Skip to main content

Advertisement

Log in

Redefining the role of biomarkers in heart failure trials: expert consensus document

  • Published:
Heart Failure Reviews Aims and scope Submit manuscript

Abstract

Heart failure is a growing cardiovascular disease with significant epidemiological, clinical, and societal implications and represents a high unmet need. Strong efforts are currently underway by academic and industrial researchers to develop novel treatments for heart failure. Biomarkers play an important role in patient selection and monitoring in drug trials and in clinical management. The present review gives an overview of the role of available molecular, imaging, and device-derived digital biomarkers in heart failure drug development and highlights capabilities and limitations of biomarker use in this context.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1

Similar content being viewed by others

References

  1. FDA-NIH Biomarker Working Group (2016) BEST (Biomarkers, EndpointS, and other Tools) Resource. 2016

  2. Januzzi JL Jr, Felker GM (2013) Surfing the biomarker tsunami at JACC: heart failure. JACC Heart Fail 1(3):213–215

    Article  PubMed  Google Scholar 

  3. Spinale FG (2007) Myocardial matrix remodeling and the matrix metalloproteinases: influence on cardiac form and function. Physiol Rev 87(4):1285–1342

    Article  CAS  PubMed  Google Scholar 

  4. Kong P, Christia P, Frangogiannis NG (2014) The pathogenesis of cardiac fibrosis. Cell Mol Life Sci 71(4):549–574

    Article  CAS  PubMed  Google Scholar 

  5. Cohn JN (1995) Structural basis for heart failure. Ventricular remodeling and its pharmacological inhibition. Circulation 91(10):2504–2507

    Article  CAS  PubMed  Google Scholar 

  6. Curigliano G et al (2012) Cardiovascular toxicity induced by chemotherapy, targeted agents and radiotherapy: ESMO Clinical Practice Guidelines. Ann Oncol 23(Suppl 7):vii155–vii166

    Article  PubMed  Google Scholar 

  7. Dibbs Z et al (1999) Cytokines in heart failure: pathogenetic mechanisms and potential treatment. Proc Assoc Am Physicians 111(5):423–428

    CAS  PubMed  Google Scholar 

  8. Weber KT, Brilla CG (1991) Pathological hypertrophy and cardiac interstitium. Fibrosis and renin-angiotensin-aldosterone system. Circulation 83(6):1849–1865

    Article  CAS  PubMed  Google Scholar 

  9. Olivetti G et al (1997) Apoptosis in the failing human heart. N Engl J Med 336(16):1131–1141

    Article  CAS  PubMed  Google Scholar 

  10. Guerra S et al (1999) Myocyte death in the failing human heart is gender dependent. Circ Res 85(9):856–866

    Article  CAS  PubMed  Google Scholar 

  11. Heger J, Schulz R, Euler G (2016) Molecular switches under TGFbeta signalling during progression from cardiac hypertrophy to heart failure. Br J Pharmacol 173(1):3–14

    Article  CAS  PubMed  Google Scholar 

  12. Prabhu SD, Frangogiannis NG (2016) The biological basis for cardiac repair after myocardial infarction: from inflammation to fibrosis. Circ Res 119(1):91–112

    Article  CAS  PubMed  Google Scholar 

  13. Terentyev D, Hamilton S (2016) Regulation of sarcoplasmic reticulum Ca2+ release by serine-threonine phosphatases in the heart. J Mol Cell Cardiol 101:156–164

    Article  CAS  PubMed  Google Scholar 

  14. Braunwald E (2013) Heart failure. JACC Heart Fail 1(1):1–20

    Article  PubMed  Google Scholar 

  15. Brown, D.A., et al. (2016) Expert consensus document: mitochondrial function as a therapeutic target in heart failure. Nat Rev Cardiol

  16. Mullens W et al (2009) Importance of venous congestion for worsening of renal function in advanced decompensated heart failure. J Am Coll Cardiol 53(7):589–596

    Article  PubMed  PubMed Central  Google Scholar 

  17. Floras JS, Ponikowski P (2015) The sympathetic/parasympathetic imbalance in heart failure with reduced ejection fraction. Eur Heart J 36(30):1974–182b

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Dunlap ME, Bhardwaj A, Hauptman PJ (2015) Autonomic modulation in heart failure: ready for prime time? Curr Cardiol Rep 17(11):103

