Predicting 10-year Major Adverse Cardiac Events Using Multi-Source Modalities with XGBoost: Establishing a Baseline for Multimodal Fusion in Cardiac Risk Assessment

BackgroundAccurate prediction of major adverse cardiac events (MACE) is critical for long-term cardiovascular risk management. Traditional risk scores offer only moderate performance. Leveraging multi-source data may improve individualized risk stratification. MethodsIn this retrospective study of patients who underwent non-contrast cardiac- gated CT between 2010 and 2023 across Emory-affiliated hospitals, XGBoost models were trained on structured tabular data using sequential feature integration to predict 10-year MACE. Features included coronary artery calcium (CAC), other imaging- derived metrics, clinical risk scores, electrocardiogram parameters, and laboratory biomarkers. Performance was mainly assessed using AUC-ROC and AUC-PRC. A 5- fold cross-validation strategy was employed, repeated across 10 randomized seeds. Statistical significance was evaluated using two-sided t-tests with 95% confidence. ResultsThis retrospective study included 25,514 adult patients (mean age 57 {+/-} 10 years; 57% men), of whom 2.93% experienced MACE within 10 years. The final model incorporating all features, achieved the highest performance with an AUC-ROC of 0.883 {+/-} 0.012, a 30.8% improvement over CAC (0.675 {+/-} 0.015), 28.9%-32.2% over clinical risk scores, with p <0.01 for all. AUC-PRC was 0.289 {+/-} 0.028 compared to 0.056-0.104 for clinical risk scores and 0.067 for CAC. SHAP analysis identified creatinine, hemoglobin A1c, body mass index, glomerular filtration rate, and CAC volume as the most influential features. ConclusionSequential integration of structured clinical and imaging-derived data significantly improves MACE prediction. This model establishes a robust and interpretable benchmark for future research in multimodal fusion and cardiovascular risk stratification.