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Reducing Heart Failure Readmissions: Case Studies Utilizing Biomarkers for Risk Stratification HeartFailureCases.com Reducing Heart Failure Readmissions: Case Studies Utilizing Biomarkers for Risk Stratification • Accredited by Educational Review Systems (ERS) • Supported by an educational grant from: Critical Diagnostics • Content support provided by Medavera, Inc. • Date of release: April 1, 2014 • Date of expiration: March 31, 2016 • Estimated time to complete this educational activity: 1 Hour AW201153 Rev.1 2 Continuing Education Credit(s) • Physicians: 1.0 hour – This Enduring Material activity, Reducing Heart Failure Readmissions: Case Studies Utilizing Biomarkers for Risk Stratification, has been reviewed and is acceptable for up to 1.00 Prescribed credit(s) by the American Academy of Family Physicians. AAFP certification begins 04/01/2014. Physicians should claim only the credit commensurate with the extent of their participation in the activity. Program expires 3/31/2016. • Nursing: 1.0 hours – Educational Review Systems is an approved provider of continuing education in nursing by ASNA, an accredited provider by the ANCC/Commission on Accreditation. Provider #5-115. This program is approved for one (1.0) hour. Educational Review Systems is also approved for nursing continuing education by the State of California and the District of Columbia. • Laboratorians: 1.0 hour – Educational Review Systems is approved as a provider of continuing education programs in the clinical laboratory sciences by the ASCLS P.A.C.E. Program. This program is approved for 1 hour of continuing education credit. AW201153 Rev.1 3 Continuing Education Credit(s) • Statement of Need – There are approximately 5.8 million people in the U.S. with heart failure resulting in 1 million annual hospitalizations. With rehospitalization rates reaching nearly 25%, heart failure care has been targeted by the Centers for Medicare & Medicaid Services (CMS) for improvements. Biomarkers specific to the determinants of heart failure readmissions may play an increasingly prominent role in risk assessment of patients, tailoring of therapy, and possible reduction of short term hospital readmissions. The current climate is demanding solutions and additional education and discussion is needed. • Intended Audience – Health care professionals (physicians, nurses, laboratorians etc.) involved in the care of patients with heart failure. AW201153 Rev.1 4 Continuing Education Credit(s) • Instructions For CME credit, please view the slides and 1. Take the online post-test Or 2. Download and print the CME application and fax to 678.401.0259. Questions? Call Medavera, Inc. at : 417.890.9722 Or email: [email protected] AW201153 Rev.1 5 Medical Advisors for Activity James L. Januzzi Jr, MD, FACC, FESC Roman W. Desanctis Endowed Clinical Scholar Director, Cardiac ICU, Massachusetts General Hospital Associate Professor of Medicine, Harvard Medical School Boston, Massachusetts Aurelia M. O’Connell, PhD, ACNP, BC, RN, PHN, FAHA Associate Professor Azusa Pacific University School of Nursing Azusa, California AW201153 Rev.1 6 Faculty Disclosures AW201153 Rev.1 7 Faculty Commercial Interest Honorarium James L. Januzzi Jr Roche Critical Diagnostics Siemens BG Medicine Grant Grant, Honorarium Grant Grant Aurelia M. O’Connell None None Learning Objectives 1. Review current statistics on heart failure incidence, trends, and readmission rates. 2. Identify CMS initiatives to reduce Medicare readmissions and penalties that are and will continue to be assigned. 3. Summarize the changes in the 2013 update to the ACCF/AHA Guideline for the Management of Heart Failure. 4. Assess various biomarkers used in heart failure prognostication in pathophysiology, clinical trial evidence, and clinical rationale. 5. Apply information learned in case studies to real-life care scenarios to risk stratify heart failure patients. 6. Utilize information provided toward implementation of strategies that will reduce heart failure readmissions within one’s own institution. AW201153 Rev.1 8 Heart Failure Statistics and Trends AW201153 Rev.1 9 Heart Failure Statistics • Heart failure (HF) is one of the most rapidly increasing cardiovascular disorders. • Leading cause of hospitalization in individuals over 65 years of age1 • Third leading cause of hospitalization in the U.S. in all age groups2 HF is the most common cause of readmission.3 Rates approach 30% within 60-90 days of discharge.4 1Krumholz AW201153 Rev.1 10 HM, Chen YT, Wang Y et al. Am Heart J. 2000;139(1 Pt 1):72–7.. 