    Article  PubMed  Google Scholar 

  19. Steinberg BA et al (2012) Trends in patients hospitalized with heart failure and preserved left ventricular ejection fraction: prevalence, therapies, and outcomes. Circulation 126(1):65–75

    Article  PubMed  Google Scholar 

  20. Owan TE et al (2006) Trends in prevalence and outcome of heart failure with preserved ejection fraction. N Engl J Med 355(3):251–259

    Article  CAS  PubMed  Google Scholar 

  21. Lee DS et al (2009) Relation of disease pathogenesis and risk factors to heart failure with preserved or reduced ejection fraction: insights from the Framingham Heart Study of the National Heart, Lung, and Blood Institute. Circulation 119(24):3070–3077

    Article  PubMed  PubMed Central  Google Scholar 

  22. Gheorghiade M et al (2013) Rehospitalization for heart failure: problems and perspectives. J Am Coll Cardiol 61(4):391–403

    Article  PubMed  Google Scholar 

  23. Jacobs L et al (2014) Heart ‘omics’ in AGEing (HOMAGE): design, research objectives and characteristics of the common database. J Biomed Res 28(5):349–359

    PubMed  PubMed Central  Google Scholar 

  24. Schelbert, E.B., et al (2015) Myocardial fibrosis quantified by extracellular volume is associated with subsequent hospitalization for heart failure, death, or both across the spectrum of ejection fraction and heart failure stage. J Am Heart Assoc. 4(12)

  25. Clark GM (2008) Prognostic factors versus predictive factors: examples from a clinical trial of erlotinib. Mol Oncol 1(4):406–412

    Article  PubMed  Google Scholar 

  26. Desai AS (2013) Are serial BNP measurements useful in heart failure management? Serial natriuretic peptide measurements are not useful in heart failure management: the art of medicine remains long. Circulation 127(4):509–516 discussion 516

    Article  PubMed  Google Scholar 

  27. Gaggin HK et al (2013) Soluble concentrations of the interleukin receptor family member ST2 and beta-blocker therapy in chronic heart failure. Circ Heart Fail 6(6):1206–1213

    Article  CAS  PubMed  Google Scholar 

  28. Rich S, McLaughlin VV (2003) Endothelin receptor blockers in cardiovascular disease. Circulation 108(18):2184–2190

    Article  CAS  PubMed  Google Scholar 

  29. Reinstadler SJ et al (2015) Copeptin testing in acute myocardial infarction: ready for routine use? Dis Markers 2015:614145

    PubMed  PubMed Central  Google Scholar 

  30. Cotter G et al (2015) Growth differentiation factor 15 (GDF-15) in patients admitted for acute heart failure: results from the RELAX-AHF study. Eur J Heart Fail 17(11):1133–1143

    Article  CAS  PubMed  Google Scholar 

  31. Samuel CS et al (2014) Serelaxin is a more efficacious antifibrotic than enalapril in an experimental model of heart disease. Hypertension 64(2):315–322

    Article  CAS  PubMed  Google Scholar 

  32. Yeh ET et al (2004) Cardiovascular complications of cancer therapy: diagnosis, pathogenesis, and management. Circulation 109(25):3122–3131

    Article  PubMed  Google Scholar 

  33. Sawaya H et al (2011) Early detection and prediction of cardiotoxicity in chemotherapy-treated patients. Am J Cardiol 107(9):1375–1380

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Jansweijer, J.A., et al. (2016) Truncating titin mutations are associated with a mild and treatable form of dilated cardiomyopathy. Eur J Heart Fail

  35. Teo LY, Moran RT, Tang WH (2015) Evolving approaches to genetic evaluation of specific cardiomyopathies. Curr Heart Fail Rep 12(6):339–349

    Article  PubMed  Google Scholar 

  36. van Spaendonck-Zwarts KY et al (2013) Genetic analysis in 418 index patients with idiopathic dilated cardiomyopathy: overview of 10 years’ experience. Eur J Heart Fail 15(6):628–636

    Article  PubMed  Google Scholar 

  37. Mullard A (2012) Cholesterol-lowering blockbuster candidates speed into phase III trials. Nat Rev Drug Discov 11(11):817–819

