Development and temporal evaluation of an emergency severity index-informed Bayesian sequential model for hospital admission prediction using time-to-urination

Triage tools in routine emergency care are largely static and may miss simple dynamic bedside cues available after presentation. We developed and temporally evaluated an emergency severity index (ESI)-informed Bayesian sequential prediction model for hospital admission using time-to-urination (TTU) in a prospective single-center cohort of ambulance-transported emergency department patients in Japan (February–August 2025; \(n=2221\) ). The outcome was hospital admission at emergency department disposition. ESI was used as the initial pretest risk layer, TTU as a dynamic updating cue, and age and sex as refinement variables. Population-level fit to the cumulative admission curve was strong. In nested model comparison, ESI alone yielded an AUROC of 0.661 (95% CI 0.640–0.680), adding TTU improved discrimination to 0.677 (95% CI 0.658–0.698), and further adjustment for age and sex yielded the best performance (AUROC 0.741, 95% CI 0.722–0.760). Recalibration improved probability alignment without materially changing discrimination. Calibration deteriorated later in the post-arrival period, suggesting that the model is most informative in the early post-arrival window. This framework is designed to augment, rather than replace, existing triage systems.