2Heart Disease and Stroke Statistics—2012 Update. Circulation. 2012;125:e2-220. 3Jencks SF, Williams MV, Coleman EA. N Engl J Med. 2009;360:1418-28. 4Gheorghiade M, Vaduganathan M, Fonarow GC et al. J Am Coll Cardiol. 2013;61:391-403. Hospital Discharges for HF Are Increasing 1979-2009 Discharge in Thousands 700 600 500 400 Female Male 300 200 100 0 1979 1980 1985 1990 1995 2000 2005 2009 Years AW201153 Rev.1 11 National Hospital Discharge Survey/National Center for Health Statistics and National Heart, Lung, and Blood Institute. 2008. Projected Prevalence and Cost of HF 4 90 3.5 80 3 70 2.5 60 2 1.5 1 0.5 25% Increase 2010 2015 2020 2025 2030 Year AW201153 Rev.1 50 40 30 20 0 12 Projected U.S. Direct Costs for Heart Failure Billions ($) Percent (%) Projected U.S. Prevalence of Heart Failure 10 215% Increase 0 2010 2015 2020 2025 2030 Year Konstam MA. Circulation.2012;125:820-7. What is the Problem With Readmissions? AW201153 Rev.1 13 CMS and Heart Failure • Medically unnecessary treatment of HF is one of the most claimed improper payments.1 • HF is the 4th highest diagnosis in recovered payments by the Center for Medicare & Medicaid Services (CMS).1 • Average cost per rehospitalization is $22,700 per patient.2 • Providers must differentiate themselves based on quality of care, patient satisfaction, and transparency. 1CMS. AW201153 Rev.1 14 2Agency The Medicare Recovery Audit Contractor(RAC) Program: An Evaluation of the 3-year Demonstration. 2008. for Healthcare Research and Quality, US Department of Health & Human Services (hcupnet.ahrq.gov). 2010. HF readmissions are a key opportunity to remain competitive in an increasingly transparent environment. Low Readmission Rates Kaiser Foundation Hospital Los Angeles, CA Mercy Hospital Buffalo, NY Baylor University Medical Center Dallas, TX High Readmission Rates University Hospital of Brooklyn Brooklyn, NY AW201153 Rev.1 15 Boston Medical Center Boston, MA Kendall Regional Medical Center Miami, FL Patients can see readmission rates for any hospital at www.medicare.gov/HospitalCompare. CMS and Medicare Readmission Penalties • Nearly 25% of all patients hospitalized for heart failure are readmitted within 30 days. • CMS has labeled HF as an area of excessive readmission. • CMS penalties will ensue to reduce readmission rates. Penalties Will Reduce Medicare Payments Percent of Payments Received 101 100 99 98 97 96 95 1% Loss FY 2012 AW201153 Rev.1 16 FY 2013 2% Loss FY 2014 3% Loss FY 2015 http://www.ama-assn.org/amednews/2012/08/27/gvsb0827.htm. American Medical Association. Accessed online 12/28/2012. Rehospitalization Diagnoses • In a recently published study, 30-day readmissions were analyzed in 329,308 patients from 2007 to 2009. • Heart failure was the most common diagnosis for rehospitalization. Most Common Rehospitalization Diagnoses AW201153 Rev.1 17 Heart Failure Acute MI Pneumonia Heart failure (%) 35.2 19.3 8.53 Renal disorders (%) 8.11 5.28 5.27 Pneumonias (%) 4.98 4.89 22.4 Arrhythmias and conduction disorders (%) 4.04 4.95 2.68 Septicemia/shock (%) 3.55 3.96 5.95 Cardiorespiratory failure (%) 3.50 3.14 4.69 Dharmarajan K, Hsieh AF, Lin Z, et al. J Am Med Assoc. 2013;309:355-63. Readmission and Index Hospitalization • Also, in this analysis, heart failure was the most common readmission diagnosis regardless of index hospitalization. • The prior admission is referred to as the “index hospitalization.” In the event that there is more than one discharge from an acute care hospital within a 30-day period, the index hospitalization is the hospitalization closest in time to the readmission. Parameter Heart Failure Acute MI Pneumonia Readmission rate (%) 24.8 19.9 18.3 Readmissions for indication of index hospitalization (%) 35.2 10.0 22.4 12 10 12 61.0 67.6 62.6 Median time to 30-day readmission (d) Readmissions in post discharge days 1 to 15 (%) AW201153 Rev.1 18 Dharmarajan K, Hsieh AF, Lin Z, et al. J Am Med Assoc. 2013;309:355-63. Conclusions of Readmissions Analysis “The diagnoses associated with 30-day readmission are diverse and are not associated with patient demographic characteristics or time after discharge for older patients initially hospitalized with HF, acute MI, or pneumonia. Although a high percentage of 30-day readmissions occurred relatively soon after hospitalization, readmissions remained frequent during days 16 through 30 after discharge regardless of patient age, sex, or race.” “This heightened vulnerability of recently hospitalized patients to a broad spectrum of conditions throughout the post discharge period favors a generalized approach to preventing readmissions that is broadly applicable across potential readmission diagnoses and effective for at least the full month after hospitalization.” “Strategies that are specific to particular diseases or periods may only address a fraction of patients at risk for rehospitalization.” AW201153 Rev.1 19 Dharmarajan K, Hsieh AF, Lin Z et al. J Am Med Assoc. 