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.
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.
Authors: Yi Su; Freddy M H Lam; Jason Leung; Wing-Hoi Cheung; Suzanne C Ho; Timothy Kwok Journal: Calcif Tissue Int Date: 2020-05-30 Impact factor: 4.333
Authors: H Johansson; J A Kanis; E V McCloskey; A Odén; J-P Devogelaer; J-M Kaufman; A Neuprez; M Hiligsmann; O Bruyere; J-Y Reginster Journal: Osteoporos Int Date: 2010-03-30 Impact factor: 4.507
Authors: Kathryn M Ryder; Steven R Cummings; Lisa Palermo; Suzanne Satterfield; Douglas C Bauer; Adrianne C Feldstein; John T Schousboe; Ann V Schwartz; Kristine Ensrud Journal: J Gen Intern Med Date: 2008-05-06 Impact factor: 5.128
Authors: Peter M Wayne; Julie E Buring; Roger B Davis; Ellen M Connors; Paolo Bonato; Benjamin Patritti; Mary Fischer; Gloria Y Yeh; Calvin J Cohen; Danette Carroll; Douglas P Kiel Journal: BMC Musculoskelet Disord Date: 2010-03-01 Impact factor: 2.362