Fracture-risk calculators: Has their time come?

50% of postmenopausal women and 10%–20% of older men, causing substantial morbidity, mortality and cost to society. Prevention of osteoporotic fractures is an important component of health care that relies on the accurate prediction of who is likely to sustain a fracture. Although predicting events is not easy, models that predict fracture have been developed and then validated in external populations. In this issue, Langsetmo and colleagues report the results of their validation study of one such model involving a Canadian cohort. We review the history of fracture prediction and discuss the use of fracture-risk calculators. In 1994, a study group sponsored by the World Health Organization (WHO) recommended that postmenopausal women be grouped into four diagnostic categories of fracture risk (normal, osteopenia, osteoporosis and severe osteoporosis) based on bone mineral density Tscores and history of fracture. Those recommendations were widely adopted and led to measurement of bone mineral density occupying a central place in strategies for fracture prevention. Nevertheless, numerous other risk factors for fracture exist (many of which are independent of bone mineral density), and most osteoporotic fractures occur among women who do not fulfill the WHO criteria for osteoporosis. For these reasons, models that integrate other risk factors for fracture in addition to bone mineral density might improve our predictive ability. Predictive models have been reported for numerous conditions, but only a small proportion have entered clinical practice. Before clinical use, a model must be validated in populations different from those in which it was developed. Two key factors should be considered: discrimination and calibration. Discrimination is the ability of the model to distinguish between people with and without the outcome. It is assessed by the area under the receiver operating curve (or equivalent C statistic); a value of 1 indicates a perfect model, greater than 0.8 indicates excellent performance, 0.6 to 0.8 indicates moderate performance, less than 0.6 indicates weak performance, and 0.5 indicates performance no better than chance. Calibration is a measure of the goodness of fit of the model, and is assessed by comparing observed risk with predicted risk in a cohort divided by quintiles or deciles of predicted risk, and in subgroups defined by relevant clinical factors. Langsetmo and colleagues describe a carefully conducted validation study of an absolute risk calculator for fracture developed from the Dubbo Osteoporosis Epidemiology Study in Australia. The Dubbo calculator incorporates four clinical risk factors (age, sex, history of fractures and recent falls) with bone mineral density to produce estimated fiveand ten-year risks of hip and osteoporotic fracture. The authors report that the Dubbo calculator has moderate predictive ability (greater for hip fracture than the broader category of osteoporotic fracture), is generally well-calibrated and is an improvement on the existing WHO recommendations and, in men, the Canadian Association of Radiologists recommendations. A more widely known fracture-risk calculator is FRAX. A recent validation study of the country-specific FRAX-Canada tool involving the Manitoba Bone Density Program cohort showed similar findings, with good calibration and moderate predictive ability that was greater for hip fracture than major osteoporotic fracture. Absolute risk calculators for fracture have theoretical and practical advantages over earlier category-based systems. Fracture risk is expressed as a continuum, removing the somewhat arbitrary categorizations that otherwise occur. Clinically relevant reclassification of risk occurs, particularly in two settings: younger people with low bone mineral density but no clinical risk factors often have low risk of fracture over the short to medium term, whereas older people without osteoporosis by WHO criteria often have Fracture-risk calculators: Has their time come?