Development and validation of a prehospital-stage prediction tool for traumatic brain injury: a multicentre retrospective cohort study in Korea

• Published in: BMJ open Vol. 12; no. 1; p. e055918
• Main Authors: Choi, Yeongho, Park, Jeong Ho, Hong, Ki Jeong, Ro, Young Sun, Song, Kyoung Jun, Shin, Sang Do
• Format: Journal Article
• Language: English
• Published: England British Medical Journal Publishing Group 12.01.2022; BMJ Publishing Group LTD; BMJ Publishing Group
• Series: Original research
• Subjects: accident & emergency medicine; Algorithms; Bias; Codes; Cohort analysis; Demographics; Emergency medical care; Emergency Medicine; Fires; Hospitals; Hypotension; Hypoxia; Machine learning; Medical prognosis; neurological injury; Physicians; Sex crimes; Trauma; trauma management; Traumatic brain injury; Variables; United States > US; South Korea; accident & emergency medicine; trauma management; neurological injury
• ISSN: 2044-6055; 2044-6055
• DOI: 10.1136/bmjopen-2021-055918

ObjectivesPredicting 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.DesignThis was a multicentre retrospective study.Setting and participantsThis 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.ResultsWe 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.ConclusionOur 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.