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Machine learning for the diagnosis of acute coronary syndrome using a 12-lead ECG: a systematic review

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Abstract

Objectives

Prompt diagnosis of acute coronary syndrome (ACS) using a 12-lead electrocardiogram (ECG) is a critical task for emergency physicians. While computerized algorithms for ECG interpretation are limited in their accuracy, machine learning (ML) models have shown promise in several areas of clinical medicine. We performed a systematic review to compare the performance of ML-based ECG analysis to clinician or non-ML computerized ECG interpretation in the diagnosis of ACS for emergency department (ED) or prehospital patients.

Methods

We searched Medline, Embase, Cochrane Central, and CINAHL databases from inception to May 18, 2022. We included studies that compared ML algorithms to either clinicians or non-ML based software in their ability to diagnose ACS using only a 12-lead ECG, in adult patients experiencing chest pain or symptoms concerning for ACS in the ED or prehospital setting. We used QUADAS-2 for risk of bias assessment. Prospero registration CRD42021264765.

Results

Our search yielded 1062 abstracts. 10 studies met inclusion criteria. Five model types were tested, including neural networks, random forest, and gradient boosting. In five studies with complete performance data, ML models were more sensitive but less specific (sensitivity range 0.59–0.98, specificity range 0.44–0.95) than clinicians (sensitivity range 0.22–0.93, specificity range 0.63–0.98) in diagnosing ACS. In four studies that reported it, ML models had better discrimination (area under ROC curve range 0.79–0.98) than clinicians (area under ROC curve 0.67–0.78). Heterogeneity in both methodology and reporting methods precluded a meta-analysis. Several studies had high risk of bias due to patient selection, lack of external validation, and unreliable reference standards for ACS diagnosis.

Conclusions

ML models have overall higher discrimination and sensitivity but lower specificity than clinicians and non-ML software in ECG interpretation for the diagnosis of ACS. ML-based ECG interpretation could potentially serve a role as a “safety net”, alerting emergency care providers to a missed acute MI when it has not been diagnosed. More rigorous primary research is needed to definitively demonstrate the ability of ML to outperform clinicians at ECG interpretation.

Résumé

Objectifs

Le diagnostic rapide du syndrome coronarien aigu (SCA) à l’aide d’un électrocardiogramme à 12 dérivations (ECG) est une tâche essentielle pour les urgentologues. Bien que la précision des algorithmes informatisés pour l’interprétation de l’ECG soit limitée, les modèles d’apprentissage automatique (ML) se sont révélés prometteurs dans plusieurs domaines de la médecine clinique. Nous avons effectué une revue systématique pour comparer la performance de l’analyse ECG basée sur le ML à l’interprétation ECG informatisée clinicienne ou non-ML dans le diagnostic du SCA pour les urgences (ED) ou les patients préhospitaliers.

Méthodes

Nous avons effectué des recherches dans les bases de données Medline, Embase, Cochrane Central et CINAHL de la création au 18 mai 2022. Nous avons inclus des études qui comparaient les algorithmes de ML à des cliniciens ou à des logiciels non basés sur ML dans leur capacité à diagnostiquer le SCA en utilisant uniquement un ECG à 12 dérivations, chez des patients adultes présentant des douleurs thoraciques ou des symptômes concernant le SCA dans le cadre de l’urgence ou préhospitalier. Nous avons utilisé QUADAS-2 pour l’évaluation du risque de biais. Prospero registration CRD42021264765.

Résultats

Notre recherche a donné 1062 résumés. 10 études satisfaisaient aux critères d’inclusion. Cinq types de modèles ont été testés, dont les réseaux neuronaux, la forêt aléatoire et le gradient boosting. Dans cinq études avec des données de performance complètes, les modèles de ML étaient plus sensibles mais moins spécifiques (plage de sensibilité 0,59-0,98, plage de spécificité 0,44-0,95) que les cliniciens (plage de sensibilité 0,22-0,93, plage de spécificité 0,63-0,98) dans le diagnostic du SCA. Dans quatre études qui l’ont rapporté, les modèles de ML avaient une meilleure discrimination (zone sous la courbe ROC plage 0,79-0,98) que les cliniciens (zone sous la courbe ROC 0,67-0,78). L’hétérogénéité de la méthodologie et des méthodes de déclaration a empêché une méta-analyse. Plusieurs études présentaient un risque élevé de biais en raison de la sélection des patients, du manque de validation externe et de normes de référence peu fiables pour le diagnostic du SCA.

Conclusions

Les modèles de ML ont globalement une discrimination et une sensibilité plus élevées mais une spécificité plus faible que les cliniciens et les logiciels non-ML dans l’interprétation de l’ECG pour le diagnostic du SCA. L’interprétation de l’ECG basée sur le ML pourrait servir de « filet de sécurité », alertant les fournisseurs de soins d’urgence d’une IM aiguë manquée lorsqu’elle n’a pas été diagnostiquée. Des recherches primaires plus rigoureuses sont nécessaires pour démontrer définitivement la capacité du ML à surpasser les cliniciens lors de l’interprétation de l’ECG.

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Data availability

All data generated or analysed during this study are included in this published article [and its supplementary information files].

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Acknowledgements

Ms. Sarah Visintini peer reviewed our Medline search strategy.

Author information

Authors and Affiliations

  1. Department of Emergency Medicine, University of Ottawa, Ottawa, ON, Canada

    Max Zworth, Hashim Kareemi, Ian Stiell & Krishan Yadav

  2. Department of Family Medicine, McMaster University Faculty of Health Sciences, Hamilton, ON, Canada

    Suzanne Boroumand

  3. Health Sciences Library, University of Ottawa, Ottawa, ON, Canada

    Lindsey Sikora

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  1. Max Zworth
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  2. Hashim Kareemi
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  3. Suzanne Boroumand
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  4. Lindsey Sikora
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Corresponding author

Correspondence to Max Zworth.

Additional information

Communicated by Daniel K. Ting.

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Zworth, M., Kareemi, H., Boroumand, S. et al. Machine learning for the diagnosis of acute coronary syndrome using a 12-lead ECG: a systematic review. Can J Emerg Med 25, 818–827 (2023). https://doi.org/10.1007/s43678-023-00572-5

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  • DOI: https://doi.org/10.1007/s43678-023-00572-5

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