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A simple tool to predict admission at the time of triage
  1. Allan Cameron1,
  2. Kenneth Rodgers2,
  3. Alastair Ireland3,
  4. Ravi Jamdar1,
  5. Gerard A McKay1
  1. 1Acute Medicine Unit, Glasgow Royal Infirmary, Glasgow, UK
  2. 2Medical School, University of Glasgow, Glasgow, UK
  3. 3Emergency Department, Glasgow Royal Infirmary, Glasgow, UK
  1. Correspondence to Dr Allan Cameron, Acute Medicine Unit, Glasgow Royal Infirmary, Jubilee building, Castle Street, Glasgow G4 0SF, UK; Allan.Cameron{at}


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.

  • clinical assessment
  • clinical assessment, effectiveness
  • clinical management
  • emergency care systems, efficiency
  • triage

This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 3.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:

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