DXA-based statistical models of shape and intensity outperform aBMD hip fracture prediction: A retrospective study

Abstract

Areal bone mineral density (aBMD) currently represents the clinical gold standard for hip fracture risk assessment. Nevertheless, it is characterised by a limited prediction accuracy, as about half of the people experiencing a fracture are not classified as at being at risk by aBMD. In the context of a progressively ageing population, the identification of accurate predictive tools would be pivotal to implement preventive actions. In this study, DXA-based statistical models of the proximal femur shape, intensity (i.e., density) and their combination were developed and employed to predict hip fracture on a retrospective cohort of post-menopausal women. Proximal femur shape and pixel-by-pixel aBMD values were extracted from DXA images and partial least square (PLS) algorithm adopted to extract corresponding modes and components. Subsequently, logistic regression models were built employing the first three shape, intensity and shape-intensity PLS components, and their ability to predict hip fracture tested according to a 10-fold cross-validation procedure. The area under the ROC curves (AUC) for the shape, intensity, and shape-intensity-based predictive models were 0.59 (95%CI 0.47-0.69), 0.80 (95%CI 0.70-0.90) and 0.83 (95%CI 0.73-0.90), with the first being significantly lower than the latter two. aBMD yielded an AUC of 0.72 (95%CI 0.59-0.82), found to be significantly lower than the shape-intensity-based predictive model. In conclusion, a methodology to assess hip fracture risk uniquely based on the clinically available imaging technique, DXA, is proposed. Our study results show that hip fracture risk prediction could be enhanced by taking advantage of the full set of information DXA contains.

Keywords: DXA images; Fracture prediction; Hip fracture; Partial least square; Statistical shape-intensity model.