Developing a multivariate model for the prediction of concussion recovery in sportspeople: a machine learning...

Sportspeople suffering from mild traumatic brain injury (mTBI) who return prematurely to sport are at an increased risk of delayed recovery, repeat concussion events and, in the longer-term, the development of chronic traumatic encephalopathy. Therefore, determining the appropriate recovery time, without unnecessarily delaying return to sport, is paramount at a professional/semi-professional level, yet notoriously difficult to predict.

To use machine learning to develop a multivariate model for the prediction of concussion recovery in sportspeople.

Demographics, injury history, Sport Concussion Assessment Tool fifth edition questionnaire and MRI head reports were collected for sportspeople who suffered mTBI and were referred to a tertiary university hospital in the West Midlands over 3 years. Random forest (RF) machine learning algorithms were trained and tuned on a 90% outcome-balanced corpus subset, with subsequent validation testing on the previously unseen 10% subset for binary prediction of greater than five missed sporting games. Confusion matrices and receiver operator curves were used to determine model discrimination.

375 sportspeople were included. A final composite model accuracy of 94.6% based on the unseen testing subset was obtained, yielding a sensitivity of 100% and specificity of 93.8% with a positive predictive value of 71.4% and a negative predictive value of 100%. The area under the curve was 96.3%.

In this large single-centre cohort study, a composite RF machine learning algorithm demonstrated high performance in predicting sporting games missed post-mTBI injury. Validation of this novel model on larger external datasets is therefore warranted.

ISRCTN16974791.