ReviewFRAX® and its applications to clinical practice
Introduction
FRAX® is a computer based algorithm (http://www.shef.ac.uk/FRAX) that provides models for the assessment of fracture probability in men and women [1], [2], [3]. The approach uses easily obtained clinical risk factors (CRFs) to estimate 10-year fracture probability. The estimate can be used alone or with femoral neck bone mineral density (BMD) to enhance fracture risk prediction. In addition, FRAX® uses Poisson regression to derive hazard functions of death as well as fracture. These hazard functions are continuous as a function of time which permit the calculation of the 10-year probability of a major osteoporotic fracture (hip, clinical spine, humerus or wrist fracture) and the 10-year probability of hip fracture. Some of the risk factors affect the risk of death as well as the fracture risk. Examples include increasing age, low body mass index (BMI), low BMD and smoking. Other risk engines calculate the probability of a clinical event (e.g. a myocardial infarct) without taking into account the possibility of death from other causes. In addition, the FRAX® model can be calibrated for different countries [1], [2], [3].
Probability of fracture is calculated in men or women from age, body mass index (BMI) computed from height and weight, and dichotomised risk variables that comprise;
a prior fragility fracture,
parental history of hip fracture,
current tobacco smoking,
ever long-term use of oral glucocorticoids,
rheumatoid arthritis,
other causes of secondary osteoporosis,
daily alcohol consumption of 3 or more units daily.
These variables are entered onto the web site. Femoral neck BMD can additionally be entered as a T-score derived from the NHANES III database for female Caucasians aged 20–29 years [4]. When entered, calculations give the 10-year probabilities as defined above with the inclusion of BMD (Fig. 1).
The relationships between risk factors and fracture risk incorporated within FRAX® have been constructed using information derived from the primary data of nine population based cohorts from around the world, including centres from North America, Europe, Asia and Australia and has been validated in 11 independent cohorts (mainly women) with a similar geographic distribution with in excess of 1 million patient years [5]. The use of primary data for the model construct permits the determination of the predictive importance in a multivariable context of each of the risk factors, as well as interactions between risk factors, and thereby optimises the accuracy by which fracture probability can be computed. The large sample permits the examination of the general relationship of each risk factor by age, sex, duration of follow up and, for continuous variables (BMD and BMI), the relationship of risk with the variable itself in a manner hitherto not possible. The use of primary data also eliminates the risk of publication bias.
In addition to the clinical risk factors, fracture probability varies markedly in different regions of the world [6]. Thus the FRAX® models need to be calibrated to those countries where the epidemiology of fracture and death is known. At present FRAX® models are available for Austria, China, Germany, France, Italy, Japan, Spain, Sweden, Switzerland, Turkey, and the UK and US. Other models are being developed, but there are relatively few other countries with sufficient information to construct FRAX® models [3], and these are listed below according to categories of hip fracture risk.
- (a)
Very high risk (e.g. Denmark, Iceland, Norway, Sweden, United States).
- (b)
High risk (e.g. Australia, Austria, Canada, Finland, Germany, Greece, Hungary, Italy, Kuwait, Netherlands, Portugal, Singapore, Switzerland, Taiwan, UK).
- (c)
Moderate risk (e.g. Argentina, China, France, Hungary, Hong Kong, Japan, Spain).
- (d)
Low risk (e.g. Cameroon, Chile, Korea, Turkey, Venezuela).
Each category of risk has been represented in the FRAX® models currently available (in italics, above). Thus in the absence of a FRAX® model for a particular country, a surrogate country should be chosen, based on the likelihood that it is representative of the index country.
The obvious application of FRAX® is in the assessment of individuals to identify those who would be candidates for pharmacological intervention, and it has been widely used since the launch of the web site, currently receiving on average 55,000 hits daily. But FRAX® should not be used in the clinic without an appreciation of its limitations as well as its strengths. There are also challenges to be faced in the construct of new clinical guidelines, the assessment of pharmacologic agents for drug registration (in Europe) and in health economics. These applications are the focus of this review.
Section snippets
Rationale for use
Until recently, the majority of clinical guidelines for the management of osteoporosis have made recommendations for intervention based predominantly on the basis of the T-score for BMD [3]. In the UK, for example, guidance for the identification of individuals at high fracture risk was provided until recently, by the Royal College of Physicians (RCP) [7], [8], [9]. The guidance was based on an opportunistic case finding strategy where physicians are alerted to the possibility of osteoporosis
Clinical guidelines
The application of this methodology to clinical practice demands a consideration of the fracture probability at which to intervene, both for treatment (an intervention threshold) and for BMD testing (assessment thresholds). These have been developed for Europe, Canada, Germany, Japan, the UK and US [11], [30], [31], [32], [33], [34]. There have been two approaches to the development of guidelines based on fracture probability. The first is to ‘translate’ current practice in the light of FRAX®,
Assessment of drug efficacy
Revised guidelines on the evaluation of medicinal products in the treatment of primary osteoporosis have been developed by the Committee for Medicinal Products for Human Use (CHMP) and came into effect at the end of May 2007 [46]. A major departure from previous guidance is that there is no longer any distinction between prevention and treatment, but an emphasis on the study of patients at risk from fracture. The preferred metric for expressing risk is the ten-year probability of fracture, in
Health economic applications of FRAX®
In order to justify resource allocation, it is becoming increasingly important to determine the cost-effectiveness of intervention. The preferred approach is cost utility analysis. This integrates the deaths and disability associated with the multiple outcomes by measuring quality of life adjusted years (QALYs). In order to estimate QALYs, each year of life is valued according to its utility to the patient that ranges from 0, the least desirable health state, to 1, perfect health. In the
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