Abstract
Introduction
Up to 60% of total hip arthroplasties (THA) in Asian populations arise from avascular necrosis (AVN), a bone disease that can lead to femoral head collapse. Current diagnostic methods to classify AVN have poor reproducibility and are not reliable in assessing the fracture risk. Femoral heads with an immediate fracture risk should be treated with a THA, conservative treatments are only successful in some cases and cause unnecessary patient suffering if used inappropriately. There is potential to improve the assessment of the fracture risk by using a combination of density-calibrated computed tomographic (QCT) imaging and engineering beam theory. The aim of this study was to validate the novel fracture prediction method against in-vitro compression tests on a series of six human femur specimens.
Methods
Six femoral heads from six subjects were tested, a subset (n=3) included a hole drilled into the subchondral area of the femoral head via the femoral neck (University of Leeds, ethical approval MEEC13-002). The simulated lesions provided a method to validate the fracture prediction model with respect of AVN.
The femoral heads were then modelled by a beam loaded with a single joint contact load. Material properties were assigned to the beam model from QCT-scans by using a density-modulus relationship. The maximum joint loading at which each bone cross-section was likely to fracture was calculated using a strain based failure criterion.
Based on the predicted fracture loads, all six femoral heads (validation set) were classified into two groups, high fracture risk and low fracture risk (Figure 1). Beam theory did not allow for an accurate fracture load to be found because of the geometry of the femoral head. Therefore the predicted fracture loads of each of the six femoral heads was compared to the mean fracture load from twelve previously analysed human femoral heads (reference set) without lesions.
The six cemented femurs were compression tested until failure. The subjects with a higher fracture risk were identified using both the experimental and beam tool outputs.
Results
The computational tool correctly identified all femoral head samples which fractured at a significantly low load in-vitro (Figure 2). Both samples with a low experimental fracture load had an induced lesion in the subchondral area (Figure 3).
Discussion
This study confirmed findings of a previous verification study on a disease models made from porcine femoral heads (Preutenborbeck et al. I-CORS2016). It demonstrated that fracture prediction based on beam theory is a viable tool to predict fracture. The tests confirmed that samples with a lesion in the weight bearing area were more likely to fracture at a low load however not all samples with a lesion fractured with a low load experimentally, indicating that a lesion alone is not a sufficient factor to predict fracture. The developed tool takes both structural and material properties into account when predicting the fracture risk. Therefore it might be superior to current diagnostic methods in this respect and it has the added advantage of being largely automated and therefore removing the majority of user bias.
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