Machine Learning vs Traditional Approaches to Predict All-Cause Mortality for Acute Coronary Syndrome: A Systematic Review and Meta-analysis.

Background: Acute coronary syndrome (ACS) remains one of the leading causes of death globally. Accurate and reliable mortality risk prediction of ACS patients is essential for developing targeted treatment strategies and improve prognostication. Traditional models for risk stratification such as the GRACE and TIMI risk scores offer moderate discriminative value, and do not incorporate contemporary predictors of ACS prognosis. Machine learning (ML) models have emerged as an alternate method that may offer improved risk assessment. This review compares ML models with traditional risk scores for predicting all-cause mortality in patients with ACS.

Methods: PubMed, Embase, Web of Science, Cochrane, CINAHL, Scopus, and IEEE XPlore databases were searched through October 30, 2024, as well as Google Scholar and manual screening of reference lists from included studies and the grey literature for studies comparing ML models with traditional statistical methods for event prediction of ACS patients. The primary outcome was comparative discrimination measured by C-statistics with 95% confidence intervals (CIs) in estimating risk of all-cause mortality.

Results: Twelve studies were included (250,510 patients). The summary C-statistic of best-performing ML models across all end points was 0.88 (95% CI 0.86-0.91), compared with 0.82 (95% CI 0.80-0.85) for traditional methods. The difference in C-statistic between ML models and traditional methods was 0.06 (P < 0.0007). Five studies undertook external validation. The PROBAST tool demonstrated high risk of bias for all studies. Common sources of bias included reporting bias and selection bias. Best-performing ML models demonstrated superior discrimination of all-cause mortality for ACS patients compared with traditional risk scores.

Conclusions: Despite outperforming well established prognostic tools such as the GRACE and TIMI scores, current clinical applications of ML approaches remain uncertain, particularly in view of the need for greater model validation.