TY  - JOUR
PY  - 2022//
TI  - Development and validation of a prehospital-stage prediction tool for traumatic brain injury: a multicentre retrospective cohort study in Korea
JO  - BMJ open
A1  - Choi, Yeongho
A1  - Park, Jeong Ho
A1  - Hong, Ki Jeong
A1  - Ro, Young Sun
A1  - Song, Kyoung Jun
A1  - Shin, Sang Do
SP  - e055918
EP  - e055918
VL  - 12
IS  - 1
N2  - OBJECTIVES: Predicting diagnosis and prognosis of traumatic brain injury (TBI) at the prehospital stage is challenging; however, using comprehensive prehospital information and machine learning may improve the performance of the predictive model. We developed and tested predictive models for TBI that use machine learning algorithms using information that can be obtained in the prehospital stage. <br><br>DESIGN: This was a multicentre retrospective study. SETTING AND PARTICIPANTS: This study was conducted at three tertiary academic emergency departments (EDs) located in an urban area of South Korea. The data from adult patients with severe trauma who were assessed by emergency medical service providers and transported to three participating hospitals between 2014 to 2018 were analysed. <br><br>RESULTS: We developed and tested five machine learning algorithms-logistic regression analyses, extreme gradient boosting, support vector machine, random forest and elastic net (EN)-to predict TBI, TBI with intracranial haemorrhage or injury (TBI-I), TBI with ED or admission result of admission or transferred (TBI with non-discharge (TBI-ND)) and TBI with ED or admission result of death (TBI-D). A total of 1169 patients were included in the final analysis, and the proportions of TBI, TBI-I, TBI-ND and TBI-D were 24.0%, 21.5%, 21.3% and 3.7%, respectively. The EN model yielded an area under receiver-operator curve of 0.799 for TBI, 0.844 for TBI-I, 0.811 for TBI-ND and 0.871 for TBI-D. The EN model also yielded the highest specificity and significant reclassification improvement. Variables related to loss of consciousness, Glasgow Coma Scale and light reflex were the three most important variables to predict all outcomes. <br><br>CONCLUSION: Our results inform the diagnosis and prognosis of TBI. Machine learning models resulted in significant performance improvement over that with logistic regression analyses, and the best performing model was EN.<p />  <p>Language: en</p>
LA  - en
SN  - 2044-6055
UR  - http://dx.doi.org/10.1136/bmjopen-2021-055918
ID  - ref1
ER  -