Multi-Center Machine Learning-Based Prediction of Mortality in ICU Patients with Hypocalcemia

Abstract Background Hypocalcemia occurs frequently in intensive-care units (ICUs) and is independently associated with excess mortality. Conventional severity scores—such as APACHE II and SOFA—assign fixed weights to a limited set of variables and therefore fail to capture the nonlinear, high-dimensional physiology characteristic of hypocalcemic patients. Although machine-learning (ML) approaches can enhance risk stratification, no interpretable model tailored to this cohort has been available. Methods We harmonised de-identified data from MIMIC-III, MIMIC-IV and two Grade III Level A hospitals in China, generating a multicentre cohort of 13,979 adult ICU admissions with total serum calcium < 2.12 mmol L -1 . MIMIC-IV records were randomly divided into a training set (n = 7,749) and an internal-validation set (n = 1,550). External validation employed MIMIC-III (n = 4,771) and the Chinese multicentre dataset (n = 209). Predictors were filtered with least-absolute-shrinkage-and-selection operator (LASSO) regression and applied to eight ML algorithms: logistic regression, k-nearest neighbors (KNN), support-vector machine, decision tree, random forest, artificial neural network, eXtreme Gradient Boosting (XGBoost) and LightGBM. Model discrimination, calibration and clinical utility were quantified using the area under the receiver-operating-characteristic curve (AUC), F1-score, sensitivity, specificity, calibration plots, decision-curve analysis (DCA) and clinical-impact curves (CIC). SHapley Additive exPlanations (SHAP) were used for interpretability, and the final model was deployed as a public web application. Results LASSO retained 20 predictive variables; is_noninvasive_ventilator and hospital length of stay were the most influential in SHAP analysis. XGBoost provided the highest discrimination (AUC = 0.914; F1 = 0.844), surpassing logistic regression (AUC = 0.896; F1 = 0.829), LightGBM (AUC = 0.909; F1 = 0.816) and conventional ICU scores. Calibration curves, DCA and CIC confirmed consistent performance and superior net benefit across internal and external validation cohorts. Conclusions We present and externally validate an interpretable, high-performance ML model that predicts in-hospital mortality in hypocalcemic ICU patients more accurately than established scoring systems. The SHAP-enabled web interface provides real-time, patient-specific risk estimates, facilitating data-driven clinical decisions within the early, critical window of ICU care.