Key result
Multicenter registry protocol launches to train machine learning models for SCD in HFrEF.
Why the study?
Using LVEF ≤35% to select heart failure patients for primary prevention ICDs is inefficient, and existing models are inadequate to identify low LVEF patients at high risk of sudden cardiac death.
Can machine learning models accurately predict sudden cardiac death in heart failure patients with low LVEF?
Observational (n=2,500)
Yes
Can machine learning models accurately predict sudden cardiac death in heart failure patients with low LVEF?
This study protocol outlines a multicenter registry to develop and validate machine learning models for predicting sudden cardiac death in heart failure patients with LVEF ≤35%, aiming to optimize ICD patient selection.
Registry protocol for ML SCD prediction in low-LVEF HF leaves open better risk stratification; no practice change pending validation.
INTRODUCTION: Left 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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Meng et al. (2019) conducted an observational in Heart failure with reduced ejection fraction (n=2,500). Machine learning prediction models vs. Traditional multivariable Cox proportional hazards regression models was evaluated on All-cause death and sudden cardiac death (SCD). This study protocol describes a planned multicenter registry to develop and validate machine learning models for predicting sudden cardiac death in heart failure patients with LVEF ≤35%; no results are available yet.
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