A predictive model to analyze factors affecting the presence of mild whiplash-associated disorders in minor motor vehicle crashes based on the Korean In-Depth Accidents Study (KIDAS) Database
Creators: name: Lee, Hee Young
name: Youk, Hyun
name: Kim, Oh Hyun
name: Kong, Joon Seok
name: Kang, Chan Young
name: Sung, Sil
name: Jang, Ji Yun
name: Kim, Ho Jung
name: Kim, Sang Chul
name: Lee, Kang Hyun
Publisher: name: Taylor & Francis
Year: 2019
Subjects: subject: Biotechnology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: FOS: Earth and related environmental sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: FOS: Computer and information sciences
subject: Marine Biology
subject: Science Policy

description: <b>Objectives:</b> We aimed to analyze factors affecting the severity of mild whiplash-associated disorders (WADs) and to develop a predictive model to evaluate the presence of mild WAD in minor motor vehicle crashes (MVCs). <b>Methods:</b> We used the Korean In-Depth Accident Study (KIDAS) database, which collects data from 4 regional emergency centers, to obtain data from 2011 to 2017. The Collision Deformation Classification code was obtained as vehicle’s damage information, and Abbreviated Injury Scale (AIS), Maximum Abbreviated Injury Scale (MAIS), and Injury Severity Score (ISS) were used as occupant’s injury information. The degree of WAD was determined using the Quebec Task Force (QTF) classification, comprised of 5 stages (QTF 0–4), depending on the occupant’s pain and the physician’s findings. QTF 1 was defined as mild WAD, and we used QTF 0 to define those who were uninjured. For KIDAS data between 2011 and 2016, a logistic regression model was used to identify factors affecting the occurrence of mild WAD and a predictive model was constructed. Internal validity was estimated using random bootstrapping, and external validity was evaluated by applying 2017 KIDAS data. Of the 2,629 occupants in the KIDAS database from 2011 to 2016, after applying several exclusion conditions, 459 occupants were used to develop the predictive model. The external validity of the derived predictive model was assessed using the 13 MVC occupants from the 2017 KIDAS database meeting our inclusion criteria. Among the 137 MVC occupants from the 2017 KIDAS database for analysis of the external validity of the derived predictive model, the predictive model was verified for 13 MVC occupants. <b>Results:</b> Logistic regression analysis was used to derive a predictive model based on sex, age, body mass index, type of vehicle, belt status, seating row, crush type, and crush extent. This predictive model had an explanatory power of 65.5% to determine an actual QTF of 0 and 1 (c-statistics: 0.655). As a result of the external validity analysis of the predictive model using data from the 2017 KIDAS database (<i>N</i> = 13), sensitivity, specificity, and accuracy were 0.500, 0.857, and 0.692, respectively. <b>Conclusions:</b> Using the predictive model, the results of the external validity analysis showed low sensitivity but high specificity. This predictive model provided meaningful results, with a high success rate for determining no injury to an occupant. Given our study results, future research is needed to create a more accurate predictive model that includes relevant technical and sociological factors.