2013;309:355-63. HF Readmission Comorbidities Heart Failure Renal Disorders Arrhythmias Pneumonias Cardiac Failure The large number of potential comorbidities dramatically increases the complexity of heart failure readmissions. Septicemia/Shock AW201153 Rev.1 20 Dharmarajan K, Hsieh AF, Lin Z et al. J Am Med Assoc. 2013;309:355-63. CMS Readmission Adjustment Scenario HF Admissions 200 Average Payment $20,000 Actual Readmissions Expected Readmissions 50 51 Hospital-specific readmissions adjustment factor 51 / 50 = 1.02 – 1 = 0.02 Current readmission penalty formula: 200 AW201153 Rev.1 x $20,000 x 0.02 = $80,000 Loss http://cms.gov/Medicare/Medicare-Fee-for-Service-Payment/AcuteInpatientPPS/Readmissions-Reduction-Program.html. Accessed 01/01/13. 21 CMS Readmission Penalties in 2014 • 2,225 hospitals will loose up to 2% of Medicare reimbursements for a total of $227 million in fines – – – – Decreased fines for 1,371 hospitals Increased fines for 1,074 hospitals Fine 283 hospitals that were not fined in FY2013 18 hospitals will receive the maximum 2% penalty • FY2014 will include a modification that allows for planned readmissions – Would have reduced FY2013 penalties by 1.5% for HF AW201153 Rev.1 http://www.advisory.com/daily-briefing/2013/08/05/cms-2225-hospitals-will-pay-readmissions-penalties-next-year. http://www.advisory.com/Daily-Briefing/2013/05/01/CMS-wants-to-exclude-more-readmissions-from-penalty-program. Accessed online 2/1/2014. 22 What is Next in 2015? • Maximum penalty will be raised to 3% • The number of conditions eligible for penalties will be expanded. – Chronic lung disease – Elective hip and knee replacements • CMS may include a rate for ALL of a hospital’s readmissions as part of its penalty calculations. AW201153 Rev.1 23 http://www.advisory.com/daily-briefing/2013/08/05/cms-2225-hospitals-will-pay-readmissions-penalties-next-year. Accessed online 2/1/2014. What is Working to Help Reduce Heart Failure Readmissions? AW201153 Rev.1 24 What is NOT Working? • Decreased length of stay (LOS)1 – Decreased LOS is correlated with increased readmissions and post-discharge mortality. • Patient compliance2 – Despite a higher risk profile, non-adherent patients have a shorter LOS and mortality risk. • Outdated guidelines – Previous 2009 HF guidelines did not include new biomarker data. – 2013 guidelines have been updated to include information on new biomarkers.3 1Bueno AW201153 Rev.1 25 H, Ross JS, Wang Y et al. J Am Med Assoc. 2010;303:2141-7. AV, Fonarow GC, Hernandez AF et al. Am Heart J. 2009;158:644-52. 3Yancy CW, Jessup M, Bozkurt B et al. J Am Coll Cardiol. 2013;62(16):e147-239. Epub 2013 Jun 5. 2Ambardekar What is Working? • Basoor’s Heart Failure Checklist© – 27-question discharge checklist – Cut 30-day readmissions from 19% to 6% – Readmission rates continued to be lower after six months. • AW201153 Rev.1 42% to 23% without the checklist http://www.cardiosource.org/News-Media/Media-Center/News-Releases/2012/03/HF-Checklist.aspx. Accessed 2/3/14. Basoor A, Doshi NC, Cotant JF et al. Cong Heart Fail. 2013;19:200-6. 26 What is Working? • Brigham and Women’s Hospital – 10,731 discharges – 2,398 readmissions • Computerized algorithm for 30-day readmissions – Based on past administrative discharge data • Prediction score identified independent factors which can be used to calculate risk. – 879 readmissions were identified as potentially avoidable AW201153 Rev.1 27 Donze J, Aujesky D, Williams D, Schnipper JL. J Am Med Assoc Intern Med. 2013;173(8):632-8. What is Working? • Seven risk factors were identified as part of the 30-day readmissions algorithm. • HOSPITAL Risk Prediction Model 1. Hemoglobin at discharge 2. Discharge from Oncology 3. Sodium at discharge 4. Procedure during index admissions 5. Index Type 6. Admissions in the last 12 months 7. Length of stay AW201153 Rev.1 28 Donze J, Aujesky D, Williams D, Schnipper JL. J Am Med Assoc Intern Med. 2013;173(8):632-8. What is Working? • For each of the seven factors, risk is calculated with 1-2 points per factor based on severity. • Patients with 7 or more points have an 18% risk of potentially avoidable readmission within 30 days. • Model can be used before discharge to assess the risk of readmissions. – May be used to