Multicenter Machine Learning-Based Prediction of Mortality in ICU Patients With Hypocalcemia

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.

We harmonized de-identified data from MIMIC-III, MIMIC-IV, and two Grade III Level A hospitals in China, generating a multicenter 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 multicenter 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.

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.

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.