Machine learning in sudden cardiac death risk prediction : a systematic review
© The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please email: journals.permissionsoup.com..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2022 |
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| Erschienen: |
2022 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology - 24(2022), 11 vom: 22. Nov., Seite 1777-1787 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Barker, Joseph [VerfasserIn] |
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| Links: |
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| Themen: |
Deep learning |
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| Anmerkungen: |
Date Completed 28.11.2022 Date Revised 22.03.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.1093/europace/euac135 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM347166180 |
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| 245 | 1 | 0 | |a Machine learning in sudden cardiac death risk prediction |b a systematic review |
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| 520 | |a © The Author(s) 2022. Published by Oxford University Press on behalf of the European Society of Cardiology. All rights reserved. For permissions, please email: journals.permissionsoup.com. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Systematic Review | |
| 650 | 4 | |a Deep learning | |
| 650 | 4 | |a Implantable cardioverter-defibrillator | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Prediction | |
| 650 | 4 | |a Sudden cardiac death | |
| 650 | 4 | |a Systematic review | |
| 700 | 1 | |a Li, Xin |e verfasserin |4 aut | |
| 700 | 1 | |a Khavandi, Sarah |e verfasserin |4 aut | |
| 700 | 1 | |a Koeckerling, David |e verfasserin |4 aut | |
| 700 | 1 | |a Mavilakandy, Akash |e verfasserin |4 aut | |
| 700 | 1 | |a Pepper, Coral |e verfasserin |4 aut | |
| 700 | 1 | |a Bountziouka, Vasiliki |e verfasserin |4 aut | |
| 700 | 1 | |a Chen, Long |e verfasserin |4 aut | |
| 700 | 1 | |a Kotb, Ahmed |e verfasserin |4 aut | |
| 700 | 1 | |a Antoun, Ibrahim |e verfasserin |4 aut | |
| 700 | 1 | |a Mansir, John |e verfasserin |4 aut | |
| 700 | 1 | |a Smith-Byrne, Karl |e verfasserin |4 aut | |
| 700 | 1 | |a Schlindwein, Fernando S |e verfasserin |4 aut | |
| 700 | 1 | |a Dhutia, Harshil |e verfasserin |4 aut | |
| 700 | 1 | |a Tyukin, Ivan |e verfasserin |4 aut | |
| 700 | 1 | |a Nicolson, William B |e verfasserin |4 aut | |
| 700 | 1 | |a Ng, G Andre |e verfasserin |4 aut | |
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