Survival Analysis-Based Human Head Injury Risk Curves: Focus on Skull Fracture

Head contact-induced loads can result in skull fractures and/or brain injuries. While skull fractures have been produced from post-mortem human cadaver surrogates (PMHS), injury probability curves describing their structural responses have not been developed. The objectives of this study were to develop skull fracture-based injury risk curves and describe human tolerances using survival analysis. Published PMHS data in this journal were used. Mean age, stature, and weight of 12 PMHS were: 66.6 +/- 2.3 years, 1.71 +/- 2.9 m, and 76.4 +/- 4.6 kg. A testing device applied contact loading to the head. Failure force, deflection, energy, and linear and secant stiffness variables were used to develop probability curves. Parametrical survival analysis included identifying most optimal distribution, ensuring that the chosen distribution is not significantly different from the nonparametrical model, determining +/- 95% confidence interval bounds and Normalized Confidence Interval Sizes (NICS), obtaining quality indices for each risk curve, and determining their hierarchical sequence using the Brier score metric (BSM). Lognormal distribution was the most optimal distribution for all variables, except failure force, for which Weibull distribution was optimal. Tightness-of-fit of risk curves for failure force, energy, and deflection were better than linear and secant stiffness variables. Force best represented skull fracture response based on BSM and NCIS, followed by deflection and energy, while two stiffness variables were least preferred metrics. These structural response-based set of risk curves, hitherto not reported, form a fundamental dataset for validating/assessing accuracy of outputs from computational models and serve as hierarchical skull fracture injury criteria under head contact loads.