ArticleEmergency medicine journal : EMJ2015
A simple tool to predict admission at the time of triage.
Article in Emergency medicine journal : EMJ, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers, 2 of them syntheses that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
43 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Strategies to identify medical patients suitable for management through same-day emergency care services: A systematic review.Clinical medicine (London, England) · 2024Pooled it
- Predicting inhospital admission at the emergency department: a systematic review.Emergency medicine journal : EMJ · 2022Pooled it
- Applying Sydney Triage to Admission Risk Tool (START) to improve patient flow in emergency departments: a multicentre randomised, implementation study.BMC emergency medicine · 2024Trial
- Enhancing the accuracy of a multivariable prediction model to identify medical patients suitable for same day emergency care services.BMJ health & care informatics · 2026Article
- Predicting emergency department disposition using machine learning and large language models to support proactive capacity management: a multicenter retrospective study.BMC emergency medicine · 2026Article
- Unsupervised Clustering of 41,728 Emergency Department Visits: Insights into Patient Profiles and KTAS Reliability.Healthcare (Basel, Switzerland) · 2025Article
- Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments.BMJ health & care informatics · 2025Article
- Provision of medical same day emergency care services within the UK: analysis from the Society for Acute Medicine Benchmarking Audit.BMJ open · 2025Article
- The variable relationship between the National Early Warning Score on admission to hospital, the primary discharge diagnosis, and in-hospital mortality.Internal and emergency medicine · 2025Observational
- Triage Data-Driven Prediction Models for Hospital Admission of Emergency Department Patients: A Systematic Review.Healthcare informatics research · 2025Article
- Drinking from the Holy Grail-Does a Perfect Triage System Exist? And Where to Look for It?Journal of personalized medicine · 2024Review
- The association of demographics, frailty and multiple health conditions with outcomes from acute medical admissions to hospitals in England: exploratory analysis of an administrative dataset.Future healthcare journal · 2023Article
- Predicting Hospital Ward Admission from the Emergency Department: A Systematic Review.Journal of personalized medicine · 2023Review
- Medical Admission Prediction Score (MAPS); a simple tool to predict medical admissions in the emergency department.PloS one · 2023Article
- Article
- Benchmarking emergency department prediction models with machine learning and public electronic health records.Scientific data · 2022Article
- Reducing Crowding in Emergency Departments With Early Prediction of Hospital Admission of Adult Patients Using Biomarkers Collected at Triage: Retrospective Cohort Study.JMIR bioinformatics and biotechnology · 2022Article
- Development of a low-dimensional model to predict admissions from triage at a pediatric emergency department.Journal of the American College of Emergency Physicians open · 2022Article
- Leveraging Large-Scale Electronic Health Records and Interpretable Machine Learning for Clinical Decision Making at the Emergency Department: Protocol for System Development and Validation.JMIR research protocols · 2022Article
- How do we identify acute medical admissions that are suitable for same day emergency care?Clinical medicine (London, England) · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo create and validate a simple clinical score to estimate the probability of admission at the time of triage.
methodsThis 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.
results215 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).
conclusionsThis 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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.