Medication use, lifestyle variables, other illnesses improves FRAX risk prediction
MedWire News: The addition of other illnesses, medication use, and behavioral factors to the fracture risk assessment tool (FRAX) helps to further identify men at risk for fracture, research shows.
"The full model was especially powerful for identifying elderly men at high risk of hip fractures and those with high risk of two fractures," report Liisa Byberg (Uppsala University, Sweden) and colleagues in the Journal of Bone and Mineral Research.
Fractures frequently occur in individuals without osteoporosis and are not easily predicted by conventional risk-prediction tools.
Bone architecture and the risk for falls both significantly affect the risk for fracture and are influenced by variables such as age, lifestyle behaviors, diseases, and medication use.
Using data from a longitudinal cohort study of Swedish men, the researchers assessed the prognostic value for any fracture and hip fracture using the FRAX algorithm and other different risk models.
For men over 50 years old and men over 82 years old, the fracture rates were 5.7 per 1000 person-years and 25.9 per 1000 person-years, respectively. The rates of hip fracture in these men were 2.9 per 1000 person-years and 11.7 per 1000 person-years, respectively.
Depending on age, the FRAX risk model plus comorbidity, medication use, and behavioral factors explained 25% to 45% of all fractures and 80% to 92% of hip fractures. The prognostic values of the FRAX risk algorithm alone ranged from 7% to 17% for all fractures and 41% to 60% for hip fractures.
The net reclassification index (NRI) that compared the FRAX model with a model that included FRAX variables, comorbidity, medication use, and lifestyle variables ranged from 40% to 53%.
The NRI for hip fractures ranged from 40% to 87%.
"One criticism against the FRAX algorithm is that it does not include history of falls," write Byberg and colleagues.
Including a full history of falls only contributed to the full risk prediction model in subjects over 82 years old, perhaps because falls history might be related to other factors in the model, they note.
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