Why the study?
Early identification of arrhythmia in patients with acute myocardial infarction is essential for clinical decision-making, prompting the use of machine learning methods to develop predictive models.
Do machine learning models improve the prediction of tachyarrhythmia during admission in patients with acute myocardial infarction compared to the GRACE risk score?
Do machine learning models improve the prediction of tachyarrhythmia during admission in patients with acute myocardial infarction compared to the GRACE risk score?
Machine learning models, particularly artificial neural networks, can effectively predict the occurrence of tachyarrhythmias during admission in patients with acute myocardial infarction, outperforming traditional GRACE risk variables.
Answers are grounded in this paper and cite their sources.
ANN models may aid post-AMI arrhythmia risk stratification beyond GRACE; leaves open prospective validation before clinical use.
BACKGROUND: Early identification of the occurrence of arrhythmia in patients with acute myocardial infarction plays an essential role in clinical decision-making. The present study attempted to use machine learning (ML) methods to build predictive models of arrhythmia after acute myocardial infarction (AMI). METHODS: A total of 2084 patients with acute myocardial infarction were enrolled in this study. (All data is available on Github: https://github.com/wangsuhuai/AMI-database1.git) . The primary outcome is whether tachyarrhythmia occurred during admission containing atrial arrhythmia, ventricular arrhythmia, and supraventricular tachycardia. All data is randomly divided into a training set (80%) and an internal testing set (20%). Apply three machine learning algorithms: decision tree, random forest (RF), and artificial neural network (ANN) to learn the training set to build a model, then use the testing set to evaluate the prediction performance, and compare it with the model built by the Global Registry of Acute Coronary Events (GRACE) risk variable set. RESULTS: Three ML models predict the occurrence of tachyarrhythmias after AMI. After variable selection, the artificial neural network (ANN) model has reached the highest accuracy rate, which is better than the model constructed using the Grace variable set. After applying SHapley Additive exPlanations (SHAP) to make the model interpretable, the most important features are abnormal wall motion, lesion location, bundle branch block, age, and heart rate. Among them, RBBB (odds ratio [OR]: 4.21; 95% confidence interval [CI]: 2.42-7.02), ≥ 2 ventricular walls motion abnormal (OR: 3.26; 95% CI: 2.01-4.36) and right coronary artery occlusion (OR: 3.00; 95% CI: 1.98-4.56) are significant factors related to arrhythmia after AMI. CONCLUSIONS: We used advanced machine learning methods to build prediction models for tachyarrhythmia after AMI for the first time (especially the ANN model that has the best performance). The current study can supplement the current AMI risk score, provide a reliable evaluation method for the clinic, and broaden the new horizons of ML and clinical research. Trial registration Clinical Trial Registry No.: ChiCTR2100041960.
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Wang et al. (2021) studied this question.
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