Bone Abstracts

In the recent years, worldwide the FRAX algorithm is used more extensively to estimate the risk of fragility fractures. Since 2009, the Ukrainian Scientific Medical Centre on Osteoporosis has been extensively using the FRAX algorithm to estimate the fragility fracture risk.

The aim of the study was to establish the normative data for the Austrian model of FRAX algorithm. We examined 3405 women aged 40–89. For the statistical analysis of results the STATISTICA-7.0 and SPSS-17 software were used.

Results: Analyzing the relationship between FRAX-1 and FRAX-2 scores for all fragility fractures and hip fractures we found strong correlations for different subgroups, the results suggest that the FRAX model without the BMD scores has a sufficient informative value and could be used in the decision making on treatment initiation.

The analysis of FRAX scores revealed that FRAX-all and FRAX-hip scores of 11.5 and 2.5 respectively, when the Austrian model is applied to the postmenopausal women, are the criteria for the osteoporotic treatment initiation. FRAX-all and FRAX-hip scores of 7.0 and 1.5 (when the Austrian model is applied to the postmenopausal women), respectively, are the criteria for a further investigation with DXA scan.

This set of criteria could be used in decision making on the antiosteoporotic therapy initiation, especially if DXA scan is not available. However, the researchers should remember that the BMD score is a significant characteristic of the bone tissue state that could be used in monitoring effectiveness of the treatment.

Finally, the results of our study have defined a new approach toward an antiosteoporotic treatment initiation in the Ukrainian women, however, the limitation of this study is explained by using the models developed for different populations, and it could be a source of system error due to the regional features of osteoporosis and its complications in the Ukrainian population. It was a reason to start a multicenter epidemiological survey on fragility fractures prevalence.