    Article  PubMed  Google Scholar 

  38. Scandinavian Simvastatin Survival Study Group (1994) Randomised trial of cholesterol lowering in 4444 patients with coronary heart disease: the Scandinavian Simvastatin Survival Study (4S). Lancet 344(8934):1383–1389

    Google Scholar 

  39. Milting H et al (2015) The TMEM43 Newfoundland mutation p.S358L causing ARVC-5 was imported from Europe and increases the stiffness of the cell nucleus. Eur Heart J 36(14):872–881

    Article  PubMed  Google Scholar 

  40. Hodgkinson KA et al (2013) The natural history of a genetic subtype of arrhythmogenic right ventricular cardiomyopathy caused by a p.S358L mutation in TMEM43. Clin Genet 83(4):321–331

    Article  CAS  PubMed  Google Scholar 

  41. Lee JY et al (2013) A genome-wide association study of a coronary artery disease risk variant. J Hum Genet 58(3):120–126

    Article  CAS  PubMed  Google Scholar 

  42. van der Zwaag PA et al (2012) Phospholamban R14del mutation in patients diagnosed with dilated cardiomyopathy or arrhythmogenic right ventricular cardiomyopathy: evidence supporting the concept of arrhythmogenic cardiomyopathy. Eur J Heart Fail 14(11):1199–1207

    Article  PubMed  PubMed Central  Google Scholar 

  43. van Rijsingen IA et al (2014) Outcome in phospholamban R14del carriers: results of a large multicentre cohort study. Circ Cardiovasc Genet 7(4):455–465

    Article  PubMed  Google Scholar 

  44. Cao C, Moult J (2014) GWAS and drug targets. BMC Genomics 15(Suppl 4):S5

    Article  PubMed  PubMed Central  Google Scholar 

  45. Kraigher-Krainer E et al (2014) Impaired systolic function by strain imaging in heart failure with preserved ejection fraction. J Am Coll Cardiol 63(5):447–456

    Article  PubMed  Google Scholar 

  46. Kim RJ et al (2000) The use of contrast-enhanced magnetic resonance imaging to identify reversible myocardial dysfunction. N Engl J Med 343(20):1445–1453

    Article  CAS  PubMed  Google Scholar 

  47. Schelbert EB, Messroghli DR (2016) State of the art: clinical applications of cardiac T1 mapping. Radiology 278(3):658–676

    Article  PubMed  Google Scholar 

  48. Sado DM et al (2015) Noncontrast myocardial T1 mapping using cardiovascular magnetic resonance for iron overload. J Magn Reson Imaging 41(6):1505–1511

    Article  PubMed  Google Scholar 

  49. Thompson RB et al (2013) T(1) mapping with cardiovascular MRI is highly sensitive for Fabry disease independent of hypertrophy and sex. Circ Cardiovasc Imaging 6(5):637–645

    Article  PubMed  Google Scholar 

  50. Banypersad SM et al (2015) T1 mapping and survival in systemic light-chain amyloidosis. Eur Heart J 36(4):244–251

    Article  PubMed  Google Scholar 

  51. Miller CA et al (2013) Comprehensive validation of cardiovascular magnetic resonance techniques for the assessment of myocardial extracellular volume. Circ Cardiovasc Imaging 6(3):373–383

    Article  PubMed  Google Scholar 

  52. White SK et al (2013) T1 mapping for myocardial extracellular volume measurement by CMR: bolus only versus primed infusion technique. JACC Cardiovasc Imaging 6(9):955–962

    Article  PubMed  Google Scholar 

  53. Fontana M et al (2012) Comparison of T1 mapping techniques for ECV quantification. Histological validation and reproducibility of ShMOLLI versus multibreath-hold T1 quantification equilibrium contrast CMR. J Cardiovasc Magn Reson 14:88

    Article  PubMed  PubMed Central  Google Scholar 

  54. de Meester de Ravenstein C et al (2015) Histological validation of measurement of diffuse interstitial myocardial fibrosis by myocardial extravascular volume fraction from Modified Look-Locker imaging (MOLLI) T1 mapping at 3 T. J Cardiovasc Magn Reson 17:48

    Article  PubMed  PubMed Central  Google Scholar 

  55. Zeng M et al (2016) Histological validation of cardiac magnetic resonance T1 mapping for detecting diffuse myocardial fibrosis in diabetic rabbits. J Magn Reson Imaging 44(5):1179–1185

