Ledoux, Didier, EU - European Union, FRB - King Baudouin Foundation, ZNS Hannelore Kohl Stiftung, One Mind, EC - European Commission, FWO - Research Foundation Flanders, Van Deynse, Helena, Cools, Wilfried, De Deken, Viktor-Jan, Depreitere, Bart, Hubloue, Ives, Tisseghem, Ellen, Putman, Koen, Åkerlund, Cecilia, Amrein, Krisztina, Andelic, Nada, Zoerle, Tommaso, Vrije Universiteit Brussel Bruxelles (VUB), University of Oslo (UiO), University of Tromsø (UiT), Espace de réflexion éthique Grand Est (EREGE Lorraine), Service de médecine physique et réadaptation CHU Raymond-Poincaré, Hôpital Raymond Poincaré AP-HP, Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Innsbruck Medical University = Medizinische Universität Innsbruck (IMU), Centre Hospitalier Universitaire CHU Grenoble (CHUGA), Université Grenoble Alpes (UGA)
Source:
Disability and Health Journal, 18, 2 CENTER-TBI Collaborators & et al. 2024, ' One-year employment outcome prediction after traumatic brain injury: A CENTER-TBI study. ', Disability and health journal . https://doi.org/10.1016/j.dhjo.2024.101716
Traumatic brain injury (TBI) can come with long term consequences for functional outcome that can complicate return to work.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?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.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 %.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.
Traumatic brain injury (TBI) can come with long term consequences for functional outcome that can complicate return to work.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?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.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 %.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.
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