Can Patient Variables Measured on Arrival to the Emergency Department Predict Disposition in Medium-acuity Patients?

Abstract

BackgroundEmergency 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. ObjectiveWe 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. MethodsOur 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. ResultsAll 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. ConclusionWe 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.