{"doi":"10.1002/ejhf.2528","title":"Improving clinical trial efficiency using a machine learning‐based risk score to enrich study populations","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.","journal":"European Journal of Heart Failure","year":2022,"id":252579,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":23,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9585,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":694048,"name":"C. Campagnari","orcid":"0000-0002-8978-8177","position":1,"is_corresponding":false},{"id":28286,"name":"Brian Claggett","orcid":"0000-0002-4215-9218","position":2,"is_corresponding":false},{"id":367214,"name":"Eric Adler","orcid":"0000-0002-4765-0188","position":3,"is_corresponding":false},{"id":63394,"name":"LIVIU KLEIN","orcid":"0000-0002-0547-1480","position":4,"is_corresponding":false},{"id":335339,"name":"Faraz S. Ahmad","orcid":"0000-0002-2613-2541","position":5,"is_corresponding":false},{"id":2632,"name":"Adriaan A. Voors","orcid":"0000-0002-5417-4415","position":6,"is_corresponding":false},{"id":27970,"name":"Scott D. Solomon","orcid":"0000-0003-3698-9597","position":7,"is_corresponding":false},{"id":694598,"name":"Avi Yagil","orcid":null,"position":8,"is_corresponding":false},{"id":361828,"name":"Barry Greenberg","orcid":"0000-0002-6605-9385","position":9,"is_corresponding":false},{"id":260469,"name":"Karola Jering","orcid":"0000-0002-5243-1959","position":0,"is_corresponding":true}],"reference_count":31,"raw_metadata":null,"created_at":"2026-07-19T00:24:50.819822Z","pmid":"35508918","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}