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Can Patient Variables Measured on Arrival to the Emergency Department Predict Disposition in Medium-acuity Patients?
J Emerg Med. 2017 May; 52(5):769-779.JE

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.

CONCLUSION

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+Show Affiliations

Department of Emergency Medicine, University of Virginia, Charlottesville, Virginia.Department of Emergency Medicine, University of Virginia, Charlottesville, Virginia.Public Health Sciences, University of Virginia, Charlottesville, Virginia.

Pub Type(s)

Journal Article
Observational Study

Language

eng

PubMed ID

28012828

Citation

Riordan, John P., et al. "Can Patient Variables Measured On Arrival to the Emergency Department Predict Disposition in Medium-acuity Patients?" The Journal of Emergency Medicine, vol. 52, no. 5, 2017, pp. 769-779.
Riordan JP, Dell WL, Patrie JT. Can Patient Variables Measured on Arrival to the Emergency Department Predict Disposition in Medium-acuity Patients? J Emerg Med. 2017;52(5):769-779.
Riordan, J. P., Dell, W. L., & Patrie, J. T. (2017). Can Patient Variables Measured on Arrival to the Emergency Department Predict Disposition in Medium-acuity Patients? The Journal of Emergency Medicine, 52(5), 769-779. https://doi.org/10.1016/j.jemermed.2016.11.018
Riordan JP, Dell WL, Patrie JT. Can Patient Variables Measured On Arrival to the Emergency Department Predict Disposition in Medium-acuity Patients. J Emerg Med. 2017;52(5):769-779. PubMed PMID: 28012828.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Can Patient Variables Measured on Arrival to the Emergency Department Predict Disposition in Medium-acuity Patients? AU - Riordan,John P, AU - Dell,Wayne L, AU - Patrie,James T, Y1 - 2016/12/21/ PY - 2016/06/24/received PY - 2016/10/25/revised PY - 2016/11/03/accepted PY - 2016/12/26/pubmed PY - 2018/4/11/medline PY - 2016/12/26/entrez KW - clinical guidelines KW - general EM practice SP - 769 EP - 779 JF - The Journal of emergency medicine JO - J Emerg Med VL - 52 IS - 5 N2 - 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. CONCLUSION: 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. SN - 0736-4679 UR - https://www.unboundmedicine.com/medline/citation/28012828/Can_Patient_Variables_Measured_on_Arrival_to_the_Emergency_Department_Predict_Disposition_in_Medium_acuity_Patients L2 - https://linkinghub.elsevier.com/retrieve/pii/S0736-4679(16)31006-X DB - PRIME DP - Unbound Medicine ER -