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<p>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; <InlineEquation ID="IEq1"><EquationSource Format="TEX">\(n=2221\)</EquationSource></InlineEquation>). 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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月17日

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  • Atsushi Senda,
  • Yuki Takatsu,
  • Ryokan Ikebe,
  • Hiroshi Suginaka,
  • Koji Morishita,
  • Akira Endo

摘要

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.