Prediction of in-hospital cardiac arrest on general wards using calibrated machine learning.

OBJECTIVE: To identify time-windowed clinical predictors of in-hospital cardiac arrest (IHCA) and develop a temporally validated, calibrated machine-learning early warning for general wards. METHODS: Retrospective matched case-control study at a tertiary hospital in Taiwan (2019-2021) including 115 IHCA cases and 115 controls matched on age and ward. Patients with do-not-resuscitate (DNR) orders were excluded. Demographics, comorbidities and laboratory results were extracted from electronic health records. Vital signs and level of consciousness were sampled in 16-24, 8-16 and 1-8-hour windows before the index time and summarised as the modified early warning score (MEWS). Models were trained with fivefold cross-validation and isotonic probability calibration and tested on a temporally held-out 2021 cohort. RESULTS: Atrial fibrillation (OR 6.30), heart failure (OR 2.98), end-stage renal disease (OR 2.54), potassium (OR 1.81 per 1 mEq/L) and white blood count (OR 1.06 per 1 k/µL) were independent predictors. MEWS was associated with IHCA up to 16 hours, whereas in the final 8-hour lower SpO₂ predominated (OR 1.30 per 1% decrease; 95% CI 1.08 to 1.54). Calibrated XGBoost achieved Area Under the Receiver Operating Characteristic (AUROC) curve 0.89 and average precision (AP) 0.88; at sensitivity 0.95, specificity was 0.47 in the matched test set. CONCLUSIONS: Combining comorbidity burden, laboratory indices and time-windowed physiology enabled high-sensitivity IHCA warning. Because matching inflates event prevalence, AP, positive predictive value and calibration require recalibration to real-world prevalence; prospective multicentre validation is needed.