Machine Learning for Predicting Discharge Disposition After Traumatic Brain Injury.

Journal Article (Journal Article)

BACKGROUND: Current traumatic brain injury (TBI) prognostic calculators are commonly used to predict the mortality and Glasgow Outcome Scale, but these outcomes are most relevant for severe TBI. Because mild and moderate TBI rarely reaches severe outcomes, there is a need for novel prognostic endpoints. OBJECTIVE: To generate machine learning (ML) models with a strong predictive capacity for trichotomized discharge disposition, an outcome not previously used in TBI prognostic models. The outcome can serve as a proxy for patients' functional status, even in mild and moderate patients with TBI. METHODS: Using a large data set (n = 5292) of patients with TBI from a quaternary care center and 84 predictors, including vitals, demographics, mechanism of injury, initial Glasgow Coma Scale, and comorbidities, we trained 6 different ML algorithms using a nested-stratified-cross-validation protocol. After optimizing hyperparameters and performing model selection, isotonic regression was applied to calibrate models. RESULTS: When maximizing the microaveraged area under the receiver operating characteristic curve during hyperparameter optimization, a random forest model exhibited top performance. A random forest model was also selected when maximizing the microaveraged area under the precision-recall curve. For both models, the weighted average area under the receiver operating characteristic curves was 0.84 (95% CI 0.81-0.87) and the weighted average area under the precision-recall curves was 0.85 (95% CI 0.82-0.88). CONCLUSION: Our group presents high-performing ML models to predict trichotomized discharge disposition. These models can assist in optimization of patient triage and treatment, especially in cases of mild and moderate TBI.

Full Text

Duke Authors

Cited Authors

  • Satyadev, N; Warman, PI; Seas, A; Kolls, BJ; Haglund, MM; Fuller, AT; Dunn, TW

Published Date

  • June 1, 2022

Published In

Volume / Issue

  • 90 / 6

Start / End Page

  • 768 - 774

PubMed ID

  • 35319523

Electronic International Standard Serial Number (EISSN)

  • 1524-4040

Digital Object Identifier (DOI)

  • 10.1227/neu.0000000000001911


  • eng

Conference Location

  • United States