Sevick MA, Handler SM. Medication nonadherence: a diagnosable and treatable medical condition. JAMA. 2013;309(20):2105-06. 15. Mann DM, Glazer NL, Winter M, et al. A pilot study identifying statin nonadherence with visit-to-visit variability of low-density lipoprotein cholesterol. Am J Cardiol. 2013;111(10):1437-42. 16. Muntner P, Levitan EB, Joyce C, et al. Association between antihypertensive medication adherence and visit-to-visit variability of blood pressure. J Clin Hypertens (Greenwich). 2013;15(2):112-17. Vol. 21, No. 3 www.amcp.org Association of Visit-to-Visit Variability of Hemoglobin A1c and Medication Adherence 17. Boston University Clinical and Translational Science Institute. Massachusetts Healthcare Disparities Repository (MHDR). Informatics for Integrating Biology & the Bedside (i2b2). Available at: http://www.bu.edu/ i2b2/mhdr/. Accessed January 30, 2015. 18. Hess LM, Raebel MA, Conner DA, Malone DC. Measurement of adherence in pharmacy administrative databases: a proposal for standard definitions and preferred measures. Ann Pharmacother. 2006;40(7-8):1280-88. 19. Zhang Y, Baik SH, Chang CC, Kaplan CM, Lave JR. Disability, race/ethnicity, and medication adherence among Medicare myocardial infarction survivors. Am Heart J. 2012;164(3):425-433.e4. 20. Mann DM, Allegrante JP, Natarajan S, Halm EA, Charlson M. Predictors of adherence to statins for primary prevention. Cardiovasc Drugs Ther. 2007;21(4):311-16. 21. Mann DM, Woodward M, Muntner P, Falzon L, Kronish I. Predictors of nonadherence to statins: a systematic review and meta-analysis. Ann Pharmacother. 2010;44(9):1410-21. 29. Sugawara A, Kawai K, Motohashi S, et al. HbA(1c) variability and the development of microalbuminuria in type 2 diabetes: Tsukuba Kawai Diabetes Registry 2. Diabetologia. 2012;55(8):2128-31. 30. Rodríguez-Segade S, Rodríguez J, García López JM, Casanueva FF, Camiña F. Intrapersonal HbA(1c) variability and the risk of progression of nephropathy in patients with Type 2 diabetes. Diabet Med. 2012;29(12):1562-66. 31. Ma WY, Li HY, Pei D, et al. Variability in hemoglobin A1c predicts allcause mortality in patients with type 2 diabetes. J Diabetes Complications. 2012;26(4):296-300. 32. Rothwell PM, Howard SC, Dolan E, et al. Prognostic significance of visitto-visit variability, maximum systolic blood pressure, and episodic hypertension. Lancet. 2010;375(9718):895-905. 22. Ismail-Beigi F. Clinical practice. Glycemic management of type 2 diabetes mellitus. N Engl J Med. 2012;366(14):1319-27. 33. Muntner P, Shimbo D, Tonelli M, Reynolds K, Arnett DK, Oparil S. The relationship between visit-to-visit variability in systolic blood pressure and all-cause mortality in the general population: findings from NHANES III, 1988 to 1994. Hypertension. 2011;57(2):160-66. 23. Bersot T. Drug therapy for hypercholesterolemia and dyslipiedmia. In: Brunton L, Chabner B, Knollmann B, eds. Goodman and Gilman’s The Pharmacological Basis of Therapeutics. 12th ed. New York: McGraw-Hill; 2011. 34. Andrade SE, Kahler KH, Frech F, Chan KA. Methods for evaluation of medication adherence and persistence using automated databases. Pharmacoepidemiol Drug Saf. 2006;15(8):565-74. 24. Gregg EW, Chen H, Wagenknecht LE, et al. Association of an intensive lifestyle intervention with remission of type 2 diabetes. JAMA. 2012;308(23):2489-96. 35. Claxton AJ, Cramer J, Pierce C. A systematic review of the associations between dose regimens and medication compliance. Clin Ther. 2001;23(8):1296-310. 25. Knowler WC, Barrett-Connor E, Fowler SE, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393-403. 36. Piette JD, Heisler M, Wagner TH. Problems paying out-of-pocket medication costs among older adults with diabetes. Diabetes Care. 2004;27(2):384-91. 26. Herman WH, Ma Y, Uwaifo G, et al. Differences in A1C by race and ethnicity among patients with impaired glucose tolerance in the Diabetes Prevention Program. Diabetes Care. 2007;30(10):2453-57. 37. Glasgow RE, Hampson SE, Strycker LA, Ruggiero L. Personal-model beliefs and social-environmental barriers related to diabetes self-management. Diabetes Care. 1997;20(4):556-61. 27. Ziemer DC, Kolm P, Weintraub WS, et al. Glucose-independent, blackwhite differences in hemoglobin A1c levels: a cross-sectional analysis of 2 studies. Ann Intern Med. 2010;152(12):770-77. 38. Schillinger D, Grumbach K, Piette J, et al. Association of health literacy with diabetes outcomes. JAMA. 2002;288(4):475-82. 28. Wadén J, Forsblom C, Thorn LM, et al. A1c variability predicts incident cardiovascular events, microalbuminuria, and overt diabetic nephropathy in patients with type 1 diabetes. Diabetes. 2009;58(11):2649-55. 39. Lin EH, Katon W, Von Korff M, et al. Relationship of depression and diabetes self-care, medication adherence, and preventive care. Diabetes Care. 2004;27(9):2154-60. www.amcp.org Vol. 21, No. 3 March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 237 Consider once-weekly TANZEUM for formulary inclusion • The lowest Wholesale Acquisition Cost (WAC) in the GLP-1 receptor agonist class1,a — WAC comparison does not imply comparable safety or effectiveness and does not imply identical indications — No Phase III clinical trial data are available comparing the efficacy of TANZEUM to Bydureon® (exenatide extended-release for injectable suspension), Byetta® (exenatide) Injection, or Trulicity™ (dulaglutide) injection, for subcutaneous use. In a head-to-head trial of TANZEUM vs Victoza, TANZEUM provided less HbA1c reduction than Victoza® (liraglutide [rDNA origin] injection), solution for subcutaneous use and the treatment difference was statistically significant • Available in 2 dosage strengths at the same WAC price1,2: 30-mg and 50-mg, single-dose pens • The safety and efficacy for TANZEUM have been evaluated in a clinical trial program comprising 8 Phase III studies and 2365 patients who received TANZEUM2 a WAC is the listed price to wholesalers and warehousing chains, not including prompt pay, stocking or distribution allowances, or other discounts, rebates, or chargebacks. The listed price may not represent prices charged to other customers, including specialty distributors. WAC does not reflect the price paid by consumers.1 Indications and Usage for TANZEUM TANZEUM is indicated as an adjunct to diet and exercise to improve glycemic control in adults with type 2 diabetes mellitus. Limitations of Use: • TANZEUM is not recommended as first-line therapy for patients inadequately controlled on diet and exercise. • TANZEUM has not been studied in patients with a history of pancreatitis. Consider other antidiabetic therapies in patients with a history of pancreatitis. • TANZEUM is not indicated in the treatment of patients with type 1 diabetes mellitus or for the treatment of patients with diabetic ketoacidosis. TANZEUM is not a substitute for insulin in these patients. • TANZEUM has not been studied in patients with severe gastrointestinal disease, including severe gastroparesis. The use of TANZEUM is not recommended in patients with pre-existing severe gastrointestinal disease. • TANZEUM has not been studied in combination with prandial insulin. Important Safety Information for TANZEUM WARNING: RISK OF THYROID C-CELL TUMORS Thyroid C-cell tumors have been observed in rodent studies with glucagon-like peptide-1 (GLP-1) receptor agonists at clinically relevant exposures. It is unknown whether TANZEUM causes thyroid C-cell tumors, including medullary thyroid carcinoma (MTC), in humans. TANZEUM is contraindicated in patients with a personal or family history of MTC or in patients with Multiple Endocrine Neoplasia syndrome type 2 (MEN 2). Routine serum calcitonin or thyroid ultrasound monitoring is of uncertain value in patients treated with TANZEUM. Patients should be counseled regarding the risk and symptoms of thyroid tumors. CONTRAINDICATIONS Hypersensitivity: TANZEUM is contraindicated in patients with a prior serious hypersensitivity reaction to albiglutide or to any of the product components. Continued on next page Important Safety Information for TANZEUM (cont’d) WARNINGS AND PRECAUTIONS Risk of Thyroid C-cell Tumors: Counsel patients regarding the risk for MTC with the use of TANZEUM and inform them of symptoms of thyroid tumors (e.g., a mass in the neck, dysphagia, dyspnea, persistent hoarseness). Patients with thyroid nodules noted on physical examination or neck imaging should be referred to an endocrinologist for further evaluation. Routine monitoring of serum calcitonin or using thyroid ultrasound is of uncertain value for early detection of MTC in patients treated with TANZEUM. If serum calcitonin is measured and found to be elevated, the patient should be referred to an endocrinologist for further evaluation. Acute Pancreatitis: In clinical trials, acute pancreatitis has been reported in association with TANZEUM. After initiation of TANZEUM, observe patients carefully for signs and symptoms of pancreatitis (including persistent severe abdominal pain, sometimes radiating to the back and which may or may not be accompanied by vomiting). If pancreatitis is suspected, promptly discontinue TANZEUM. If pancreatitis is confirmed, TANZEUM should not be restarted. TANZEUM has not been studied in patients with a history of pancreatitis to determine whether these patients are at increased risk for pancreatitis. Consider other antidiabetic therapies in patients with a history of pancreatitis. Hypoglycemia with Concomitant Use of Insulin Secretagogues or Insulin: The risk of hypoglycemia is increased when TANZEUM is used in combination with insulin secretagogues (e.g., sulfonylureas) or insulin. Therefore, patients may require a lower dose of sulfonylurea or insulin to reduce the risk of hypoglycemia in this setting. Hypersensitivity Reactions: Across 8 Phase III clinical trials, a serious hypersensitivity reaction with pruritus, rash, and dyspnea occurred in a patient treated with TANZEUM. If hypersensitivity reactions occur, discontinue use of TANZEUM; treat promptly per standard of care and monitor until signs and symptoms resolve. Renal Impairment: In patients treated with GLP-1 receptor agonists, there have been postmarketing reports of acute renal failure and worsening of chronic renal failure, which may sometimes require hemodialysis. Some of these events were reported in patients without known underlying renal disease. A majority of reported events occurred in patients who had experienced nausea, vomiting, diarrhea, or dehydration. In a trial of TANZEUM in patients with renal impairment, the frequency of such gastrointestinal reactions increased as renal function declined. Because these reactions may worsen renal function, use caution when initiating or escalating doses of TANZEUM in patients with renal impairment. Monitor renal function in patients with renal impairment reporting severe adverse gastrointestinal reactions. Macrovascular Outcomes: There have been no clinical trials establishing conclusive evidence of macrovascular risk reduction with TANZEUM or any other antidiabetic drug. ADVERSE REACTIONS The most common adverse reactions, excluding hypoglycemia, reported in ≥5% of patients treated with TANZEUM and more commonly than in patients treated with placebo, are: upper respiratory tract infection (14.2 vs 13.0); diarrhea (13.1 vs 10.5); nausea (11.1 vs 9.6); injection site reaction (10.5 vs 2.1); cough (6.9 vs 6.2); back pain (6.7 vs 5.8); arthralgia (6.6 vs 6.4); sinusitis (6.2 vs 5.8); influenza (5.2 vs 3.2). DRUG INTERACTIONS TANZEUM delays gastric emptying and may impact absorption of concomitantly administered oral medications. Caution should be exercised when oral medications are concomitantly administered with TANZEUM. USE IN SPECIFIC PATIENT POPULATIONS Pediatric Use: Safety and effectiveness of TANZEUM have not been established in pediatric patients (younger than 18 years). A1C = glycosylated hemoglobin; GLP-1 = glucagon-like peptide-1. References: 1. Data on file. GSK. 2. Prescribing Information for TANZEUM. Please see Brief Summary of Prescribing Information, including Boxed Warning, for TANZEUM on the following pages. www.GSKSource.com Bydureon and Byetta are registered trademarks of the AstraZeneca group of companies. Trulicity is a trademark of Eli Lilly and Company. Victoza is a registered trademark of Novo Nordisk A/S. TANZEUM ™ BRIEF SUMMARY (albiglutide) for injection, for subcutaneous use The following is a brief summary only; see full prescribing information for complete product information. WARNING: RISK OF THYROID C-CELL TUMORS • Thyroid C-cell tumors have been observed in rodent studies with glucagon-like peptide-1 (GLP-1) receptor agonists at clinically relevant exposures. It is unknown whether TANZEUM™ causes thyroid C-cell tumors, including medullary thyroid carcinoma (MTC), in humans [see Warnings and Precautions (5.1)]. • TANZEUM is contraindicated in patients with a personal or family history of MTC or in patients with Multiple Endocrine Neoplasia syndrome type 2 (MEN 2). Routine serum calcitonin or thyroid ultrasound monitoring is of uncertain value in patients treated with TANZEUM. Patients should be counseled regarding the risk and symptoms of thyroid tumors [see Contraindications (4.1), Warnings and Precautions (5.1)]. 1 INDICATIONS AND USAGE TANZEUM is indicated as an adjunct to diet and exercise to improve glycemic control in adults with type 2 diabetes mellitus [see Clinical Studies (14) of full prescribing information]. Limitations of Use: TANZEUM is not recommended as first-line therapy for patients inadequately controlled on diet and exercise [see Warnings and Precautions (5.1)]. TANZEUM has not been studied in patients with a history of pancreatitis [see Warnings and Precautions (5.2)]. Consider other antidiabetic therapies in patients with a history of pancreatitis. TANZEUM is not indicated in the treatment of patients with type 1 diabetes mellitus or for the treatment of patients with diabetic ketoacidosis. TANZEUM is not a substitute for insulin in these patients. TANZEUM has not been studied in patients with severe gastrointestinal (GI) disease, including severe gastroparesis. The use of TANZEUM is not recommended in patients with pre-existing severe gastrointestinal disease [see Adverse Reactions (6.1)]. TANZEUM has not been studied in combination with prandial insulin. 4 CONTRAINDICATIONS 4.1 Medullary Thyroid Carcinoma: TANZEUM is contraindicated in patients with a personal or family history of medullary thyroid carcinoma (MTC) or in patients with Multiple Endocrine Neoplasia syndrome type 2 (MEN 2) [see Warnings and Precautions (5.1)]. 4.2 Hypersensitivity: TANZEUM is contraindicated in patients with a prior serious hypersensitivity reaction to albiglutide or to any of the product components [see Warnings and Precautions (5.4)]. 5 WARNINGS AND PRECAUTIONS 5.1 Risk of Thyroid C-cell Tumors: Nonclinical studies in rodents of clinically relevant doses of GLP-1 receptor agonists showed dose-related and treatment-duration-dependent increases in the incidence of thyroid C-cell tumors (adenomas and carcinomas). Carcinogenicity studies could not be conducted with TANZEUM because drug-clearing, anti-drug antibodies develop in animals used for these types of studies [see Nonclinical Toxicology (13.1)]. It is unknown whether GLP-1 receptor agonists are associated with thyroid C-cell tumors, including MTC in humans [see Boxed Warning, Contraindications (4.1)]. Across 8 Phase III clinical trials [see Clinical Studies (14) of full prescribing information], MTC was diagnosed in 1 patient receiving TANZEUM and 1 patient receiving placebo. Both patients had markedly elevated serum calcitonin levels at baseline. TANZEUM is contraindicated in patients with a personal or family history of MTC or in patients with MEN 2. Counsel patients regarding the risk for MTC with the use of TANZEUM and inform them of symptoms of thyroid tumors (e.g., a mass in the neck, dysphagia, dyspnea, persistent hoarseness). The clinical value of routine monitoring of serum calcitonin to diagnose MTC in patients at risk for MTC has not been established. Elevated serum calcitonin is a biological marker of MTC. Patients with MTC usually have calcitonin values >50 ng/L. Patients with thyroid nodules noted on physical examination or neck imaging should be referred to an endocrinologist for further evaluation. Routine monitoring of serum calcitonin or using thyroid ultrasound is of uncertain value for early detection of MTC in patients treated with TANZEUM. Such monitoring may increase the risk of unnecessary procedures, due to the low specificity of serum calcitonin testing for MTC and a high background incidence of thyroid disease. If serum calcitonin is measured and found to be elevated, the patient should be referred to an endocrinologist for further evaluation. 