    Article  PubMed  Google Scholar 

  56. Inui K et al (2016) Superiority of the extracellular volume fraction over the myocardial T1 value for the assessment of myocardial fibrosis in patients with non-ischemic cardiomyopathy. Magn Reson Imaging 34(8):1141–1145

    Article  PubMed  Google Scholar 

  57. McDiarmid AK et al (2015) Single bolus versus split dose gadolinium administration in extra-cellular volume calculation at 3 Tesla. J Cardiovasc Magn Reson 17(1):6

    Article  PubMed  PubMed Central  Google Scholar 

  58. Schelbert EB et al (2011) Myocardial extravascular extracellular volume fraction measurement by gadolinium cardiovascular magnetic resonance in humans: slow infusion versus bolus. J Cardiovasc Magn Reson 13:16

    Article  PubMed  PubMed Central  Google Scholar 

  59. Kawel N et al (2012) T1 mapping of the myocardium: intra-individual assessment of post-contrast T1 time evolution and extracellular volume fraction at 3T for Gd-DTPA and Gd-BOPTA. J Cardiovasc Magn Reson 14:26

    Article  PubMed  PubMed Central  Google Scholar 

  60. Chin CW et al (2014) Optimization and comparison of myocardial T1 techniques at 3T in patients with aortic stenosis. Eur Heart J Cardiovasc Imaging 15(5):556–565

    Article  PubMed  Google Scholar 

  61. Singh A et al (2015) Myocardial T1 and extracellular volume fraction measurement in asymptomatic patients with aortic stenosis: reproducibility and comparison with age-matched controls. Eur Heart J Cardiovasc Imaging 16(7):763–770

    Article  PubMed  Google Scholar 

  62. Liu S et al (2012) Diffuse myocardial fibrosis evaluation using cardiac magnetic resonance T1 mapping: sample size considerations for clinical trials. J Cardiovasc Magn Reson 14:90

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Mohammed SF et al (2015) Coronary microvascular rarefaction and myocardial fibrosis in heart failure with preserved ejection fraction. Circulation 131(6):550–559

    Article  PubMed  Google Scholar 

  64. Schwartzkopff B et al (2000) Repair of coronary arterioles after treatment with perindopril in hypertensive heart disease. Hypertension 36(2):220–225

    Article  CAS  PubMed  Google Scholar 

  65. Kato, S., et al. (2016) Impairment of coronary flow reserve evaluated by phase contrast cine-magnetic resonance imaging in patients with heart failure with preserved ejection fraction. J Am Heart Assoc. 5(2)

  66. Rommel KP et al (2016) Extracellular volume fraction for characterization of patients with heart failure and preserved ejection fraction. J Am Coll Cardiol 67(15):1815–1825

    Article  PubMed  Google Scholar 

  67. Zile MR et al (2015) Myocardial stiffness in patients with heart failure and a preserved ejection fraction: contributions of collagen and titin. Circulation 131(14):1247–1259

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Brilla CG, Funck RC, Rupp H (2000) Lisinopril-mediated regression of myocardial fibrosis in patients with hypertensive heart disease. Circulation 102(12):1388–1393

    Article  CAS  PubMed  Google Scholar 

  69. Diez J et al (2002) Losartan-dependent regression of myocardial fibrosis is associated with reduction of left ventricular chamber stiffness in hypertensive patients. Circulation 105(21):2512–2517

    Article  CAS  PubMed  Google Scholar 

  70. Tamarappoo BK et al (2012) Vulnerable myocardial interstitium in patients with isolated left ventricular hypertrophy and sudden cardiac death: a postmortem histological evaluation. J Am Heart Assoc 1(3):e001511

    Article  PubMed  PubMed Central  Google Scholar 

  71. Schelbert EB et al (2014) Therapeutic targets in heart failure: refocusing on the myocardial interstitium. J Am Coll Cardiol 63(21):2188–2198

    Article  PubMed  Google Scholar 

  72. Wong TC et al (2014) Myocardial extracellular volume fraction quantified by cardiovascular magnetic resonance is increased in diabetes and associated with mortality and incident heart failure admission. Eur Heart J 35(10):657–664