identify patients who need more intensive transitional care AW201153 Rev.1 29 Donze J, Aujesky D, Williams D, Schnipper JL. J Am Med Assoc Intern Med. 2013;173(8):632-8. What is Working? • Two rural South Dakota hospitals – Avera Tri-State Affiliates Hospitals in Souix Falls, SD – Patients were from a general hospital or a cardiac specialty hospital • Intensive transitions of care program • Self-management training of patients • Appropriate outpatient follow-up and monitoring of the patient by the health care system A 42% relative reduction in 30-day readmission rates was documented in patients participating in the program. AW201153 Rev.1 30 Huntington MK, Guzman AI, Roemen A et al. S D Med. 2013;66(9):370-3. What is Working? • The following six strategies have been associated with lower 30-day HF readmission rates: 1. 2. 3. 4. 5. Partnering with community physicians and groups Partnering with local hospitals Having nurses responsible for medication reconciliation Arranging for follow-up visits before discharge Having a process in place to send discharge or electronic summaries directly to the patient’s primary care physician 6. Assigning staff to follow up on test results after the patient is discharged The more strategies are used by a single institution, the lower the readmission rates. AW201153 Rev.1 31 Bradley EH, Curry L, Horwitz LI et al. Circ Cardiovasc Qual Outcomes. 2013;6:444-50. What Assessments Are Working? • LVF assessment1 – Readmitted patients are twice as likely to not have left ventricular function (LVF) assessments. • Blood glucose at presentation2 – Independent predictor of 30-day mortality – Easily modifiable, potential therapeutic target • Biomarker assessments3-4 – Independent predictors of mortality and readmission – Single and multimarker assessments 1Mazimba 2Mebazaa AW201153 Rev.1 32 S, Grant N, Parikh A et al. Am J Med Qual. 2012 Oct 30. [Epub ahead of print]. A, Gayat E, Lassus J et al. J Am Coll Cardiol. 2012 Jan 16. [Epub ahead of print]. 3Noveanu M, Breidthardt T, Potocki M et al. Crit Care. 2011;15:R1. 4Zaya M, Phan A, Schwarz ER. World J Cardiol. 2012;4:23–30. The 2013 ACCF/AHA Guideline for Heart Failure Management AW201153 Rev.1 33 The 2013 ACCF/AHA Guideline for Heart Failure Management: Emphasis on Care • Validated multivariable risk scores can be useful to estimate subsequent risk in HF patients. • Use of additional biomarkers for diagnosis, risk stratification, and prognosis is recommended. • Use of non-pharmacological interventions are encouraged. – Patient education, exercise, diet • New pharmacological recommendations: – Aldosterone antagonists and digoxin could be beneficial – Combined use of angiotensin-converting-enzyme inhibitor, angiotensin receptor blocker, and aldosterone antagonist is considered potentially harmful • Device therapy recommendations now include NYHA Class I and II HF. AW201153 Rev.1 34 Yancy CW, Jessup M, Bozkurt B et al. J Am Coll Cardiol. 2013;62(16):e147-239. Highlights for the 2013 ACCF/AHA Guideline for Heart Failure Management • Participation in performance improvement processes based on professionally developed clinical practice guidelines • Care coordination and transitions of care from primary care physicians to cardiologists, to palliative care and hospice • Shared decision making between patients and family members • Improvement in quality of life as well as survival and performance metrics • Importance of education, informed decisions for next steps and advanced directives AW201153 Rev.1 35 Yancy CW, Jessup M, Bozkurt B et al. J Am Coll Cardiol. 2013;62(16):e147-239. 2013 Heart Failure Guidelines Recommend Additional Biomarkers for Hospitalized and Acute Patients 2013 ACCF/AHA Guideline for the Management of Heart Failure • Markers of myocardial fibrosis are predictive of hospitalization and death in patients with HF. • These markers are also additive to natriuretic peptide levels in their prognostic value. • ST2 and Gal-3 are recognized markers for myocardial fibrosis. Class IIb 1. The usefulness of BNP- or NT-proBNP−guided therapy for acutely decompensated HF is not well established (259, 260). (Level of Evidence: C) 2. Measurement of other clinically available tests such as biomarkers of myocardial injury or fibrosis may be considered for additive risk stratification in patients with acutely decompensated HF (248, 253, 256, 257, 261-267). (Level of Evidence: A) AW201153 Rev.1 36 Yancy CW, Jessup M, Bozkurt B et al. J Am Coll Cardiol. 