Quantification of biomechanical tolerance is necessary for injury prediction and protection of vehicular occupants. This study experimentally quantified lumbar spine axial tolerance during accelerative environments simulating a variety of military and civilian scenarios. Intact human lumbar spines (T12-L5) were dynamically loaded using a custom-built drop tower. Twenty-three specimens were tested at sub-failure and failure levels consisting of peak axial forces between 2.6 and 7.9 kN and corresponding peak accelerations between 7 and 57 g. Military aircraft ejection and helicopter crashes fall within these high axial acceleration ranges. Testing was stopped following injury detection. Both peak force and acceleration were significant (p < 0.0001) injury predictors. Injury probability curves using parametric survival analysis were created for peak acceleration and peak force. Fifty-percent probability of injury (95%CI) for force and acceleration were 4.5 (3.9-5.2 kN), and 16 (13-19 g). A majority of injuries affected the L1 spinal level. Peak axial forces and accelerations were greater for specimens that sustained multiple injuries or injuries at L2-L5 spinal levels. In general, force-based tolerance was consistent with previous shorter-segment lumbar spine testing (3-5 vertebrae), although studies incorporating isolated vertebral bodies reported higher tolerance attributable to a different injury mechanism involving structural failure of the cortical shell. This study identified novel outcomes with regard to injury patterns, wherein more violent exposures produced more injuries in the caudal lumbar spine. This caudal migration was likely attributable to increased injury tolerance at lower lumbar spinal levels and a faster inertial mass recruitment process for high rate load application. Published 2017. This article is a U.S. Government work and is in the public domain in the USA. J Orthop Res 36:1747-1756, 2018.
Keywords: Weibull distribution; biomechanics; injury risk curves; lumbar spine; parametric survival analysis.
Published 2017. This article is a U.S. Government work and is in the public domain in the USA.