Development and validation of a prehospital-stage prediction tool for traumatic brain injury: a multicentre retrospective cohort study in Korea
Abstract
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. 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. 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…
How strong is the evidence?
Evidence Hub reads this as a cohort study, done in people. Its evidence level is 3 of 5 (Moderate · trial / cohort / secondary analysis). Trials without a large randomized design, cohorts and secondary analyses give moderate evidence. Reported size: 1,169 participants or records.
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