Triage of geriatric patients in the emergency department: validity and survival with the Emergency Severity Index
Abstract
Study objective We evaluate the validity of the Emergency Severity Index (version 3) (ESI) triage algorithm in a geriatric emergency department (ED) population and determine the association between ESI categorization and survival.Methods Validity was assessed using a retrospective analysis of hospitalization, ED length of stay, and observed resource use compared with resource utilization estimated at triage by the ESI. Survival analysis was conducted for the same cohort. The study was exempted by the hospital institutional review board, and the requirement for informed consent was waived.Results During the investigation, 1,087 patients older than 65 years were registered in the ED. Six patients were identified as direct admissions, and 152 were found to be repeat visitors, leaving 929 subject visits for analysis. In this cohort, hospitalization was associated with ESI triage assignment (Kendall's tau-b=-0.476; 95% confidence interval [CI] -0.524 to -0.425). The area under the receiver operating characteristic curve for the predictive ability of the ESI for hospitalization was 0.77 (95% CI 0.748 to 0.806). Length of stay was associated with ESI assignment (Kruskal-Wallis test, P=.000). The relationship between triage categorization and resource utilization was significant (Spearman's correlation=-0.683; 95% CI -0.716 to -0.647). ESI categorization was associated with vital status at 1 year (Kaplan-Meier chi2 67.85; df=4; P=.0000).Conclusion When used to triage patients older than 65 years, the ESI algorithm demonstrates validity. Hospitalization, length of stay, resource utilization, and survival were all associated with ESI categorization in this cohort.
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The triage level encoded by the Emergency Severity Index (ESI) had high information gain in the full XGBoost model.
“Not surprisingly, the triage level encoded by the Emergency Severity Index (ESI) had high information gain [44,45]”
From Predicting hospital admission at emergency department triage using machine learning (2018) · cites this paper as [45]
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Predicting hospital admission at emergency department triage using machine learning
Hong, W. S., Haimovich, A. D., & Taylor, R. A. (2018) · PLOS ONE · doi:10.1371/journal.pone.0201016 · Cited by 326 · see citing works
Predicting hospital admission at the emergency department triage: A novel prediction model
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- Authors
- Baumann, M. R., & Strout, T. D.
- Affiliations
- Maine Medical Center (US)
- Corresponding author
- Strout, Tania D.
- Venue
- Annals of Emergency Medicine
- Publisher
- Elsevier BV
- Published
- 2007-02
- Volume
- 49
- Issue
- 2
- Pages
- 234-240
- ISSN
- 0196-0644 (ISSN-L)
- OpenAlex
- W2124799218
- Cited by
- 121
- References
- 19
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 3 · 2023: 12 · 2022: 5 · 2021: 7 · 2020: 5 · 2019: 15 · 2018: 8 · 2017: 5 · 2016: 6 · 2015: 3 · 2014: 9 · 2013: 7 · 2012: 6
- FWCI
- 2.80
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Emergency Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Health Sciences › Medicine › Emergency Medicine › Trauma and Emergency Care Studies
- Health Sciences › Medicine › Epidemiology › Sepsis Diagnosis and Treatment
- Keywords
- Emergency Severity Index, geriatric emergency department, emergency department triage, hospitalization, emergency department length of stay, resource utilization (health care), survival, older adults
- MeSH terms
- Aged, Aged, 80 and over, Algorithms, Emergency Service, Hospital, Female, Hospitalization, Humans, Length of Stay, Maine, Male, Retrospective Studies, ROC Curve, Severity of Illness Index, Triage, Geriatric Assessment, Medical Records Systems, Computerized, Hospital Mortality
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