• Pongsakorn Atiksawedparit, Sasivimol Rattanasiri, Yuwares Sittichanbuncha, Mark McEvoy, Paibul Suriyawongpaisal, John Attia, and Ammarin Thakkinstian.
• Section for Clinical Epidemiology and Biostatistics, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand; Department of Emergency Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok, Thailand. Electronic address: kawhommali@hotmail.com.
• Injury. 2019 Sep 1; 50 (9): 1499-1506.

BackgroundTo develop and validate a risk stratification model of severe injury (SI) and death to identify and prioritize road traffic injury (RTI) patients for transportation to an appropriate trauma center (TC).MethodsA 2-phase multicenter-cross-sectional study with prospective data collection was collaboratively conducted using 9 dispatch centers (DC) across Thailand. Among the 9 included DC, 7 and 2 DCs were used for development and validation, respectively. RTI patients who were treated and transported to hospitals by advanced life support (ALS) response units were enrolled. Multiple logistic regression was used to derive risk prediction score of death in 48 h and SI (new injury severity score ≥ 16). Calibration/discrimination performances were explored.ResultsA total of 5359 and 2097 RTIs were used for development and external validation, respectively. Seven and 9 predictors among demographic data, mechanism of injury, physic data, EMS operation, and prehospital managements were significant predictors of death and SI, respectively. Risk prediction models fitted well with the developed data (O/E ratios of 1.00 (IQR: 0.69, 1.01) and 0.99 (IQR: 0.95, 1.05) for death and SI, respectively); and the C statistics of 0.966 (0.961, 0.972) and 0.913 (0.905, 0.922). The risk scores were further stratified as low, moderate and high risk. The derive models did not fit well with external data but they were improved after recalibrating the intercepts. However, the model was externally good/excellent discriminated with C statistics from 0.896 (0.871, 0.922) to 0.981 (0.971, 0.991).ConclusionRisk prediction models of death and SI were developed with good calibration and excellent discrimination. The model should be useful for ALS response units in proper allocation of patients.Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.

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