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Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome

Molenaar, Mitchel A; Bouma, Berto J; Asselbergs, Folkert W; Verouden, Niels J; Selder, Jasper L; Chamuleau, Steven AJ; Schuuring, Mark J; (2024) Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome. European Heart Journal - Digital Health , Article ztae001. 10.1093/ehjdh/ztae001. (In press). Green open access

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Abstract

Aims: The European Society of Cardiology guidelines recommend risk stratification with limited clinical parameters such as left ventricular (LV) function in patients with chronic coronary syndrome (CCS). Machine learning (ML) methods enable an analysis of complex datasets including transthoracic echocardiography (TTE) studies. We aimed to evaluate the accuracy of ML using clinical and TTE data to predict all-cause 5-year mortality in patients with CCS and to compare its performance with traditional risk stratification scores. // Methods and results: Data of consecutive patients with CCS were retrospectively collected if they attended the outpatient clinic of Amsterdam UMC location AMC between 2015 and 2017 and had a TTE assessment of the LV function. An eXtreme Gradient Boosting (XGBoost) model was trained to predict all-cause 5-year mortality. The performance of this ML model was evaluated using data from the Amsterdam UMC location VUmc and compared with the reference standard of traditional risk scores. A total of 1253 patients (775 training set and 478 testing set) were included, of which 176 patients (105 training set and 71 testing set) died during the 5-year follow-up period. The ML model demonstrated a superior performance [area under the receiver operating characteristic curve (AUC) 0.79] compared with traditional risk stratification tools (AUC 0.62–0.76) and showed good external performance. The most important TTE risk predictors included in the ML model were LV dysfunction and significant tricuspid regurgitation. // Conclusion: This study demonstrates that an explainable ML model using TTE and clinical data can accurately identify high-risk CCS patients, with a prognostic value superior to traditional risk scores.

Type: Article
Title: Explainable machine learning using echocardiography to improve risk prediction in patients with chronic coronary syndrome
Open access status: An open access version is available from UCL Discovery
DOI: 10.1093/ehjdh/ztae001
Publisher version: http://dx.doi.org/10.1093/ehjdh/ztae001
Language: English
Additional information: Copyright © The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.
Keywords: Coronary artery disease; Machine learning; Artificial intelligence; Prognosis; Risk; Mortality
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology
URI: https://discovery.ucl.ac.uk/id/eprint/10188161
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