Distributed Random Forest and Gradient Boosting Machine models effectively predicted 1-year major adverse cardiovascular events in STEMI patients after primary PCI, with AUCs of 0.92 and 0.91.
Cohort (n=4,514)
No
Do machine learning models accurately predict 1-year major adverse cardiovascular events in patients undergoing primary PCI for acute STEMI?
Distributed Random Forest and Gradient Boosting Machine models demonstrated excellent performance (AUC > 0.90) in predicting 1-year MACE in STEMI patients undergoing primary PCI.
Effect estimate: AUC 0.92 for DRF, 0.91 for GBM
Abstract Background Acute ST‐elevation myocardial infarction (STEMI) is a leading cause of mortality and morbidity worldwide, and primary percutaneous coronary intervention (PCI) is the preferred treatment option. Hypothesis Machine learning (ML) models have the potential to predict adverse clinical outcomes in STEMI patients treated with primary PCI. However, the comparative performance of different ML models for this purpose is unclear. Methods This study used a retrospective registry‐based design to recruit consecutive hospitalized patients diagnosed with acute STEMI and treated with primary PCI from 2011 to 2019, at Tehran Heart Center, Tehran, Iran. Four ML models, namely Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), Logistic Regression (LR), and Deep Learning (DL), were used to predict major adverse cardiovascular events (MACE) during 1‐year follow‐up. Results A total of 4514 patients (3498 men and 1016 women) were enrolled, with MACE occurring in 610 (13.5%) subjects during follow‐up. The mean age of the population was 62.1 years, and the MACE group was significantly older than the non‐MACE group (66.2 vs. 61.5 years, p < .001). The learning process utilized 70% ( n = 3160) of the total population, and the remaining 30% ( n = 1354) served as the testing data set. DRF and GBM models demonstrated the best performance in predicting MACE, with an area under the curve of 0.92 and 0.91, respectively. Conclusion ML‐based models, such as DRF and GBM, can effectively identify high‐risk STEMI patients for adverse events during follow‐up. These models can be useful for personalized treatment strategies, ultimately improving clinical outcomes and reducing the burden of disease.
Tofighi et al. (Mon,) conducted a cohort in Acute ST-elevation myocardial infarction (STEMI) (n=4,514). Machine learning models (GBM, DRF, LR, DL) was evaluated on Major adverse cardiovascular events (MACE) during 1-year follow-up (AUC 0.92 for DRF, 0.91 for GBM). Distributed Random Forest and Gradient Boosting Machine models effectively predicted 1-year major adverse cardiovascular events in STEMI patients after primary PCI, with AUCs of 0.92 and 0.91.