5.2 Acute Pancreatitis: In clinical trials, acute pancreatitis has been reported in association with TANZEUM. Across 8 Phase III clinical trials [see Clinical Studies (14) of full prescribing information], pancreatitis adjudicated as likely related to therapy occurred more frequently in patients receiving TANZEUM (6 of 2,365 [0.3%]) than in patients receiving placebo (0 of 468 [0%]) or active comparators (2 of 2,065 [0.1%]). After initiation of TANZEUM, observe patients carefully for signs and symptoms of pancreatitis (including persistent severe abdominal pain, sometimes radiating to the back and which may or may not be accompanied by vomiting). If pancreatitis is suspected, promptly discontinue TANZEUM. If pancreatitis is confirmed, TANZEUM should not be restarted. TANZEUM has not been studied in patients with a history of pancreatitis to determine whether these patients are at increased risk for pancreatitis. Consider other antidiabetic therapies in patients with a history of pancreatitis. 5.3 Hypoglycemia with Concomitant Use of Insulin Secretagogues or Insulin: The risk of hypoglycemia is increased when TANZEUM is used in combination with insulin secretagogues (e.g., sulfonylureas) or insulin. Therefore, patients may require a lower dose of sulfonylurea or insulin to reduce the risk of hypoglycemia in this setting [see Adverse Reactions (6.1)]. 5.4 Hypersensitivity Reactions: Across 8 Phase III clinical trials [see Clinical Studies (14) of full prescribing information], a serious hypersensitivity reaction with pruritus, rash, and dyspnea occurred in a patient treated with TANZEUM. If hypersensitivity reactions occur, discontinue use of TANZEUM; treat promptly per standard of care and monitor until signs and symptoms resolve [see Contraindications (4.2)]. 5.5 Renal Impairment: In patients treated with GLP-1 receptor agonists, there have been postmarketing reports of acute renal failure and worsening of chronic renal failure, which may sometimes require hemodialysis. Some of these events were reported in patients without known underlying renal disease. A majority of reported events occurred in patients who had experienced nausea, vomiting, diarrhea, or dehydration. In a trial of TANZEUM in patients with renal impairment [see Clinical Studies (14.3) of full prescribing information], the frequency of such gastrointestinal reactions increased as renal function declined [see Use in Specific Populations (8.6)]. Because these reactions may worsen renal function, use caution when initiating or escalating doses of TANZEUM in patients with renal impairment [see Dosage and Administration (2.3) of full prescribing information, Use in Specific Populations (8.6)]. 5.6 Macrovascular Outcomes: There have been no clinical trials establishing conclusive evidence of macrovascular risk reduction with TANZEUM or any other antidiabetic drug. 6 ADVERSE REACTIONS The following serious reactions are described below or elsewhere in the labeling: Risk of Thyroid C-cell Tumors [see Warnings and Precautions (5.1)], Acute Pancreatitis [see Warnings and Precautions (5.2)], Hypoglycemia with Concomitant Use of Insulin Secretagogues or Insulin [see Warnings and Precautions (5.3)], Hypersensitivity Reactions [see Warnings and Precautions (5.4)], Renal Impairment [see Warnings and Precautions (5.5)]. 6.1 Clinical Trials Experience: Because clinical trials are conducted under widely varying conditions, adverse reaction rates observed in the clinical trials of a drug cannot be directly compared with rates in the clinical trials of another drug and may not reflect the rates observed in practice. Pool of Placebo-Controlled Trials: The data in Table 1 are derived from 4 placebo-controlled trials. TANZEUM was used as monotherapy in 1 trial and as add-on therapy in 3 trials [see Clinical Studies (14) of full prescribing information]. These data reflect exposure of 923 patients to TANZEUM and a mean duration of exposure to TANZEUM of 93 weeks. The mean age of participants was 55 years, 1% of participants were 75 years or older and 53% of participants were male. The population in these studies was 48% white, 13% African/African American, 7% Asian, and 29% Hispanic/Latino. At baseline, the population had diabetes for an average of 7 years and had a mean HbA1c of 8.1%. At baseline, 17% of the population in these studies reported peripheral neuropathy and 4% reported retinopathy. Baseline estimated renal function was normal or mildly impaired (eGFR >60 mL/min/1.73 m2) in 91% of the study population and moderately impaired (eGFR 30 to 60 mL/min/1.73 m2) in 9%. Table 1 shows common adverse reactions excluding hypoglycemia associated with the use of TANZEUM in the pool of placebo-controlled trials. These adverse reactions were not present at baseline, occurred more commonly on TANZEUM than on placebo, and occurred in at least 5% of patients treated with TANZEUM. Table 1. Adverse Reactions in Placebo-controlled Trials Reported in ≥5% of Patients Treated with TANZEUMa Placebo TANZEUM Adverse Reaction (N=468) (N=923) % % Upper respiratory tract infection 13.0 14.2 Diarrhea 10.5 13.1 Nausea 9.6 11.1 Injection site reactionb 2.1 10.5 Cough 6.2 6.9 Back pain 5.8 6.7 Arthralgia 6.4 6.6 Sinusitis 5.8 6.2 Influenza 3.2 5.2 Adverse reactions reported includes adverse reactions occurring with the use of glycemic rescue medications which included metformin (17% for placebo and 10% for TANZEUM) and insulin (24% for placebo and 14% for TANZEUM). b See below for other events of injection site reactions reported. a Continued on next page Adverse Reactions (cont’d) Gastrointestinal Adverse Reactions: In the pool of placebo-controlled trials, gastrointestinal complaints occurred more frequently among patients receiving TANZEUM (39%) than patients receiving placebo (33%). In addition to diarrhea and nausea (see Table 1), the following gastrointestinal adverse reactions also occurred more frequently in patients receiving TANZEUM: vomiting (2.6% versus 4.2% for placebo versus TANZEUM), gastroesophageal reflux disease (1.9% versus 3.5% for placebo versus TANZEUM), and dyspepsia (2.8% versus 3.4% for placebo versus TANZEUM). Constipation also contributed to the frequently reported reactions. In the group treated with TANZEUM, investigators graded the severity of GI reactions as “mild” in 56% of cases, “moderate” in 37% of cases, and “severe” in 7% of cases. Discontinuation due to GI adverse reactions occurred in 2% of individuals on TANZEUM or placebo. Injection Site Reactions: In the pool of placebo-controlled trials, injection site reactions occurred more frequently on TANZEUM (18%) than on placebo (8%). In addition to the term injection site reaction (see Table 1), the following other types of injection site reactions also occurred more frequently on TANZEUM: injection site hematoma (1.9% versus 2.1% for placebo versus TANZEUM), injection site erythema (0.4% versus 1.7% for placebo versus TANZEUM), injection site rash (0% versus 1.4% for placebo versus TANZEUM), injection site hypersensitivity (0% versus 0.8% versus for placebo versus TANZEUM), and injection site hemorrhage (0.6% versus 0.7% for placebo versus TANZEUM). Injection site pruritus also contributed to the frequently reported reactions. The majority of injection site reactions were judged as “mild” by investigators in both groups (73% for TANZEUM versus 94% for placebo). More patients on TANZEUM than on placebo: discontinued due to an injection site reaction (2% versus 0.2%), experienced more than 2 reactions (38% versus 20%), had a reaction judged by investigators to be “moderate” or “severe” (27% versus 6%) and required local or systemic treatment for the reactions (36% versus 11%). Pool of Placebo- and Active-controlled Trials: The occurrence of adverse reactions was also evaluated in a larger pool of patients with type 2 diabetes participating in 7 placebo- and active-controlled trials. These trials evaluated the use of TANZEUM as monotherapy, and as add-on therapy to oral antidiabetic agents, and as add-on therapy to basal insulin [see Clinical Studies (14) of full prescribing information]. In this pool, a total of 2,116 patients with type 2 diabetes were treated with TANZEUM for a mean duration of 75 weeks. The mean age of patients treated with TANZEUM was 55 years, 1.5% of the population in these studies was 75 years or older and 51% of participants were male. Forty-eight percent of patients were white, 15% African/African American, 9% Asian, and 26% were Hispanic/Latino. At baseline, the population had diabetes for an average of 8 years and had a mean HbA1c of 8.2%. At baseline, 21% of the population reported peripheral neuropathy and 5% reported retinopathy. Baseline estimated renal function was normal or mildly impaired (eGFR >60 mL/min/1.73 m2) in 92% of the population and moderately impaired (eGFR 30 to 60 mL/min/1.73 m2) in 8% of the population. In the pool of placebo- and active-controlled trials, the types and frequency of common adverse reactions excluding hypoglycemia were similar to those listed in Table 1. Other Adverse Reactions: Hypoglycemia: The proportion of patients experiencing at least one documented symptomatic hypoglycemic episode on TANZEUM and the proportion of patients experiencing at least one severe hypoglycemic episode on TANZEUM in clinical trials [see Clinical Studies (14) of full prescribing information] is shown in Table 2. Hypoglycemia was more frequent when TANZEUM was added to sulfonylurea or insulin [see Warnings and Precautions (5.3)]. Table 2. Incidence (%) of Hypoglycemia in Clinical Trials of TANZEUMa TANZEUM Monotherapyb Placebo 30 mg Weekly N = 101 (52 Weeks) N = 101 Documented symptomaticc Severed In Combination with Metformin Trial (104 Weeks)e Documented symptomatic Severe In Combination with Pioglitazone ± Metformin (52 Weeks) Documented symptomatic Severe In Combination with Metformin and Sulfonylurea (52 Weeks) Documented symptomatic Severe In Combination with Insulin Glargine (26 Weeks) Documented symptomatic Severe 2% Placebo N = 101 4% Placebo N = 151 1% Placebo N = 115 7% Insulin Lispro N = 281 30% 0.7% 2% TANZEUM N = 302 3% TANZEUM N = 150 3% 1% TANZEUM N = 271 13% 0.4% TANZEUM N = 285 16% - Table 2. Incidence (%) of Hypoglycemia in Clinical Trials of TANZEUMa (cont’d) Insulin In Combination with TANZEUM Glargine N = 504 Metformin ± Sulfonylurea (52 Weeks) N = 241 Documented symptomatic Severe In Combination with OADs in Renal Impairment (26 Weeks) Documented symptomatic Severe 27% 0.4% 17% 0.4% Sitagliptin N = 246 TANZEUM N = 249 6% 0.8% 10% - OAD = Oral antidiabetic agents. Data presented are to the primary endpoint and include only events occurring on-therapy with randomized medications and excludes events occurring after use of glycemic rescue medications (i.e., primarily metformin or insulin). bIn this trial, no documented symptomatic or severe hypoglycemia were reported for TANZEUM 50 mg and these data are omitted from the table. cPlasma glucose concentration ≤70 mg/dL and presence of hypoglycemic symptoms. dEvent requiring another person to administer a resuscitative action. eRate of documented symptomatic hypoglycemia for active controls 18% (glimepiride) and 2% (sitagliptin). a Pneumonia: In the pool of 7 placebo- and active-controlled trials, the adverse reaction of pneumonia was reported more frequently in patients receiving TANZEUM (1.8%) than in patients in the all-comparators group (0.8%). More cases of pneumonia in the group receiving TANZEUM were serious (0.4% for TANZEUM versus 0.1% for all comparators). Atrial Fibrillation/Flutter: In the pool of 7 placebo- and active-controlled trials, adverse reactions of atrial fibrillation (1.0%) and atrial flutter (0.2%) were reported more frequently for TANZEUM than for all comparators (0.5% and 0%, respectively). In both groups, patients with events were generally male, older, and had underlying renal impairment or cardiac disease (e.g., history of arrhythmia, palpitations, congestive heart failure, cardiomyopathy, etc.). Appendicitis: In the pool of placebo- and active-controlled trials, serious events of appendicitis occurred in 0.3% of patients treated with TANZEUM compared with 0% among all comparators. Immunogenicity: In the pool of 7 placebo- and active-controlled trials, 116 (5.5%) of 2,098 patients exposed to TANZEUM tested positive for anti-albiglutide antibodies at any time during the trials. None of these antibodies were shown to neutralize the activity of albiglutide in an in vitro bioassay. Presence of antibody did not correlate with reduced efficacy as measured by HbA1c and fasting plasma glucose or specific adverse reactions. Consistent with the high homology of albiglutide with human GLP-1, the majority of patients (approximately 79%) with anti-albiglutide antibodies also tested positive for anti-GLP-1 antibodies; none were neutralizing. A minority of patients (approximately 17%) who tested positive for anti-albiglutide antibodies also transiently tested positive for antibodies to human albumin. The detection of antibody formation is highly dependent on the sensitivity and specificity of the assay. Additionally, the observed incidence of antibody (including neutralizing antibody) positivity in an assay may be influenced by several factors including assay methodology, sample handling, timing of sample collection, concomitant medications, and underlying disease. For these reasons, the incidence of antibodies to albiglutide cannot be directly compared with the incidence of antibodies of other products. Liver Enzyme Abnormalities: In the pool of placebo- and active-controlled trials, a similar proportion of patients experienced at least one event of alanine aminotransferase (ALT) increase of 3-fold or greater above the upper limit of normal (0.9% and 0.9% for all comparators versus TANZEUM). Three subjects on TANZEUM and one subject in the all-comparator group experienced at least one event of ALT increase of 10-fold or greater above the upper limit of normal. In one of the 3 cases an alternate etiology was identified to explain the rise in liver enzyme (acute viral hepatitis). In one case, insufficient information was obtained to establish or refute a drug-related causality. In the third case, elevation in ALT (10 times the upper limit of normal) was accompanied by an increase in total bilirubin (4 times the upper limit of normal) and occurred 8 days after the first dose of TANZEUM. The etiology of hepatocellular injury was possibly related to TANZEUM but direct attribution to TANZEUM was confounded by the presence of gallstone disease diagnosed on ultrasound 3 weeks after the event. Gamma Glutamyltransferase (GGT) Increase: In the pool of placebo-controlled trials, the adverse event of increased GGT occurred more frequently in the group treated with TANZEUM (0.9% and 1.5% for placebo versus TANZEUM). Heart Rate Increase: In the pool of placebo-controlled trials, mean heart rate in patients treated with TANZEUM was higher by an average of 1 to 2 bpm compared with mean heart rate in patients treated with placebo across study visits. The long-term clinical effects of the increase in heart rate have not been established [see Warnings and Precautions (5.6)]. Continued on next page 7 DRUG INTERACTIONS TANZEUM did not affect the absorption of orally administered medications tested in clinical pharmacology studies to any clinically relevant degree [see Clinical Pharmacology (12.3) of full prescribing information]. However, TANZEUM causes a delay of gastric emptying, and thereby has the potential to impact the absorption of concomitantly administered oral medications. Caution should be exercised when oral medications are concomitantly administered with TANZEUM. 