Patient-level, Temporal, and Dynamic Operational Indicators of Emergency Department Prolonged Length of Stay: A Retrospective Cohort Study
Archives of Academic Emergency Medicine,
Vol. 14 No. 1 (2026),
1 October 2025
,
Page e25
https://doi.org/10.22037/aaem.v14i1.3048
Abstract
Introduction: Emergency department length of stay (ED-LOS) is a key indicator of crowding and care quality. This study aimed to identify patient-level, temporal, and real-time operational predictors of prolonged ED-LOS.
Methods: We conducted a retrospective cohort study using routinely collected data from all ED visits at Foch Hospital, France, between January and November 2025. Prolonged ED-LOS was defined as ED-LOS >8 hours. Data were split chronologically into a training period from January to September and a held-out test period from October to November. Three logistic regression models were evaluated: Model 1 included patient-level and temporal variables; Model 2 additionally included dynamic congestion indicators; and Model 3 further included early process delays.
Results: Among 41,818 ED visits, 41,431 were included in the final analytic sample. Overall, 8,119 (19.6%) visits had ED-LOS >8 hours. In the test set, Model 1 showed good discrimination (area under the receiver operating characteristic curve (AUC): 0.752, 95% confidence interval (CI): 0.740 – 0.766), which improved modestly after adding dynamic congestion variables in Model 2 (AUC: 0.761, 95% CI: 0.749 – 0.775). Model 3 achieved the best performance (AUC: 0.804 (95% CI: 0.793 – 0.815); Brier score: 0.127 (95% CI: 0.122 – 0.132)). Older age, triage acuity level 3 (classification infirmière des malades aux urgences: CIMU ), weekend arrival, dynamic congestion at arrival, and early process delays were the main predictors of prolonged ED-LOS.
Conclusion: Based on the findings, older age, intermediate triage acuity, weekend arrival, dynamic congestion at arrival, and early process delays were the independent predictors of prolonged ED stay. The addition of dynamic congestion variables improved prediction beyond patient-level and temporal characteristics, while the strongest performance was achieved after incorporating early delays to triage and physician assessment.
- emergency department
- length of stay
- crowding
- operational indicators
- prediction model
How to Cite
References
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