M M Dinh1,2,3, K J Bein4, M Oliver5, A-S Veillard6, R Ivers7. 1. Department of Trauma Services, Royal Prince Alfred Hospital, Camperdown, Australia. dinh.mm@gmail.com. 2. Emergency Department, Royal Prince Alfred Hospital, Camperdown, NSW, 2050, Australia. dinh.mm@gmail.com. 3. Sydney Medical School, University of Sydney, Sydney, Australia. dinh.mm@gmail.com. 4. Emergency Department, Royal Prince Alfred Hospital, Camperdown, NSW, 2050, Australia. Kendall.bein@sswahs.nsw.gov.au. 5. Emergency Department, Royal Prince Alfred Hospital, Camperdown, NSW, 2050, Australia. drmatoliver@gmail.com. 6. NHMRC Clinical Trials Centre, Camperdown, Australia. anne-sophie.veillard@ctc.usyd.edu.au. 7. Injury Division, The George Institute for Global Health, Sydney, Australia. Rivers@thegeorgeinstitute.org.au.
Abstract
PURPOSE: To derive and internally validate a clinical prediction rule for trauma triage. METHODS: Ambulance presentations requiring trauma team activation between 2007 and 2011 at a single inner city major trauma centre were analysed. The primary outcome was major trauma, defined as Injury Severity Score >15, intensive care unit admission or in-hospital death. Demographic details, vital signs on arrival at hospital, mechanism of injury and injured body regions were used in the modelling process. Multivariable logistic regression was used on a randomly selected derivation sample. Receiver operating characteristic (ROC) analysis and Hosmer-Lemeshow tests were used to assess the discrimination and calibration of the derived model. The model was further tested using bootstrapping cross-validation. RESULTS: A total of 3027 patients were identified. Predictors selected for the prediction model were age ≥65 years (OR 1.58, 95 %CI 1.08-2.32, p = 0.02), abnormal vital signs (OR 3.72, 95 %CI 2.64-5.25), Glasgow Coma Scale score ≤13 (OR 14, 95 %CI 9.23-23.34 p < 0.001), penetrating injury (OR 5.13, 95 %CI 2.76-9.54, p < 0.001), multiregion injury (OR 4.72 95 %CI 3.45-6.46, p < 0.001), falls (OR 1.51 95 %CI 1.06-2.15, p = 0.02) and motor vehicle crashes (OR 0.56, 95 %CI 0.35-0.90, p = 0.02). The ROC area under the curve (AUC) for the final model was 0.85 (95 %CI 0.83-0.87) with a Hosmer-Lemeshow test statistic p = 0.83. Bootstrapping cross-validation demonstrated an identical AUC. CONCLUSION: We have derived and internally validated a trauma risk prediction rule using trauma registry data. This may assist with the formulation of revised local and regional trauma triage protocols. External validation is required before implementation.
PURPOSE: To derive and internally validate a clinical prediction rule for trauma triage. METHODS: Ambulance presentations requiring trauma team activation between 2007 and 2011 at a single inner city major trauma centre were analysed. The primary outcome was major trauma, defined as Injury Severity Score >15, intensive care unit admission or in-hospital death. Demographic details, vital signs on arrival at hospital, mechanism of injury and injured body regions were used in the modelling process. Multivariable logistic regression was used on a randomly selected derivation sample. Receiver operating characteristic (ROC) analysis and Hosmer-Lemeshow tests were used to assess the discrimination and calibration of the derived model. The model was further tested using bootstrapping cross-validation. RESULTS: A total of 3027 patients were identified. Predictors selected for the prediction model were age ≥65 years (OR 1.58, 95 %CI 1.08-2.32, p = 0.02), abnormal vital signs (OR 3.72, 95 %CI 2.64-5.25), Glasgow Coma Scale score ≤13 (OR 14, 95 %CI 9.23-23.34 p < 0.001), penetrating injury (OR 5.13, 95 %CI 2.76-9.54, p < 0.001), multiregion injury (OR 4.72 95 %CI 3.45-6.46, p < 0.001), falls (OR 1.51 95 %CI 1.06-2.15, p = 0.02) and motor vehicle crashes (OR 0.56, 95 %CI 0.35-0.90, p = 0.02). The ROC area under the curve (AUC) for the final model was 0.85 (95 %CI 0.83-0.87) with a Hosmer-Lemeshow test statistic p = 0.83. Bootstrapping cross-validation demonstrated an identical AUC. CONCLUSION: We have derived and internally validated a trauma risk prediction rule using trauma registry data. This may assist with the formulation of revised local and regional trauma triage protocols. External validation is required before implementation.
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