Real-time dynamic prediction of in-hospital mortality in hyperglycemic crisis patients using temporal deep learning.

BACKGROUND Hyperglycemic crisis is associated with substantial morbidity and mortality. Existing prognostic tools largely rely on static baseline measurements and do not exploit the longitudinal data generated during acute care. We aimed to develop a real-time dynamic prediction model for in-hospital mortality within the next 24 h in adults with hyperglycemic crisis. METHODS We performed a multicenter retrospective study using eICU-CRD and MIMIC-IV databases. We developed HCNet, a temporal deep learning architecture that integrates longitudinal trajectories with static features to generate a continuously updated 24-hour mortality risk. The eICU-CRD was used for development and internal testing; MIMIC-IV served as an external test cohort. Discrimination, calibration, and decision curve analysis were compared with those of eight machine learning models, including logistic regression (LR), random forest (RF), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), tabular data network (TabNet), and long short-term memory (LSTM). RESULTS The study included 3,436 eICU-CRD training admissions, 1,473 eICU-CRD internal test admissions, and 1,182 MIMIC-IV external test admissions. In-hospital mortality was 1.8%, 1.9%, and 3.1%, respectively. HCNet achieved an AUC of 0.941 (95% CI: 0.939 to 0.943) in internal testing and 0.913 (95% CI: 0.911 to 0.916) in external testing, outperforming all comparators (P<0.05). The strongest baseline models according to the AUC were CatBoost (internal 0.916; external 0.871) and LSTM (internal 0.911; external 0.890). Temporal analyses showed improved discrimination with increasing length of stay and as death approached, as well as a reduced false alarm burden. CONCLUSION HCNet provides real-time 24-hour mortality risk estimates for adults with hyperglycemic crisis using routinely collected data. Its generalizable performance and interpretable temporal patterns support its potential as a clinical decision support tool for early warning and escalation of care during hyperglycemic crisis.