50 mg/kg/day (39 times clinical exposure based on AUC). In pregnant mice given SC doses of 1, 5, or 50 mg/kg/day from gestation Day 6 through 15 (organogenesis), embryo-fetal lethality (post-implantation loss) and bent (wavy) ribs were observed at 50 mg/kg/day (39 times clinical exposure based on AUC), a dose associated with maternal toxicity (body weight loss and reduced food consumption). Pregnant mice were given SC doses of 1, 5, or 50 mg/kg/day from gestation Day 6 to 17. Offspring of pregnant mice given 50 mg/kg/day (39 times clinical exposure based on AUC), a dose associated with maternal toxicity, had reduced body weight pre-weaning, dehydration and coldness, and a delay in balanopreputial separation. Pregnant mice were given SC doses of 1, 5, or 50 mg/kg/day from gestation Day 15 to lactation day 10. Increased mortality and morbidity were seen at all doses (≥1 mg/kg/ day) in lactating females in mouse pre- and postnatal development studies. Mortalities have not been observed in previous toxicology studies in nonlactating or non-pregnant mice, nor in pregnant mice. These findings are consistent with lactational ileus syndrome which has been previously reported in mice. Since the relative stress of lactation energy demands is lower in humans than mice and humans have large energy reserves, the mortalities observed in lactating mice are of questionable relevance to humans. The offspring had decreased pre-weaning body weight which reversed post-weaning in males but not females at ≥5 mg/kg/day (2.2 times clinical exposure based on AUC) with no other effects on development. Low levels of albiglutide were detected in plasma of offspring. Lactating mice were given SC doses of 1, 5, or 50 mg/kg/day from lactation day 7 to 21 (weaning) under conditions that limit the impact of lactational ileus (increased caloric intake and culling of litters). Doses ≥1 mg/kg/day (exposures below clinical AUC) caused reduced weight gain in the pups during the treatment period. 8 USE IN SPECIFIC POPULATIONS 8.1 Pregnancy: Pregnancy Category C: There are no adequate and wellcontrolled studies of TANZEUM in pregnant women. Nonclinical studies have shown reproductive toxicity, but not teratogenicity, in mice treated with albiglutide at up to 39 times human exposure resulting from the maximum recommended dose of 50 mg/week, based on area under the time-concentration curve (AUC) [see Nonclinical Toxicology (13.1,13.3)]. TANZEUM should not be used during pregnancy unless the expected benefit outweighs the potential risks. Due to the long washout period for TANZEUM, consider stopping TANZEUM at least 1 month before a planned pregnancy. There are no data on the effects of TANZEUM on human fertility. Studies in mice showed no effects on fertility [see Nonclinical Toxicology (13.1)]. The potential risk to human fertility is unknown. 8.3 Nursing Mothers: There are no adequate data to support the use of TANZEUM during lactation in humans. It is not known if TANZEUM is excreted into human milk during lactation. Given that TANZEUM is an albumin-based protein therapeutic, it is likely to be present in human milk. Decreased body weight in offspring was observed in mice treated with TANZEUM during gestation and lactation [see Nonclinical Toxicology (13.3)]. A decision should be made whether to discontinue nursing or to discontinue TANZEUM, taking into account the importance of the drug 17 PATIENT COUNSELING INFORMATION See FDA-approved patient labeling (Medication Guide and Instructions for to the mother and the potential risks to the infant. 8.4 Pediatric Use: Safety Use). The Medication Guide is contained in a separate leaflet that accompanies and effectiveness of TANZEUM have not been established in pediatric patients the product. Inform patients about self-management practices, including the (younger than 18 years). 8.5 Geriatric Use: Of the total number of patients importance of proper storage of TANZEUM, injection technique, timing of dosage (N = 2,365) in 8 Phase III clinical trials who received TANZEUM, 19% (N = of TANZEUM and concomitant oral drugs, and recognition and management of 444) were 65 years and older, and <3% (N = 52) were 75 years and older. hypoglycemia. Inform patients that thyroid C-cell tumors have been observed No overall differences in safety or effectiveness were observed between in rodents treated with some GLP-1 receptor agonists, and the human these patients and younger patients, but greater sensitivity of some older relevance of this finding is unknown. Counsel patients to report symptoms of individuals cannot be ruled out. 8.6 Renal Impairment: Of the total number thyroid tumors to their physician [see Warnings and Precautions (5.1)]. Advise of patients (N = 2,365) in 8 Phase III clinical trials who received TANZEUM, patients that persistent, severe abdominal pain that may radiate to the back 54% (N = 1,267) had mild renal impairment (eGFR 60 to 89 mL/min/1.73 2 and which may (or may not) be accompanied by vomiting is the hallmark m ), 12% (N = 275) had moderate renal impairment (eGFR 30 to 59 mL/ 2 symptom of acute pancreatitis. Instruct patients to discontinue TANZEUM min/1.73 m ) and 1% (N = 19) had severe renal impairment (eGFR 15 to <30 2 promptly and to contact their physician if persistent, severe abdominal pain mL/min/1.73 m ). No dosage adjustment is required in patients with mild 2 2 occurs [see Warnings and Precautions (5.2)]. The risk of hypoglycemia is (eGFR 60 to 89 mL/min/1.73 m ), moderate (eGFR 30 to 59 mL/min/1.73 m ), 2 increased when TANZEUM is used in combination with an agent that induces or severe (eGFR 15 to <30 mL/min/1.73 m ) renal impairment. Efficacy of hypoglycemia, such as sulfonylurea or insulin. Instructions for hypoglycemia TANZEUM in patients with type 2 diabetes and renal impairment is described should be reviewed with patients and reinforced when initiating therapy with elsewhere [see Clinical Studies (14.3) of full prescribing information]. There TANZEUM, particularly when concomitantly administered with a sulfonylurea or is limited clinical experience in patients with severe renal impairment (19 insulin [see Warnings and Precautions (5.3)]. Advise patients on the symptoms subjects). The frequency of GI events increased as renal function declined. of hypersensitivity reactions and instruct them to stop taking TANZEUM and For patients with mild, moderate, or severe impairment, the respective seek medical advice promptly if such symptoms occur [see Warnings and event rates were: diarrhea (6%, 13%, 21%), nausea (3%, 5%,16%), and Precautions (5.4)]. Instruct patients to read the Instructions for Use before vomiting (1%, 2%, 5%). Therefore, caution is recommended when initiating starting therapy. Instruct patients on proper use, storage, and disposal of or escalating doses of TANZEUM in patients with renal impairment [see the pen [see How Supplied/Storage and Handling (16.2) of full prescribing Dosage and Administration (2.3) of full prescribing information, Warnings and information, Patient Instructions for Use of full prescribing information]. Precautions (5.5), Clinical Pharmacology (12.3) of full prescribing information]. Instruct patients to read the Medication Guide before starting TANZEUM and to 10 OVERDOSAGE read again each time the prescription is renewed. Instruct patients to inform No data are available with regard to overdosage in humans. Anticipated their doctor or pharmacist if they develop any unusual symptom, or if any symptoms of an overdose may be severe nausea and vomiting. In the event known symptom persists or worsens. Inform patients not to take an extra of an overdose, appropriate supportive treatment should be initiated as dose of TANZEUM to make up for a missed dose. If a dose is missed, instruct dictated by the patient’s clinical signs and symptoms. A prolonged period of patients to take a dose as soon as possible within 3 days after the missed observation and treatment for these symptoms may be necessary, taking into dose. Instruct patients to then take their next dose at their usual weekly time. account the half-life of TANZEUM (5 days). If it has been longer than 3 days after the missed dose, instruct patients to wait and take TANZEUM at the next usual weekly time. 13 NONCLINICAL TOXICOLOGY 13.1 Carcinogenesis, Mutagenesis, Impairment of Fertility: As albiglutide TANZEUM is a trademark of the GSK group of companies. is a recombinant protein, no genotoxicity studies have been conducted. Carcinogenicity studies have not been performed with albiglutide because such studies are not technically feasible due to the rapid development of drug-clearing, anti-drug antibodies in rodents. Thyroid C-cell tumors were observed in 2-year rodent carcinogenicity studies with some GLP-1 receptor agonists. The clinical relevance of rodent thyroid findings observed with GLP-1 Manufactured by GlaxoSmithKline LLC receptor agonists is unknown. In a mouse fertility study, males were treated Wilmington, DE 19808 with subcutaneous (SC) doses of 5, 15, or 50 mg/kg/day for 7 days prior to U.S. Lic. No. 1727 cohabitation with females, and continuing through mating. In a separate fertility Marketed by GlaxoSmithKline study, females were treated with SC doses of 1, 5, or 50 mg/kg/day for 7 days Research Triangle Park, NC 27709 prior to cohabitation with males, and continuing through mating. Reductions ©2014, the GSK group of companies. All rights reserved. in estrous cycles were observed at 50 mg/kg/day, a dose associated with August 2014, TNZ:2BRS maternal toxicity (body weight loss and reduced food consumption). There were no effects on mating or fertility in either sex at doses up to 50 mg/kg/ ©2014 GSK group of companies. day (up to 39 times clinical exposure based on AUC). 13.3 Reproductive All rights reserved. Printed in USA. BIG159R0 November 2014 and Developmental Toxicity: In order to minimize the impact of the drug-clearing, anti-drug antibody response, reproductive and developmental toxicity assessments in the mouse were partitioned to limit the dosing period to no more than approximately 15 days in each study. In pregnant mice given SC doses of 1, 5, or 50 mg/kg/day from gestation Day 1 to 6, there were no adverse effects on early embryonic development through implantation at RESEARCH Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database Sumesh Kachroo, PhD; Hugh Kawabata, MS; Susan Colilla, PhD, MPH; Lizheng Shi, PhD; Yingnan Zhao, PhD; Jayanti Mukherjee, PhD; Uchenna Iloeje, MD, MPH; and Vivian Fonseca, MD ABSTRACT BACKGROUND: Hypoglycemia is a major barrier to achieving optimal glycemic control and managing diabetes successfully in patients with diabetes. Falls are the most significant consequences caused by hypoglycemia episodes. Both hypoglycemia and falls lead to substantial economic burden on the health care system in the United States. OBJECTIVE: To examine the association of hypoglycemia with fall-related outcomes in elderly patients with type 2 diabetes mellitus (T2DM). METHODS: Records were obtained for T2DM patients (N = 1,147,937) from January 1, 2008, to December 31, 2011. The nonhypoglycemia patients were randomly matched 1:1 by age and gender to the hypoglycemia patients. Fall-related events (composite of fall-related outcomes) were defined using ICD-9-CM codes. Conditional logistic regression models were used to compare the fall-related events within 30 days, 90 days, 180 days, and 365 days between the 2 cohorts. RESULTS: A total of 21,613 hypoglycemia patients were matched with 21,613 nonhypoglycemic patients. Patients with hypoglycemia had higher fall-related events within 30 days, 90 days, 180 days, and 365 days (P < 0.001 for all frequency differences). Conditional logistic regression analyses showed an elevated risk for fall-related events over 365 days (aOR = 1.95, 95% CI = 1.70-2.24). Subgroup analysis showed elevated risk for patients aged < 75 years and ≥ 75 years. Elevated risks were also seen for individual fall-related outcomes of fractures, head injuries, long-term care placement, and hospital admissions. CONCLUSIONS: The risk of fall-related events over 365 days increased 2-fold among elderly patients with diabetes who experienced hypoglycemia. J Manag Care Spec Pharm. 2015;21(3):243-53 Copyright © 2015, Academy of Managed Care Pharmacy. All rights reserved. What is already known about this subject •Hypoglycemia is a major barrier to achieving optimal glycemic control and managing diabetes successfully in patients with diabetes. •Falls are the most significant consequences caused by hypoglycemia episodes. •Hypoglycemia and falls lead to substantial economic burden on the health care system in the United States. •Currently, there exists limited evidence reporting the association between hypoglycemia and fall-related events among elderly diabetes patients in the United States. www.amcp.org Vol. 21, No. 3 What this study adds •This study reported the association between hypoglycemia and fall-related events among elderly diabetes patients and highlighted an increased risk of fall-related events among elderly patients with diabetes who experienced hypoglycemia. •The analyses reported the association overall for all fall-related events as well as for the specific fall-related outcomes, which included fractures, head injury, long-term care placement, and hospitalizations. •This study provides a real-world picture of elderly diabetic patients who experience hypoglycemia. T ype 2 diabetes mellitus (T2DM) is a progressive disorder with more than 347 million people worldwide suffering from this disease.1 It results in at least a 2-fold increase in the risk of death and, hence, has become a major global concern.1 As per 2012 U.S. estimates, about 22.3 million people (~7% of the U.S. population) have been diagnosed with diabetes, leading to an estimated economic burden of $245 billion ($176 billion in direct medical costs and $69 billion in indirect costs).2 Diabetes leads to an increase in the risk of macrovascular and microvascular complications, thereby predisposing these patients to hospitalization.3-5 Oral antidiabetes medications are used to control the glycemic levels in patients with diabetes; however, some of these medications have been linked to an increased risk of hypoglycemia (abnormally low blood sugar levels < 70 milligrams per deciliter).1,4,5 Consequently, hypoglycemia has remained 1 of the critical limiting factors that has troubled health care providers in achieving optimal glycemic control and managing diabetes successfully in these patients. Hypoglycemia leads to a substantial economic burden on the health care system, as highlighted by studies from across the globe. The direct medical costs spent on treating hypoglycemia range from approximately $188 to $2,100 per episode, depending on disease severity and the extent of medical care needed, while the indirect costs are approximately $3,169 per patient per year due to lost productivity.6 A population-based Scottish study reported that the annual direct cost of treating severe hypoglycemia in the United Kingdom could exceed £13 million (~$21 million).7 A Spanish study has reported the cost of hypoglycemia as approximately €3,500 (~$4,700) per March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 243 Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database episode of severe hypoglycemia.8 A Korean study reported costs ranging from $135.50 to $1,391.00 per episode of severe hypoglycemia.9 Swedish studies report the direct costs of hypoglycemia per patient being $12.90 for a 1-month period, while the indirect costs are $14.10 for a 1-month period, with the total cost (in base case) of hypoglycemia in Sweden being approximately €4,250,000 (~$5.74 million) per year.10,11 In addition to an increase in health care utilization and costs, hypoglycemia also leads to decline in the quality of life of the patients.6,12,13 In the United States, falls remain a critical issue of concern, since they are a major cause of morbidity and mortality in the elderly population, with current literature suggesting that the economic burden of falls may reach $54.9 billion in 2020.14,15 The most common nonfatal outcome of falls is fractures, which constitutes approximately 61% of all fall-related costs, with other outcomes being injuries to internal organs, traumatic brain injuries including subdural hematomas, injuries resulting in surgical interventions, and even death.15,16 A study at 3 midwestern hospitals reported that the operational costs for fallers with serious injury (fracture, subdural hematoma, any injury resulting in surgical intervention, or death) were $13,316 higher than inpatient controls who do not experience a