The value of bone mineral density (BMD) measurements to stratify fracture probability can be enhanced in a case-finding strategy that combines BMD measurement with independent clinical risk indicators. Putative risk indicators include age and gender, BMI or weight, prior fracture, the use of corticosteroids, and possibly others. The aim of the present study was to develop a mathematical framework to quantify the impact of using combinations of risk indicators with BMD in case finding. Fracture probability can be expressed as a risk gradient, i.e. a relative risk (RR) of fracture per standard deviation (SD) change in BMD. With the addition of other continuous or categorical risk indicators a continuous distribution of risk indicators is obtained that approaches a normal distribution. It is then possible to calculate the risk of individuals compared with the average risk in the population, stratified by age and gender. A risk indicator with a gradient of fracture risk of 2 per SD identified 36% of the population as having a higher than average fracture risk. In individuals so selected, the risk was on average 1.7 times that of the general population. Where, through the combination of several risk indicators, the gradient of risk of the test increased to 4 per SD, a smaller proportion (24%) was identified as having a higher than average risk, but the average risk in this group was 3.1 times that of the population, which is a much better performance. At higher thresholds of risk, similar phenomena were found. We conclude that, whereas the change of the proportion of the population detected to be at high risk is small, the performance of a test is improved when the RR per SD is higher, indicated by the higher average risk in those identified to be at risk. Case-finding strategies that combine clinical risk indicators with BMD have increased efficiency, while having a modest impact on the number of individuals requiring treatment. Therefore, the cost-effectiveness is enhanced.
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We are grateful to the Alliance for Better Bone Health, Hologic, IGEA, Lilly, Lunar, Novartis, Pfizer, Roche, Wyeth, and the EU (FP3/5) for supporting this study and the International Osteoporosis Foundation, the International Society for Clinical Densitometry, and the National Osteoporosis Foundation for their unrestricted support of this work.
In this appendix we detail the derivation of the formulae.
Let X denote a risk variable for fracture or a combination of risk variables, which we assume to have a normal distribution. Without loss of generality we can assume that the mean is zero and the standard deviation is 1. The hazard function of fracture when X=x is given by:
The expected value E[exp(β0+β1X)] has to be equal to 1 in order to give an average risk equal to that of the population. We use the following general relationship: if Y has a normal distribution with mean μ and standard deviation σ, then E[exp(Y)]=exp(μ+σ2/2). The variable β0+β1X has a normal distribution with mean β0 and standard deviation β1.
Therefore, E[exp(β0+β1X)]=exp(β0+β12/2) and the condition that the mean is 1 implies that β0=−β12/2.
The definition of a gradient of risk (GR) for the variable X implies that an increase of 1 standard deviation corresponds to a risk ratio of GR. Moreover, the standard deviation of X is 1. Thus, exp(β0+β1(x+1))/exp(β0+β1x)=GR. This implies that β1=ln(GR), and we find that β0=−ln(GR)2/2. If the value x of the risk variable X exceeds a specific risk threshold level (RT), then, exp(β0+β1x)>RT, and thus, x>(ln(RT)−β0)/β1. The probability of this event is therefore given by
where Φ is the cumulative distribution function of the standardized normal distribution, and z is given by
Let z=−(ln(RT)−β0)/β1 The average risk (AR) in the group above the chosen risk threshold relative to the average risk in the general population is then given by
Since exp(β0+β12/2)=1, this becomes
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De Laet, C., Odén, A., Johansson, H. et al. The impact of the use of multiple risk indicators for fracture on case-finding strategies: a mathematical approach. Osteoporos Int 16, 313–318 (2005). https://doi.org/10.1007/s00198-004-1689-z
- Case finding
- Mathematical model