A Proposed Method for Determination of Distal Tibia Fracture Tolerance for Prediction of Ankle Injuries

Abstract

<div class="section abstract"><div class="htmlview paragraph">Ankle injuries continue to occur in motor vehicle collisions, particularly in female occupants. The causes of these injuries are sometimes unclear. Further understanding of ankle fracture tolerance and refinement of ankle injury prediction tools would help future injury prediction efforts. The goal of this study was to identify ankle injury types of interest and develop a test methodology to induce these injuries. Cases were examined from NHTSA’s Crash Injury Research Engineering Network (CIREN) database. 68 cases with distal tibia fracture were identified from CIREN years 2017+ (vehicle models years 2010+). The most common fractures were pilon fractures and malleolar fractures. Based on these results, a test methodology was developed to induce pilon and medial malleolar fractures in isolated cadaveric tibiae to quantify local fracture tolerance. Nineteen post-mortem human subject (PMHS) specimens (9 male and 10 female across a wide anthropometric range) were tested. To replicate the fractures, a novel method was developed to subject isolated distal tibia specimens to inferomedial oblique loading via stainless steel, 3D-printed, subject-specific, metallic pseudo-tali. These pseudo-tali were chosen to produce loading similar to what would occur from ankle eversion under compression, driving the talus into the distal tibia. Pilon fractures and medial malleolus fractures were produced, with fracture patterns similar to those observed in the CIREN cases. Boundary forces and moments, pseudo-tali displacements and rotations, and fracture timing (via high-speed video) were measured. Pre- and post- fracture bone geometry was digitized via computed tomography (CT) scans. These results demonstrated the utility of these novel methods and will help facilitate future implementation of tissue-level fracture prediction in the distal tibia of human body models, advancing future ability to predict ankle injury risk under complex loading.</div></div>