Key result
Machine learning models did not outperform Cox regression in predicting 3-year all-cause mortality after myocardial infarction (AUC 0.712-0.738 vs 0.742; P=0.59).
Why the study?
Accurate risk stratification after MI is essential, but whether machine learning offers an advantage over conventional statistical models in real-world clinical settings remains uncertain.
Do machine learning models improve the prediction of 3-year all-cause mortality compared to Cox regression in patients after myocardial infarction?
Cohort (n=965)
Do machine learning models improve the prediction of 3-year all-cause mortality compared to Cox regression in patients after myocardial infarction?
Machine learning models did not outperform traditional Cox proportional hazards regression for predicting 3-year mortality in a moderate-sized post-MI cohort.
Answers are grounded in this paper and cite their sources.
ML models offer no advantage over Cox regression for post-MI mortality prediction in moderate cohorts; leaves open utility with larger datasets or richer predictors.
BACKGROUND: Accurate risk stratification after myocardial infarction (MI) remains essential for optimizing long-term management. The advantage of machine learning (ML) over conventional statistical models in real-world clinical settings remains uncertain. AIMS: Our aim was to compare the performance of Cox proportional hazards regression and ML models in predicting 3-year all-cause mortality after MI. METHODS: This retrospective cohort study included 965 patients enrolled in a structured post-MI care program between 2018 and 2022, all with complete 3-year follow-up. A development cohort (n = 740) and a temporal validation cohort (n = 225) were defined. A Cox model and three ML models (Random Forest, XGBoost, and Random Survival Forest) were developed using baseline clinical variables. Performance was assessed at a fixed 3-year horizon using area under the receiver operating characteristic curve, calibration, and the Brier score. RESULTS: During the 3-year follow-up, 126 patients (13%) died. In temporal validation, discrimination was similar across models, with area under the receiver operating characteristic curves of 0.742 (Cox), 0.738 (Random Survival Forest), 0.727 (Random Forest), and 0.712 (XGBoost) (P = 0.59). Random Survival Forest achieved the lowest Brier score (0.105). In multivariable Cox analysis, age, comorbidity index, log-transformed creatinine, and log-transformed C-reactive protein were independently associated with mortality. CONCLUSIONS: In a moderate-sized real-world cohort with limited predictors, ML models did not outperform Cox regression in predicting 3-year mortality after MI. Age, comorbidity burden, renal dysfunction, and inflammatory status emerged as the most important predictors of mortality. These findings require external validation in independent cohorts before clinical implementation.
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Szymańska-Łyczkowska et al. (2026) conducted a cohort in myocardial infarction (n=965). Machine learning models vs. Cox proportional hazards regression was evaluated on 3-year all-cause mortality (p=0.59). Machine learning models did not outperform Cox regression in predicting 3-year all-cause mortality after myocardial infarction (AUC 0.712-0.738 vs 0.742; P=0.59).
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