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额外的临床风险因素能否改善绝经后女性骨折风险评估工具(FRAX)的性能?来自女性健康倡议观察性研究和临床试验的结果。

Do Additional Clinical Risk Factors Improve the Performance of Fracture Risk Assessment Tool (FRAX) Among Postmenopausal Women? Findings From the Women's Health Initiative Observational Study and Clinical Trials.

作者信息

Crandall Carolyn J, Larson Joseph, Cauley Jane A, Schousboe John T, LaCroix Andrea Z, Robbins John A, Watts Nelson B, Ensrud Kristine E

机构信息

Division of General Internal Medicine and Health Services Research David Geffen School of Medicine at University of California Los Angeles CA USA.

Fred Hutchinson Cancer Research Center Seattle WA USA.

出版信息

JBMR Plus. 2019 Nov 30;3(12):e10239. doi: 10.1002/jbm4.10239. eCollection 2019 Dec.

Abstract

The ability of the fracture risk assessment tool (FRAX) to discriminate between women who do and do not experience major osteoporotic fractures (MOFs) is suboptimal. Adding common clinical risk factors may improve discrimination. We used data from the Womens Health Initiative, a prospective study of women aged 50 to 79 years at baseline ( = 99,413; = 5722 in BMD subset) enrolled at 40 US clinical centers. The primary outcome was incident MOFs assessed annually during 10 years follow-up. For prediction of incident MOF, we examined the area under the receiver operatic characteristic curve (AUC) and net reclassification index (NRI) of the FRAX model alone and FRAX plus additional risk factors (singly or together: type 2 diabetes mellitus, frequent falls [≥2 falls in the past year], vasomotor symptoms, self-reported physical function score [RAND 36-item Health Survey subscale), and lumbar spine BMD). For NRI calculations, high risk was defined as predicted MOF risk ≥20%. We also assessed calibration as observed MOF events/expected MOF events. The AUC value for FRAX without BMD information was 0.65 (95% CI, 0.65 to 0.66). Compared with the FRAX model (without BMD), the AUC value was not improved by the addition of vasomotor symptoms, diabetes, or frequent falls, but was minimally increased by adding physical function score (AUC 0.66, 95% CI, 0.66 to 0.67). FRAX was well-calibrated for MOF prediction. The NRI of FRAX + additional variables versus FRAX alone was 5.7% ( < 0.001) among MOF cases and -1.7% among noncases ( > 0.99). Additional variables (diabetes, frequent falls, vasomotor symptoms, physical function score, or lumbar spine BMD) did not yield meaningful improvements in NRI or discrimination of FRAX for MOFs. Future studies should assess whether tools other than FRAX provide superior discrimination for prediction of MOFs. © 2019 The Authors. published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.

摘要

骨折风险评估工具(FRAX)区分发生和未发生主要骨质疏松性骨折(MOF)女性的能力欠佳。添加常见临床风险因素可能会改善区分效果。我们使用了来自女性健康倡议的数据,这是一项对40个美国临床中心招募的基线年龄在50至79岁的女性进行的前瞻性研究(n = 99,413;骨密度亚组n = 5722)。主要结局是在10年随访期间每年评估的新发MOF。为了预测新发MOF,我们单独检查了FRAX模型以及FRAX加上其他风险因素(单独或一起:2型糖尿病、频繁跌倒[过去一年中≥2次跌倒]、血管舒缩症状、自我报告的身体功能评分[兰德36项健康调查子量表]和腰椎骨密度)的受试者操作特征曲线下面积(AUC)和净重新分类指数(NRI)。对于NRI计算,高风险定义为预测的MOF风险≥20%。我们还评估了校准情况,即观察到的MOF事件/预期的MOF事件。没有骨密度信息的FRAX的AUC值为0.65(95%CI,0.65至0.66)。与FRAX模型(无骨密度)相比,添加血管舒缩症状、糖尿病或频繁跌倒并没有改善AUC值,但添加身体功能评分使其略有增加(AUC 0.66,95%CI,0.66至0.67)。FRAX在预测MOF方面校准良好。在MOF病例中,FRAX加上其他变量相对于单独的FRAX的NRI为5.7%(P < 0.001),在非病例中为-1.7%(P > 0.99)。其他变量(糖尿病、频繁跌倒、血管舒缩症状、身体功能评分或腰椎骨密度)在NRI或FRAX对MOF的区分方面没有产生有意义的改善。未来的研究应评估除FRAX之外的工具是否在预测MOF方面提供更好的区分效果。© 2019作者。由Wiley Periodicals, Inc.代表美国骨与矿物质研究学会出版。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/05f2/6894725/a4102aa3a3a8/JBM4-3-na-g001.jpg

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