Citation

Lee SH, Lee CH, Hwang SH, Kang DH. World Neurosurg. 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Elsevier Publishing)

DOI

10.1016/j.wneu.2022.06.130

PMID

35787963

Abstract

BACKGROUND: Machine learning (ML) has been used to predict the outcomes of traumatic brain injury (TBI). However, few studies have reported the use of ML models to predict early death. This study aimed to develop ML models for early death prediction and to compare performance with the corticosteroid randomization after significant head injury (CRASH) model.

METHODS: We retrospectively reviewed TBI patients between February 2017 and August 2021. The patients were randomly assigned to a training set and a test set. Predictive variables included clinical findings, laboratory values, and computed tomography findings. The ML models (random forest, support vector machine [SVM], logistic regression) were developed with the training set. The CRASH model is a prognostic model that was developed based on 10,008 patients included in the CRASH trial. The ML and CRASH models were applied to the test set to evaluate the performance.

RESULTS: A total of 423 patients were included; 317 and 106 patients were randomly assigned to the training and test sets, respectively. The area under the curve (AUC) was highest in the SVM (0.952, 95% CI 0.906-0.990) and lowest in the CRASH model (0.942, 95% CI 0.886-0.999). There were no significant differences between the AUCs of the ML and CRASH models (P =.899 for random forest versus the CRASH model, P =.760 for SVM versus the CRASH model, P =.806 for logistic regression versus the CRASH model).

CONCLUSIONS: The ML models may have comparable performances compared to the CRASH model despite being developed with a smaller sample size.

Language: en

Keywords

trauma; machine learning; traumatic brain injury; Corticosteroid randomization after significant head injury model; early death; prognostic model