VALIDATING PROGNOSTIC HEALING SIMULATIONS IN AN OVINE MODEL

Fracture management remains a significant clinical challenge, with nonunion occurring in approximately 5% of fractures. Tibial shaft fractures are particularly concerning with approximately 25% of these fractures remaining unhealed after five months, with reoperation rates up to 12%. Prognostic fracture healing simulations offer the potential to predict healing outcomes, aiding in treatment selection and enabling early intervention for complications. However, these simulations have historically lacked robust ground truth validation. Two recent studies utilized in vivo sensors in ovine osteotomy models to validate the time course of healing in well-reduced fractures 1 as well as healing outcomes (union, delayed union, nonunion) in clinically relevant fixation configurations. Data of 24 sheep were utilized from two previously completed tibial osteotomy experiments 2,3 . The osteotomies were stabilized with medial locking compression plating. One group used a custom fixator that allowed for controlled axial motion equipped with a displacement sensor on 3 mm gaps. The second group used the AO Fracture monitor which measured plate strain on gap sizes ranging from 0.6 – 30 mm. Specimen specific finite element (FE) models were constructed with computed tomography (CT) scans in Simpleware (v17, Synopsys). Prognostic fracture healing simulations were performed via iterative FE analyses in Abaqus (v2021, Dassault Systems) using Python (3.11) and MATLAB (R2023b, MathWorks). In group 1, a paired sample T-Test found that there was no difference in healing time between the in vivo ground truth measurement and the simulated in silico sensor prediction. In group 2, an unsupervised Affinity Propagation clustering algorithm was employed to determine any group differences between the healing trajectories. The predicted groups aligned exactly with the sensor-based classification of the specimen into union, delayed union, and nonunion. This work highlights the potential of predictive bone fracture healing simulations and was the first step in an in vivo sensor based validation. Techniques such as these could assist in nonunion risk detection at earlier stages, improving patient care and outcomes.