• Y-T Chen, P D Miller, E Barrett-Connor, T W Weiss, S G Sajjan, and E S Siris.
• Department of Outcomes Research and Management, Merck & Co., Inc., 770 Sumneytown Pike, West Point, PA 19486, USA. yating_chen@merck.com
• Osteoporos Int. 2007 Sep 1; 18 (9): 1287-96.

UnlabelledUsing data from NORA, we used 18 potential risk factors in a classification and regression tree analysis to build two algorithms. These algorithms correctly identified postmenopausal women between the ages of 50 and 64 years who were at the highest risk of osteoporotic fracture within 36 months.IntroductionThe objective was to develop algorithms that best predict short-term fracture risk (3 years) in postmenopausal women 50-64 years old.MethodsData were from 91,652 women who responded to follow-up surveys as part of National Osteoporosis Risk Assessment (NORA) study. Peripheral bone mineral density (BMD) and risk factors obtained at baseline; incident osteoporotic fractures obtained from follow-up surveys. Eighteen risk factors were entered into a classification and regression tree analysis to build two algorithms, one with and one without BMD.ResultsTwo thousand and seven (2.2%) women reported new osteoporotic fractures. Prior fracture, a peripheral BMD T-score ConclusionsNORA-based algorithms may be useful for health care providers to guide further assessment and management decisions to prevent fractures in younger postmenopausal women.

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