2013;62(16):e147-239 Recommendations for Biomarkers in HF Setting COR LOE Diagnosis or exclusion of HF Ambulatory, Acute I A Prognosis of HF Ambulatory, Acute I A Ambulatory IIa B Acute IIb C Ambulatory IIb B Natriuretic peptides Achieve GDMT Guidance for acutely decompensated HF therapy Biomarkers of myocardial fibrosis (Gal-3, ST2) Additive risk stratification COR, class of recommendations; GDMT, guidance directed medical therapy; LOE, level of evidence AW201153 Rev.1 37 Adapted from Yancy CW, Jessup M, Bozkurt B et al. J Am Coll Cardiol. 2013;62(16):e147-239 Which Biomarkers Are Being Used for Patient Risk Stratification? AW201153 Rev.1 38 Heart Failure Biomarkers Have Distinct Mechanisms of Action Myocardial Insult Myocyte Stretch BNP, NT-proBNP Mycardial Injury Troponins Oxidative Stress Myeloperoxidase, oxLDL AW201153 Rev.1 39 Maladaptive Remodeling Neurohormonal Activation Inflammation C-reactive protein, tumor necrosis factor-α, Fas, interleukins, osteoprotegerin, adiponectin Hypertrophy/fibrosis Matrix metalloproteinases, collagen propeptides, galectin-3, soluble ST2 Apoptosis Renin Angiotensin System Renin, angiotensin II, aldosterone Sympathetic Nervous System Norepinephrine, chromogranin A Arginine Vasopressin System Arginine vasopressin Kim HN, Januzzi JL. Curr Treat Options Cardiovasc Med. 2010;12:519-31. What Is a Useful HF Biomarker? Criteria Able to be used for serial testing Changes only reflect disease progression Not affected by changes during acute phase of HF No differences with regards to age, gender, body mass index, or other medical conditions Levels decrease in response to successful therapy Compliments or exceeds effectiveness of existing tests Low reference change value (RCV) (Value attributed to normal biological variation) AW201153 Rev.1 40 Wu A. eJIFCC. 2012;23:1-5. Biomarkers in HF Classification NYHA Class1 I II III IV Symptoms1 Predictive Biomarkers2 Cardiac disease, but no symptoms and no limitation in ordinary physical activity, e.g. shortness of breath when walking, climbing stairs etc. Mild symptoms (mild shortness of breath and/or angina) and slight limitation during ordinary activity. Yes Marked limitation in activity due to symptoms, even during less-than-ordinary activity, e.g. walking short distances (20–100 m). Comfortable only at rest. Yes Severe limitations. Experiences symptoms even while at rest. Bedbound patients. Yes Biomarkers can be predictive of NYHA classes > II.2 1The AW201153 Rev.1 41 Criteria Committee of the New York Heart Association. Nomenclature and Criteria for Diagnosis of Diseases of the Heart and Great Vessels. 9th ed. Boston, Mass: Little, Brown & Co; 1994:253-6. 2Silva Marques J, Luz-Rodrigues H, David C et al. Rev Port Cardiol. 2012;31:701-10. Role of Biomarkers in HF Readmissions • Biomarkers may predict which patients are at increased risk for readmission. – Original presentation of HF or other cardiac event • Early intervention – Serial monitoring may allow for interventions at an earlier stage, thereby reducing readmissions. Which biomarkers would be useful for reducing readmission rates? AW201153 Rev.1 42 Dunlay SM & Jaffe AS. Clin Chem. 2013;59:737-39. Heart Failure Biomarker Capabilities Diagnosis Prognosis Therapy Guidance ++++ ++++ ++ GDF-15 − +++ unknown Highly sensitive troponins + ++++ unknown CRP − ++ unknown TNF-α − ++ unknown IL-6 − ++ unknown MPO − ++ unknown NGAL − ++++ unknown Biomarker NT-proBNP and BNP NT-proBNP, N-terminal prohormone of brain natriuretic peptide; BNP, brain natriuretic peptide; GDF-15, growth differentiation factor-15; CRP, C-reactive protein; TNF-α, tumor necrosis factor-α; IL-6, interleukin-6; MPO, myeloperoxidase; NGAL, neutrophil gelatinase-associated lipocalin. AW201153 Rev.1 43 Adapted from van Kimmenade, Januzzi JL. Clin Chem. 2012;58:127-38. Galectin-3 Mechanism Cardiac Stress Increases Galectin-3 Cardiac Remodeling Left Ventricular Dysfunction Myocardial Fibrosis Gal-3 Gal-3 Gal-3 Cardiac Fibrosis/Remodeling with Collagen Crosslinking Collagen Myofibroblast Differentiation Myofibroblast Activation Conversion of Procollagen to Collagen Spontaneous Aggregation of Collagen Galectin-3 Increases Cardiac Fibrosis and Remodeling AW201153 Rev.1 44 Galectin-3 Is a Marker of NYHA Class Galectin-3 (ng/mL) 100 P < 0.001 80 60 40 20 0 Healthy Control II III IV NYHA Class AW201153 Rev.1 45 Chen K, Jiang RJ, Wang CQ et al. Eur Rev Med Pharmacol Sci. 2013;23:1005-11. Galectin-3 Vs. NT-proBNP 1.0 1.0 0.8 0.8 0.6 0.4 0.4 0.2 0.0 0.0 0.6 0.8 1.0 1-Specificity AW201153 Rev.1 0.6 0.2 0.0 0.2 0.4 46 NT-proBNP ROC Curve Sensitivity Sensitivity Gal-3 ROC Curve 0.0 0.2 0.4 0.6 0.8 1.0 1-Specificity Chen K, Jiang RJ, Wang CQ et al. Eur Rev Med Pharmacol Sci. 2013;23:1005-11. Cumulative Incidence of HF (%) Cumulative Incidence of HF Increases with Higher Galectin-3 Quartiles 10 Quartile 1 Quartile 2 Quartile 3 Quartile 4 8 6 4 2 0 0 2 4 835 842 835 834 811 808 801 789 760 762 755 712 No. at Risk Quartile 1 Quartile 2 Quartile 3 Quartile 4 AW201153 Rev.1 47 6 8 10 747 736 726 662 702 661 647 591 278 235 233 228 Years Ho JE, Liu C, Lyass A et al. J Am Coll Cardiol. 