Early Risk Stratification for 30-Day Mortality After In-Hospital Cardiac Arrest: SHAP Interpretable CatBoost Model with m-NUTRIC and Micronutrient Biomarkers.

Background/

Objectives

Predicting 30-day mortality after in-hospital cardiac arrest (IHCA) remains challenging. We developed an interpretable CatBoost model that incorporates the m-NUTRIC score, age, and selected micronutrient biomarkers (i.e., magnesium, zinc, vitamin D, and vitamin B12). We compared its performance with that of logistic regression and quantified variable contributions using SHAP.

Methods

Variables were extracted from the electronic medical records of 880 patients with IHCA admitted to a medical intensive care unit. The CatBoost and logistic regression models were trained on a stratified 80/20 split. The decision threshold was optimized using the Youden index (0.482). Discrimination (ROC-AUC with bootstrap confidence intervals), classification metrics, precision-recall analysis, calibration, and decision curve analysis were reported.

Results

CatBoost achieved a ROC-AUC of 0.850 (95% confidence interval [CI]: 0.822-0.879) in the training set and 0.827 (95% CI: 0.760-0.887) in the internal test set, outperforming logistic regression (0.797; 95% CI: 0.720-0.861). The test set accuracy, precision, recall, F1-score, specificity, and average precision were 0.761, 0.847, 0.790, 0.817, 0.702, and 0.909, respectively. The Brier score was 0.186. Decision curve analysis showed net benefit across threshold probabilities of 0.20-0.70. The SHAP analysis identified m-NUTRIC and age as the dominant predictors, whereas micronutrients served as complementary contextual factors.

Conclusions

The CatBoost model consistently outperformed the logistic regression and warrants prospective multicenter validation.