Original Research

Calcified Tissue International

, Volume 89, Issue 2, pp 172-177

First online:

Clinical Risk Factors for Osteoporosis in Ireland and the UK: A Comparison of FRAX and QFractureScores

  • N. M. CumminsAffiliated withClinical Materials Unit, Materials and Surface Science Institute, University of Limerick Email author 
  • , E. K. PokuAffiliated withCranfield Health, Cranfield University
  • , M. R. TowlerAffiliated withInamori School of Engineering, Alfred University
  • , O. M. O’DriscollAffiliated withMedical Engineering Design and Innovation Centre, Cork Institute of Technology
  • , S. H. RalstonAffiliated withSchool of Molecular and Clinical Medicine, Western General Hospital

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Recently two algorithms have become available to estimate the 10-year probability of fracture in patients suspected to have osteoporosis on the basis of clinical risk factors: the FRAX algorithm and QFractureScores algorithm (QFracture). The aim of this study was to compare the performance of these algorithms in a study of fracture patients and controls recruited from six centers in the United Kingdom and Ireland. A total of 246 postmenopausal women aged 50–85 years who had recently suffered a low-trauma fracture were enrolled and their characteristics were compared with 338 female controls who had never suffered a fracture. Femoral bone mineral density was measured by dual-energy X-ray absorptiometry, and fracture risk was calculated using the FRAX and QFracture algorithms. The FRAX algorithm yielded higher scores for fracture risk than the QFracture algorithm. Accordingly, the risk of major fracture in the overall study group was 9.5% for QFracture compared with 15.2% for FRAX. For hip fracture risk the values were 2.9% and 4.7%, respectively. The correlation between FRAX and QFracture was R = 0.803 for major fracture and R = 0.857 for hip fracture (P ≤ 0.0001). Both algorithms yielded high specificity but poor sensitivity for prediction of osteoporosis. We conclude that the FRAX and QFracture algorithms yield similar results in the estimation of fracture risk. Both of these tools could be of value in primary care to identify patients in the community at risk of osteoporosis and fragility fractures for further investigation and therapeutic intervention.


Clinical risk factor Osteoporosis Fracture FRAX QFractureScores