Effect of Time Since Baseline Data Collection, Mechanism of Injury, and Service Academy Sub-population on Concussion Risk Prediction in Military Service Academy Cadets and Midshipmen: An Observational Study

BACKGROUND: Concussion is a common but serious health concern among athlete and military populations. To improve understanding of concussion, the NCAA and U.S. Department of Defense established the Concussion Assessment, Research, and Education (CARE) Consortium, which collected comprehensive baseline assessment data from collegiate athletes and military service academy members. In this study, we used baseline assessments collected in service academy cadets and midshipmen to estimate the likelihood of concussion in the following academic year. Using eXtreme Gradient Boosting, we determined how the predictive accuracy of baseline assessments changes over the first year after testing and compared concussion risk prediction across two sub-populations (varsity/club sport athletes and intramural cadets) and two injury mechanisms (concussions occurring during military training/physical education classes versus sports-related concussions). Exploratory analyses also identified and ranked the most influential variables contributing to risk prediction, offering insights that could guide future risk assessment and prevention efforts. RESULTS: The analytic dataset included 16,642 participants from four U.S. military academies between the 2015-2016 and 2019-2020 academic years after data preprocessing. We used eXtreme Gradient Boosting to predict concussion risk using baseline variables collected within the same academic year. Prediction accuracy decreased as time passed since the baseline assessment both in the overall model and within each sub-population and injury mechanism model, with the overall prediction dropping from 0.68 to less than 0.64. Concussion predictions were more accurate for intramural cadets and for injuries sustained during military training or physical education, compared to varsity/club sport athletes and sports-related concussions. CONCLUSIONS: Given the significant time and resource investment required to conduct baseline concussion testing, baseline assessments being used to predict concussion risk should be performed as close to time periods of greatest risk as possible. Future research is needed to develop streamlined population- and injury mechanism-specific predictive models using a smaller set of key baseline measures to facilitate more frequent baseline testing to optimize risk prediction.