Simulation-Based Development, Stress Testing, and External Physiological Benchmarking of a DKA Early-Warning Framework Intended for Future Nurse-Facing Evaluation

Background/Objectives: We evaluated a simulation-based diabetic ketoacidosis (DKA) early-warning framework and examined whether machine learning advantages reflected simulator assumptions. Methods: Twenty thousand synthetic 15 h episodes were generated at 5 min intervals. Validation-frozen beta-hydroxybutyrate (BHB) rules and XGBoost models were compared. Post hoc analyses assessed phenotype-specific glucose, counterfactual vital signs, comparator thresholds, and calibration. Public IOBP2 data were reconstructed for physiological benchmarking. Results: Among 449 reference-DKA test episodes, the original BHB+trend rule achieved 98.9% sensitivity and 1.060 false alerts/day; multimodal XGBoost achieved 89.5% and 0.456/day. At BHB ≥3.0 mmol/L, median glucose was 152.9 mg/dL in classical-DKA versus 304.5 mg/dL in non-DKA hyperglycemia, demonstrating a generator shortcut. With phenotype-identical vital signs, dual/multimodal AUPRC were 0.6044/0.6036; removing glucose and vital-sign phenotype cues yielded 0.5142/0.5164. An extended validation grid changed the BHB+trend cutoff and achieved 91.5% sensitivity at 0.761 false alerts/day. The adult IOBP2 benchmark contained 854 paired observations from 165 adults. Observed glucose medians exceeded synthetic medians by 108.7–243.4 mg/dL across five BHB strata. Conclusions: Conditional XGBoost discrimination depends on simulator construction and does not establish physiological superiority. IOBP2 exposes substantial glucose-distribution mismatch and does not validate DKA prediction or kinetics. Prospective clinical and nurse-facing assessment remains necessary.