The effect of fracture recency on observed 5-year fracture probability: A study based on the FRISBEE cohort.

Introduction: Prediction models, especially the FRAX®, are largely used to estimate the fracture risk at ten years, but the current algorithm does not take into account the time elapsed after a fracture. Kanis et al. recently proposed correction factors allowing to adjust the FRAX® score for fracture recency. The objective of this work was to analyze the effect of fracture recency in the FRISBEE cohort.
Methods: We identified in the FRISBEE cohort subjects who sustained a validated fracture during the first 5 years following an incident MOF. We calculated their estimated 5-year risk of fracture using FRAX® uncorrected, adjusted for recency and further adjusted for the MOF/hip ratios calibration factors previously derived for the Belgian FRAX®. We compared the fracture risk estimated by FRAX® before and after these corrections to the observed incidence of validated fractures in our cohort.
Results: In our ongoing cohort, 376 subjects had a first non-traumatic incident validated MOF after inclusion; 81 had a secondary fracture during the 5 years follow-up period after this index fracture. The FRAX® score significantly under-evaluated the observed incidence of fractures in our cohort by 54.7 % (fracture rate of 9.7 %; 95 % CI, 6.8-12.9 %) if uncorrected (p < 0.001) and by 32.6 % after correction for recency (14.5 %; 95 % CI, 11.1-18.2 %) (p = 0.01). The calibration for MOF/hip ratios improved the prediction (17.5 %; 95 % CI: 13.7-21.4 %) (p = 0.2). After correcting for recency and for calibration, the predicted value was over-evaluated by 22 % (fracture rate of 26.1 %; 95 % CI, 21.6-30.5 %) but this over-evaluation was not significant (p = 0.1).
Conclusion: Our data indicate that the correction of the FRAX® score for fracture recency improves fracture prediction. However, correction for calibration and recency tends to overestimate fracture risk in this population of elderly women.
Competing Interests: We wish to declare that there are no known conflicts of interest associated with this publication that could be perceived as prejudicing the impartiality of the research reported.
(© 2023 The Authors.)