{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,17]],"date-time":"2026-08-17T14:08:35Z","timestamp":1786975715788,"version":"build-2736575974"},"reference-count":38,"publisher":"American Medical Association (AMA)","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"\n Importance<\/jats:title>\n For younger postmenopausal women, clinical guidelines recommend using osteoporosis risk prediction tools to identify candidates with low bone mineral density (BMD). However, the performance of these tools is not well quantified.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n To examine the performance of Osteoporosis Risk Assessment Instrument (ORAI) and Osteoporosis Index of Risk (OSIRIS), compared with Osteoporosis Self-Assessment Tool (OST), in identifying the presence of osteoporotic BMD in younger postmenopausal women.<\/jats:p>\n <\/jats:sec>\n \n Design, Setting, and Participants<\/jats:title>\n This cross-sectional study used data from the Women\u2019s Health Initiative Bone Density Substudy, which was conducted at 3 clinical centers in Tucson and Phoenix, Arizona; Pittsburgh, Pennsylvania; and Birmingham, Alabama. Participants were healthy postmenopausal women aged 50 to 64 years with BMD measurements evaluated using the 3 risk prediction tools: OSIRIS, ORAI, and OST. Risk factors and other participant characteristics were compared across osteoporosis status. Data were collected from October 1993 to December 1998 and analyzed between September 23, 2023, and April 10, 2024.<\/jats:p>\n <\/jats:sec>\n \n Exposures<\/jats:title>\n The primary exposures were OSIRIS, ORAI, and OST risk scores.<\/jats:p>\n <\/jats:sec>\n \n Main Outcomes and Measures<\/jats:title>\n Primary outcome was osteoporosis defined by BMD T score of \u20132.5 or lower at 1 or more of 3 anatomical locations: femoral neck, total hip, and\/or lumbar spine. The tools were evaluated via area under the receiver operating characteristic curve (AUC) at published score cutoffs and at alternate cutoffs.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Among 6067 included participants (mean [SD] age at baseline, 57.7 [4.1] years), the prevalence of osteoporosis was 14.1% (n\u2009=\u2009857) at any 1 of 3 anatomical sites. AUC for identifying osteoporosis at any site was 0.633 (95% CI, 0.633-0.634) for OSIRIS, 0.663 (95% CI, 0.663-0.664) for ORAI, and 0.654 (95% CI, 0.654-0.655) for OST.<\/jats:p>\n <\/jats:sec>\n \n Conclusions and Relevance<\/jats:title>\n In this cross-sectional study, 3 guideline-recommended osteoporosis risk assessment tools had fair to moderate discrimination in identifying osteoporosis defined by lowest BMD at any 1 of 3 skeletal sites. Screening is essential to reducing individual and societal burden of osteoporosis and related fractures, and this study showed a gap in identifying younger postmenopausal women using common clinical risk factors.<\/jats:p>\n <\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2025.0626","type":"journal-article","created":{"date-parts":[[2025,3,18]],"date-time":"2025-03-18T11:02:03Z","timestamp":1742295723000},"page":"e250626","source":"Crossref","is-referenced-by-count":8,"title":["Identifying Younger Postmenopausal Women With Osteoporosis Using USPSTF-Recommended Osteoporosis Risk Assessment Tools"],"prefix":"10.1001","volume":"8","author":[{"given":"Henry W.","family":"Zheng","sequence":"first","affiliation":[{"name":"Medical and Imaging Informatics, UCLA (University of California, Los Angeles), Los Angeles"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex A. 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