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

Barker, Joseph; Li, Xin; Khavandi, Sarah; Koeckerling, David; Mavilakandy, Akash; Pepper, Coral; Bountziouka, Vasiliki; Chen, Long; Kotb, Ahmed; Antoun, Ibrahim; Mansir, John; Smith-Byrne, Karl; Schlindwein, Fernando S; Dhutia, Harshil; Tyukin, Ivan; Nicolson, William B; Andre Ng, G (2022). Machine learning in sudden cardiac death risk prediction: a systematic review. Europace, 24(11), pp. 1777-1787. Oxford University Press 10.1093/europace/euac135

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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.

Item Type:	Journal Article (Review Article)
Division/Institute:	04 Faculty of Medicine > Department of Cardiovascular Disorders (DHGE) > Clinic of Angiology
UniBE Contributor:	Köckerling, David
Subjects:	600 Technology > 610 Medicine & health
ISSN:	1099-5129
Publisher:	Oxford University Press
Language:	English
Submitter:	Pubmed Import
Date Deposited:	07 Oct 2022 10:07
Last Modified:	05 Dec 2022 16:26
Publisher DOI:	10.1093/europace/euac135
PubMed ID:	36201237
Uncontrolled Keywords:	Deep learning Implantable cardioverter-defibrillator Machine learning Prediction Sudden cardiac death Systematic review
URI:	https://boris.unibe.ch/id/eprint/173556