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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/51442
Title: Prehospital prediction of severe injury in road traffic injuries: A multicenter cross-sectional study
Authors: Pongsakorn Atiksawedparit
Sasivimol Rattanasiri
Yuwares Sittichanbuncha
Mark McEvoy
Paibul Suriyawongpaisal
John Attia
Ammarin Thakkinstian
University of Newcastle, Faculty of Health and Medicine
Faculty of Medicine, Ramathibodi Hospital, Mahidol University
Keywords: Medicine
Issue Date: 1-Sep-2019
Citation: Injury. Vol.50, No.9 (2019), 1499-1506
Abstract: © 2019 The Authors Background: To 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). Methods: A 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. Results: A 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). Conclusion: Risk 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.
URI: http://repository.li.mahidol.ac.th/dspace/handle/123456789/51442
metadata.dc.identifier.url: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85066495336&origin=inward
ISSN: 18790267
00201383
Appears in Collections:Scopus 2019

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