首页> 外文期刊>Journal of bone and mineral research: the official journal of the American Society for Bone and Mineral Research >Fracture risk predictions based on statistical shape and density modeling of the proximal femur
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Fracture risk predictions based on statistical shape and density modeling of the proximal femur

机译:基于股骨近端统计形状和密度模型的骨折风险预测

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Increased risk of skeletal fractures due to bone mass loss is a major public health problem resulting in significant morbidity and mortality, particularly in the case of hip fractures. Current clinical methods based on two-dimensional measures of bone mineral density (areal BMD or aBMD) are often unable to identify individuals at risk of fracture. We investigated predictions of fracture risk based on statistical shape and density modeling (SSDM) methods using a case-cohort sample of individuals from the Osteoporotic Fractures in Men (MrOS) study. Baseline quantitative computed tomography (QCT) data of the right femur were obtained for 513 individuals, including 45 who fractured a hip during follow-up (mean 6.9 year observation, validated by physician review). QCT data were processed for 450 individuals (including 40 fracture cases) to develop individual models describing three-dimensional bone geometry and density distribution. Comparison of mean fracture and non-case models indicated complex structural differences that appear to be responsible for resistance to hip fracture. Logistic regressions were used to model the relation of baseline hip BMD and SSDM weighting factors to the occurrence of hip fracture. Area under the receiver operating characteristic (ROC) curve (AUC) for a prediction model based on weighting factors and adjusted by age was significantly greater than AUC for a prediction model based on aBMD and age (0.94 versus 0.83, respectively). The SSDM-based prediction model adjusted by age correctly identified 55% of the fracture cases (and 94.7% of the non-cases), whereas the clinical standard aBMD correctly identified 10% of the fracture cases (and 91.3% of the non-cases). SSDM identifies subtle changes in combinations of structural bone traits (eg, geometric and BMD distribution traits) that appear to indicate fracture risk. Investigation of important structural differences in the proximal femur between fracture and no-fracture cases may lead to improved prediction of those at risk for future hip fracture.
机译:由于骨质流失而导致骨骼骨折的风险增加是一个重大的公共卫生问题,会导致很高的发病率和死亡率,尤其是在髋部骨折的情况下。当前基于骨骼矿物质密度(面积BMD或aBMD)的二维测量的临床方法通常无法识别有骨折风险的个体。我们使用来自男性骨质疏松性骨折(MrOS)研究的病例队列样本,基于统计形状和密度建模(SSDM)方法调查了骨折风险的预测。获得了513例右股骨的基线定量计算机断层扫描(QCT)数据,其中包括45例在随访期间髋骨骨折的患者(平均6.9年观察,经医师审查确认)。处理了450例患者(包括40例骨折病例)的QCT数据,以开发描述三维骨几何形状和密度分布的个体模型。平均骨折模型和非病例模型的比较表明,复杂的结构差异似乎是造成髋部骨折抵抗力的原因。使用Logistic回归对基线髋骨BMD和SSDM权重因子与髋部骨折发生之间的关系进行建模。对于基于加权因子的预测模型并根据年龄进行调整的接收器工作特性(ROC)曲线(AUC)下面积明显大于基于aBMD和年龄的预测模型的AUC(分别为0.94和0.83)。根据年龄调整的基于SSDM的预测模型可正确识别55%的骨折病例(非病例的94.7%),而临床标准aBMD可正确识别10%的骨折病例(非病例的91.3%) )。 SSDM识别结构骨性状(例如,几何和BMD分布性状)组合中的细微变化,这些变化似乎表明存在骨折危险。对骨折和未骨折病例之间在股骨近端重要结构差异的研究可能会改善对有未来髋部骨折风险的人的预测。

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