Bayesian Sequential Modeling of Time-to-Urination for Dynamic ED Triage

Triage tools in routine emergency care are largely static, failing to exploit simple behavioral cues clinicians notice in real time. Here, we developed a Bayesian, sequentially updating framework that integrates incoming cues to produce calibrated, time-consistent risk. Using a prospective single-center cohort of ambulance arrivals in Japan (February-August 2025; n=2,221), we evaluated time to first urination (TTU) as a proof-of-concept bedside cue for predicting hospital admission. Population-level fit to the cumulative admission curve was excellent (integrated squared error 0.002; RMSE 0.003; Kolmogorov-Smirnov 0.008; coverage 0.98). At the patient level, performance improved markedly with age/sex adjustment (AUC(t) 0.70 vs. 0.50 unadjusted), with lower Brier scores and positive calibration slopes. Platt recalibration refined probability scaling without altering discrimination, and decision-curve analysis showed small, favorable net benefit at common thresholds. This framework is readily extensible to multimodal inputs and external validation and is designed to complement, not replace, existing triage systems.