Research Article: Development and external validation of a machine learning model for predicting 28-day mortality in patients with acute myocardial infarction complicated by malignant arrhythmia: a study using the MIMIC database and a Chinese cohort
Abstract:
Acute myocardial infarction (AMI) complicated by malignant ventricular arrhythmia (MVA) carries high 28-day mortality. Existing risk scores inadequately capture this population. We aimed to develop and externally validate an interpretable machine learning model for predicting 28-day mortality in AMI-MVA patients.
This retrospective study included 952 AMI-MVA patients from MIMIC-IV (training n =?668, internal validation n =?284) and 100 patients from Maoming People's Hospital, China (external validation). Feature selection integrated multivariable logistic regression, LASSO, and Boruta, yielding eight predictors: age, lactate, fasting blood glucose, RDW, antiplatelet agents, beta-blockers, ACEI/ARB, and norepinephrine. Seven machine learning algorithms were compared by AUC, calibration, and decision curve analysis. Treatment-free sensitivity models, propensity-score matching (PSM), and inverse-probability-of-treatment-weighting (IPTW) were performed to assess confounding by indication.
28-day mortality was 32.46% (309/952). The SVM model (linear kernel, C =?0.1) achieved AUC 0.865 (95% CI 0.824–0.909) in internal validation and 0.898 (95% CI 0.835–0.949) externally, with excellent calibration (Brier score 0.128). SHAP analysis identified beta-blockers, lactate, and ACEI/ARB as the three most influential predictors. A treatment-free sensitivity model (biomarkers only) achieved AUC 0.801 (95% CI 0.748–0.851) internally and 0.771 (95% CI 0.675–0.853) externally. PSM and IPTW attenuated but did not eliminate treatment-outcome associations, consistent with a mixture of genuine prognostic signal and residual confounding.
This interpretable SVM model supports bedside risk stratification for AMI-MVA patients within the first 24?h of ICU admission. The model should be understood as a prognostic snapshot rather than an early prediction tool. Prospective, multicentre validation with standardised severity scores and coronary-anatomy variables is required before routine clinical use.
Introduction:
Acute myocardial infarction (AMI) complicated by malignant ventricular arrhythmia (MVA) carries high 28-day mortality. Existing risk scores inadequately capture this population. We aimed to develop and externally validate an interpretable machine learning model for predicting 28-day mortality in AMI-MVA patients.
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