Anja A Ebker-White1,2, Kendall J Bein1, Michael M Dinh1,3. 1. Emergency Department, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia. 2. Faculty of Nursing, The University of Sydney, Sydney, New South Wales, Australia. 3. Discipline of Emergency Medicine, The University of Sydney, Sydney, New South Wales, Australia.
Abstract
OBJECTIVE: The present study aims to prospectively validate the Sydney Triage to Admission Risk Tool (START) to predict ED disposition. METHODS: This was a prospective validation study at two metropolitan EDs in Sydney, Australia. Consecutive triage encounters were observed by a trained researcher and START scores calculated. The primary outcome was patient disposition (discharge or inpatient admission) from the ED. Multivariable logistic regression was used to estimate area under curve of receiver operator characteristic (AUC ROC) for START scores as well as START score in combination with other variables such as frailty, general practitioner referral, overcrowding and major medical comorbidities. RESULTS: There were 894 patients analysed during the study period. The START score when applied to the data had AUC ROC of 0.80 (95% CI 0.77-0.83). The inclusion of other clinical variables identified at triage did not improve the overall performance of the model with an AUC ROC of 0.81 (95% CI 0.78-0.84) in the present study. CONCLUSION: The overall performance of the START tool with respect to model discrimination and accuracy has been prospectively validated. Further clinical trials are required to test the clinical effectiveness of the tool in improving patient flow and overall ED performance.
OBJECTIVE: The present study aims to prospectively validate the Sydney Triage to Admission Risk Tool (START) to predict ED disposition. METHODS: This was a prospective validation study at two metropolitan EDs in Sydney, Australia. Consecutive triage encounters were observed by a trained researcher and START scores calculated. The primary outcome was patient disposition (discharge or inpatient admission) from the ED. Multivariable logistic regression was used to estimate area under curve of receiver operator characteristic (AUC ROC) for START scores as well as START score in combination with other variables such as frailty, general practitioner referral, overcrowding and major medical comorbidities. RESULTS: There were 894 patients analysed during the study period. The START score when applied to the data had AUC ROC of 0.80 (95% CI 0.77-0.83). The inclusion of other clinical variables identified at triage did not improve the overall performance of the model with an AUC ROC of 0.81 (95% CI 0.78-0.84) in the present study. CONCLUSION: The overall performance of the START tool with respect to model discrimination and accuracy has been prospectively validated. Further clinical trials are required to test the clinical effectiveness of the tool in improving patient flow and overall ED performance.