ArticleBMJ open2019
Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China.
Article in BMJ open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 44 citations in OpenAlex.
- Prediction of cardiac arrest in patients with heart failure in Sweden: a registry study with development of a machine learning model.BMJ open · 2026Observational
- Real-World Data Needs Real-World Doctors: When Automation Advances Faster Than Clinical Workflow.Journal of participatory medicine · 2026Article
- Short-term study of atrial shunt and improvement of functional mitral regurgitation.Journal of cardiothoracic surgery · 2023Article
- Development of machine learning models for mortality risk prediction after cardiac surgery.Cardiovascular diagnosis and therapy · 2022Article
- Machine learning techniques for arrhythmic risk stratification: a review of the literature.International journal of arrhythmia · 2022Article
- Prediction of long-term hospitalisation and all-cause mortality in patients with chronic heart failure on Dutch claims data: a machine learning approach.BMC medical informatics and decision making · 2021Article
- Score and Correlation Coefficient-Based Feature Selection for Predicting Heart Failure Diagnosis by Using Machine Learning Algorithms.Computational and mathematical methods in medicine · 2021Article
- Sudden Cardiac Death Prediction in Non-ischemic Dilated Cardiomyopathy: a Multiparametric and Dynamic Approach.Current cardiology reports · 2020Review
- Machine learning can predict survival of patients with heart failure from serum creatinine and ejection fraction alone.BMC medical informatics and decision making · 2020Article
- Machine learning for predicting cardiac events: what does the future hold?Expert review of cardiovascular therapy · 2020Review
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Authors and funding
21 authors at 15 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionLeft ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF. METHODS AND ANALYSIS: We will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study. ETHICS AND DISSEMINATION: The study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences. TRIAL REGISTRATION NUMBER: ChiCTR-POC-17011842; Pre-results.
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