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 Women's Health Initiative, a prospective study of women aged 50 to 79 years at baseline (n = 99,413; n = 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% (p < 0.001) among MOF cases and -1.7% among noncases (p > 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. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.