2012;60(14):1249–56. Cumulative Incidence of Death (%) Cumulative Incidence of All-Cause Mortality Increases with Higher Galectin-3 Quartiles 25 Quartile 1 Quartile 2 Quartile 3 Quartile 4 20 15 10 5 0 0 2 4 835 841 842 831 811 809 807 785 772 764 763 714 No. at Risk Quartile 1 Quartile 2 Quartile 3 Quartile 4 AW201153 Rev.1 48 6 8 10 751 743 736 674 707 672 661 609 281 232 238 238 Years Ho JE, Liu C, Lyass A et al. J Am Coll Cardiol. 2012;60(14):1249–56. Association of Galectin-3 with Clinical Characteristics 95% CI ≤ 75th Percentilea >75th Percentileb Difference Lower Upper P-value Age 66 27–99 67 31–94 −1.0 −5.1 7.1 0.856 African–American Male History of HF History of Renal Disease Heart Rate Respiratory Rate Systolic BP BNP BUN Sodium Creatinine Five Day Event Thirty Day Event 59 88 111 27 85 20 147 731 17 139 1.2 3 22 39.10% 58.30% 73.50% 17.90% 49–165 12–90 89–262 9.0–4850.0 4.0–83.0 100.0–146.0 0.5–6.8 2.00% 14.60% 12 30 44 28 86 20 139 1191 35 138 2.4 0 14 24.00% 60.00% 88.00% 56.00% 50–140 12–32 96–221 26.0–5050.0 10.0–105.0 128.0–147.0 0.9–9.6 0.00% 28.00% 15.10% 1.70% 14.50% 38.10% 0.5 0 −8.5 459.5 18 −1.0 1.2 −2.0% 13.40% −0.3 −14.0 1.1 22.8 −5.3 1.8 −19.2 −68.3 8.7 −2.0 0.6 −5.3 1.1 27.6 16.5 24.4 52 6.3 −1.8 2.2 987.3 27.3 0 1.8 5.7 27.9 0.053 0.83 0.024 <0.001 0.533 0.946 0.043 0.025 <0.001 0.135 <0.001 0.422 0.032 a7.3–29.7 ng/ml. ng/ml. BNP, B-type natriuretic peptide; BUN, blood urea nitrogen. b29.9–82.9 AW201153 Rev.1 49 Fermann GJ, Lindsell CJ, Storrow AB et al. Biomarkers. 2012;17:706–13. Potential Uses of Gal-3 as a Biomarker of HF • Quantification of absolute risk in heart failure • There is a strong association of Gal-3 level with renal dysfunction – Different prognostic cut points of Gal-3 should be used for patients with renal disease. • Gal-3 may play an important role in delineating patients who may benefit from therapies versus those who may not – Low levels of Gal-3 may identify a subgroup that benefits from rosuvastatin AW201153 Rev.1 50 Ahmad T, Felker GM. J Am Heart Assoc. 2012;1:e004374. Myocardial Fibrosis Markers in All-Cause Mortality and Cardiovascular Mortality • Another marker of cardiac fibrosis, ST2 is independently associated with all-cause and cardiovascular mortality. • Incorporation of ST2 into a full-adjusted model for all-cause mortality improved discrimination and calibration, and reclassified significantly better. • Incorporation of another myocardial fibrosis marker, Gal-3, showed no significant increase in discrimination or reclassification and worse calibration metrics. AW201153 Rev.1 51 Bayes-Genis A, de Antonio M, Vila J et al. J Am Coll Cardiol. 2014;63:158-66. ST2 Mechanism AW201153 Rev.1 52 Patients with Elevated sST2 Get More Benefit from Intensive Therapy P < 0.001 Mean Cardiovascular Events 2.5 2 P < 0.001 for trend 1.5 P = 0.09 OR 6.0 1 OR 2.5 0.5 OR 1.7 Ref 0 Low sST2/ High sST2/ High sST2/ Low sST2/ High-dose BB Low-dose BB High-dose BB Low-dose BB *BB, beta blocker AW201153 Rev.1 53 Gaggin HK, Motiwala S, Bhardwaj A et al. Circ Heart Fail. 2013;6:1206-13. ST2 is Associated With Long-Term Outcomes • In univariate analysis, ST2 was significantly associated with: – Death or hospitalization (hazard ratio, 1.48; P < 0.0001) – Cardiovascular death or HF hospitalization (hazard ratio, 2.14; P < 0.0001) – All-cause mortality (hazard ratio, 2.33; P < 0.0001) • In multivariate models, ST2 was independently associated with outcomes after adjustments for clinical variables and amino-terminal NT-proBNP. AW201153 Rev.1 54 Felker GM, Fiuzat M, Thompson V et al. Circ Heart Fail. 2013;6:1172-9. Survival in HF with Reduced EF According to sST2 Survival Probability (%) 100 90 80 sST2 < 43.8 ng/mL 70 60 50 sST2 > 43.8 ng/mL 40 30 Log-Rank test: P = 0.0021 20 0 500 1000 1500 2000 2500 3000 Time (Days) AW201153 Rev.1 55 Gruson D, Lepoutre T, Ahn SA, Rousseau MF. Int J Cardiol. 2014. Epub ahead of print. Percentage of CV Death in HF with Reduced EF According to sST2 and BNP Percent Cardiovascular Death 90 80 70 60 50 84% 40 77% 34% 30 sST2 < 43.8 and BNP < 380 AW201153 Rev.1 56 sST2 > 43.8 or BNP > 380 sST2 < 43.8 and BNP < 380 Gruson D, Lepoutre T, Ahn SA, Rousseau MF. Int J Cardiol. 