fall, with an additional 6.3 days of hospital stay needed for the average faller in the general population.16 The study also indicated that the fallers were significantly more likely to have diabetes with preexisting organ damage. A recent study of Medicare-covered patients with T2DM by Johnston et al. (2012) reported 70% higher adjusted odds of fall-related fractures among patients with hypoglycemic events as compared with patients without hypoglycemic events.15 Signorovitch et al. (2013) reported that in patients with T2DM who received antidiabetes drugs without insulin, hypoglycemia was associated with a significantly higher risk of accidents that resulted in hospital visits (hazard ratio [HR] = 1.39, 95% confidence interval [CI] = 1.21-1.59), accidental falls (HR = 1.36, 95% CI = 1.13-1.65), and motor vehicle accidents (HR = 1.82, 95% CI = 1.18-2.80).17 Currently, a dearth of literature exists on studies evaluating the association between hypoglycemia and various key fall-related outcomes. The objective of this study was to examine the association between hypoglycemia and fall-related events (a composite of all fall-related outcomes) among elderly diabetes patients. This examination included the association overall for all fall-related events, as well as for the specific fall-related outcomes, which included fractures, head injury, long-term care placement, and hospitalizations. ■■ Methods A retrospective cohort study was conducted in patients with T2DM. Records were obtained from Truven Health MarketScan Medicare Supplemental Database for patients with at least 2 records of T2DM diagnosis from January 1, 2008, to December 244 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 31, 2011. Patients were required to be aged ≥ 65 years at index date (first T2DM date in the study period). The first date of a recorded hypoglycemia diagnosis (International Classification of Diseases, Ninth Revision, Clinical Modification [ICD-9-CM] codes 250.8, 251.0, 251.1, and 251.2) was defined as the cohort entry date for patients in the hypoglycemia group (HG).18 A 6-month baseline and 1-year follow-up period from cohort entry date was required. Patients in the nonhypoglycemia group (NHG) were 1:1 randomly matched by age and gender. For the NHG patients, their cohort entry dates were kept the same as their respective matched patients from the HG. Fall-related events were defined as ICD-9-CM codes 800.x-995.x, with a fall being the external cause defined as ICD-9-CM E-codes E880-E888, which were recorded within ± 2 days of each other in any order.15,19 In administrative claims data, the ICD-9-CM diagnosis coding for fracture sites does not possess a level of specificity that allows identification of whether a fall specifically caused a fracture. To determine whether a fall caused a fracture, ICD-9-CM Index to External Cause of Injury codes (E codes) is still employed, even though E codes may be flawed. The approach is the best we can do at this time, and the limitation of using E codes to define fall-related events is recognized by the Centers for Disease Control and Prevention.20 ICD-9-CM codes were also used to capture the 2 individual outcomes: hospitalizations (inpatients) and long-term care placements. In addition to ICD-9-CM codes, provider type codes, place of service codes, discharge status codes, and procedure codes were used to identify long-term care placement patients. ICD-9-CM codes for fractures included 800.x-839.x, with a fall being the external cause defined as ICD-9-CM E-codes E880-E888. Head injuries were defined as ICD-9-CM codes 800.x-804.x, 850.x-854.x, 905.0, 907.0, and 873, with a fall being the external cause defined as ICD-9-CM E-codes between E880-E888. The ICD-9-CM codes for fractures and head injuries were verified and finalized from the ICD-9-CM coding book. If 2 claims were observed within 30 days of each other, they were assessed as the result of the same fall event. Statistical Analysis Two study cohorts were formed. The first cohort (HG) included those patients who had at least 1 claim for hypoglycemia. The data of the first such claim during the study period was considered the patient’s cohort entry date. The 6-month period immediately prior to this cohort entry date was considered the baseline period. The 12-month period following this cohort entry date was the follow-up period. The NHG cohort was randomly formed, by assigning a cohort entry date to those patients who did not have a hypoglycemia claim such that the mean days between the index date of the patient and this cohort entry date was the same as in the HG cohort. The NHG cohort was 1:1 randomly matched by age and gender to patients in the HG cohort. Vol. 21, No. 3 www.amcp.org Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database TABLE 1 Demographic Characteristics, Comorbidities, and Medication Use in the Baseline Period Hypoglycemia Patients N = 21,613 Demographic Characteristics Age group 65-69 70-74 75-79 80-84 85-89 90-94 95-100 ≥ 100 Gender Male Female U.S. census region North East North Central South West Unknown Plan type Comprehensive EPO HMO POS PPO POS with capitation CDHP HDHP Missing Falls in the baseline period a Yes No Medication Metformin Sulfonylurea Thiazolidinediones Insulinb Comorbiditiesc Cardiovascular events Stroke Obesity Dyslipidemia Atherosclerosis Hypertension Nephropathy Diabetic foot problem Diabetes with neurological manifestations Dental disease Osteoporosis Chronic kidney disease Hyperparathyroidism Impaired vitamin D deficiency Depression Arthritis N 8,678 4,849 4,511 2,595 840 128 11 1 % 40 22 21 12 4 1 0 0 Nonhypoglycemia Patients N = 21,613 N % McNemar’s Test Statistic P Value 8,678 4,849 4,511 2,595 840 128 11 1 40.15 22.44 20.87 12.01 3.89 0.59 0.05 0.00 NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS 10,494 11,119 48.55 51.45 10,494 11,119 48.55 51.45 NS NS NS NS 4,068 7,395 6,069 3,562 519 18.82 34.22 28.08 16.48 2.40 3,865 6,639 6,321 3,858 930 17.88 30.72 29.25 17.85 4.30 6.42 60.59 7.32 14.36 - 0.0116 < 0.0001 0.0070 0.0002 - 8,749 102 3,250 401 8,135 15 81 11 869 40.48 0.47 15.04 1.86 37.64 0.07 0.37 0.05 4.02 7,511 36 3,045 504 9,625 33 159 12 688 34.75 0.17 14.09 2.33 44.53 0.15 0.74 0.06 3.18 154.00 31.56 7.94 11.85 216.21 6.75 25.78 0.04 - < 0.0001 < 0.0001 0.0051 0.0006 < 0.0001 0.0133 < 0.0001 1.0000 - 637 20,976 2.95 97.05 238 21,375 1.10 98.90 184.90 184.90 < 0.0001 < 0.0001 4,883 8,651 2,794 4,531 22.6 40.0 12.9 21.0 5,302 4,594 2,081 1,474 24.5 21.3 9.6 6.8 22.70 1,728.37 119.64 1,739.94 < 0.0001 < 0.0001 < 0.0001 < 0.0001 945 1,798 851 8,519 1,990 14,431 273 7 11 30 1,035 4,224 592 591 1,547 4,570 4.4 8.3 3.9 39.4 9.2 66.8 1.3 0.0 0.1 0.1 4.8 19.5 2.7 2.7 7.2 21.1 289 681 472 7,151 1,030 11,214 87 3 3 24 789 1,626 201 345 742 3,300 1.3 3.2 2.2 33.1 4.8 51.9 0.4 0.0 0.0 0.1 3.7 7.5 0.9 1.6 3.4 15.3 364.07 526.23 113.19 188.04 327.04 984.41 96.64 1.60 4.57 0.67 35.35 1,313.15 195.25 67.24 300.43 249.29 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 0.3438 0.0574 0.4966 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 < 0.0001 www.amcp.org Vol. 21, No. 3 March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 245 Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database TABLE 1 Demographic Characteristics, Comorbidities, and Medication Use in the Baseline Period (continued) Hypoglycemia Patients N = 21,613 Nonhypoglycemia Patients N = 21,613 McNemar’s Demographic Characteristics N % N % Test Statistic P Value Leg and foot amputation 43 0.2 8 0.0 24.02 < 0.0001 Impaired vision 1,430 6.6 637 2.9 317.44 < 0.0001 Lack of urine control 649 3.0 414 1.9 53.15 < 0.0001 Dizziness 1,964 9.1 924 4.3 397.35 < 0.0001 Low blood pressure 1,269 5.9 417 1.9 444.79 < 0.0001 Fainting 1,847 8.5 621 2.9 638.52 < 0.0001 5,084 23.5 2,319 10.7 1,234.10 < 0.0001 Any mental health-related disorderd Note: The patient counts in this table are not mutually exclusive, since a patient might have multiple medications in the baseline. a Fall events during 6 months immediately prior to the baseline period. bHCPCS codes as well as NDCs were used to identify patients with insulin. cComorbidities were identified in the 6-month period prior to the baseline period. d ICD-9-CM codes from 290-319 were used to search for patients with any mental health-related disorders. CDHP = consumer-driven health plan; EPO = exclusive provider organization; HCPCS = Healthcare Common Procedure Coding System; HDHP = high-deductible health plan; HMO = health maintenance organization; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification; NDC = National Drug Code; POS = point of service; PPO = preferred provider organization; NS = not significant. Comparison of the baseline categorical variables was reported as percentages and baseline continuous variables as means ± standard deviation (SD) for both of these cohorts. McNemar’s test statistic was used to test the comparison of baseline characteristics between cohorts. Due to the matched study design, conditional logistic regression models were used to compare fall-related events within 30 days, 90 days, 180 days, and 365 days between the 2 cohorts in the follow-up period. Adjusted odds ratios (aOR) and corresponding 95% CIs, controlling for baseline characteristics and comorbidities, were estimated from logistic regression models. Understanding that matching on age and gender do not sufficiently control for all potential confounders, the regression models were adjusted for Charlson Comorbidity Index (CCI) scores. Subgroup analyses for age (aged < 75 years and ≥ 75 years) were also conducted. The association between hypoglycemia and fall-related events among elderly diabetes patients with recurrent hypoglycemia was also evaluated. All analyses were done using SAS 9.2 (SAS Institute, Inc., Carey, NC). Statistical significance was defined as a P < 0.05 (two-tailed). ■■ Results A total of 1,147,937 patients with at least 2 records of T2DM diagnosis from January 1, 2008, to December 31, 2011, were available for the analyses. A total of 21,613 hypoglycemia patients were matched with 21,613 patients with no hypoglycemia. Table 1 provides the baseline demographics and comorbidities data. In the hypoglycemia cohort, most of the patients were aged 65-69 years (40%), female (51.4%), from the North Central region (34.2%), and had comprehensive insurance (40.5%). The most common comorbidities in the HG included hypertension (66.8%) followed by dyslipidemia 246 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 (39.4%), any mental health-related disorder (23.5%), arthriitis (21.1%), and chronic kidney disease (19.5%). A significantly higher proportion of patients with hypoglycemia had comorbidities compared with patients with no hypoglycemia, and this was true for all comorbidities (Table 1; P ≤ 0.001 for all comorbidities, except diabetic foot problem [P = 0.344], diabetes with neurological manifestations [P = 0.057], and dental disease [P = 0.497]). The mean CCI score was 1.99 (± 2.31) for hypoglycemia patients, compared with 0.95 (± 1.55) for nonhypoglycemia patients (P < 0.001). Forty percent of the patients in the HG had been prescribed sulfonylureas. Patients with hypoglycemia consistently had higher fallrelated events: 235 events (1.09%) among the HG patients and 37 events (0.17%) among the NHG patients within 30 days; 373 events (1.73%) and 118 events (0.55%) within 90 days; 520 events (2.41%) and 204 events (0.94%) within 180 days; and 720 events (3.33%) and 351 events (1.62%) within 1 year. All frequency differences between HG patients and NHG patients were statistically significant (P < 0.001; Table 2). Conditional logistic regression analyses showed an elevated risk of fallrelated events over 365 days (aOR = 1.95, 95% CI = 1.70-2.24). The subgroup analyses by age for the 365-day analysis showed elevated risk for both age groups: aged < 75 years (aOR = 2.20, 95% CI = 1.77-2.73) and aged ≥ 75 years (aOR = 1.77, 95% CI = 1.48-2.12). Elevated risks were seen for other time points as well, which included within 30 days, within 90 days, and within 180 days. These analyses highlight the greatest risk of fall-related events within the first 30 days (aOR = 5.86, 95% CI = 4.08-8.43), with the risk gradually decreasing over time. Table 2 shows the results for all fall-related events by groups. The analyses shown in Table 2 were replicated using relative risk (RR) as a different summary measure (see Appendix A). Vol. 21, No. 3 www.amcp.org Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database TABLE 2 Fall-Related Events by Group (Including 30-Day to 1-Year Definition Sensitivity Analysis) Hypoglycemia Group N = 21,613 Nonhypoglycemia Group N = 21,613 Eventsa,b Conditional Logistic Regressiond Adjusted Odds Ratioc 5.86 10.34 4.52 2.91 3.07 2.77 2.40 2.56 2.27 1.95 2.20 1.77 95% Confidence Limits Fall Total N % N % Lower Within 30 days 272 235 1.09 37 0.17 4.08 Aged < 75 years 117 107 0.50 10 0.05 4.94 Aged ≥ 75 years 155 128 0.59 27 0.12 2.94 Within 90 days 491 373 1.73 118 0.55 2.34 Aged < 75 years 211 165 0.76 46 0.21 2.17 Aged ≥ 75 years 280 208 0.96 72 0.33 2.09 Within 180 days 724 520 2.41 204 0.94 2.02 Aged < 75 years 319 238 1.10 81 0.37 1.95 Aged ≥ 75 years 405 282 1.30 123 0.57 1.81 Within 365 days 1,071 720 3.33 351 1.62 1.70 Aged < 75 years 470 335 1.55 135 0.62 1.77 Aged ≥ 75 years 601 385 1.78 216 1.00 1.48 a Includes patients with baseline fall events. b Composite fall events identified by ICD-9-CM codes 800.x-995.x and E codes E880-E888 occurring within 2 days. c By baseline characteristics, Charlson Comorbidity Index, and comorbidities. d Matched on age and gender. E code = ICD-9-CM Index to External Cause of Injury code; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification. The interpretation of the results remained the same, regardless of the summary measure used. The analyses shown in Appendix A were also done excluding any patient in a matched pair who used insulin at baseline (see Appendix B). Compared with Appendix A, the risk of falls in the HG were even higher when insulin users were excluded (particularly notable at 30-day and 90-day estimates). For the analyses on individual fall-related outcomes (Table 3), conditional logistic regression analyses showed an elevated risk of fracture events for the 365-day analysis (aOR = 2.16, 95% CI = 1.74-2.67). All analyses presented in this paragraph are for the 365-day time period. The subgroup analyses by age showed elevated risk for both age groups: aged < 75 years (aOR = 2.30, 95% CI = 1.63-3.24) and aged ≥ 75 years (aOR = 2.07, 95% CI = 1.58-2.72). Elevated risks were also seen for head injuryrelated events (aOR = 1.63, 95% CI = 1.22-2.19). The subgroup analyses by age showed elevated risk for both age groups: aged < 75 years (aOR = 1.77, 95% CI = 1.08-2.89) and aged ≥ 75 years (aOR = 1.56, 95% CI = 1.08-2.24). For the analyses of all fall-related hospital admissions, conditional logistic regression analyses showed an elevated risk (aOR = 2.20, 95% CI = 1.573.08). The subgroup analyses by age showed elevated risk for both age groups: < 75 years (aOR = 2.24, 95% CI = 1.22-4.13) and ≥ 75 years (aOR = 2.20, 95% CI = 1.45-3.34). Elevated risks were also seen for patients with long-term care placement days (aOR = 2.59, 95% CI = 1.93-3.46). The subgroup analyses by age showed elevated risk for both age groups: < 75 years (aOR = 3.75, 95% CI = 2.10-6.69) and ≥ 75 years group (aOR = 2.20, 95% CI = 1.57-3.10). The analyses shown in Table www.amcp.org Vol. 21, No. 3 Upper 8.43 21.62 6.94 3.62 4.36 3.67 2.85 3.35 2.85 2.24 2.73 2.12 2 were also replicated using RR as a different summary measure (see Appendix C). Again, the interpretation of the results remained the same, regardless of the summary measure used. For all the individual fall-related outcomes, elevated risks were also seen for other time points as well, which included within 30 days, within 90 days, and within 180 days (data not shown). For individual fall-related outcomes as well, the analyses highlighted the greatest risk within the first 30 days, with the risk gradually decreasing over time (data not shown). Additional analyses on all composite fall-related events in patients with recurring hypoglycemia (Table 4) also showed an elevated risk over 365 days (aOR = 2.43, 95% CI = 1.95-3.02). The subgroup analyses by age for the 365-day analysis showed elevated risk for both age