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July 20, 2025Journal of Medical Internet ResearchOpen Access

Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis

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Authors

MYM YuHYHae Young YooGHGa In Han

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Overview

Systematic review shows machine learning outperforms conventional risk scores in predicting adverse events in acute myocardial infarction patients, suggesting better prognostic capabilities.

Key Points

  • Machine learning models showed a higher area under the receiver operating characteristic curve (0.88) compared to conventional risk scores (0.79) for predicting mortality risk.
  • Ninety studies included 89,702 patients with acute myocardial infarction who underwent percutaneous coronary intervention.
  • The primary predictors for mortality identified were age, systolic blood pressure, and Killip class in both model types.
  • Understanding the limitations of both machine learning and traditional scores is crucial for accurate clinical application in predicting major events.

Cite This Study

Yu et al. (2025) studied this question.

synapsesocial.com/papers/689a02c9e6551bb0af8ccf93https://doi.org/10.2196/76215
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Comparison of the Prognostic Performance of Various Machine Learning Models in Patients with Acute Myocardial Infarction: Results from the COREA-AMI Registry2025
  2. 2Risk Prediction of Major Adverse Cardiovascular Events Within One Year After Percutaneous Coronary Intervention in Patients With Acute Coronary Syndrome: Machine Learning–Based Time-to-Event Analysis2025
  3. 3Enhancing one-year mortality prediction in STEMI patients post-PCI: an interpretable machine learning model with risk stratification2025
  4. 4External Validation of Clinical Risk Scores and Machine Learning Models for Predicting 30-Day Cardiovascular Risk After Noncardiac Surgery: The PERICARE Study2026
  5. 5Predicting Mortality After Percutaneous Coronary Intervention in a Multiethnic Southeast Asian Population: Insights From Machine Learning2026