Two new scores for predicting osteoporotic fracture could help to identify high risk patients who are most likely to benefit from interventions to reduce their risk, concludes a study published on bmj.com today.
Osteoporotic fracture is a major cause of illness and a considerable burden to health services. Guidelines suggest a targeted approach for identifying high-risk patients, so the challenge is now to improve the methods of identifying those patients who are most likely to benefit from interventions to reduce their risk of fracture.
Researchers from the University of Nottingham set out to develop and validate two new fracture risk algorithms (QFractureScores) for estimating the individual risk of osteoporotic fracture or hip fracture over 10 years. They also tested its performance against the established FRAX (fracture risk assessment) algorithm.
In the study, QFractureScores potentially improved on other algorithms by including additional risk factors, such as falls, type 2 diabetes, cardiovascular disease and use of hormone replacement therapy.
And because QFractureScores were developed in the clinical setting in which they will be used, they are likely to provide more appropriate risk estimates of fracture risk in the UK population, write the authors.
The results also suggest that the QFractureScores are likely to be at least as effective at identifying patients at high risk of hip fracture within primary care as the FRAX algorithm.
The authors conclude: "These new algorithms can predict risk of fracture in primary care populations in the UK without laboratory measurements and are therefore suitable for use in both clinical settings and for self-assessment (www.qfracture.org).
"QFractureScores could be used to identify patients at high risk of fracture who might benefit from interventions to reduce their risk."