groups: < 75 years (aOR = 2.32, 95% CI = 1.64-3.27) and ≥ 75 years (aOR = 2.47, 95% CI = 1.85-3.28). Elevated risks were seen for other time points as well, which included within 30 days, within 90 days, and within 180 days. The greatest risk of fall-related events was within the first 30 days (aOR = 12.41, 95% CI = 6.24-24.67), with the risk gradually decreasing over time. We also ran the regression analysis for all fall outcomes adjusting for CCI and falls at baseline. The change in the aORs was very minimal (less than a 10% difference in aORs was found) when we included falls in the baseline period as a covariate in addition to the CCI score in the regression model. For example, aOR = 1.95, 95% CI = 1.70-2.24 for all fall-related events within 365 days instead of 2.16 as previously noted. Hence, we have presented the results for the model adjusted for the CCI score only. March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 247 Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database TABLE 3 Specific Fall-Related Outcomes (Within 365 Days Only) by Age Subgroup Hypoglycemia Group N = 21,613 Nonhypoglycemia Group N = 21,613 Conditional Logistic Regressiond 95% Confidence Limits Adjusted Fall Eventsa,b Total Odds Ratioc N % N % Lower Upper All fracture-related events 445 308 1.43 137 0.63 2.16 1.74 2.67 Aged < 75 years 182 129 0.60 53 0.25 2.30 1.63 3.24 Aged ≥ 75 years 263 179 0.83 84 0.39 2.07 1.58 2.72 All head injury-related events 222 145 0.67 77 0.36 1.63 1.22 2.19 Aged < 75 years 85 58 0.27 27 0.12 1.77 1.08 2.89 Aged ≥ 75 years 137 87 0.40 50 0.23 1.56 1.08 2.24 All fall-related hospital admissions 184 128 0.59 56 0.26 2.20 1.57 3.08 Aged < 75 years 62 46 0.21 16 0.07 2.24 1.22 4.13 Aged ≥ 75 years 122 82 0.38 40 0.19 2.20 1.45 3.34 All composite fall-related outcomes for 260 186 2.20 74 0.87 2.59 1.93 3.46 patients with long-term care placement Aged < 75 years 88 69 0.81 19 0.22 3.751 2.10 6.69 172 117 1.38 55 0.65 2.204 1.57 3.10 Aged ≥ 75 years a Includes patients with baseline falls events. b Composite fall events identified by ICD-9-CM codes 800.x-995.x and E codes E880-E888 occurring within 2 days. c Adjusted odds ratio by baseline characteristics, Charlson Comorbidity Index, and comorbidities. d Matched on age and gender. E code = ICD-9-CM Index to External Cause of Injury code; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification. ■■ Discussion This study highlights an increased risk of fall-related events among elderly patients with diabetes who experienced hypoglycemia. In our conditional logistic regression analysis, we observed a 2-fold risk of fall-related events over a 365day period among elderly diabetic patients who experienced hypoglycemia compared with patients without hypoglycemic events. Higher risks were also observed for individual fallrelated outcomes. Signorovitch et al. reported that hypoglycemia was associated with a significantly higher risk of accidental falls resulting in hospital visits.17 Johnston et al. have also reported 70% higher regression-adjusted odds of fall-related fractures among patients with hypoglycemic events as compared with patients without hypoglycemic events after 1 year of follow-up.15 However, Johnston et al. only looked at the fracture outcome. Our study looked at fall-related events overall, as well as examined other key fall-related outcomes that included fractures, head injury, long-term care placement, and hospitalizations. Fractures, along with head injuries, may lead to serious complications. Some of the possible fall-inducing symptoms that may be connected with a symptomatic presentation of hypoglycemia have been reported previously in the literature.15 A more detailed study is needed to find if a causal link exists between the 2 or if the falls are a result of the neurologic, vascular, and ophthalmic complications associated with diabetes. A more focused approach is also needed to understand our observations in patients with recurring hypoglycemia. The larger ORs that were observed in patients with recurrent hypoglycemia suggest higher risks in those patients. 248 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 These patients may require a more careful and monitored approach in the management of diabetes. For all of the analyses, we observed the greatest risk of fallrelated events (as well as for individual fall-related outcomes) within the first 30 days. Education is the key to minimizing the occurrence of falls. Patients, as well as their caregivers, should be better educated about the high risk of falls, especially within the first 30 days of a hypoglycemic episode, and counseling should be provided so appropriate steps can be taken to minimize the occurrence of falls. In addition to educating patients and caregivers on hypoglycemia-related topics such as definition, symptoms, and risk factors, they should be also trained to recognize and treat these episodes. Our findings also highlight the need for a better patient-monitoring strategy immediately after a hypoglycemic event in diabetic patients. Previously published literature has evaluated the association between fractures and medications used by diabetic patients. A few studies have evaluated the risk for bone fractures associated with exposure to insulin or other oral hypoglycemic agents.21-23 These studies show that a patient’s functional level and risk factors for falls should be considered during the drug selection decision-making process, since some of the medications may increase fracture risk and thereby worsen fall-related outcomes. In addition, these studies also show that falls are most likely to occur during hypoglycemic episodes. Some studies, however, contradict these findings.15,21,22 Consequently, focused research evaluation of the impact of diabetes medications on risk of falls is needed. The recently published American Diabetes Association and European Association Vol. 21, No. 3 www.amcp.org Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database TABLE 4 All Composite Fall-Related Outcomes for Patients with Recurring Hypoglycemia (Including 30-Day to 1-Year Definition Sensitivity Analysis), Adjusted for CCI Score Recurrent Hypoglycemia Group N = 8,472c Nonhypoglycemia Group N = 8,472 Conditional Logistic Regressione 95% Confidence Limits Adjusted Fall Eventsa,b Total Odds Ratiod N % N % Lower Upper Within 30 days 137 128 1.51 9 0.11 12.41 6.24 24.67 Within 90 days 241 193 2.28 48 0.57 3.65 2.61 5.09 Within 180 days 345 261 3.08 84 0.99 3.06 2.34 4.00 Within 365 days 491 349 4.12 142 1.68 2.43 1.95 3.02 aComposite fall events identified by ICD-9-CM codes 800.x-995.x and E codes E880-E888 occurring within 2 days. bBaseline fall event is allowed; 1:1 match was used. cOut of 21,613 hypoglycemia patients, only 8,472 patients had recurrent hypoglycemia (defined as at least 2 diagnosis of hypoglycemia on 2 different dates). For these 8,472 patients in the hypoglycemic group, the corresponding 8,472 nonhypoglycemic patients were identified. d Adjusted odds ratio by baseline characteristics, CCI, and comorbidities. eMatched on age and gender. CCI = Charlson Comorbidity Index; E code = ICD-9-CM Index to External Cause of Injury code; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification. for the Study of Diabetes position statement has also raised concerns about hypoglycemia and sulfonylurea use among the elderly, since they are at highest risk for hypoglycemia.4,5 This position statement highlights the fact that sulfonylureas are associated with risk of hypoglycemia and recommend that medications predisposing patients to hypoglycemia should be avoided, since it may worsen myocardial ischemia and may cause dysrhythmias. Existing literature on the association between sulfonylurea use and risk of falls and fall-related fractures is sparse and includes studies that have reported conflicting findings.24 Future studies are needed to understand this association given that falls induced by hypoglycemia are the hypothesized mechanism for fractures related to sulfonylurea use. Continuous and intermittent sulfonylurea availability has also been reported as a predictor for hypoglycemia-related hospitalization.25 A detailed understanding of sulfonylureas is a critical need, given that an estimated 50%-66% T2DM patients use sulfonylureas alone or in combination with other diabetes medications.26 Physicians treating elderly diabetes patients need to make treatment decisions that minimize the risk of hypoglycemia thereby avoiding the ensuing complications of falls. Falls lead to an increase in the direct and indirect health care utilization and cost burden, so it is critical to take preventive steps to minimize hypoglycemia in elderly diabetic patients.15 Minimizing the hypoglycemic events in these patients may also lead to improvement in their quality of life.15 In addition, patients with hypoglycemia may need additional education and other preventive measures to reduce the risk and clinical impact of falls. Research on the merits of diabetes education in T2DM patients has highlighted that the more knowledge and awareness patients have regarding the disease, the more www.amcp.org Vol. 21, No. 3 efficient their glycemic control is.27 Health care professionals should guide patients in identifying the proper educational resources, as well as educate them on key topics such as influence of nutrition and lifestyle, self-monitoring of blood glucose, knowledge regarding hemoglobin A1c, as well as the need for proper glucose, lipid, and blood pressure control to minimize the development of macrovascular complications. Hypoglycemia is not often recognized as a risk that could lead to potential health-related consequences in T2DM patients.28 In 2011, it was reported that patients with diabetes are aware of the importance of controlling blood sugar, but they may not know the risks associated with extremely low blood sugar.29 A recent publication has revealed that there could be underreporting of this issue because of patient discomfort in disclosing hypoglycemia with treating physicians due to fear of losing driving licenses or jobs, especially with regard to serious or frequent events.30 A recent survey also revealed that almost one-third of patients surveyed (32%) said they do not regularly discuss hypoglycemia with their physicians, in part because of patients’ limited understanding of hypoglycemia and lack of time, highlighting a need for improving patient and physician communication.31 Hypoglycemia unawareness further complicates the situation, which leads to more underreporting of hypoglycemic incidences.32 In addition to underreporting, the longer-term impact of hypoglycemia is also not well characterized. If one looks only at emergency room visits and hospitalizations, the true impact of hypoglycemia will be underestimated. A recent survey conducted in Germany, France, and the United Kingdom has shown the potential scale in a real-world setting of what the authors refer to as “hidden costs” associated with hypoglycemia, including absenteeism from work and disturbance of daily life due to fear of hypoglycemia.33 March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 249 Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database Finally, frequently occurring hypoglycemia is associated with increased morbidity and mortality. Mild episodes can cause unpleasant symptoms and disrupt daily activities, while severe hypoglycemia can result in disorientation and unusual behavior and may be life threatening. Severe hypoglycemia was a predictor of cardiovascular mortality in the Action to Control Cardiovascular Risk in Diabetes trial, with a previous occurrence of severe hypoglycemia being an important predictor of a primary cardiovascular event.34 Also, in the Veterans Affairs Diabetes Trial, hypoglycemia was an important predictor of cardiovascular death.34 Although a causal relationship was not definitely established, these trials highlight that hypoglycemia might precipitate other morbidities, such as dementia, as well.31 A recent study has reported a bidirectional association between hypoglycemia and dementia in elderly diabetic patients.35 Dementia could be associated with a decreased ability to manage medications. This is an important finding, since existing literature suggests an increased prevalence of hip fractures in elderly patients with dementia.36 Limitations The key strength of this study is that it provides a real-world picture of elderly diabetic patients who experience hypoglycemia. Some of the limitations of this study include the use of ICD-9-CM codes for extracting relevant information. As such, there is potential for misclassification, since it is possible that some of these codes may be incorrectly entered in the database or on the claim form or might not have been entered at all. Potential for residual confounding also exists. The administrative databases may also lack information of some critical covariates. Also, this study may have missed patients who may have had severe hypoglycemia that resulted in death before hospital admission. This study may also have missed episodes of mild hypoglycemia that did not result in a doctor’s visit or medical claim. The exclusion of events (e.g., fall or death) prior to the index date for the HG may introduce selection bias of lower risk than the general population. We also excluded death in the comparison group (bias of lower risk). There may be other limitations with regard to the generalizability of the study population, since the data do not include uninsured patients and are weighted towards patients who are able to afford supplemental Medicare health insurance, which may not represent the general U.S. population. In the database, we do not have information on whether these patients were previously employed or whether they had supplement health insurance from a previous employer. Due to nature of this study, estimating attributable risks for hypoglycemia was beyond the study’s scope. Also, our study design only used a simple matching mechanism. We used conditional logistic regression to ana250 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 lyze the matched pairs on age and sex, for the comparison of hypoglycemia groups, and these groups ended up having different frequencies in baseline characteristics. A more extensive matching or propensity score matching will be used in a future study to strengthen the argument and verify the associations found in this study. ■■ Conclusions This study shows an increased risk of fall-related events among elderly diabetic patients who experience hypoglycemia. This highlights the need to take preventive measures to reduce the incidence of hypoglycemia in elderly diabetic patients, which may thereby lead to a reduction of fall-related events. Welldesigned and conducted prospective studies are needed to further evaluate our findings. Authors SUMESH KACHROO, PhD, was, at the time of this study, Associate Director, Global Health Economics and Outcomes Research, BristolMyers Squibb, Nassau Park, New Jersey. HUGH KAWABATA, MS, is Director, CORDS, and SUSAN COLILLA, PhD, MPH, is Research Scientist, CORDS, Bristol-Myers Squibb, Hopewell, New Jersey. LIZHENG SHI, PhD, is Regents Associate Professor, Global Health Systems and Development, Tulane University, New Orleans, Louisiana, and Affiliated Researcher, Southeast Louisiana Veterans Health Care System, New Orleans; YINGNAN ZHAO, PhD, is Affiliated Researcher, Southeast Louisiana Veterans Health Care System, New Orleans, and Assistant Professor, College of Pharmacy, Xavier University of Louisiana, New Orleans; JAYANTI MUKHERJEE, PhD, is Group Director, WWHEOR, Bristol-Myers Squibb, Wallingford, Connecticut; UCHENNA ILOEJE, MD, MPH, is Vice President, Alexion Pharmaceuticals, Cheshire, Connecticut; and VIVIAN FONSECA, MD, is Professor, Health Sciences Center, Tulane University, New Orleans, Louisiana. AUTHOR CORRESPONDENCE: Lizheng Shi, PhD, Regents Associate Professor, School of Public Health and Tropic Medicine, Tulane University, 1440 Canal St., New Orleans, LA 70112. Tel.: 504.988.6548; Fax: 504.988.3783; E-mail: [email protected]. DISCLOSURES This study was funded by an unrestrictive grant from Bristol-Myers Squibb to Tulane University. Iloeje and Kachroo were employees of Bristol-Myers Squibb when this study was conducted and have no conflict of interest with regards to this study. Kawabata, Colilla, and Mukherjee are employees of Bristol-Myers Squibb. Zhao and Shi declare no conflict of interest with regards to this study. Fonseca has received grants from Novo Nordisk, Asahi, Eli Lilly, Abbott, and Endo Barrier, as well as honoria for consulting and lectures from GlaxoSmithKine, Takeda, Novo Nordisk, Sanofi-Aventis, Eli Lilly, Daiichi Sankyo, Pamlabs, Astra-Zeneca, Abbott, Bristol-Myers Squibb, Boehringer Ingelheim, and Janssen. Results of this manuscript were presented at the European Association for the Study of Diabetes 49th Conference; Barcelona, Spain; 2013. Vol. 21, No. 3 www.amcp.org Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database Fonseca, Kachroo, Shi, Mukherjee, and Iloeje conceptualized the study design. Shi and Kawabata coordinated data analyses. Colilla conducted data analysis and consulted in the analysis plan. Shi and Kachroo led the development of the manuscript. All authors participated in interpreting the results and commenting on the manuscript. REFERENCES 1. Danaei G, Finucane MM, Lu Y, et al. National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet. 2011;378(9785):31-40. 