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Machine learning based model for risk prediction after ST-Elevation myocardial infarction: Insights from the North India ST elevation myocardial infarction (NORIN-STEMI) registry.

Authors :
Shetty MK
Kunal S
Girish MP
Qamar A
Arora S
Hendrickson M
Mohanan PP
Gupta P
Ramakrishnan S
Yadav R
Bansal A
Zachariah G
Batra V
Bhatt DL
Gupta A
Gupta M
Source :
International journal of cardiology [Int J Cardiol] 2022 Sep 01; Vol. 362, pp. 6-13. Date of Electronic Publication: 2022 May 13.
Publication Year :
2022

Abstract

Background: Risk prediction following ST-Elevation Myocardial Infarction (STEMI) in resource limited countries is critical to identify patients at an increased risk of mortality who might benefit from intensive management.<br />Methods: North India ST-Elevation Myocardial Infarction (NORIN-STEMI) is an ongoing registry that has prospectively enrolled 3,635 STEMI patients. Of these, 3191 patients with first STEMI were included. Patients were divided into two groups: development (n=2668) and validation (unseen) dataset (n=523). Various ML strategies were used to train and tune the model based on validation dataset results that included 31 clinical characteristics. These models were compared in sensitivity, specificity, F1-score, receiver operating characteristic area under the curve (AUC), and overall accuracy to predict mortality at 30 days. ML model decision making was analyzed using the Shapley Additive exPlanations (ShAP) summary plot.<br />Results: At 30 days, the mortality was 7.7%. On the validation dataset, Extra Tree ML model had the best predictive ability with sensitivity: 85%, AUC: 79.7%, and Accuracy: 75%. ShAP interpretable summary plot determined delay in time to revascularization, baseline cardiogenic shock, left ventricular ejection fraction <30%, age, serum creatinine, heart failure on presentation, female sex, and moderate-severe mitral regurgitation to be major predictors of all-cause mortality at 30 days (P<0.001 for all).<br />Conclusion: ML models lead to an improved mortality prediction following STEMI. ShAP summary plot for the interpretability of the AI model helps to understand the model's decision in identifying high-risk individuals who may benefit from intensified follow-up and close monitoring.<br /> (Copyright © 2022 Elsevier B.V. All rights reserved.)

Details

Language :
English
ISSN :
1874-1754
Volume :
362
Database :
MEDLINE
Journal :
International journal of cardiology
Publication Type :
Academic Journal
Accession number :
35577162
Full Text :
https://doi.org/10.1016/j.ijcard.2022.05.023