• Maryse C Cnossen, Ethan A Winkler, John K Yue, David O Okonkwo, Alex B Valadka, Ewout W Steyerberg, Hester F Lingsma, Geoffrey T Manley, and TRACK-TBI Investigators.
• Center for Medical Decision Making, Department of Public Health, Erasmus Medical Center, Rotterdam, the Netherlands.
• J. Neurotrauma. 2017 Aug 15; 34 (16): 2396-2409.

AbstractPost-concussive symptoms occur frequently after mild traumatic brain injury (mTBI) and may be categorized as cognitive, somatic, or emotional. We aimed to: 1) assess whether patient demographics and clinical variables predict development of each of these three symptom categories, and 2) develop a prediction model for 6-month post-concussive symptoms. Patients with mTBI (Glasgow Coma Scale score 13-15) from the prospective multi-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) Pilot study (2010-2012) who completed the Rivermead Post Concussion Symptoms Questionnaire (RPQ) at 6 months post-injury were included. Linear regression was utilized to determine the predictive value of candidate predictors for cognitive, somatic, and emotional subscales individually, as well as the overall RPQ. The final prediction model was developed using least absolute shrinkage and selection operator shrinkage and bootstrap validation. We included 277 mTBI patients (70% male; median age 42 years). No major differences in the predictive value of our set of predictors existed for the cognitive, somatic, and emotional subscales, and therefore one prediction model for the RPQ total scale was developed. Years of education, pre-injury psychiatric disorders, and prior TBI were the strongest predictors of 6-month post-concussive symptoms. The total set of predictors explained 21% of the variance, which decreased to 14% after bootstrap validation. Demographic and clinical variables at baseline are predictive of 6-month post-concussive symptoms following mTBI; however, these variables explain less than one-fifth of the total variance in outcome. Model refinement with larger datasets, more granular variables, and objective biomarkers are needed before implementation in clinical practice.

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