Disabil Health J. 2024 Oct 10:101716. doi: 10.1016/j.dhjo.2024.101716. Online ahead of print.
ABSTRACT
BACKGROUND: Traumatic brain injury (TBI) can come with long term consequences for functional outcome that can complicate return to work.
OBJECTIVES: This study aims to make accurate patient-specific predictions on one-year return to work after TBI using machine learning algorithms. Within this process, specific research questions were defined: 1 How can we make accurate predictions on employment outcome, and does this require follow-up data beyond hospitalization? 2 Which predictors are required to make accurate predictions? 3 Are predictions accurate enough for use in clinical practice?
METHODS: This study used the core CENTER-TBI observational cohort dataset, collected across 18 European countries between 2014 and 2017. Hospitalized patients with sufficient follow-up data were selected for the current analysis (N = 586). Data regarding hospital stay and follow-up until three months post-injury were used to predict return to work after one year. Three distinct algorithms were used to predict employment outcomes: elastic net logistic regression, random forest and gradient boosting. Finally, a reduced model and corresponding ROC-curve was created.
RESULTS: Full models without follow-up achieved an area under the curve (AUC) of about 81 %, which increased up to 88 % with follow-up data. A reduced model with five predictors achieved similar results with an AUC of 90 %.
CONCLUSION: The addition of three-month follow-up data causes a notable increase in model performance. The reduced model – containing Glasgow Outcome Scale Extended, pre-injury job class, pre-injury employment status, length of stay and age – matched the predictive performance of the full models. Accurate predictions on post-TBI vocational outcomes contribute to realistic prognosis and goal setting, targeting the right interventions to the right patients.
PMID:39482193 | DOI:10.1016/j.dhjo.2024.101716
