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Review
. 2025 Aug 5;86(5):374-395.
doi: 10.1016/j.jacc.2025.05.061.

Optimizing the Primary Prevention of Sudden Cardiac Death in Patients With Heart Failure

Affiliations
Review

Optimizing the Primary Prevention of Sudden Cardiac Death in Patients With Heart Failure

Nicolai Wallace et al. J Am Coll Cardiol. .

Abstract

Implantable cardioverter-defibrillators (ICDs) protect patients from sudden cardiac death (SCD). Landmark trials demonstrating their efficacy for primary prevention in patients with heart failure (HF) used reduced left ventricular ejection fraction (LVEF) as a major inclusion criterion and current recommendations for ICD implantation rely on this variable in patient selection. However, contemporary medical management has reduced the risk of SCD in patients with reduced LVEF so that an increasingly large proportion of this population never requires the protection offered by the device. Although SCD is the major cause of cardiovascular mortality in HF patients with preserved LVEF, ICDs are not recommended for primary prevention in this subset of the population. Advances in patient management, diagnostic testing, and data processing over the past 30 years have made it apparent that recommendations for ICD use for primary prevention of SCD are no longer optimal. This review summarizes the declining incidence of SCD and reasons for the widening gap between risk of SCD and current guideline recommendations for use of ICDs in the HF population. It discusses limitations in our ability to predict risk of SCD that need to be addressed and the potential impact of ongoing clinical trials on recommendations for ICD use for primary prevention of SCD. Patient-related variables including those available from diagnostic tests that could be used to generate prediction models that more accurately identify magnitude of risk of SCD in individual patients are identified. The use of artificial intelligence processing of data from diagnostic tests to facilitate and standardize extraction of predictive variables and the use of machine learning algorithms for developing risk prediction models are discussed. The review concludes by describing a dynamic approach for generating novel risk prediction models that could better align risk of SCD with the benefits of ICD implantation in patients with HF and that could evolve over time as additional treatment strategies that alter risk of SCD are introduced in the future.

Keywords: artificial intelligence; guideline-directed medical therapy; guidelines; heart failure; implantable cardioverter-defibrillator; sudden cardiac death.

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Conflict of interest statement

Funding Support and Author Disclosures Dr Krummen has performed consulting for and has equity with Vektor Medical. Dr Greenberg is a member of the Data and Safety Monitoring Board for studies sponsored by Anthem, Axon, Boehringer Ingelheim, Cellular Dynamics, Corvia, Cytokinetics, EBR Systems, Faraday, Impulse Dynamics, Inventiva, Ionis, Merck, Tenaya, Viking, and Windtree; has performed consulting activities for Astellas, Affinia, AstraZeneca, Bristol Myers Squibb, Ionis, Secretome, and Tenaya; and has research contracts with Rocket Pharma. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

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