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A multiscale model to predict current absolute risk of femoral fracture in a postmenopausal population

机译:多尺度模型,以预测绝经后群体股骨骨折的当前绝对风险

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Osteoporotic hip fractures are a major healthcare problem. Fall severity and bone strength are important risk factors of hip fracture. This study aims to obtain a mechanistic explanation for fracture risk in dependence of these risk factors. A novel modelling approach is developed that combines models at different scales to overcome the challenge of a large space-time domain of interest and considers the variability of impact forces between potential falls in a subject. The multiscale model and its component models are verified with respect to numerical approximations made therein, the propagation of measurement uncertainties of model inputs is quantified, and model predictions are validated against experimental and clinical data. The main results are model predicted absolute risk of current fracture (ARF0) that ranged from 1.93 to 81.6% (median 36.1%) for subjects in a retrospective cohort of 98 postmenopausal British women (49 fracture cases and 49 controls); ARF0 was computed up to a precision of 1.92 percentage points (pp) due to numerical approximations made in the model; ARF0 possessed an uncertainty of 4.00pp due to uncertainties in measuring model inputs; ARF0 classified observed fracture status in the above cohort with AUC=0.852 (95% CI 0.753-0.918), 77.6% specificity (95% CI 63.4-86.5%) and 81.6% sensitivity (95% CI 68.3-91.1%). These results demonstrate that ARF0 can be computed using the model with sufficient precision to distinguish between subjects and that the novel mechanism of fracture risk determination based on fall dynamics, hip impact and bone strength can be considered validated.
机译:骨质疏松症骨折是一个主要的医疗保健问题。秋季严重程度和骨骼强度是髋部骨折的重要危险因素。本研究旨在获得根据这些风险因素的裂缝风险的机制解释。开发了一种新的建模方法,其结合了不同尺度的模型来克服兴趣的大型时域的挑战,并考虑潜在潜在潜在的撞击力的变化。多尺度模型及其组件模型是关于其中所做的数值近似验证的,量化模型输入的测量不确定性的传播,并且模型预测针对实验和临床数据进行了验证。主要结果是模型预测目前骨折(ARF0)的绝对风险,其范围为98例绝经期英国女性的回顾性队列中的对象(49裂缝和49个控件);由于模型中的数值近似,ARF0达到1.92个百分点(PP)的精度;由于测量模型输入中的不确定性,ARF0具有4.00pp的不确定性; ARF0分类在上述队列中的裂缝状态,AUC = 0.852(95%CI 0.753-0.918),特异性77.6%(95%CI 63.4-86.5%)和81.6%的灵敏度(95%CI 68.3-91.1%)。这些结果表明,可以使用具有足够精度的模型来计算ARF0,以区分受试者,并且可以考虑基于下降动态,臀部撞击和骨骼强度的裂缝风险测定的新机制。

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