Abstract
Aims: Prognostic enrichment strategies can make trials more efficient, although potentially at the cost of diminishing external validity. Whether using a risk score to identify a population at increased mortality risk could improve trial efficiency is uncertain. We aimed to assess whether Machine learning Assessment of RisK and EaRly mortality in Heart Failure (MARKER-HF), a previously validated risk score, could improve clinical trial efficiency. Methods and results: Mortality rates and association of MARKER-HF with all-cause death by 1 year were evaluated in four community-based heart failure (HF) and five HF clinical trial cohorts. Sample size required to assess effects of an investigational therapy on mortality was calculated assuming varying underlying MARKER-HF risk and proposed treatment effect profiles. Patients from community-based HF cohorts (n = 11 297) had higher observed mortality and MARKER-HF scores than did clinical trial patients (n = 13 165) with HF with either reduced ejection fraction (HFrEF) or preserved ejection fraction (HFpEF). MARKER-HF score was strongly associated with risk of 1-year mortality both in the community (hazard ratio [HR] 1.48, 95% confidence interval [CI] 1.44–1.52) and clinical trial cohorts with HFrEF (HR 1.41, 95% CI 1.30–1.54), and HFpEF (HR 1.74, 95% CI 1.53–1.98), per 0.1 increase in MARKER-HF. Using MARKER-HF to identify patients for a hypothetical clinical trial assessing mortality reduction with an intervention, enabled a reduction in sample size required to show benefit. Conclusion: Using a reliable predictor of mortality such as MARKER-HF to enrich clinical trial populations provides a potential strategy to improve efficiency by requiring a smaller sample size to demonstrate a clinical benefit.
Original language | English (US) |
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Pages (from-to) | 1418-1426 |
Number of pages | 9 |
Journal | European Journal of Heart Failure |
Volume | 24 |
Issue number | 8 |
DOIs | |
State | Published - Aug 2022 |
Funding
K.S.J. is supported by the National Institutes of Health (Training Grant 5‐ T32 HL007604). B.C. reports consulting fees from Amgen, Boehringer Ingelheim, Cardurion, Corvia, Myokardia, Novartis. E.A. reports consulting fees from Abiomed, Novartis, Abbott, AstraZeneca, Ionis, Sana Biotechnology, Medtronic, Rocket, Papillon Therapeutics, Res Pharmaceuticals, Lexio Pharmaceuticals, Cytokinetics. A.A.V. reports consulting fees from Amgen, AstraZeneca, Bayer AG, Boehringer Ingelheim, Cytokinetics, MyoKardia, Novo Nordisk, Novartis, Roche Diagnostics, Servier, and Vifor Pharma. S.S. reports research grants from Actelion, Alnylam, Amgen, AstraZeneca, Bellerophon, Bayer, BMS, Celladon, Cytokinetics, Eidos, Gilead, GSK, Ionis, Lilly, Mesoblast, MyoKardia, NIH/NHLBI, Neurotronik, Novartis, NovoNordisk, Respicardia, Sanofi Pasteur, Theracos, and has consulted for Abbott, Action, Akros, Alnylam, Amgen, Arena, AstraZeneca, Bayer, Boehringer Ingelheim, BMS, Cardior, Cardurion, Corvia, Cytokinetics, Daiichi‐Sankyo, GSK, Lilly, Merck, Myokardia, Novartis, Roche, Theracos, Quantum Genomics, Cardurion, Janssen, Cardiac Dimensions, Tenaya, Sanofi‐Pasteur, Dinaqor, Tremeau, CellProThera, Moderna, American Regent, Sarepta. B.G. reports consulting fees from ACI, Actelion, Axon, AstraZeneca, Boehringer Ingelheim, Cytokinetics, EBR Systems, Impulse Dynamics, EBR Systems, Ionis, Jaan, Moderna, Merck, Vifor, Viking, Windtree. All other authors have nothing to disclose. Conflict of interest:
Keywords
- Clinical trial efficiency
- Heart failure
- Machine learning
- Prognostic enrichment
- Risk scores
- Trial enrolment strategies
ASJC Scopus subject areas
- Cardiology and Cardiovascular Medicine