2. American Diabetes Association. Economic costs of diabetes in the U.S. in 2012. Diabetes Care. 2013;36(4):1033-46. 3. American Diabetes Association. Standards of medical care in diabetes— 2013. Diabetes Care. 2013;36(Suppl 1):S11-66. 4. Inzucchi SE, Bergenstal RM, Buse JB, et al. Management of hyperglycaemia in type 2 diabetes: a patient-centered approach. Position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetologia. 2012;55(6):1577-96. 5. Inzucchi SE, Bergenstal RM, Buse JB, et al. Management of hyperglycemia in type 2 diabetes: a patient-centered approach: position statement of the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Diabetes Care. 2012;35(6):1364-79. 6. Liu S, Zhao Y, Hempe JM, Fonseca V, Shi L. Economic burden of hypoglycemia in patients with Type 2 diabetes. Expert Rev Pharmacoecon Outcomes Res. 2012;12(1):47-51. 7. Leese GP, Wang J, Broomhall J, et al. Frequency of severe hypoglycemia requiring emergency treatment in type 1 and type 2 diabetes: a populationbased study of health service resource use. Diabetes Care. 2003;26(4):1176-80. 8. Brito-Sanfiel M, Diago-Cabezudo J, Calderon A. Economic impact of hypoglycemia on healthcare in Spain. Expert Rev Pharmacoecon Outcomes Res. 2010;10(6):649-60. 9. Ha WC, Oh SJ, Kim JH, et al. Severe hypoglycemia is a serious complication and becoming an economic burden in diabetes. Diabetes Metab J. 2012;36(4):280-84. 10. Lundkvist J, Berne C, Bolinder B, Jönsson L. The economic and quality of life impact of hypoglycemia. Eur J Health Econ. 2005;6(3):197-202. 11. Jönsson L, Bolinder B, Lundkvist J. Cost of hypoglycemia in patients with Type 2 diabetes in Sweden. Value Health. 2006;9(3):193-98. 12. Zhang Y, Weiffer H, Modha R, Balar B, Pollack M, Krishnarajah G. The burden of hypoglycemia in type 2 diabetes: a systematic review of patient and economic perspectives. J Clin Outcomes Manage. 2010;17(12):547-57. 13. Williams SA, Shi L, Brenneman SK, Johnson JC, Wegner JC, Fonseca V. The burden of hypoglycemia on healthcare utilization, costs, and quality of life among type 2 diabetes mellitus patients. J Diabetes Complications. 2012;26(5):399-406. 14. Englander F, Hodson TJ, Terregrossa RA. Economic dimensions of slip and fall injuries. J Forensic Sci. 1996;41(5):733-46. 15. Johnston SS, Conner C, Aagren M, Ruiz K, Bouchard J. Association between hypoglycaemic events and fall-related fractures in Medicare-covered patients with type 2 diabetes. Diabetes Obes Metab. 2012;14(7):634-43. 19. Peel NM, Kassulke DJ, McClure RJ. Population-based study of hospitalised fall-related injuries in older people. Inj Prev. 2002;8(4):280-83. 20. Annest JL, Fingerhut LA, Gallagher SS, et al. Strategies to improve external cause-of-injury coding in state-based hospital discharge and emergency department data systems: recommendations of the CDC Workgroup for Improvement of External Cause-of-Injury Coding. MMWR Recomm Rep. 2008;57(RR-1):1-15. 21. Monami M, Cresci B, Colombini A, et al. Bone fractures and hypoglycemic treatment in type 2 diabetic patients: a case-control study. Diabetes Care. 2008;31(2):199-203. 22. Berlie HD, Garwood CL. Diabetes medications related to an increased risk of falls and fall-related morbidity in the elderly. Ann Pharmacother. 2010;44(4):712-17. 23. Kennedy RL, Henry J, Chapman AJ, Nayar R, Grant P, Morris AD. Accidents in patients with insulin-treated diabetes: increased risk of lowimpact falls but not motor vehicle crashes—a prospective register-based study. J Trauma. 2002;52(4):660-66. 24. Lapane KL, Yang S, Brown MJ, Jawahar R, Pagliasotti C, Rajpathak S. Sulfonylureas and risk of falls and fractures: a systematic review. Drugs Aging. 2013;30(7):527-47. 25. Quilliam BJ, Simeone JC, Ozbay AB. Risk factors for hypoglycemiarelated hospitalization in patients with type 2 diabetes: a nested case-control study. Clin Ther. 2011;33(11):1781-91. 26. Chelliah A, Burge MR. Hypoglycaemia in elderly patients with diabetes mellitus: causes and strategies for prevention. Drugs Aging. 2004;21(8):511-30. 27. Ozcelik F, Yiginer O, Arslan E, et al. Association between glycemic control and the level of knowledge and disease awareness in type 2 diabetic patients. Pol Arch Med Wewn. 2010;120(10):399-406. 28. Lingvay I. Hypoglycemia in type 2 diabetes—consequences and risk assessment. US Endocrinology. 2011;7(2):95-102. Available at: http://www. touchendocrinology.com/articles/hypoglycemia-type-2-diabetes-consequences-and-risk-assessment. Accessed February 6, 2015. 29. American Association of Clinical Endocrinologists. AACE releases new guidelines for diabetes management. Endocrine Today. May 2011. Available at: http://www.healio.com/endocrinology/diabetes/news/print/endocrinetoday/%7B1cf478a0-c6b3-4389-9f65-15640b7cdcd7%7D/aace-releases-newguidelines-for-diabetes-management. Accessed February 5, 2015. 30. Kenny C. When hypoglycemia is not obvious: diagnosing and treating under-recognized and undisclosed hypoglycemia. Prim Care Diabetes. 2014;8(1):3-11. 31. Global survey: need for improved hypoglycemia management for T2 diabetics. DiabetesCare.net. October 6, 2011. Available at: http://www. diabetescare.net/article/title/global-survey-need-for-improved-hypoglycemiamanagement-for-t2-diabetics. Accessed February 5, 2015. 32. Desouza CV, Bolli GB, Fonseca V. Hypoglycemia, diabetes, and cardiovascular events. Diabetes Care. 2010;33(6):1389-94. 33. Willis WD, Diago-Cabezudo JI, Madec-Hily A, Aslam A. Medical resource use, disturbance of daily life and burden of hypoglycemia in insulin-treated patients with diabetes: results from a European online survey. Expert Rev Pharmacoecon Outcomes Res. 2013;13(1):123-30. 16. Wong CA, Recktenwald AJ, Jones ML, Waterman BM, Bollini ML, Dunagan WC. The cost of serious fall-related injuries at three Midwestern hospitals. Jt Comm J Qual Patient Saf. 2011;37(2):81-87. 34. Mathieu C, Filozof C, Barnett AH. Hypoglycaemia in type 2 diabetes— clinical consequences and impact on treatment. European Endocrinology. 2010;6(1):29-34. Available at: http://www.touchendocrinology.com/articles/ hypoglycaemia-type-2-diabetes-clinical-consequences-and-impact-treatment. Accessed February 5, 2015. 17. Signorovitch JE, Macaulay D, Diener M, et al. Hypoglycaemia and accident risk in people with type 2 diabetes mellitus treated with non-insulin antidiabetes drugs. Diabetes Obes Metab. 2013;15(4):335-41. 35. Yaffe K, Falvey CM, Hamilton N, et al. Association between hypoglycemia and dementia in a biracial cohort of older adults with diabetes mellitus. JAMA Intern Med. 2013;173(14):1300-06. 18. Zhao Y, Campbell CR, Fonseca V, Shi L. Impact of hypoglycemia associated with antihyperglycemic medications on vascular risks in veterans with type 2 diabetes. Diabetes Care. 2012;35(5):1126-32. 36. Scandol JP, Toson B, Close JC. Fall-related hip fracture hospitalisations and the prevalence of dementia within older people in New South Wales, Australia: an analysis of linked data. Injury. 2013;44(6):776-83. www.amcp.org Vol. 21, No. 3 March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 251 Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database Appendix A All Fall-Related Events by Group Hypoglycemia Group N = 21,613 Nonhypoglycemia Group N = 21,613 Eventsa,b Conditional Logistic Regressionc Relative Risk 5.6621 9.5423 4.2285 2.8259 3.2003 2.5870 2.3149 2.6740 2.0818 1.8874 2.2469 1.6640 95% Confidence Limits Fall Total N % N % Lower Within 30 days 272 235 1.09 37 0.17 3.9817 Aged < 75 years 117 107 0.50 10 0.05 4.9815 Aged ≥ 75 years 155 128 0.59 27 0.12 2.7634 Within 90 days 491 373 1.73 118 0.55 2.2841 Aged < 75 years 211 165 0.76 46 0.21 2.2907 Aged ≥ 75 years 280 208 0.96 72 0.33 1.9619 Within 180 days 724 520 2.41 204 0.94 1.9625 Aged < 75 years 319 238 1.10 81 0.37 2.0675 Aged ≥ 75 years 405 282 1.30 123 0.57 1.6774 Within 365 days 1,071 720 3.33 351 1.62 1.6573 Aged < 75 years 470 335 1.55 135 0.62 1.8327 Aged ≥ 75 years 601 385 1.78 216 1.00 1.4043 a Includes patients with baseline falls events. b Composite fall events identified by ICD-9-CM codes 800.x-995.x and E codes E880-E888 occurring within 2 days. c Matched on age and gender, and relative risk estimate adjusted for Charlson Comorbidity Index. E code = ICD-9-CM Index to External Cause of Injury code; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification. Appendix B Upper 8.0518 18.2789 6.4703 3.4962 4.4709 3.4113 2.7307 3.4583 2.5838 2.1494 2.7548 1.9718 All Fall-Related Events by Group (Sensitivity Analyses: Excluding Insulin Users) Hypoglycemia Group N = 15,925 Nonhypoglycemia Group N = 15,925 Conditional Logistic Regressionc 95% Confidence Limits Relative Fall Eventsa,b Total N % N % Risk Lower Upper Within 30 days 209 186 1.17 23 0.14 7.1266 4.6003 11.0401 Aged < 75 years 86 81 0.51 5 0.03 14.3684 5.8114 35.5251 Aged ≥ 75 years 123 105 0.66 18 0.11 5.1225 3.0845 8.5072 Within 90 days 380 293 1.84 87 0.55 2.9691 2.3206 3.7987 Aged < 75 years 156 124 0.78 32 0.20 3.3897 2.2701 5.0615 Aged ≥ 75 years 224 169 1.06 55 0.35 2.7262 1.9938 3.7276 Within 180 days 564 407 2.56 157 0.99 2.3435 1.9437 2.8256 Aged < 75 years 235 178 1.12 57 0.36 2.8214 2.0809 3.8254 Aged ≥ 75 years 329 229 1.44 100 0.63 2.0744 1.6355 2.6311 Within 365 days 834 561 3.52 273 1.71 1.8934 1.6354 2.1921 Aged < 75 years 343 249 1.56 94 0.59 2.4182 1.9016 3.0750 Aged ≥ 75 years 491 312 1.96 179 1.12 1.6193 1.3442 1.9507 a Includes patients with baseline falls events. b Composite fall events identified by ICD-9-CM codes 800.x-995.x and E codes E880-E888 occurring within 2 days. c Matched on age and gender, and relative risk estimate adjusted for Charlson Comorbidity Index. E code = ICD-9-CM Index to External Cause of Injury code; ICD-9-CM = International Classification of Diseases, Ninth Revision, Clinical Modification. 252 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Vol. 21, No. 3 www.amcp.org Association Between Hypoglycemia and Fall-Related Events in Type 2 Diabetes Mellitus: Analysis of a U.S. Commercial Database Appendix C Specific Fall-Related Outcomes (Within 365 Days Only) by Age Subgroups Hypoglycemia Group N = 21,613 Nonhypoglycemia Group N = 21,613 Eventsa,b Fall Total N % N % All fracture-related events 445 308 1.43 137 0.63 Aged < 75 years 182 129 0.60 53 0.25 Aged ≥ 75 years 263 179 0.83 84 0.39 All head injury-related events 222 145 0.67 77 0.36 Aged < 75 years 85 58 0.27 27 0.12 Aged ≥ 75 years 137 87 0.40 50 0.23 All fall-related hospital admissions 184 128 0.59 56 0.26 Aged < 75 years 62 46 0.21 16 0.07 Aged ≥ 75 years 122 82 0.38 40 0.19 All composite fall-related outcomes for 260 186 2.20 74 0.87 patients with long-term care placement Aged < 75 years 88 69 0.81 19 0.22 Aged ≥ 75 years 172 117 1.38 55 0.65 a Includes patients with baseline falls events. b Composite fall events identified by ICD-9-CM codes 800.x-995.x and E codes E880-E888 occurring within 2 days. c Matched on age and gender, and relative risk estimate adjusted for Charlson Comorbidity Index. www.amcp.org Vol. 21, No. 3 March 2015 JMCP Conditional Logistic Regressionc Relative Risk 2.1061 2.2662 2.0048 1.7696 2.0061 1.6413 2.1541 2.4424 2.0532 2.3623 3.3522 2.0200 95% Confidence Limits Lower 1.7140 1.6339 1.5376 1.3279 1.2513 1.1422 1.5596 1.3283 1.4042 1.7954 Upper 2.5878 3.1433 2.6139 2.3582 3.2162 2.3585 2.9753 4.4910 3.0022 3.1084 2.0132 1.4537 5.5817 2.8071 Journal of Managed Care & Specialty Pharmacy 253 2015 MEMBERSHIP APPLICATION [ MEMBER INFORMATION l Mr. l Ms. l Mrs. first name l Dr. last name please enter the amcp member who referred you for membership (if applicable). referred by ] DEMOGRAPHIC INFORMATION title please tell us: organization name l l l l l l I. 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AMCP — The Solution to Your Challenges Join Today at www.amcp.org IN THEIR WORDS “ AMCP webinars are a must-see for anyone wanting the latest, most collaborative information from top leaders in the health care industry. ” Dr. Caroline Atwood AMCP VALUED MEMBER SINCE 2009 Academy of Managed Care Pharmacy | 100 N Pitt Street | Suite 400 | Alexandria, VA 22314 | Tel 703/683-8416 | Fax 703/683-8417 | www.amcp.org RESEARCH Pharmacist-Coordinated Multidisciplinary Hospital Follow-up Visits Improve Patient Outcomes Jamie J. Cavanaugh, PharmD, CPP, BCPS; Kimberly N. Lindsey, PharmD; Betsy B. Shilliday, PharmD, CDE, CPP, BCACP; and Shana P. Ratner, MD ABSTRACT BACKGROUND: The Affordable Care Act of 2010 allows for the adjustment of reimbursement to health care centers based on 30-day readmission rates. High readmission rates may be explained by multiple events at discharge, including medication errors that occur during the transition of care from inpatient to outpatient. Pharmacist involvement at discharge has been shown to improve health outcomes in patients with chronic disease; however, there is limited knowledge regarding the benefits of a clinic appointment with a pharmacist postdischarge. OBJECTIVE: To compare hospital readmission rates and interventions in a multidisciplinary team visit coordinated by a clinical pharmacist practitioner with those conducted by a physician-only team within an internal medicine hospital follow-up program. METHODS: A retrospective observational study was completed. Patients seen between May 2012 and January 2013 in 1 of the 2 hospital followup program models (multidisciplinary team or physician-only team) were included. RESULTS: A total of 140 patient visits were included for 124 patients. Patients seen by the multidisciplinary team had a 30-day readmission rate of 14.3% compared with 34.3% by the physician-only team (P=0.010). Interventions completed during the visits, including addressing nonadherence, initiating a new medication, and discontinuing a medication were also statistically different between the groups, with the multidisciplinary team completing these interventions more frequently. CONCLUSIONS: Hospital follow-up visits coordinated by the multidisciplinary team decreased 30-day hospital readmission rates compared with follow-up visits by a physician-only team. J Manag Care Spec Pharm. 2015;21(3):256-60 Copyright © 2015, Academy of Managed Care Pharmacy. All rights reserved. What is already known about this subject •Pharmacist involvement at discharge and postdischarge follow-up phone calls are beneficial in improving adherence and decreasing hospital readmission rates, respectively. •Pharmacist involvement during postdischarge clinic visits has been associated with a decrease in 60-day hospital readmission rates. 