ArticleJACC. Asia2022
Machine Learning Risk Prediction for Incident Heart Failure in Patients With Atrial Fibrillation.
Article in JACC. Asia, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 26 citations in OpenAlex.
- Pooled it
- Machine-learning versus traditional approaches for atherosclerotic cardiovascular risk prognostication in primary prevention cohorts: a systematic review and meta-analysis.European heart journal. Quality of care & clinical outcomes · 2023Pooled it
- Big Data and Trustworthy AI for Heart Failure: A Review.Circulation. Heart failure · 2026Review
- Early warning and stratification of the elderly cardiopulmonary dysfunction-related diseases: multicentre prospective study protocol.BMJ open · 2025Article
- Risk stratification of major arrhythmia events in Japanese patients with Brugada syndrome using machine learning models.Heart rhythm O2 · 2025Article
- Polygenic risk-based prediction of heart failure in young patients with atrial fibrillation: an analysis from UK Biobank.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Machine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis.European heart journal. Digital health · 2025Article
- Machine learning and deep learning predictive models for prognosis in patients with atrial fibrillation: a systematic review and meta-analysis.BMJ digital health & AI · 2025Article
- Improving cardiovascular risk prediction with machine learning: a focus on perivascular adipose tissue characteristics.Biomedical engineering online · 2024Article
- Artificial Intelligence Predicts Hospitalization for Acute Heart Failure Exacerbation in Patients Undergoing Myocardial Perfusion Imaging.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Association of left atrial enlargement with heart failure events in non-valvular atrial fibrillation patients with preserved left ventricular ejection fraction.European heart journal open · 2024Article
- A review of evaluation approaches for explainable AI with applications in cardiology.Artificial intelligence review · 2024Article
- Utility of left ventricular ejection fraction in atrial fibrillation patients without pre-existing heart failure.ESC heart failure · 2023Article
- Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation.Life (Basel, Switzerland) · 2023Review
- Exploring the Intersection of Artificial Intelligence and Clinical Healthcare: A Multidisciplinary Review.Diagnostics (Basel, Switzerland) · 2023Review
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Corrections and comments
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Authors and funding
12 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Atrial fibrillation (AF) increases the risk of heart failure (HF); however, little focus is placed on the risk stratification for, and prevention of, incident HF in patients with AF. Objectives: This study aimed to construct and validate a machine learning (ML) prediction model for HF hospitalization in patients with AF. Methods: The Fushimi AF Registry is a community-based prospective survey of patients with AF in Fushimi-ku, Kyoto, Japan. We divided the data set of the registry into derivation (n = 2,383) and validation (n = 2,011) cohorts. An ML model was built to predict the incidence of HF hospitalization using the derivation cohort, and predictive ability was examined using the validation cohort. Results: HF hospitalization occurred in 606 patients (14%) during a median follow-up period of 4.4 years in the entire registry. Data of transthoracic echocardiography and biomarkers were frequently nominated as important predictive variables across all 6 ML models. The ML model based on a random forest algorithm using 7 variables (age, history of HF, creatinine clearance, cardiothoracic ratio on x-ray, left ventricular [LV] ejection fraction, LV end-systolic diameter, and LV asynergy) had high prediction performance (area under the receiver operating characteristics curve [AUC]: 0.75) and was significantly superior to the Framingham HF risk model (AUC: 0.67; Conclusions: The ML model revealed important predictors and helped us to stratify the risk of HF, providing opportunities for the prevention of HF in patients with AF.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.