One-year employment outcome prediction after traumatic brain injury: A CENTER-TBI study
Van Deynse H., Cools W., De Deken VJ., Depreitere B., Hubloue I., Tisseghem E., Putman K., Åkerlund C., Amrein K., Andelic N., Andreassen L., Anke A., Antoni A., Audibert G., Azouvi P., Azzolini ML., Bartels R., Barzó P., Beauvais R., Beer R., Bellander BM., Belli A., Benali H., Berardino M., Beretta L., Blaabjerg M., Bragge P., Brazinova A., Brinck V., Brooker J., Brorsson C., Buki A., Bullinger M., Cabeleira M., Caccioppola A., Calappi E., Calvi MR., Cameron P., Lozano GC., Carbonara M., Cavallo S., Chevallard G., Chieregato A., Citerio G., Clusmann H., Coburn M., Coles J., Cooper JD., Correia M., Čović A., Curry N., Czeiter E., Czosnyka M., Dahyot-Fizelier C., Dark P., Dawes H., De Keyser V., Degos V., Della Corte F., Boogert HD., Đilvesi Đ., Dixit A., Donoghue E., Dreier J., Dulière GL., Ercole A., Esser P., Ezer E., Fabricius M., Feigin VL., Foks K., Frisvold S., Furmanov A., Gagliardo P., Galanaud D., Gantner D., Gao G., George P., Ghuysen A., Giga L., Glocker B., Golubovic J., Gomez PA., Gratz J., Gravesteijn B., Grossi F., Gruen RL., Gupta D., Haagsma JA., Haitsma I., Helbok R., Helseth E., Horton L., Huijben J., Hutchinson PJ., Jacobs B., Jankowski S., Jarrett M., Jiang JY., Johnson F.
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.