Personalized fracture risk assessment: room for improvement
Area under the receiver operating characteristic curve of the FRAX and Garvan models for predicting total or major osteoporotic fractures (A) and hip fracture (B) from published validation studies.
The Garvan Fracture Risk Calculator is now 10 years old. In this occasion, I was invited to write a review of fracture risk assessment for the journal Osteoporosis and Sarcopenia. In this review, I also put forward a number of ideas for improving the accuracy of assessment. Here is the abstract of the review:
Fragility fracture is a serious clinical event, because it is associated with increased risk of mortality and reduced quality of life. The risk of fracture is determined by multiple risk factors, and their effects may be interactional. Over the past 10 years, a number of predictive models (e.g., FRAX, Garvan Fracture Risk Calculator, and Qfracture) have been developed for individualized assessment of fracture risk. These models use different risk profiles to estimate the probability of fracture over 5- and 10-year period. The ability of these models to discriminate between those individuals who will and will not have a fracture (i.e., area under the receiver operating characteristic curve [AUC]) is generally acceptable-to-good (AUC, 0.6 to 0.8), and is highly variable between populations. The calibration of existing models is poor, particularly in Asian populations. There is a strong need for the development and validation of new prediction models based on Asian data for Asian populations. We propose approaches to improve the accuracy of existing predictive models by incorporating new markers such as genetic factors, bone turnover markers, trabecular bone score, and time-variant factors. New and more refined models for individualized fracture risk assessment will help identify those most likely to sustain a fracture, those most likely to benefit from treatment, and encouraging them to modify their risk profile to decrease risk.