Development of a Concussion Risk Function for a Youth Population Using Head Linear and Rotational Acceleration

Abstract

Physical differences between youth and adults, which include incomplete myelination, limited neck muscle development, and a higher head-body ratio in the youth population, likely contribute towards the increased susceptibility of youth to concussion. Previous research efforts have considered the biomechanics of concussion for adult populations, but these known age-related differences highlight the necessity of quantifying the risk of concussion for a youth population. This study adapted the previously developed Generalized Acceleration Model for Brian Injury Threshold (GAMBIT) that combines linear and rotational head acceleration to model the risk of concussion for a youth population with the Generalized Acceleration Model for Concussion in Youth (GAM-CY). Survival analysis was used in conjunction with head impact data collected during participation in youth football to model risk between individuals who sustained medically-diagnosed concussions (n = 15). Receiver operator characteristic curves were generated for peak linear acceleration, peak rotational acceleration, and GAM-CY, all of which were observed to be better injury predictors than random guessing. GAM-CY was associated with an area under the curve of 0.89 (95% confidence interval: 0.82-0.95) when all head impacts experienced by the concussed players were considered. Concussion tolerance was observed to be lower for youth athletes, with average peak linear head acceleration of 62.4 ± 29.7 g compared to 102.5 ± 32.7 g for adults and average peak rotational head acceleration of 2609 ± 1591 rad/s² compared to 4412 ± 2326 rad/s². These data provide further evidence of age-related differences in concussion tolerance and may be used for the development of youth-specific protective designs.

Keywords: Biomechanics; Football; Helmet; Mild traumatic brain injury; Risk curve.

Figure 1

Empirical probability density function (PDF) of subconcussive and concussive head impacts. The distribution of subconcussive head impacts was heavily right-skewed, while the distribution of concussive head impacts was less well-defined. The median peak acceleration values were 19.5 g and 970 rad/s² for the subconcussive head impacts and 63.8 g and 2599 rad/s/s for the concussive head impacts. The median GAM-CY value for subconcussive head impacts was 0.49 and 1.36 for concussive head impacts.

Figure 2

60% of concussive head impacts were among a player’s top 10 hardest head impacts. All concussive head impacts were within the top quartile of a player’s head impacts when considering the combination of linear and rotational kinematics.

Figure 3

Log-normal distribution fit to concussion data (solid line) with 95% confidence bounds (dashed lines). Most concussive head impacts were associated with lower values of GAM-CY. Fewer concussive head impacts were observed for higher values of GAM-CY (> 2), so the 95% confidence bounds are much wider at these values. Gray lines represent log-normal distributions fit from the bootstrap samples for the concussion data.

Figure 4

Risk of concussion as a function of linear and rotational head acceleration. Most concussive head impacts were associated with average risk of concussion below 20%.

Figure 5

ROC curves for GAM-CY, PLA, and PRA. All parameters were significantly better than random guessing (dashed line), with peak rotational acceleration (PRA) offering the least predictive capability among all metrics.

Figure 6

Comparing concussed athletes to non-concussed athletes. The median values for the 95^th percentile GAM-CY and risk-weighted exposure were higher for the concussed cohort than for the non-concussed cohort.