来自 GO 研究的绝经后妇女骨折风险预测:FRAX、Garvan 和 POL-RISK 算法之间的比较。

IF 4.3 3区 材料科学 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
W Pluskiewicz, A Werner, M Bach, P Adamczyk, B Drozdzowska
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引用次数: 0

摘要

在这项纵向回顾性研究中,对一组 457 名女性进行了 FRAX、Garvan 和 POL-RISK 算法预测骨质疏松性骨折能力的比较。结果表明,所有工具的灵敏度和特异性均存在显著差异。为每种计算器分别设定了新的骨折高风险阈值:FRAX 重度骨折的阈值为 6.3%,Garvan 任何骨折的阈值为 20.0%,POL-RISK 任何骨折的阈值为 18.0%。这样的阈值可以提高所有三种计算器的诊断准确性:这项纵向回顾性研究旨在比较三种评估骨折风险的工具:材料:研究对象包括格利维采骨质疏松症(GO)研究中的 457 名绝经后妇女,平均年龄为 64.21 ± 5.94 岁。研究人员收集了所有参与者与骨折相关的临床因素的综合数据。使用 Prodigy 设备(美国 GE 公司)对股骨近端进行了骨密度测量。采用 FRAX、Garvan 和 POL-RISK 算法确定骨折风险。收集了过去10年骨质疏松性骨折发生率的数据:在 72 个观察期内,63 名受试者发生了骨质疏松性骨折。为了初步比较分析诊断工具的预测价值,使用了 10%的骨折风险阈值。就 FRAX 而言,仅在 11 名发生骨折的受试者中观察到骨折概率超过 10%;因此,仅在 22.9% 的女性中正确预测了骨折。Garvan 的预测值为 90.5%,POL-RISK 的预测值为 98.4%。因此,FRAX 的真阳性值很低,而 Garvan 和 POL-RISK 的假阳性值很高。根据 ROC 曲线,为每种计算器分别设定了新的骨折高风险阈值:FRAX 的重大骨折风险阈值为 6.3%,Garvan 的任何骨折风险阈值为 20.0%,POL-RISK 的任何骨折风险阈值为 18.0%。这样的阈值提高了所有比较过的骨折预测工具的诊断准确性:目前的研究表明,不同的骨折风险评估工具虽然具有相似的临床目的,但在做出治疗决定时需要不同的临界值。根据这种方法更好地识别需要治疗的患者,可能有助于减少新骨折的发生。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Fracture risk prediction in postmenopausal women from GO Study: the comparison between FRAX, Garvan, and POL-RISK algorithms.

Fracture risk prediction in postmenopausal women from GO Study: the comparison between FRAX, Garvan, and POL-RISK algorithms.

In the longitudinal, retrospective study, the ability of the FRAX, Garvan, and POL-RISK algorithms to predict osteoporotic fractures was compared in a group of 457 women. Using the rigid threshold of 10% showed a significant discrepancy in sensitivity and specificity of all tools. New thresholds for high risk of fractures were established for each calculator separately: 6.3% for FRAX major fracture, 20.0% for Garvan any fracture, and 18.0% for POL-RISK any fracture. Such thresholds allow for improving the diagnostic accuracy of all three calculators.

Introduction: The aim of the longitudinal, retrospective study was to compare three tools designed to assess fracture risk: FRAX, Garvan, and POL-RISK in their prediction of fracture incidence.

Material: The study group consisted of 457 postmenopausal women with a mean age of 64.21 ± 5.94 years from the Gliwice Osteoporosis (GO) Study. Comprehensive data on clinical factors related to fractures were collected for all participants. Bone densitometry was performed at the proximal femur using the Prodigy device (GE, USA). Fracture risk was established using the FRAX, Garvan, and POL-RISK algorithms. Data on the incidence of osteoporotic fractures were collected over the last 10 years.

Results: During the period of observation 72, osteoporotic fractures occurred in 63 subjects. For a preliminary comparison of the predictive value of analyzed diagnostic tools, the fracture risk threshold of 10% was used. For FRAX, the fracture probability exceeding 10% was observed only in 11 subjects who experienced fractures; thus, the fracture was properly predicted only in 22.9% of women. For Garvan, the respective value was 90.5%, and for POL-RISK, it was 98.4%. That gave a very low true positive value for FRAX and a very high false positive value for Garvan and POL-RISK. Based on ROC curves, new thresholds for high risk of fractures were established for each calculator separately: 6.3% for FRAX major fracture, 20.0% for Garvan any fracture, and 18.0% for POL-RISK any fracture. Such thresholds improve the diagnostic accuracy of all compared fracture prediction tools.

Conclusion: The current study showed that different fracture risk assessment tools, although having similar clinical purposes, require different cut-off thresholds for making therapeutic decisions. Better identification of patients requiring therapy based on such an approach may help reduce the number of new fractures.

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