2014. Epub ahead of print. ST2 is Predictive of HF Severity and Risk • Elevated concentrations of sST2 are strongly associated with HF severity. • Predict increased risks of complications AW201153 Rev.1 57 Januzzi JL. J Cardiovasc Trans Res. 2013;6:493–500. The Impact of ST2 in Patient Management • Change in ST2 shows a stronger relationship with outcome than baseline or change in natriuretic peptides (NPs).1 • Increased ST2 = worse short term outcomes1 – Independent of NPs and serial measurements – Adds independent prognostic information in HF • ST2 is a potent marker of risk in HF when used with NT-proBNP.2 – Reclassifies 14.9% into more appropriate risk categories • Among candidate biomarkers, ST2 is among the minority that has data supporting its potential to meaningfully guide therapy.3 14.9% Increase 1Boisot S, Beede J, Isakson S. J Cardiac Fail. 2008;14:732-8. B, French B, McCloskey K et al. Circ Heart Fail. 2011;4;180-7. 3Daniels LB, Clopton P, Iqbal N et al. Am Heart J. 2010;160:721-8. 2Ky AW201153 Rev.1 58 Regression Model Predicting Total CV Events with Three Biomarkers Variable P-Value Traditional clinical and biochemical variables Age 0.98 Male 0.96 Any prior CV events 0.04 Diabetes 0.86 Smoker 0.91 NYHA grade 3 or 4 0.02 NT-proBNP* 0.06 Traditional variables + sST2* < 0.001 Traditional variables + GDF-15* 0.004 Traditional variables + hsTnT* 0.04 Traditional variables + hsTnT+ 0.20 GDF-15 0.02 Traditional variables + hsTnT + 0.35 GDF-15 + 0.05 sST2* < 0.001 *NT-proBNP was scaled by 0.01; sST2 and hsTnT were scaled by 0.1; hsTnT was scaled by 0.001. AW201153 Rev.1 59 Adapted from Gaggin HK, Szymonifka J, Bhardwaj A et al. J Am Coll Cardiol. 2014;2:65-72. Heart Failure Biomarker Attributes BNP1 NTproBNP1 cTnT2,3 Gal-31 ST21 Changes only reflect disease progression No No No Yes Yes No differences with regards to age, gender, body mass index, or other medical conditions No No No Yes Yes Levels decrease in response to successful therapy Yes Yes No Yes Yes Current Standard Current Standard Yes Yes Yes No (113%) No (98%) No (86%) No (63%) Yes (29.8%) Criteria Compliments/exceeds effectiveness of existing tests Low RCV RCV, reference change value. 1Wu 2Frankenstein L, AW201153 Rev.1 60 A. eJIFCC. 2012;23:1-5. Wu AH, Hallermayer K et al. Clin Chem. 2011;57(7):1068-71. Sato Y, Kita T, Takatsu Y, Kimura T. Heart. 2004;90:1110-3. Case Studies Utilizing Biomarkers for Risk Stratification AW201153 Rev.1 61 Case Disclosure The following cases are taken from actual patient cases but to protect confidentiality, identities are not revealed and some information may contain composite information to illustrate medical teaching points. AW201153 Rev.1 62 Case 1: Late to Seek Treatment • 64 year-old obese Hispanic female • Severe dyspnea on exertion, which had progressively worsened over the previous week. • Hypertension, self-treated with "herbal drops," and atrial fibrillation for the previous 10 years – Treated with homeopathic remedies and chiropractic care. • Post-carotid endarterectomy in 1988 AW201153 Rev.1 63 Case 1: Late to Seek Treatment • First-time diagnosis of heart failure – – – – – – – – AW201153 Rev.1 64 Bibasilar crackles, without wheezes or rhonchi Blood pressure 160/90 mmHg Heart rate 70 and irregular Respirations 18/minute Temperature 97ºF Oxygen saturation 98% on room air Heart sounds were normal EKG showed atrial fibrillation without ectopy Case 1: Findings • Treated on admission with digitalis, diltiazem, diuretics, and anticoagulation. • BNP = 150 pg/mL • Echocardiogram revealed left ventricular hypertrophy, mild anterior lateral hypokinesis, mild left atrial enlargement, and aortic sclerosis without stenosis. • Adenosine cardiolyte stress test was negative for ischemia but showed globally decreased left ventricular function. • Resting ejection fraction was estimated at 35%. • Chest x-ray revealed cardiomegaly and mild interstitial edema. AW201153 Rev.1 65 Case 1: Diagnosis and Follow-up • Discharged with a diagnosis of heart failure of idiopathic cause. • Placed on anticoagulation, digitalis, and ACE inhibitor. • Referred to the outpatient heart failure clinic for further follow up and repeat biomarkers to assist in further risk stratification. AW201153 Rev.1 66 Case 1: Questions Raised • Why was the patient so late to receive the diagnosis of heart failure? • Were the biomarkers level reflective of the patient’s course of disease? • Why was the patient discharged? • What might be done in the heart failure outpatient clinic to lower the chance of readmission? AW201153 Rev.1 67 Case 1: Answers Why was the patient so late to receive