Longitudinal CT-based finite element analyses provide objective fracture healing measures in an ovine tibia model.

Abstract

Measuring the healing status of a bone fracture is important to determine the clinical care a patient receives. Implantable devices can directly and continuously assess the healing status of fracture fixation constructs, while subject-specific virtual biomechanical tests can noninvasively determine callus structural integrity at single time points. Despite their potential for objectification, both methods are not yet integrated into clinical practice with further evidence of their benefits required. This study correlated continuous data from an implantable sensor assessing healing status through implant load monitoring with computer tomography (CT) based longitudinal finite element (FE) simulations in a large animal model. Eight sheep were part of a previous preclinical study utilizing a tibial osteotomy model and equipped with such a sensor. Sensor signal was collected over several months, and CT scans were acquired at six interim time points. For each scan, two FE analyses were performed: a virtual torsional rigidity test of the bone and a model of the bone-implant construct with the sensor. The longitudinal simulation results were compared to the sensor data at corresponding time points and a cohort-specific empirical healing rule was employed. Healing status predicted by both in silico simulations correlated significantly with the sensor data at corresponding time points and correctly identified a delayed and a nonunion in the cohort. The methodology is readily translatable with the potential to be applied to further preclinical or clinical cohorts to find generalizable healing criteria. Virtual mechanical tests can objectively measure fracture healing progressing using longitudinal CT scans.