Previously, we showed that case-specific non-linear finite element (FE) models are better at predicting the load to failure of metastatic femora than experienced clinicians. In this study we improved our FE modelling and increased the number of femora and characteristics of the lesions. We retested the robustness of the FE predictions and assessed why clinicians have difficulty in estimating the load to failure of metastatic femora. A total of 20 femora with and without artificial metastases were mechanically loaded until failure. These experiments were simulated using case-specific FE models. Six clinicians ranked the femora on load to failure and reported their ranking strategies. The experimental load to failure for intact and metastatic femora was well predicted by the FE models (R. 2. = 0.90 and R. 2. = 0.93, respectively). Ranking metastatic femora on load to failure was well performed by the FE models (τ = 0.87), but not by the clinicians (0.11 < τ < 0.42). Both the FE models and the clinicians allowed for the characteristics of the lesions, but only the FE models incorporated the initial bone strength, which is essential for accurately predicting the risk of fracture. Accurate prediction of the risk of fracture should be made possible for clinicians by further developing FE models.

Aims

The sacroiliac joint (SIJ) is the only mechanical connection between the axial skeleton and lower limbs. Following iliosacral resection, there is debate on whether reconstruction of the joint is necessary. There is a paucity of data comparing the outcomes of patients undergoing reconstruction and those who are not formally reconstructed.

Aims. Accurate estimations of the risk of fracture due to metastatic bone disease in the femur is essential in order to avoid both under-treatment and over-treatment of patients with an impending pathological fracture. The purpose of the current retrospective in vivo study was to use CT-based finite element analyses (CTFEA) to identify a clear quantitative differentiating factor between patients who are at imminent risk of fracturing their femur and those who are not, and to identify the exact location of maximal weakness where the fracture is most likely to occur. Methods. Data were collected on 82 patients with femoral metastatic bone disease, 41 of whom did not undergo prophylactic fixation. A total of 15 had a pathological fracture within six months following the CT scan, and 26 were fracture-free during the five months following the scan. The Mirels score and strain fold ratio (SFR) based on CTFEA was computed for all patients. A SFR value of 1.48 was used as the threshold for a pathological fracture. The sensitivity, specificity, positive, and negative predicted values for Mirels score and SFR predictions were computed for nine patients who fractured and 24 who did not, as well as a comparison of areas under the receiver operating characteristic curves (AUC of the ROC curves). Results. The sensitivity of SFR was 100% compared with 88% for the Mirels score, and the specificity of SFR was 67% compared with 38% for the Mirels score. The AUC was 0.905 for SFR compared with 0.578 for the Mirels score (p = 0.008). Conclusion. All the patients who sustained a pathological fracture of the femur had an SFR of > 1.48. CTFEA was far better at predicting the risk of fracture and its location accurately compared with the Mirels score. CTFEA is quick and automated and can be incorporated into the protocol of CT scanners. Cite this article: Bone Joint J 2020;102-B(5):638–645

Aims

The early mortality in patients with hip fractures from bony metastases is unknown. The objectives of this study were to quantify 30- and 90-day mortality in patients with proximal femoral metastases, and to create a mortality prediction tool based on biomarkers associated with early death.

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The aim of this study was to validate the Mirels score in predicting pathological fractures in metastatic disease of the lower limb.

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To assess complications and failure mechanisms of osteoarticular allograft reconstructions for primary bone tumours.