Assessing Acute-Care 30-Day Mortality Prediction Using Clinical Features in CHoRUS Clinical Care for AI and MIMIC-IV

Background: Mortality prediction models often combine early electronic health record data, but the relative prognostic value of baseline vulnerability, physiological severity, treatment exposure, and procedure burden remains unclear. Objective: To compare routinely available first-24-hour clinical domains for visit-level 30-day mortality prediction and assess whether domain-level patterns replicated in MIMIC-IV. Methods: We used CHoRUS, an OMOP-formatted acute-care dataset, with independent domain-level replication in MIMIC-IV. CHoRUS included 22,098 visits among 5,892 unique patients, with 1,004 30-day mortality events and 4.5% mortality prevalence. MIMIC-IV included 23,000 acute-care visits among 10,006 unique patients, with 819 events and 3.6% prevalence. Across both datasets, 45,098 visits and 15,898 unique patients were analyzed. Predictors were restricted to the first 24 hours after visit start. Performance was evaluated using AUPRC, AUROC, Brier score, calibration, sensitivity at 90% specificity, highest-risk 10% analyses, decision-curve analysis, and SHAP summaries. Because 30-day mortality was infrequent, the classification task was class-imbalanced. Accordingly, AUPRC was interpreted relative to the prevalence-based no-skill baseline, rather than as an absolute measure alone. Results and Conclusion: Physiological severity produced the largest improvement beyond baseline in CHoRUS, with median AUPRC 0.38 and median AUROC 0.86, and showed the same primary domain-level pattern in MIMIC-IV. Treatment exposure and procedure burden provided smaller gains. In CHoRUS, the best pairwise model combined baseline, physiological severity, and procedure burden features, with median AUPRC 0.41; the all-domain model was slightly lower, with median AUPRC 0.40 and median AUROC 0.86. In MIMIC-IV, the all-domain model had the highest median AUPRC, 0.25, only modestly above the best pairwise model. First-24-hour physiological severity features therefore provided the most consistent prognostic information across datasets, supporting parsimonious, clinically interpretable acute-care risk models centered on high-quality early physiological data.