the diagnosis of heart failure? Many patients do not have access to healthcare and/or do not have health insurance. They do not seek care until they are highly symptomatic. Many do not have primary care providers and their cardiovascular risk factors are not treated early. Thus, they often develop heart failure and are seen and diagnosed during their first admission. AW201153 Rev.1 68 Case 1: Answers Was the natriuretic peptide level reflective of the patient’s course of disease? Natriuretic peptide (NP) levels are lower in people with obesity, in those patients with and without heart failure.1 It has been shown that increases in NP levels from less than to more than the cutpoint were associated with increased risk of events but further increases did not add to risk and only substantial natriuretic peptide decreases (> 80%) reduced mortality risk.2 1Daniels, AW201153 Rev.1 69 LB, Clopton P, Bhalla V et al. Am Heart J. 2006 May;151(5):999-1005. WL, Hartman KA, Grill DE et al. Clin Chem. 2009 Jan;55(1):78-84. 2Miller Case 1: Answers Why was the patient discharged? Once the patient is stabilized, patients are often discharged and have outpatient follow-up at a heart failure clinic where they will receive education, a weight scale, and optimization of outpatient medications. AW201153 Rev.1 70 Case 1: Answers What might be done in the heart failure outpatient clinic to lower the chance of readmission? In addition to what is being done at the outpatient clinic, ST2 levels may be obtained in conjunction with NP levels which may allow better risk stratification and may help monitor progress with treatment. Soluble ST2 values identify those patients with a more remodeled ventricle and decompensated hemodynamic profile1 and may identify HF patients at higher risk of sudden cardiac death.2 1Shah AW201153 Rev.1 71 2Pascual-Figal RV, Chen-Tournoux AA, Picard MH et al. Circ Heart Fail. 2009 Jul;2(4):311-9. DA, Ordoñez-Llanos J, Tornel PL et al. J Am Coll Cardiol. 2009 Dec 1;54(23):2174-9. Case 2: Multiple Infarcts New HF • 53 year-old male with new HF diagnosis of Stage C NYHA Class III – Ischemic heart disease and multiple prior MIs • LVEF is 25% with severe mitral regurgitation (MR) and atrial fibrillation • First office visit – 20 mg QD of lisinopril – 6.25 mg BID of carvedilol – 40 mg QD of furosemide ST2 levels were obtained when a rise in NT-proBNP was noted during an office visit. AW201153 Rev.1 72 Case 2: Multiple Infarcts New HF Biomarker Levels and Therapy in Subsequent Visits β blocker increased 70 Asymptomatic decompensation (increase in loop diuretic) 8000 7000 60 ST2 (ng/mL) 5000 40 4000 30 Spironolactone added 20 3000 2000 ST2 NT-proBNP 10 1000 0 0 1 AW201153 Rev.1 73 NT-proBNP (pg/mL) 6000 50 2 3 4 5 ST2 cutoff (35 ng/mL) NT-proBNP HF target (1000 pg/mL) NT-proBNP cutoff (400 pg/mL) 6 7 Visits 8 9 10 11 12 ST2 is intended to be used with other clinical observations, clinician should not select treatment solely based on ST2 values. Case 2: Multiple Infarcts New HF Therapies and Findings • End of titration – 25 mg BID of carvedilol – 20 mg of lisinopril – 25 mg of spironolactone – 40 mg BID of furosemide • Rise in ST2 was recognized when NT-proBNP was elevated – Paralleled the increase in NT-proBNP – Indicated higher risk than NT-proBNP alone ST2 values were elevated in conjunction with elevated NT-proBNP and dropped in response to HF therapies. AW201153 Rev.1 74 Case 2: Findings NT-proBNP levels rose with asymptomatic decompensation but ST2 levels indicated a higher risk than NT-proBNP alone. AW201153 Rev.1 75 Conclusions AW201153 Rev.1 76 Conclusions • Heart failure is a rapidly increasing and costly cardiovascular disease. AW201153 Rev.1 77 Conclusions • There is both need and urgency to reduce readmissions from heart failure. AW201153 Rev.1 78 Conclusions • Heart failure presents with many other comorbidities and reducing readmissions has proven challenging. AW201153 Rev.1 79 Conclusions • Novel biomarkers for heart failure play an important role in the prognosis and treatment of patients and may have potential to reduce readmission rates. AW201153 Rev.1 80 Thank you for your participation in this activity. You may now download the slides and obtain your CME credit. To obtain CME credit for this activity please do ONE of the following: 1. Take and submit the online CME post-test. Please allow 24 hours for certificate delivery. 2. Download and print the CME application. Fill out the application and answer the post-test questions and fax to 678.401.0259 for credit. Please allow four weeks for certificate delivery. AW201153 Rev.1 81