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Which model to predict fracture risk?

Nature Reviews Endocrinology volume 10, pages 194195 (2014) | Download Citation

Prediction of fracture risk is increasingly used to guide clinical use of antiosteoporosis drugs. Data from a large primary care prospective study in 10 countries has now been used to generate an empirical composite 5-year fracture risk model based on clinical data (excluding BMD). This model performed better than current widely used models.

Key points

  • Osteoporotic fractures are an increasing health and societal burden worldwide

  • Low BMD and/or several clinical risk factors can be modelled into a 5–10-year empirical fracture risk predictor model; such models are already used to guide clinical treatment

  • Existing predictor models enable an accurate prediction in about 70% of patients. An extensive long-term observational study (GLOW) shows that such predictions can be further improved on the basis of extensive clinical data

  • Further improvement of predictor models seems to be possible by combining existing clinical predictor models with improved measurements of bone mass and quality and complex genetic analysis

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Acknowledgements

We join the authors of the FitzGerald et al. manuscript in dedicating these comments to our friend Steven Boonen.

Author information

Affiliations

  1. Clinical and Experimental Endocrinology, KU Leuven, Herestraat 49 ON1 Box 902, 3000 Leuven, Belgium.

    • Roger Bouillon
    •  & Dirk Vanderschueren

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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Roger Bouillon.

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DOI

https://doi.org/10.1038/nrendo.2014.15

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