A simple tool to predict admission at the time of triage
Open accessAbstract
Aim: To create and validate a simple clinical score to estimate the probability of admission at the time of triage. Methods: This was a multicentre, retrospective, cross-sectional study of triage records for all unscheduled adult attendances in North Glasgow over 2 years. Clinical variables that had significant associations with admission on logistic regression were entered into a mixed-effects multiple logistic model. This provided weightings for the score, which was then simplified and tested on a separate validation group by receiving operator characteristic (ROC) analysis and goodness-of-fit tests. Results: 215 231 presentations were used for model derivation and 107 615 for validation. Variables in the final model showing clinically and statistically significant associations with admission were: triage category, age, National Early Warning Score (NEWS), arrival by ambulance, referral source and admission within the last year. The resulting 6-variable score showed excellent admission/discharge discrimination (area under ROC curve 0.8774, 95% CI 0.8752 to 0.8796). Higher scores also predicted early returns for those who were discharged: the odds of subsequent admission within 28 days doubled for every 7-point increase (log odds=+0.0933 per point, p<0.0001). Conclusions: This simple, 6-variable score accurately estimates the probability of admission purely from triage information. Most patients could accurately be assigned to ‘admission likely’, ‘admission unlikely’, ‘admission very unlikely’ etc., by setting appropriate cut-offs. This could have uses in patient streaming, bed management and decision support. It also has the potential to control for demographics when comparing performance over time or between departments.
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The model's performance (AUROC 0.856) is state-of-the-art and comparable with recent admission models (AUROC 0.825-0.877).
“This represents state-of-the-art performance comparable with recent admission models (e.g. AUROC 0.825–0.87722–25).”
From Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19 (2021) · cites this paper as [23]
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“…of large and varied data sets to improve AI prediction performance, 27 the requirement for external validation to reduce the potential of bias, 28 expanding research through multi-centre studies, 29 ensuring the AI prediction tool outperforms the clinician 28 and it is effectively integrated…”
Background
- Authors
- Cameron, A., Rodgers, K., Ireland, A., Jamdar, R., & McKay, G. A.
- Affiliations
- Glasgow Royal Infirmary (GB)
- University of Glasgow (GB)
- Corresponding author
- Cameron, Allan
- ORCID
- Cameron, Allan
- Venue
- Emergency Medicine Journal
- Publisher
- BMJ
- Published
- 2014-01-13
- Volume
- 32
- Issue
- 3
- Pages
- 174-179
- ISSN
- 1351-0622 (ISSN-L)
- 1472-0205
- 1472-0213
- PMC
- PMC4345772
- OpenAlex
- W2096367109
- Cited by
- 99
- References
- 28
- Citations by year
- 2026: 3 · 2025: 8 · 2024: 8 · 2023: 8 · 2022: 8 · 2021: 20 · 2020: 9 · 2019: 10 · 2018: 9 · 2017: 9 · 2016: 4 · 2015: 1 · 2014: 2
- FWCI
- 5.69
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc
- Locations
- Emergency Medicine Journal (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) (acceptedVersion)
- Topics
- Health Sciences › Medicine › Emergency Medicine › Emergency and Acute Care Studies
- Health Sciences › Health Professions › Emergency Medical Services › Healthcare Operations and Scheduling Optimization
- Health Sciences › Medicine › Emergency Medicine › Trauma and Emergency Care Studies
- Keywords
- triage, clinical risk score, National Early Warning Score, ambulance arrival, hospital bed management, clinical decision support
- MeSH terms
- Adult, Aged, Cross-Sectional Studies, Emergency Service, Hospital, Female, Humans, Male, Middle Aged, Models, Theoretical, Patient Admission, Retrospective Studies, Severity of Illness Index, Triage, Logistic Models, Risk Assessment, Young Adult
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