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Machine learning in sudden cardiac death risk prediction: a systematic review

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journal contribution
posted on 17.11.2022, 16:01 authored by Joseph Barker, Xin Li, Sarah Khavandi, David Koeckerling, Akash Mavilakandy, Coral Pepper, Vasiliki Bountziouka, Long Chen, Ahmed Kotb, Ibrahim Antoun, John Mansir, Karl Smith-Byrne, Fernando S Schlindwein, Harshil Dhutia, Ivan Tyukin, William B Nicolson, G Andre Ng
Abstract Aims Most patients who receive implantable cardioverter defibrillators (ICDs) for primary prevention do not receive therapy during the lifespan of the ICD, whilst up to 50% of sudden cardiac death (SCD) occur in individuals who are considered low risk by conventional criteria. Machine learning offers a novel approach to risk stratification for ICD assignment. Methods and results Systematic search was performed in MEDLINE, Embase, Emcare, CINAHL, Cochrane Library, OpenGrey, MedrXiv, arXiv, Scopus, and Web of Science. Studies modelling SCD risk prediction within days to years using machine learning were eligible for inclusion. Transparency and quality of reporting (TRIPOD) and risk of bias (PROBAST) were assessed. A total of 4356 studies were screened with 11 meeting the inclusion criteria with heterogeneous populations, methods, and outcome measures preventing meta-analysis. The study size ranged from 122 to 124 097 participants. Input data sources included demographic, clinical, electrocardiogram, electrophysiological, imaging, and genetic data ranging from 4 to 72 variables per model. The most common outcome metric reported was the area under the receiver operator characteristic (n = 7) ranging between 0.71 and 0.96. In six studies comparing machine learning models and regression, machine learning improved performance in five. No studies adhered to a reporting standard. Five of the papers were at high risk of bias. Conclusion Machine learning for SCD prediction has been under-applied and incorrectly implemented but is ripe for future investigation. It may have some incremental utility in predicting SCD over traditional models. The development of reporting standards for machine learning is required to improve the quality of evidence reporting in the field.

Funding

National Institute of Heath Research (NIHR) Academic Clinical Fellowship

Neurocardiac interaction in malignant ventricular arrhythmias and sudden cardiac death

British Heart Foundation

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NIHR Leicester Biomedical Research Centre

Development of a successful novel technology for sudden cardiac death risk stratification for clinical use - LifeMap

Medical Research Council

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History

Author affiliation

Department of Cardiovascular Sciences, University of Leicester

Version

AM (Accepted Manuscript)

Published in

EP Europace

Pagination

euac135

Publisher

Oxford University Press (OUP)

issn

1099-5129

eissn

1532-2092

Copyright date

2022

Available date

17/11/2022

Spatial coverage

England

Language

en