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■ 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]
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reprints
(continued on page 186)
Journal of Managed Care & Specialty Pharmacy® (ISSN 2376-1032) is published 12 times per year and is
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184 Journal of Managed Care & Specialty Pharmacy
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March 2015
Vol. 21, No. 3
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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
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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
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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
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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.
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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
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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
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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.
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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
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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
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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.
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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
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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
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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.
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■■  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
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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).
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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.
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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.
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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
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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.
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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.
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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.
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www.psoriasis.org/research/science-of-psoriasis/statistics. Accessed
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Undertreatment, treatment trends, and treatment dissatisfaction among
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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.
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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
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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.
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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
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Adalimumab, and Ustekinumab in the Management of Moderate-to-Severe Psoriasis
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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
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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.
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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
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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
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32. Schabert VF, Watson C, Joseph GJ, Iversen P, Burudpakdee C, Harrison
DJ. Costs of tumor necrosis factor blockers per treated patient using
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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
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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.
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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
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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
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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).
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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
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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
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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,
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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).
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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
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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
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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
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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
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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
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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
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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
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Journal of Managed Care & Specialty Pharmacy 217
Increased Relapse Activity for Multiple Sclerosis Natalizumab Users Who Become Nonpersistent: A Retrospective Study
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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.
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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
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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
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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
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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
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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:
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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
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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
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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
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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
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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
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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
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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
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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).
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Resource Utilization and Costs Associated with Using Insulin Therapy
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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
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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.
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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.
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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.
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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
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March 2015
Vol. 21, No. 3
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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.
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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
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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;
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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
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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
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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.
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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-
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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
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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
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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
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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.
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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].
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March 2015
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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
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agonists. The clinical relevance of rodent thyroid findings observed with GLP-1
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receptor agonists is unknown. In a mouse fertility study, males were treated
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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
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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
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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
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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
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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.
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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
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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
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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
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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
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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.
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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
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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.
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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
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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
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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.
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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
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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
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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.