    Article  CAS  PubMed  Google Scholar 

  73. Izawa H et al (2005) Mineralocorticoid receptor antagonism ameliorates left ventricular diastolic dysfunction and myocardial fibrosis in mildly symptomatic patients with idiopathic dilated cardiomyopathy: a pilot study. Circulation 112(19):2940–2945

    CAS  PubMed  Google Scholar 

  74. Sharma V et al (2015) Stratifying patients at the risk of heart failure hospitalization using existing device diagnostic thresholds. Heart Lung 44(2):129–136

    Article  PubMed  Google Scholar 

  75. Whellan DJ et al (2010) Combined heart failure device diagnostics identify patients at higher risk of subsequent heart failure hospitalizations: results from PARTNERS HF (Program to Access and Review Trending Information and Evaluate Correlation to Symptoms in Patients With Heart Failure) study. J Am Coll Cardiol 55(17):1803–1810

    Article  PubMed  Google Scholar 

  76. Small RS et al (2014) Implantable device diagnostics on day of discharge identify heart failure patients at increased risk for early readmission for heart failure. Eur J Heart Fail 16(4):419–425

    Article  PubMed  Google Scholar 

  77. Charitos EI et al (2014) How often should we monitor for reliable detection of atrial fibrillation recurrence? Efficiency considerations and implications for study design. PLoS One 9(2):e89022

    Article  PubMed  PubMed Central  Google Scholar 

  78. Whellan DJ, Adams S, Bowerman L (2011) Review of advanced heart failure device diagnostics examined in clinical trials and the potential benefit from monitoring capabilities. Prog Cardiovasc Dis 54(2):107–114

    Article  PubMed  Google Scholar 

  79. Bryan J, Dudoit S, Fridlyand J, Goldstein, D, Keles, S, Pollard, K (2010) Report on the statistical genomics in Biomedical Research Workshop. , Banff International Research Station for Mathematical Innovation and Discovery: https://www.birs.ca/workshops/2010/10w5076/report10w5076.pdf

  80. Scruggs SB et al (2015) Harnessing the heart of big data. Circ Res 116(7):1115–1119

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Dupuy A, Simon RM (2007) Critical review of published microarray studies for cancer outcome and guidelines on statistical analysis and reporting. J Natl Cancer Inst 99(2):147–157

    Article  PubMed  Google Scholar 

  82. Shah SJ et al (2015) Phenomapping for novel classification of heart failure with preserved ejection fraction. Circulation 131(3):269–279

    Article  PubMed  Google Scholar 

  83. Agueusop, I. and R. Vonk (2011) Biomarker based patient selection - Effects on drug development, in ISCB: Ottawa, CAN

  84. Writing Committee M et al (2013) 2013 ACCF/AHA guideline for the management of heart failure: a report of the American College of Cardiology Foundation/American Heart Association Task Force on practice guidelines. Circulation 128(16):e240–e327

    Article  Google Scholar 

  85. Erkilet G et al (2013) The biomarker plasma galectin-3 in advanced heart failure and survival with mechanical circulatory support devices. J Heart Lung Transplant 32(2):221–230

    Article  PubMed  Google Scholar 

  86. Pascual-Figal DA et al (2016) Clinical relevance of sST2 in cardiac diseases. Clin Chem Lab Med 54(1):29–35

    Article  CAS  PubMed  Google Scholar 

  87. Wollert KC, Kempf T, Wallentin L (2017) Growth differentiation factor 15 as a biomarker in cardiovascular disease. Clin Chem 63(1):140–151

    Article  PubMed  Google Scholar 

  88. Gandhi PU et al (2016) Insulin-like growth factor-binding protein-7 as a biomarker of diastolic dysfunction and functional capacity in heart failure with preserved ejection fraction: results from the RELAX trial. JACC Heart Fail 4(11):860–869

    Article  PubMed  Google Scholar 

  89. Barroso MC et al (2016) Serum insulin-like growth factor-1 and its binding protein-7: potential novel biomarkers for heart failure with preserved ejection fraction. BMC Cardiovasc Disord 16(1):199

    Article  PubMed  PubMed Central  Google Scholar 

  90. Kramer F, Dinh W (2016) Molecular and digital biomarker supported decision making in clinical studies in cardiovascular indications. Arch Pharm (Weinheim) 349(6):399–409

