A Translational Predictive Analytics Framework for Explainable Risk Assessment: Transforming High-Dimensional Surgical Data into Clinical Decision Support Tiers (S-CRI)

Clinical prediction rules often suffer from a translation gap, balancing high-dimensional statistical accuracy against practical bedside interpretability. This study presents the Surgical Complication Risk Index (S-CRI), an explainable, data-decoupled risk-stratification framework designed to predict post-operative complications using multi-center electronic health registry records (N = 19,965). To ensure strict validation integrity, data partitioning (70% development, n = 13,975; 30% independent holdout testing, n = 5990) was executed before any engineering or risk-tier group isolation. A parsimonious multivariate logistic regression model was fitted within the development cohort, utilizing five predictors: length of stay (LOS) accrued up to the morning of assessment, two institutional categorical groupings, and two historical entry-diagnosis empirical risk tiers. To bridge the translational gap, all fractional regression coefficients were scaled by the baseline anchor and rounded to the nearest whole integer, yielding a simple bedside scorecard where 1 point = 1 inpatient day. On the completely blinded independent holdout cohort, the whole-integer S-CRI demonstrated robust discriminative performance with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.8741 (95% CI: 0.864–0.884) and a Precision–Recall AUC of 0.5785. Setting a baseline operational threshold ≥ 0 yielded an accuracy of 88.18%, a specificity of 96.43%, and a sensitivity of 35.43%, while an optimized integer screening cutoff score of ≥−4 maximized screening capacity (sensitivity: 63.95%; specificity: 91.68%). By enforcing strict temporal landmark constraints to eliminate reverse causality and removing all out-of-sample data leakage, the S-CRI provides an objective, transparent, and interpretable clinical decision support mechanism for early inpatient risk stratification, designed as a supplementary clinical decision-support aid, rather than as a definitive diagnostic replacement for independent clinical judgment.