An approach to identifying osteopenic women at increased short-term risk of fracture.

Background: Identification and management of women to reduce fractures is often limited to T scores less than -2.5, although many fractures occur with higher T scores. We developed a classification algorithm that identifies women with osteopenia (T scores of -2.5 to -1.0) who are at increased risk of fracture within 12 months of peripheral bone density testing.
Methods: A total of 57 421 postmenopausal white women with baseline peripheral T scores of -2.5 to -1.0 and 1-year information on new fractures were included. Thirty-two risk factors for fracture were entered into a classification and regression tree analysis to build an algorithm that best predicted future fracture events.
Results: A total of 1130 women had new fractures in 1 year. Previous fracture, T score at a peripheral site of -1.8 or less, self-rated poor health status, and poor mobility were identified as the most important determinants of short-term fracture. Fifty-five percent of the women were identified as being at increased fracture risk. Women with previous fracture, regardless of T score, had a risk of 4.1%, followed by 2.2% in women with T scores of -1.8 or less or with poor health status, and 1.9% for women with poor mobility. The algorithm correctly classified 74% of the women who experienced a fracture.
Conclusions: This classification tool accurately identified postmenopausal women with peripheral T scores of -2.5 to -1.0 who are at increased risk of fracture within 12 months. It can be used in clinical practice to guide assessment and treatment decisions.