ArticleJACC. Asia2024
Predicting Mortality and Hospitalization in Heart Failure With Preserved Ejection Fraction by Using Machine Learning.
Article in JACC. Asia, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Prognostic value of peak tricuspid regurgitation velocity in hospitalized patients with heart failure with preserved ejection fraction: an multi-institutional study.Annals of medicine · 2026Article
- Artificial intelligence and emergency physicians in the USA: enhancing clinical excellence while preserving physicians' judgment.World journal of emergency medicine · 2026Article
- Machine learning-based prediction of mortality and hospitalization in diabetic patients with heart failure with preserved ejection fraction: the GUARDIAN-P risk score.European heart journal. Digital health · 2026Article
- Machine Learning Applications for Risk Stratification in Heart Failure with Preserved Ejection Fraction: A New Era in Cardiology.Diagnostics (Basel, Switzerland) · 2026Review
- Machine Learning Models Incorporating Nursing Care Needs to Predict 180-Day Prognosis in Patients With Heart Failure: Validation With Discrimination and Calibration Analyses.Circulation reports · 2026Article
- Interpretative machine learning for predicting 60-day mortality in burn patients with suspected infection.World journal of emergency medicine · 2026Article
- Development and validation of machine learning-based in-hospital mortality predictive models for acute aortic syndrome in emergency departments.World journal of emergency medicine · 2026Article
- Beyond the chain of survival: a scoping review of artificial intelligence applications in cardiac arrest.World journal of emergency medicine · 2026Review
- Predicting heart failure decompensation: focus on non-invasive monitoring.Cardiology journal · 2026Review
- Machine learning-based identification of heart failure with reduced ejection fraction using routine laboratory indicators.Frontiers in cardiovascular medicine · 2026Article
- Article
- Event Prediction in HFpEF Using Machine Learning: Will This Promising Model Be Applied in Practice?JACC. Asia · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Few studies have incorporated echocardiography and laboratory data to predict clinical outcomes in heart failure with preserved ejection fraction (HFpEF). Objectives: This study aimed to use machine learning to find predictors of heart failure (HF) hospitalization and cardiovascular (CV) death in HFpEF. Methods: From the Chang Gung Research Database in Taiwan, 6,092 HFpEF patients (2,898 derivation, 3,194 validation) identified between 2008 and 2017 were followed until 2019. A random survival forest model, using 58 variables, was developed to predict the composite outcome of HF hospitalization and CV death. Results: During 2.9-year follow-up, 37.7% of derivation and 36.0% of validation cohort patients experienced HF hospitalization or CV death. The study identified 15 predictive indicators, including age ≥65 years, B-type natriuretic peptide level ≥600 pg/mL, left atrium size ≥46 mm, atrial fibrillation, frequency of HF hospitalization within 3 years, body mass index <30 kg/m Conclusions: Machine learning identified 15 predictors of HF hospitalization and CV death in HFpEF patients, helping doctors identify high-risk individuals for tailored treatment.
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