256 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 What this study adds •Standardized multidisciplinary hospital follow-up visits, coordinated by a clinical pharmacist practitioner, had a lower 30-day readmission rate than physician-only visits. •Interventions such as addressing adherence, initiating medications, and discontinuing medications may be contributing factors to lower readmission rates seen in the multidisciplinary team model. P harmacist involvement in managing patients with chronic disease has been shown to improve health outcomes.1-3 However, few published studies document the role of a face-to-face clinic visit with a pharmacist in the transition from hospital to outpatient care. A smooth transition of care is especially important in improving the well-being of patients and decreasing preventable hospital readmissions. One in five Medicare patients are readmitted within 30 days.4 The 2010 Affordable Care Act calls for reduction of payment to hospitals with 30-day readmission rates higher than the average riskadjusted readmission rates, potentially resulting in significant financial repercussions for health care centers. Multiple errors may occur during the transition from inpatient to outpatient care, especially related to medication therapy. These errors include discontinuation of prescribed medications; noncompliance, including failure to fill medications or failure to be adherent; and delays in filling medications.5-7 In the geriatric population, the most frequently reported unwanted events after discharge were related to prescription medication regimens mainly due to patients getting incorrect drugs or dosages.8 Literature to date shows that some pharmacist interventions before and after discharge decrease medication-related errors. Pharmacist counseling before discharge improves overall medication adherence after discharge from 34.8% to 55.2% when compared with standard of care (i.e., information session with a nurse).9 Pharmacist counseling at discharge and via phone within 3 to 5 days of discharge reduced preventable, medication-related emergency department visits or hospital readmissions from 8% to 1% when compared with usual care, which included medication review by a pharmacist and counseling from a nurse.10 Patients receiving general medication Vol. 21, No. 3 www.amcp.org Pharmacist-Coordinated Multidisciplinary Hospital Follow-up Visits Improve Patient Outcomes therapy management by a clinical pharmacist postdischarge had a 60-day readmission rate of 18.2% compared with 43.1% who did not have a clinic visit with a pharmacist.11 While evidence suggests that inpatient pharmacists at discharge and ambulatory care pharmacists after discharge improve adherence and/or decrease preventable hospital readmissions, little is known about the role and impact of the outpatient ambulatory care pharmacist as part of a multidisciplinary team postdischarge. Little is also known about the impact of an ambulatory care pharmacist as part of a multidisciplinary team on decreasing 30-day hospital readmission rates. A pilot hospital follow-up program began seeing patients in the University of North Carolina (UNC) Internal Medicine Clinic in March 2012. The goal was to improve the transition of care for patients being discharged from UNC Hospitals and to decrease the hospital readmission rate from a baseline of 19% (based on February 2012 Medicaid data) by seeing patients in the clinic within 7 days of discharge. Patients could be referred by the care manager, or other provider, or scheduled based on an internally developed risk stratification status independent of reasons for admission or payer status. The care manager sought to first schedule moderate- and high-risk patients and then offered appointments to low-risk patients if appointments were available. During each hospital follow-up clinic visit, the purpose was to complete a thorough medication review, as well as address lifestyle interventions and barriers to care. Other visit components included a thorough patient history, a physical exam as appropriate, ordering of laboratory tests and medications if needed, and educating patients on self-management and symptoms needing immediate medical attention.12 There were 2 models in the hospital follow-up program: a multidisciplinary team model and a physician-only team model. The multidisciplinary team included a clinical pharmacist practitioner (CPP) and an attending physician. Within the state of North Carolina, CPPs are licensed pharmacists who are recognized as advanced practice providers. CPPs may prescribe medication therapy and order appropriate monitoring tests within an agreed upon protocol and under the supervision of an attending physician. The physician-only team included a medical resident and an attending physician. The initial intent of the physician-only team was to provide additional learning opportunities for medical residents and expand clinic access. The medical residents were trained by the pharmacist to perform the tasks required for the program and were provided a standard program template outlining the visit components. Both team types received assistance from a care manager in scheduling the appointments and addressing barriers to care, such as transportation and obtaining medications prior to the visit. The care manager was also available to both team types during the clinic visit as needed. The same group of attending physicians saw patients with the CPP and the residents. www.amcp.org Vol. 21, No. 3 The multidisciplinary team saw patients 7 half-days per week, and the physician-only team saw patients on 2 different half-days of the week to maximize overall clinic availability. Patients were scheduled to be seen by the multidisciplinary team or the physician-only team based on the patient’s preferred appointment time. The difference in team structure was not explained to the patient prior to scheduling. The overall hospital follow-up program, regardless of team type, has been shown to prevent one 30-day hospital readmission for every 7 patients seen, an absolute risk reduction of 16.7%, when compared with usual care.12 While it is clear that the program as a whole has improved transitions, it is unknown if there is any difference in 30-day hospital readmission rates between the 2 distinct team types carrying out the intervention. The purpose of this study was to compare the transitions of care, measured by hospital readmission rates and medication interventions, in a multidisciplinary team visit coordinated by a CPP with those conducted by a physician-only team in a hospital follow-up program. ■■ Methods After the establishment of the hospital follow-up program, a retrospective review of the 2 hospital follow-up models was conducted using an integrated electronic health record system that included inpatient and outpatient records. More patient visits were completed by the multidisciplinary team than the physician-only team during the study period. To maintain an equal number of patient visits in each study group, all patients meeting the inclusion criteria seen by the physician-only team were included. Patients seen by the multidisciplinary team were first matched to the physician-only team patients by discharge date and then selected at random. If a patient selected at random did not meet the inclusion criteria, the next random patient was reviewed for inclusion until an equal number of patient visits were selected for each team type within the matched time period. Inclusion/Exclusion Criteria A convenience sample of patients discharged from UNC Hospitals and seen in the UNC Internal Medicine Clinic followup program between May 2012 and January 2013 were included in this study. Patients were excluded if they were admitted for planned chemotherapy, same-day gastrointestinal procedures, discharged to hospice, or discharged to skilled nursing facilities. Study Variables The primary endpoint was 30-day hospital readmission at UNC Hospitals. The secondary endpoint was improvement in transition of care as measured by interventions such as frequency of medication discontinuation, recommendation of lower cost alternatives, initiation of new medications, identification of and March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 257 Pharmacist-Coordinated Multidisciplinary Hospital Follow-up Visits Improve Patient Outcomes addressing nonadherence, identification of wrong duration of medication, and medication dose adjustment. Fisher’s exact test was used to determine if there was a difference between the 2 groups for the primary and secondary endpoints. ■■ Results Data were collected on a total of 140 patient visits (n = 70 patient visits per group) completed by the hospital follow-up program. A total of 78 patient visits were completed by the physician-only team during the study time period, 8 of which did not meet inclusion criteria. A total of 311 patient visits were completed by the multidisciplinary team, 241 of which were not included after matching by discharge date to patients in the physician-only group, random selection to maintain equal group size, and meeting exclusion criteria. Four patients in the physician-only team group and 3 patients in the multidisciplinary team group had more than 1 visit (from more than 1 hospital discharge) and were counted as separate patient visits. There were 8 patients seen by both team types once during the study time period. Patients with a visit from both team types were included as separate patient visits in each team group. The majority of patients were female, and the median age was 57 years (Table 1). Patients in each group had similar characteristics, including length of hospital stay and number of medications and medication changes at discharge. The number of days between hospital discharge and hospital follow-up visit were also similar between groups, with the majority of patients seen within 1 week. The physician-only team completed more patient visits with patients who were considered moderate and high risk for readmission compared with the multidisciplinary group based on the internal UNC readmission risk stratification. Primary/Secondary Endpoints For primary endpoints, patients seen by the multidisciplinary team had a 30-day readmission rate of 14.3% compared with 34.3% in the physician-only team (P = 0.010). For secondary endpoints, medication interventions were similar between the 2 groups for medication dose adjustments (37.9% multidisciplinary team vs. 33.8% physician-only team, P = 0.719) and recommendation of a lower cost alternative (0% multidisciplinary team vs. 2.9% physician-only team, P = 0.497). The multidisciplinary team more frequently addressed nonadherence (98.5% multidisciplinary team vs. 86.8% physician-only team, P = 0.017), initiated a new medication (60.9% multidisciplinary team vs. 37.7% physician-only team, P = 0.010), and discontinued a medication (31.4% multidisciplinary team vs. 15.7% physician-only team, P = 0.046). Subgroup Analysis A subgroup analysis including only patients at moderate or high risk for hospital readmission was completed to mitigate the potential difference in hospital readmission risk between the 2 groups. A total of 42 patient visits completed by the mul258 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 TABLE 1 Background Characteristics of Patients in Multidisciplinary Team or Physician-Only Groupsa Multidisciplinary PhysicianPatient Only Patient Characteristics per Characteristics per Patient Visit Patient Visit n = 70 n = 70 Patient Visits (IQR) Patient Visits (IQR) 58 (18) 56 (24) 47 40 3 (3) 3 (2) 13 (8) 13 (7) Age, years Sex, male % Length of hospital stay, days Number of medications at discharge Number of medication changes 4 (4) 4 (4) at discharge Days between hospital discharge 6 (4) 6 (4) and follow-up visit Internal hospital readmission risk stratification, % Low 40.0 25.7 Moderate 25.7 37.2 High 34.3 37.1 Primary reason for hospital admission, n Coronary artery disease 10 7 Urinary tract infection 7 6 Chronic obstructive pulmonary 4 5 disorder Cirrhosis 2 5 Chronic heart failure 4 2 Chronic kidney disease 4 1 Diabetes 4 0 Asthma 1 2 Depression 1 1 Other infection 13 15 20 33 Otherb aValues are presented as median. b Other cases include (number in multidisciplinary group, number in physician-only group): trauma (0, 1); cancer (0, 2); seizure (0, 3); atrial fibrillation (1, 0); stroke (2, 0); electrolyte imbalance (1, 1); hemorrhage (2, 0); blood clot (2, 1); altered mental status (3, 2); endocrine disorders other than diabetes (1, 0); gastrointestinal disorders (5, 8); and chest pain, noncardiac (3, 0). IQR = interquartile range. tidisciplinary team during the study period and 52 patient visits completed by the physician-only team were included. Four patients seen by the multidisciplinary team compared with 20 in the physician-only team experienced a 30-day readmission (9.5% multidisciplinary team vs. 38.5% physician-only team). This difference in readmission rate between groups was even greater than that seen in the primary analysis. ■■ Discussion Patients seen by the multidisciplinary team had a 58.3% relative risk reduction of 30-day hospital readmission compared with those seen by the physician-only team. The multidisciplinary team was successful in reaching the Vol. 21, No. 3 www.amcp.org Pharmacist-Coordinated Multidisciplinary Hospital Follow-up Visits Improve Patient Outcomes program’s goal of reducing hospital readmission rates from the original rate of 19% to 14.3%. These results suggest that the addition of a clinical pharmacist to a hospital follow-up team in a primary care hospital follow-up clinic may improve the transition of care and reduce readmission rates. Limitations Data collection may have been impacted by documentation variability between providers. The same CPP conducted all of the multidisciplinary team visits, while multiple medical residents conducted the physician-only team visits, which may have led to lack of consistency in carrying out the designated components of the visit or variability in documentation. However, the pharmacist individually trained each medical resident on the hospital follow-up clinic visit components, provided a standard visit template that outlined the components, and provided a standard documentation format. In addition, the same attending physicians, who were familiar with the process, worked with the CPP and the residents in an attempt to mitigate variability between the 2 teams. This study was not designed to match patients based on the internal hospital readmission risk stratification. This led to differences between the 2 groups, with the physician-only team having a higher-risk population. The subgroup analysis, however, suggests that a difference in 30-day readmission rates between the 2 team types exists even when low-risk patients are excluded from both groups. The use of convenience sampling and attempts to match patients based on discharge date may have led to bias in the results. The matches were carried out without knowledge of patient outcomes to reduce the chance of bias. Randomized selection of patients among the 2 visit types could mitigate the chance of bias in future studies. This study only looked at medications and adherence as markers of improvement in the transition of care process. Additional studies are needed to examine any association between 30-day hospital readmission rates and frequency of referrals, adherence with evidence-based care, test follow-up, and medication education. Based on the positive experience of this program, CPPs within the UNC health care system have been integrated in hospital follow-up clinics in other specialty areas. ■■ Conclusions Patients seen after hospital discharge by the multidisciplinary hospital follow-up team had a decreased risk of 30-day hospital readmission. The multidisciplinary team discontinued and initiated medications more frequently as well as addressed nonadherence more often. We hypothesize that these medication interventions were associated with the reduction in 30-day hospital readmission rates. This study supports the involvement of a clinical pharmacist as part of a multidisciplinary team in primary care hospital follow-up visits to improve the transition of care from hospital to home. www.amcp.org Vol. 21, No. 3 Authors JAMIE J. CAVANAUGH, PharmD, CPP, BCPS, is Assistant Professor of Medicine, University of North Carolina (UNC) School of Medicine; Assistant Professor of Clinical Education, UNC Eshelman School of Pharmacy; and Clinical Specialist, UNC Hospitals Department of Pharmacy, Chapel Hill. KIMBERLY N. LINDSEY, PharmD, is Pharmacist, Kaiser Permanente, Colorado. BETSY B. SHILLIDAY, PharmD, CDE, CPP, BCACP, is Associate Professor of Medicine, UNC School of Medicine; Associate Professor of Clinical Education, UNC Eshelman School of Pharmacy; and Assistant Medical Director, UNC Internal Medicine Clinic, Chapel Hill. SHANA P. RATNER, MD, is Clinical Assistant Professor of Medicine, UNC School of Medicine; Medical Director, UNC Internal Medicine Clinic; and Physician Champion, AccessCare North Carolina, Chapel Hill. AUTHOR CORRESPONDENCE: Jamie J. Cavanaugh, PharmD, CPP, BCPS, 5034 Old Clinic Bldg., CB 7110, University of North Carolina, Chapel Hill, NC 27599-7110. Tel.: 919.843.0391; Fax: 919.966.4507; E-mail: [email protected]. DISCLOSURES The authors of this study have nothing to disclose. No financial support was received in order to complete this study. Study concept and design were contributed equally by Cavanaugh, Shilliday, and Ratner, with assistance from Lindsey. Data collection was primarily conducted by Lindsey, with assistance from Cavanaugh, and all authors participated equally in data analysis. Lindsey and Cavanaugh wrote the manuscript, which was revised by Cavanaugh, Lindsey, Shilliday, and Ratner. ACKNOWLEDGMENTS The authors would like to acknowledge the UNC Chapel Hill Odum Institute for helping with the statistics in this study. REFERENCES 1. Tan EC, Stewart K, Elliott RA, George J. Pharmacist services provided in general practice clinics: a systematic review and meta-analysis. Res Social Adm Pharm. 