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

Background: Accurate 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. Methods: In 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. Results: This 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. Conclusion: Sequential 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.