title: A simple tool to predict admission at the time of triage.
abstract: OBJECTIVE
  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.
authors.affiliation: Acute Medicine Unit, Glasgow Royal Infirmary, Glasgow, UK.
authors.forename: Allan
authors.initials: A
authors.lastname: Cameron
authors.affiliation: Medical School, University of Glasgow, Glasgow, UK.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Rodgers
authors.affiliation: Emergency Department, Glasgow Royal Infirmary, Glasgow, UK.
authors.forename: Alastair
authors.initials: A
authors.lastname: Ireland
authors.affiliation: Acute Medicine Unit, Glasgow Royal Infirmary, Glasgow, UK.
authors.forename: Ravi
authors.initials: R
authors.lastname: Jamdar
authors.affiliation: Acute Medicine Unit, Glasgow Royal Infirmary, Glasgow, UK.
authors.forename: Gerard A
authors.initials: GA
authors.lastname: McKay
mesh_terms: D000328:Adult; D000368:Aged; D003430:Cross-Sectional Studies; D004636:Emergency Service, Hospital / Q000458:organization & administration* / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D010343:Patient Admission / Q000706:statistics & numerical data*; D012189:Retrospective Studies; D018570:Risk Assessment / Q000379:methods*; D012720:Severity of Illness Index; D014218:Triage; D055815:Young Adult
keywords: clinical assessment; clinical assessment, effectiveness; clinical management; emergency care systems, efficiency; triage