2014;10(4):608-22. 2. Margolis KL, Asche SE, Bergdall AR, et al. Effect of home blood pressure telemonitoring and pharmacist management on blood pressure control: a cluster randomized clinical trial. JAMA. 2013;310(1):46-56. 3. Henry TM, Smith S, Hicho M. Treat to goal: impact of clinical pharmacist referral service primarily in diabetes management. Hosp Pharm. 2013;48(8):656-61. 4. Jencks SF, Williams MV, Coleman EA. Rehospitalizations among patients in the Medicare fee-for-service program. N Engl J Med. 2009;360(14):1418-28. 5. Colivicchi F, Bassi A, Santini M, Caltagirone C. Discontinuation of statin therapy and clinical outcome after ischemic stroke. Stroke. 2007;38(10):2652-57. 6. Hohl CM, Abu-Laban RB, Brubacher JR, et al. Adherence to emergency department discharge prescriptions. CJEM. 2009;11(2):131-38. 7. Ho PM, Tsai TT, Maddox TM, et al. Delays in filling clopidogrel prescription after hospital discharge and adverse outcomes after drug-eluting stent implantation: implication for transitions of care. Circ Cardiovasc Qual Outcomes. 2010;3(3):261-66. March 2015 JMCP Journal of Managed Care & Specialty Pharmacy 259 Pharmacist-Coordinated Multidisciplinary Hospital Follow-up Visits Improve Patient Outcomes 8. Mesteig M, Helbostad JL, Sletvold O, Røsstad T, Saltvedt I. Unwanted incidents during transition of geriatric patients from hospital to home: a prospective observational study. BMC Health Serv Res. 2010;10:1. 11. Bellone JM, Barner JC, Lopez DA. Postdischarge interventions by pharmacists and impact on hospital readmission rates. J Am Pharm Assoc. 2012;52(3):358-62. 9. Shah M, Norwood CA, Farias S, Ibrahim S, Chong PH, Fogelfeld L. Diabetes transitional care from inpatient to outpatient setting: pharmacist discharge counseling. J Pharm Pract. 2013;26(2):120-24. 12. Cavanaugh JJ, Jones CD, Embree G, et al. Implementation Science Workshop: primary care-based multidisciplinary readmission prevention program. J Gen Intern Med. 2014;29(5):798-804. 10. Schnipper JL, Kirwin JL, Cotugno MC, et al. Role of pharmacist counseling in preventing adverse drug events after hospitalization. Arch Intern Med. 2006;166(5):565-71. 260 Journal of Managed Care & Specialty Pharmacy JMCP March 2015 Vol. 21, No. 3 www.amcp.org BRIEF SUMMARY OF PRESCRIBING INFORMATION FOR GRANIX® (tbo-filgrastim) injection, for subcutaneous use SEE PACKAGE INSERT FOR FULL PRESCRIBING INFORMATION 1 INDICATIONS AND USAGE GRANIX is indicated to reduce the duration of severe neutropenia in patients with nonmyeloid malignancies receiving myelosuppressive anti-cancer drugs associated with a clinically significant incidence of febrile neutropenia. 4 CONTRAINDICATIONS None. 5 WARNINGS AND PRECAUTIONS 5.1 Splenic Rupture Splenic rupture, including fatal cases, can occur following administration of human granulocyte colony-stimulating factors. In patients who report upper abdominal or shoulder pain after receiving GRANIX, discontinue GRANIX and evaluate for an enlarged spleen or splenic rupture. 5.2 Acute Respiratory Distress Syndrome (ARDS) Acute respiratory distress syndrome (ARDS) can occur in patients receiving human granulocyte colony-stimulating factors. Evaluate patients who develop fever and lung infiltrates or respiratory distress after receiving GRANIX, for ARDS. Discontinue GRANIX in patients with ARDS. 5.3 Allergic Reactions Serious allergic reactions including anaphylaxis can occur in patients receiving human granulocyte colony-stimulating factors. Reactions can occur on initial exposure. The administration of antihistamines‚ steroids‚ bronchodilators‚ and/or epinephrine may reduce the severity of the reactions. Permanently discontinue GRANIX in patients with serious allergic reactions. Do not administer GRANIX to patients with a history of serious allergic reactions to filgrastim or pegfilgrastim. 5.4 Use in Patients with Sickle Cell Disease Severe and sometimes fatal sickle cell crises can occur in patients with sickle cell disease receiving human granulocyte colony-stimulating factors. Consider the potential risks and benefits prior to the administration of human granulocyte colony-stimulating factors in patients with sickle cell disease. Discontinue GRANIX in patients undergoing a sickle cell crisis. 5.5 Capillary Leak Syndrome Capillary leak syndrome (CLS) can occur in patients receiving human granulocyte colonystimulating factors and is characterized by hypotension, hypoalbuminemia, edema and hemoconcentration. Episodes vary in frequency, severity and may be life-threatening if treatment is delayed. Patients who develop symptoms of capillary leak syndrome should be closely monitored and receive standard symptomatic treatment, which may include a need for intensive care. 5.6 Potential for Tumor Growth Stimulatory Effects on Malignant Cells The granulocyte colony-stimulating factor (G-CSF) receptor through which GRANIX acts has been found on tumor cell lines. The possibility that GRANIX acts as a growth factor for any tumor type, including myeloid malignancies and myelodysplasia, diseases for which GRANIX is not approved, cannot be excluded. 6 ADVERSE REACTIONS The following potential serious adverse reactions are discussed in greater detail in other sections of the labeling: • Splenic Rupture [see Warnings and Precautions (5.1)] • Acute Respiratory Distress Syndrome [see Warnings and Precautions (5.2)] • Serious Allergic Reactions [see Warnings and Precautions (5.3)] • Use in Patients with Sickle Cell Disease [see Warnings and Precautions (5.4)] • Capillary Leak Syndrome [see Warnings and Precautions (5.5)] • Potential for Tumor Growth Stimulatory Effects on Malignant Cells [see Warnings and Precautions (5.6)] The most common treatment-emergent adverse reaction that occurred at an incidence of at least 1% or greater in patients treated with GRANIX at the recommended dose and was numerically two times more frequent than in the placebo group was bone pain. 6.1 Clinical Trials Experience Because clinical trials are conducted under widely varying conditions, adverse reaction rates observed in the clinical trials of a drug cannot be directly compared to rates in the clinical trials of another drug and may not reflect the rates observed in clinical practice. GRANIX clinical trials safety data are based upon the results of three randomized clinical trials in patients receiving myeloablative chemotherapy for breast cancer (N=348), lung cancer (N=240) and non-Hodgkin’s lymphoma (N=92). In the breast cancer study, 99% of patients were female, the median age was 50 years, and 86% of patients were Caucasian. In the lung cancer study, 80% of patients were male, the median age was 58 years, and 95% of patients were Caucasian. In the non-Hodgkin’s lymphoma study, 52% of patients were male, the median age was 55 years, and 88% of patients were Caucasian. In all three studies a placebo (Cycle 1 of the breast cancer study only) or a non-US-approved filgrastim product were used as controls. Both GRANIX and the non-US-approved filgrastim product were administered at 5 mcg/kg subcutaneously once daily beginning one day after chemotherapy for at least five days and continued to a maximum of 14 days or until an ANC of ≥10,000 x 106/L after nadir was reached. Bone pain was the most frequent treatment-emergent adverse reaction that occurred in at least 1% or greater in patients treated with GRANIX at the recommended dose and was numerically two times more frequent than in the placebo group. The overall incidence of bone pain in Cycle 1 of treatment was 3.4% (3.4% GRANIX, 1.4% placebo, 7.5% non-USapproved filgrastim product). Leukocytosis In clinical studies, leukocytosis (WBC counts > 100,000 x 106/L) was observed in less than 1% patients with non-myeloid malignancies receiving GRANIX. No complications attributable to leukocytosis were reported in clinical studies. Additional Adverse Reactions Other adverse reactions known to occur following administration of human granulocyte colony-stimulating factors include myalgia, headache, vomiting, Sweet’s syndrome (acute febrile neutrophilic dermatosis), cutaneous vasculitis and thrombocytopenia. 6.2 Immunogenicity As with all therapeutic proteins, there is a potential for immunogenicity. The incidence of antibody development in patients receiving GRANIX has not been adequately determined. 7 DRUG INTERACTIONS No formal drug interaction studies between GRANIX and other drugs have been performed. Drugs which may potentiate the release of neutrophils‚ such as lithium‚ should be used with caution. Increased hematopoietic activity of the bone marrow in response to growth factor therapy has been associated with transient positive bone imaging changes. This should be considered when interpreting bone-imaging results. 8 USE IN SPECIFIC POPULATIONS 8.1 Pregnancy Pregnancy Category C Risk Summary There are no adequate and well-controlled studies of GRANIX in pregnant women. In animal reproduction studies, treatment of pregnant rabbits with tbo-filgrastim resulted in increased spontaneous abortion and fetal malformations at systemic exposures substantially higher than the human exposure. GRANIX should be used during pregnancy only if the potential benefit justifies the potential risk to the fetus. Animal Data In an embryofetal developmental study, pregnant rabbits were administered subcutaneous doses of tbo-filgrastim during the period of organogenesis at 1, 10 and 100 mcg/kg/day. Increased abortions were evident in rabbits treated with tbo-filgrastim at 100 mcg/kg/day. This dose was maternally toxic as demonstrated by reduced body weight. Other embryofetal findings at this dose level consisted of post-implantation loss‚ decrease in mean live litter size and fetal weight, and fetal malformations such as malformed hindlimbs and cleft palate. The dose of 100 mcg/kg/day corresponds to a systemic exposure (AUC) of approximately 50-90 times the exposures observed in patients treated with the clinical tbo-filgrastim dose of 5 mcg/kg/day. 8.3 Nursing Mothers It is not known whether tbo-filgrastim is secreted in human milk. Because many drugs are excreted in human milk, caution should be exercised when GRANIX is administered to a nursing woman. Other recombinant G-CSF products are poorly secreted in breast milk and G-CSF is not orally absorbed by neonates. 8.4 Pediatric Use The safety and effectiveness of GRANIX in pediatric patients have not been established. 8.5 Geriatric Use Among 677 cancer patients enrolled in clinical trials of GRANIX, a total of 111 patients were 65 years of age and older. No overall differences in safety or effectiveness were observed between patients age 65 and older and younger patients. 8.6 Renal Impairment The safety and efficacy of GRANIX have not been studied in patients with moderate or severe renal impairment. No dose adjustment is recommended for patients with mild renal impairment. 8.7 Hepatic Impairment The safety and efficacy of GRANIX have not been studied in patients with hepatic impairment. 10 OVERDOSAGE No case of overdose has been reported. ©2014 Cephalon, Inc., a wholly-owned subsidiary of Teva Pharmaceutical Industries Ltd. All rights reserved. GRANIX is a registered trademark of Teva Pharmaceutical Industries Ltd. Manufactured by: Distributed by: Sicor Biotech UAB Teva Pharmaceuticals USA, Inc. Vilnius, Lithuania North Wales, PA 19454 U.S. License No. 1803 Product of Israel GRX-40580 January 2015 This brief summary is based on TBO-004 GRANIX full Prescribing Information. Take a bite out of G-CSF acquisition costs Based on wholesale acquisition cost (WAC) of all short-acting G-CSF products as of November 11, 2013. WAC represents published catalogue or list prices and may not represent actual transactional prices. Please contact your supplier for actual prices. GRANIX® is an option in short-acting G-CSF therapy » A 71% reduction in duration of severe neutropenia vs placebo (1.1 days vs 3.8 days, p<0.0001)1 – Efficacy was evaluated in a multinational, multicenter, randomized, controlled, Phase III study of chemotherapy-naïve patients with high-risk breast cancer receiving doxorubicin (60 mg/m2 IV bolus)/docetaxel (75 mg/m2)1 » The safety of GRANIX was established in 3 Phase III trials, with 680 patients receiving chemotherapy for either breast cancer, lung cancer, or non-Hodgkin lymphoma (NHL)1 » Now offering a new presentation for self-administration Indication » GRANIX is a leukocyte growth factor indicated for reduction in the duration of severe neutropenia in patients with nonmyeloid malignancies receiving myelosuppressive anticancer drugs associated with a clinically significant incidence of febrile neutropenia. Important Safety Information » Splenic rupture: Splenic rupture, including fatal cases, can occur following the administration of human granulocyte colony-stimulating factors (hG-CSFs). Discontinue GRANIX and evaluate for an enlarged spleen or splenic rupture in patients who report upper abdominal or shoulder pain after receiving GRANIX. » Acute respiratory distress syndrome (ARDS): ARDS can occur in patients receiving hG-CSFs. Evaluate patients who develop fever and lung infiltrates or respiratory distress after receiving GRANIX, for ARDS. Discontinue GRANIX in patients with ARDS. » Allergic reactions: Serious allergic reactions, including anaphylaxis, can occur in patients receiving hG-CSFs. Reactions can occur on initial exposure. Permanently discontinue GRANIX in patients with serious allergic reactions. Do not administer GRANIX to patients with a history of serious allergic reactions to filgrastim or pegfilgrastim. » Use in patients with sickle cell disease: Severe and sometimes fatal sickle cell crises can occur in patients with sickle cell disease receiving hG-CSFs. Consider the potential risks and benefits prior to the administration of GRANIX in patients with sickle cell disease. Discontinue GRANIX in patients undergoing a sickle cell crisis. » Capillary leak syndrome (CLS): CLS can occur in patients receiving hG-CSFs and is characterized by hypotension, hypoalbuminemia, edema and hemoconcentration. Episodes vary in frequency, severity and may be life-threatening if treatment is delayed. Patients who develop symptoms of CLS should be closely monitored and receive standard symptomatic treatment, which may include a need for intensive care. » Potential for tumor growth stimulatory effects on malignant cells: The granulocyte colony-stimulating factor (G-CSF) receptor, through which GRANIX acts, has been found on tumor cell lines. The possibility that GRANIX acts as a growth factor for any tumor type, including myeloid malignancies and myelodysplasia, diseases for which GRANIX is not approved, cannot be excluded. » Most common treatment-emergent adverse reaction: The most common treatment-emergent adverse reaction that occurred in patients treated with GRANIX at the recommended dose with an incidence of at least 1% or greater and two times more frequent than in the placebo group was bone pain. Please see brief summary of Full Prescribing Information on adjacent page. For more information, visit GRANIXhcp.com. Reference: 1. GRANIX® (tbo-filgrastim) Injection Prescribing Information. North Wales, PA: Teva Pharmaceuticals; 2014. ©2015 Cephalon, Inc., a wholly-owned subsidiary of Teva Pharmaceutical Industries Ltd. GRANIX is a registered trademark of Teva Pharmaceutical Industries Ltd. All rights reserved. GRX-40442 January 2015. Printed in USA.