Clalit Research Institute, Clalit Health Services, Tel Aviv, Israel.
Department of Computer Science, Ben-Gurion University, Beer Sheva, Israel.
Nat Med. 2020 Jan;26(1):77-82. doi: 10.1038/s41591-019-0720-z. Epub 2020 Jan 13.
Methods for identifying patients at high risk for osteoporotic fractures, including dual-energy X-ray absorptiometry (DXA) and risk predictors like the Fracture Risk Assessment Tool (FRAX), are underutilized. We assessed the feasibility of automatic, opportunistic fracture risk evaluation based on routine abdomen or chest computed tomography (CT) scans. A CT-based predictor was created using three automatically generated bone imaging biomarkers (vertebral compression fractures (VCFs), simulated DXA T-scores and lumbar trabecular density) and CT metadata of age and sex. A cohort of 48,227 individuals (51.8% women) aged 50-90 with available CTs before 2012 (index date) were assessed for 5-year fracture risk using FRAX with no bone mineral density (BMD) input (FRAXnb) and the CT-based predictor. Predictions were compared to outcomes of major osteoporotic fractures and hip fractures during 2012-2017 (follow-up period). Compared with FRAXnb, the major osteoporotic fracture CT-based predictor presented better receiver operating characteristic area under curve (AUC), sensitivity and positive predictive value (PPV) (+1.9%, +2.4% and +0.7%, respectively). The AUC, sensitivity and PPV measures of the hip fracture CT-based predictor were noninferior to FRAXnb at a noninferiority margin of 1%. When FRAXnb inputs are not available, the initial evaluation of fracture risk can be done completely automatically based on a single abdomen or chest CT, which is often available for screening candidates.
方法用于识别骨质疏松性骨折高危患者，包括双能 X 射线吸收法（DXA）和骨折风险评估工具（FRAX）等风险预测指标，这些方法未得到充分利用。我们评估了基于常规腹部或胸部计算机断层扫描（CT）自动进行机会性骨折风险评估的可行性。使用三种自动生成的骨骼成像生物标志物（椎体压缩性骨折（VCF）、模拟 DXA T 评分和腰椎小梁密度）和年龄和性别 CT 元数据创建了基于 CT 的预测器。在 2012 年之前（索引日期），评估了一个 48227 人的队列，这些人年龄在 50-90 岁之间，有可用的 CT，使用不包括骨密度（BMD）输入的 FRAX（FRAXnb）和基于 CT 的预测器评估了 5 年骨折风险。将预测结果与 2012-2017 年（随访期）的主要骨质疏松性骨折和髋部骨折结果进行了比较。与 FRAXnb 相比，主要骨质疏松性骨折 CT 预测器的接受者操作特征曲线下面积（AUC）、灵敏度和阳性预测值（PPV）分别提高了 1.9%、2.4%和 0.7%。髋部骨折 CT 预测器的 AUC、灵敏度和 PPV 测量值在不劣于 1%的非劣效性边界内与 FRAXnb 相当。当 FRAXnb 输入不可用时，可以根据单个腹部或胸部 CT 完全自动进行初始骨折风险评估，通常可用于筛选候选者。
