Prognosis of fracture: evaluation of predictive accuracy of the FRAX™ algorithm and Garvan nomogram
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We evaluated the prognostic accuracy of fracture risk assessment tool (FRAX™) and Garvan algorithms in an independent Australian cohort. The results suggest comparable performance in women but relatively poor fracture risk discrimination in men by FRAX™. These data emphasize the importance of external validation before widespread clinical implementation of prognostic tools in different cohorts.
Absolute risk assessment is now recognized as a preferred approach to guide treatment decision. The present study sought to evaluate accuracy of the FRAX™ and Garvan algorithms for predicting absolute risk of osteoporotic fracture (hip, spine, humerus, or wrist), defined as major in FRAX™, in a clinical setting in Australia.
A retrospective validation study was conducted in 144 women (69 fractures and 75 controls) and 56 men (31 fractures and 25 controls) aged between 60 and 90 years. Relevant clinical data prior to fracture event were ascertained. Based on these variables, predicted 10-year probabilities of major fracture were calculated from the Garvan and FRAX™ algorithms, using US (FRAX-US) and UK databases (FRAX-UK). Area under the receiver operating characteristic curves (AUC) was computed for each model.
In women, the average 10-year probability of major fracture was consistently higher in the fracture than in the nonfracture group: Garvan (0.33 vs. 0.15), FRAX-US (0.30 vs. 0.19), and FRAX-UK (0.17 vs. 0.10). In men, although the Garvan model yielded higher average probability of major fracture in the fracture group (0.32 vs. 0.14), the FRAX™ algorithm did not: FRAX-US (0.17 vs. 0.19) and FRAX-UK (0.09 vs. 0.12). In women, AUC for the Garvan, FRAX-US, and FRAX-UK algorithms were 0.84, 0.77, and 0.78, respectively, vs. 0.76, 0.54, and 0.57, respectively, in men.
In this analysis, although both approaches were reasonably accurate in women, FRAX™ discriminated fracture risk poorly in men. These data support the concept that all algorithms need external validation before clinical implementation.
KeywordsFracture risk Prognostic models Validation
The authors would like to thank the staff at St. Vincent’s Hospital Outpatient Clinics and Medical Records for their assistance with data collection. We are also grateful for the untied financial support by educational grants from Merck Sharp and Dohme, Sanofi-Aventis, Procter & Gamble Australia, Novartis and St. Vincent’s Hospital Department of Nuclear Medicine.
Conflicts of interest
John A. Eisman’s research, including the Dubbo Osteoporosis Epidemiology Study, has been supported by and/or he has provided consultation to Amgen, deCode, Eli Lilly, GE-Lunar, Merck Sharp and Dohme, Novartis, Roche-GSK, Sanofi-Aventis, Servier and Wyeth Australia. He was the editor-in-chief for the Journal of Bone and Mineral Research between 2003 and 2007 and serves in the following committees: Department of Health and Aging, Australian Government and Royal Australasian College of General Practitioners.
Jacqueline R Center has been supported by and/or given educational talks for Eli Lilly, Merck Sharp and Dohme, and Sanofi-Aventis.
Other authors have no conflicts of interest.
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