    Article  CAS  Google Scholar 

  91. Ibrahim, N.E., et al. (2016) Established and emerging roles of biomarkers in heart failure clinical trials. Circ Heart Fail. 9(9)

  92. Gheorghiade M et al (2015) Effect of vericiguat, a soluble guanylate cyclase stimulator, on natriuretic peptide levels in patients with worsening chronic heart failure and reduced ejection fraction: the SOCRATES-REDUCED randomized trial. JAMA 314(21):2251–2262

    Article  CAS  PubMed  Google Scholar 

  93. Claggett B et al (2015) Estimating the long-term treatment benefits of sacubitril-valsartan. N Engl J Med 373(23):2289–2290

    Article  PubMed  Google Scholar 

  94. Pitt B et al (2015) Rationale and design of MinerAlocorticoid Receptor antagonist Tolerability Study-Heart Failure (ARTS-HF): a randomized study of finerenone vs. eplerenone in patients who have worsening chronic heart failure with diabetes and/or chronic kidney disease. Eur J Heart Fail 17(2):224–232

    Article  CAS  PubMed  Google Scholar 

  95. McMurray JJ et al (2014) Angiotensin-neprilysin inhibition versus enalapril in heart failure. N Engl J Med 371(11):993–1004

    Article  PubMed  Google Scholar 

  96. Mebazaa A et al (2007) Levosimendan vs dobutamine for patients with acute decompensated heart failure: the SURVIVE randomized trial. JAMA 297(17):1883–1891

    Article  CAS  PubMed  Google Scholar 

  97. Clinical Trials.gov, https://clinicaltrials.gov/ct2/show/NCT02040233

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Frank Kramer.

Ethics declarations

Conflict of interest

F.K., W.D., and R.V. are full-time employees of BAYER AG.

H.N.S. has no conflicts to declare.

J.J. received grant support from Siemens, Singulex, and Prevencio and consulting income from Roche Diagnostics, Critical Diagnostics, Sphingotec, Bayer, Philips, and Novartis and participates in clinical endpoint committees/data safety monitoring boards for Novartis, Amgen, Janssen, and Boehringer Ingelheim and is supported in part by the Hutter Family Professorship Endowment in Cardiology at the Harvard Medical School.

F.Z. reports personal fees from Janssen, Bayer, Pfizer, Novartis, Boston Scientific, Resmed, Amgen, CVRx, Quantum Genomics, Eli Lilly, Takeda, and General Electric, outside the submitted work.

J.P.vT. has no conflicts to declare.

E.B.S. has no conflicts to declare.

H.M. supported by the Deutsche Forschungsgemeinschaft (DFG), the Deutsches Zentrum für Herz-Kreislauferkrankungen (DZHK), and the Erich & Hanna Klessmann-Foundation, Germany, and received fees and research funding from the Bayer AG, Germany.

J.S. and R.C. are full-time employees of MEDTRONIC Inc.

K.W. has no conflicts to declare.

B.P. received consulting income from Bayer Healthcare, Stealth Peptides, Novartis, Astra Zeneca, Daiichi Sankyo, and Vifor Pharma.

J.B. received research support from the National Institutes of Health, European Union, and Patient Centered Outcomes Research Institute and is a consultant to Amgen, Astra-Zeneca, Bayer, Boehringer Ingelheim, BMS, CVRx, Janssen, Medtronic, Novartis, Relypsa, and ZS Pharma.

M.G. received grant support from AbbVie Inc., AstraZeneca, Bayer Pharma AG, Cardiocell LLC, Cardiorentis Ltd., GlaxoSmithKline, Johnson & Johnson, Medtronic, Merck, Novartis Pharma AG, Ono Pharmaceuticals USA, Otsuka Pharmaceuticals, Sanofi-Aventis, Sigma Tau, Solvay Pharmaceuticals, Stealth BioTherapeutics, Sticares InterACT, and Takeda Pharmaceuticals North America Inc.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kramer, F., Sabbah, H.N., Januzzi, J.J. et al. Redefining the role of biomarkers in heart failure trials: expert consensus document. Heart Fail Rev 22, 263–277 (2017). https://doi.org/10.1007/s10741-017-9608-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10741-017-9608-5

Keywords

Navigation