Preclinical validation of finite element models for predicting in vivo residual plate bending using continuous implant sensor data.

Abstract

<h4>Background/objective</h4>Plate failure, including bending, is a critical issue in orthopedic fracture fixation, with clinical failure rates of 3.5%-19%, burdening patients and healthcare systems. Preclinical ovine models have observed similar plate bending due to overloading. Finite element (FE) models could be capable of predicting failures but lack <i>in vivo</i> loading data for validation. The AO Fracture Monitor is an implantable sensor that can continuously track implant deformation, offering a proxy for implant loading and the potential to bridge this gap. This study aimed to preclinically validate an FE simulation methodology for predicting overloading bending of locking plates in an ovine tibia osteotomy model using AO Fracture Monitor data, emphasizing its potential for clinical translation.<h4>Methods</h4>Tibiae of eleven sheep with osteotomy gaps (0.6 - 30 mm) were instrumented with stainless steel or titanium locking plates equipped with AO Fracture Monitors in a prior study. Residual plate bending angles were measured using co-registered CT scans at 0 and 4 weeks post-operation, with bending defined as ≥ 1°. Animal-specific FE models, incorporating virtual AO Fracture Monitors and non-linear implant material properties, were developed to determine sensor signals at the construct's yield point. <i>In vivo</i> sensor signals were compared to the virtual plasticity threshold to predict CT-based residual bending outcomes.<h4>Results</h4>Within 4 weeks, plate bending angles ranged from 0.4° to 10.4°, with overloading bending observed in 6 animals. The FE methodology correctly predicted bending/no-bending outcomes in 9 of 11 animals, achieving 100% sensitivity and 60% specificity.<h4>Conclusions</h4>This sensor-validated FE methodology robustly predicted <i>in vivo</i> plate bending, offering a promising tool for reducing implant failure. These findings highlight the methodology's ability to detect clinically relevant bending outcomes. By integrating real-time loading data, it supports the development of personalized rehabilitation strategies, enhancing clinical outcomes in fracture fixation.<h4>The translational potential of this article</h4>This validated FE methodology, leveraging AO Fracture Monitor data, can be adapted for human use to tailor rehabilitation protocols immediately post-surgery and provide real-time feedback to patients and clinicians if loading exceeds safe thresholds. This approach could minimize implant failure, reduce revision surgeries, and enhance patient recovery.