ArticleEBioMedicine2023
Treatment response to spironolactone in patients with heart failure with preserved ejection fraction: a machine learning-based analysis of two randomized controlled trials.
Article in EBioMedicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Prognostic value of anion gap for patients with heart failure: a systematic review and meta-analysis.BMC cardiovascular disorders · 2024Pooled it
- Heterogeneous Treatment Effects in HFpEF: Distinguishing Drug-Specific Response from Prognostic Phenotypes Across Randomized Trials.medRxiv : the preprint server for health sciences · 2026Article
- Heart Failure with Preserved Ejection Fraction in Women: Sex-Specific Insights Across the Continuum from Diagnosis to Outcomes.Current cardiology reports · 2026Review
- Review
- Artificial Intelligence in Heart Failure with Preserved Ejection Fraction.Diagnostics (Basel, Switzerland) · 2026Review
- Magnesium-assisted hydrogen improves isoproterenol-induced heart failure.Medical gas research · 2025Article
- AI Tools for Heart Failure Management: A Comprehensive Review of Potential, Pitfalls, and Predictive Analytics.Cureus · 2025Review
- Artificial intelligence in cardiovascular pharmacotherapy: applications and perspectives.European heart journal · 2025Review
- Review
- Association of body roundness index with cardiovascular disease in patients with cardiometabolic syndrome: a cross-sectional study based on NHANES 2009-2018.Frontiers in endocrinology · 2025Review
- A study on the methylation patterns ofHeliyon · 2024Article
- Single-cell transcriptomics reveals writers of RNA modification-mediated immune microenvironment and cardiac resident Macro-MYL2 macrophages in heart failure.BMC cardiovascular disorders · 2024Article
- Drug Therapy for Acute and Chronic Heart Failure with Preserved Ejection Fraction with Hypertension: A State-of-the-Art Review.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2024Review
- Role of Artificial Intelligence and Machine Learning to Create Predictors, Enhance Molecular Understanding, and Implement Purposeful Programs for Myocardial Recovery.Methodist DeBakey cardiovascular journal · 2024Review
- Comparison of Metoprolol and Sacubitril/Valsartan in Chinese Patients With Heart Failure: A Retrospective Cohort Study.Dose-response : a publication of International Hormesis SocietyArticle
- J-Shaped Association Between Respiratory Rate and In-Hospital Mortality in Acute Myocardial Infarction Patients Complicated by Congestive Heart Failure in Intensive Care Unit.Dose-response : a publication of International Hormesis SocietyArticle
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWhether there is a subset of patients with heart failure with preserved ejection fraction (HFpEF) that benefit from spironolactone therapy is unclear. We applied a machine learning approach to identify responders and non-responders to spironolactone among patients with HFpEF in two large randomized clinical trials.
methodsUsing a reiterative cluster allocating permutation approach, patients from the derivation cohort (Aldo-DHF) were identified according to their treatment response to spironolactone with respect to improvement in E/e'. Heterogenous features of response ('responders' and 'non-responders') were characterized by an extreme gradient boosting (XGBoost) algorithm. XGBoost was used to predict treatment response in the validation cohort (TOPCAT). The primary endpoint of the validation cohort was a combined endpoint of cardiovascular mortality, aborted cardiac arrest, or heart failure hospitalization. Patients with missing variables for the XGboost model were excluded from the validation analysis.
findingsOut of 422 patients from the derivation cohort, reiterative cluster allocating permutation identified 159 patients (38%) as spironolactone responders, in whom E/e' significantly improved (p = 0.005). Within the validation cohort (n = 525) spironolactone treatment significantly reduced the occurrence of the primary outcome among responders (n = 185, p log rank = 0.008), but not among patients in the non-responder group (n = 340, p log rank = 0.52).
interpretationMachine learning approaches might aid in identifying HFpEF patients who are likely to show a favorable therapeutic response to spironolactone.
fundingSee Acknowledgements section at the end of the manuscript.
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