title: Can Patient Variables Measured on Arrival to the Emergency Department Predict Disposition in Medium-acuity Patients?
abstract: BACKGROUND
  Emergency department crowding has led to innovative "front end" care models to safely and efficiently care for medium and lower acuity patients. In the United States, most treatment algorithms rely on the emergency severity index (ESI) triage tool to sort patients. However, there are no objective criteria used to differentiate ESI 3 patients.
  
  
  OBJECTIVE
  We seek to derive and validate a model capable of predicting patient discharge disposition (DD) using variables present on arrival to the emergency department for ESI 3 patients.
  
  
  METHODS
  Our retrospective cohort study included adult patients with an ESI triage designation 3 treated in an academic emergency department over the course of 2 successive years (2013-2015). The main outcome was DD. Two datasets were used in the modeling process. One dataset, the derivation dataset (n = 25,119), was used to develop the statistical model, while the second dataset, the validation dataset (n = 24,639), was used to evaluate the statistical model's prediction performance.
  
  
  RESULTS
  All variables included in the derivation model were uniquely associated with DD status (p < 0.001). We assessed multivariate adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for age (2.50 [95% CI 2.35-2.65]), arrival mode (1.85 [95% CI 1.74-1.96]), heart rate (1.31 [95% CI 1.26-1.37]), sex (1.35 [95% CI 1.28-1.43]), oxygen saturation (1.06 [95% CI 1.01-1.10]), temperature (1.10 [95% CI 1.06-1.15]), systolic blood pressure (1.18 [95% CI 1.12-1.25]), diastolic blood pressure (1.16 [95% CI 1.09-1.22]), respiratory rate (1.05 [95% CI 1.01-1.10]), and pain score (1.13 [95% CI 1.06-1.21]). The validation C-statistic was 0.73.
  
  
  CONCLUSIONS
  We derived and validated a model and created a nomogram with acceptable discrimination of ESI 3 patients on arrival for purposes of predicting DD. Incorporating these variables into the care of these patients could improve patient flow by identifying patients who are likely to be discharged.
authors.affiliation: Department of Emergency Medicine, University of Virginia, Charlottesville, Virginia.
authors.forename: John P
authors.initials: JP
authors.lastname: Riordan
authors.affiliation: Department of Emergency Medicine, University of Virginia, Charlottesville, Virginia.
authors.forename: Wayne L
authors.initials: WL
authors.lastname: Dell
authors.affiliation: Public Health Sciences, University of Virginia, Charlottesville, Virginia.
authors.forename: James T
authors.initials: JT
authors.lastname: Patrie
mesh_terms: D000328:Adult; D000368:Aged; D016009:Chi-Square Distribution; D003441:Crowding; D004636:Emergency Service, Hospital / Q000458:organization & administration / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007902:Length of Stay; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D049451:Nomograms; D062072:Patient Acuity; D015203:Reproducibility of Results; D012189:Retrospective Studies; D012720:Severity of Illness Index; D011795:Surveys and Questionnaires; D014218:Triage / Q000379:methods* / Q000706:statistics & numerical data; D014768:Virginia
keywords: clinical guidelines; general EM practice