Background: Younger postmenopausal women (PMW) are at risk for fractures. Little guidance is available for the management of osteoporosis to prevent fractures in PMW age ≤65 years. We developed classification algorithms, with and without bone mineral density (BMD) results, to identify women at increased risk for fracture within 3 years using National Osteoporosis Risk Assessment (NORA) data.
Methods: 91,562 Caucasian PMW who responded to either the 1st or 2nd follow-up surveys were 50 to 64 years old at baseline. All participants received a BMD test at one of the peripheral sites (heel, forearm, or finger). New fractures at the hip, spine, rib, forearm and wrist were self-reported. We used CART 4.0 to build the decision tree. Eighteen risk factors available in the NORA database with < 10% missing were used for analysis.
Results: 2007 women had new fractures in 3 years. Prior fracture, peripheral T-score ≤-1.1, and self-rated fair/poor health status were identified as the most important determinants for fracture risk. This algorithm correctly identified 64% of the women who had a fracture and 59% of the women who did not have a fracture. Women identified as high risk by the algorithm were nearly 3 times more likely to have a fracture than women not identified as high risk. In the absence of BMD information, prior fracture, self-rated fair/poor health status, and hormone therapy use were the most important determinants of fracture risk. This alternative algorithm correctly identified 68% of the women who had a fracture and 50% of the women who did not have a fracture.
Conclusion: Two simple classification algorithms identify younger PMW age 50-64 who are at 2 to 3 times increased risk for fracture in 3 years. These algorithms can be used in clinical practice to guide assessment and treatment decisions to prevent fractures.
Disclosure Information:
Faculty Member's Name: Ya-Ting Chen, PhD
Other Financial or Material Support: Employee of Merck & Co., Inc.
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