Department of Mechanical Engineering, McMaster University, ABB-C308, 1280 Main St. West, Hamilton, Ontario, L8S 4L8, Canada.
Department of Medicine, McMaster University, Hamilton, Ontario, Canada.
Osteoporos Int. 2020 Oct;31(10):1925-1933. doi: 10.1007/s00198-020-05444-7. Epub 2020 May 15.
A new technique to enhance hip fracture risk prediction in older adults was presented and assessed. The new method dramatically improved prediction at high specificity levels using only a standard clinical diagnostic scan. This has the potential to be implemented in clinical practice to enhance patient fragility diagnosis.
Diagnosis of osteoporosis is based on the measurement of bone mineral density (BMD) using dual-energy X-ray absorptiometry (DXA) scans. However, studies have shown this to be insufficient to accurately predict hip fractures. Therefore, complementary methods are needed to enhance hip fracture risk prediction to identify vulnerable patients.
Hip DXA scans were obtained for 192 subjects from the Canadian Multicenter Osteoporosis Study (CaMos), 50 of whom had experienced a hip fracture within 5 years of the scan. 2D statistical shape and appearance modeling was performed to account for the effect of the femur's geometry and BMD distribution on hip fracture risk. Statistical shape modeling (SSM), and statistical appearance modeling (SAM) were also used separately to predict the fracture risk based solely on the femur's geometry and BMD distribution, respectively. Combined with BMD, age, and body mass index (BMI), logistic regression was performed to estimate the fracture risk over the 5-year period.
Using the new technique, hip fractures were correctly predicted in 78% of cases compared with 36% when using the T-score. The accuracy of the prediction was not greatly reduced when using SSM and SAM (78% and 74% correct, respectively). Various geometric and BMD distribution traits were identified in the fractured and non-fractured groups.
2D SSAM can dramatically improve hip fracture prediction at high specificity levels and estimate the year of the impending fracture using standard clinical images. This has the potential to be implemented in clinical practice to estimate hip fracture risk.
提出并评估一种新的技术,以提高老年人髋部骨折风险预测的准确性。
对 192 名来自加拿大骨质疏松多中心研究(CaMos)的受试者进行髋部 DXA 扫描,其中 50 名在扫描后 5 年内发生了髋部骨折。采用二维统计形状和外观建模来解释股骨几何形状和骨密度分布对髋部骨折风险的影响。单独使用统计形状建模(SSM)和统计外观建模(SAM)分别基于股骨的几何形状和骨密度分布来预测骨折风险。结合骨密度、年龄和体重指数(BMI),进行逻辑回归分析以估计 5 年内的骨折风险。
使用新方法,髋部骨折的预测准确率为 78%,而 T 评分的准确率为 36%。仅使用 SSM 和 SAM 时,预测的准确率并未大幅降低(分别为 78%和 74%正确)。在骨折组和非骨折组中,确定了各种几何形状和骨密度分布特征。
2D SSAM 可以在高特异性水平上显著提高髋部骨折的预测准确性,并通过标准临床图像来估计即将发生骨折的年份。这有可能在临床实践中实施,以评估髋部骨折风险。