Dr Oglesby is a stockholder in Eli Lilly and Company. All other authors have no conflict of interest
Optimization of BMD Measurements to Identify High Risk Groups for Treatment—A Test Analysis
Article first published online: 1 JUN 2004
Copyright © 2004 ASBMR
Journal of Bone and Mineral Research
Volume 19, Issue 6, pages 906–913, June 2004
How to Cite
Johansson, H., Oden, A., Johnell, O., Jonsson, B., de Laet, C., Oglesby, A., McCloskey, E. V., Kayan, K., Jalava, T. and Kanis, J. A. (2004), Optimization of BMD Measurements to Identify High Risk Groups for Treatment—A Test Analysis. J Bone Miner Res, 19: 906–913. doi: 10.1359/jbmr.2004.19.6.906
- Issue published online: 2 DEC 2009
- Article first published online: 1 JUN 2004
- Manuscript Accepted: 6 FEB 2004
- Manuscript Revised: 30 DEC 2003
- Manuscript Received: 3 JUL 2003
- fracture possibility;
- BMD tests;
- intervention threshold
The aim of this study was to optimize the use of BMD measurements in case finding strategies. The use of clinical risk factors with and without BMD was explored in a random sample of the Sheffield female population ≥75 years of age. The use of clinical risk factors alone could identify women well above or well below a threshold of fracture risk. BMD assessment can be confined to a minority of women (∼20%) in whom the measurement aids in prognostication of fracture.
Introduction: The aim of this study was to develop a methodology to optimize the role of BMD measurements in a case finding strategy. We studied 2113 women ≥75 years of age randomly selected from Sheffield, UK, and adjacent regions. Baseline assessment included hip BMD and clinical risk factors. Outcomes included death and fracture in women followed for 6723 person-years.
Materials and Methods: Poisson models were used to identify significant risk factors for all fractures and for death with and without BMD and the hazard functions were used to compute fracture probabilities. Women were categorized by fracture probability with and without a BMD assessment. A 10-year fracture probability threshold of 35% was taken as an intervention threshold. Discordance in categorization of risk (i.e., above or below the threshold probability) between assessment with and without BMD was examined by logistic regression as probabilities of re-classification. Age, prior fracture, use of corticosteroids, and low body mass index were identified as significant clinical risk factors.
Results: A total of 16.8% of women were classified as high risk based on these clinical risk factors. The average BMD in these patients was ∼1 SD lower than in low-risk women; 21.5% of women were designated to be at high risk with the addition of BMD. Fifteen percent of all women were reclassified after adding BMD to clinical risk factors, most of whom lay near the intervention threshold. When a high probability of reclassification was accepted (without a BMD test) for high risk to low risk (p1 ≤ 0.8) and a low probability accepted for low to high risk (p2 ≤ 0.2), BMD tests would be required in only 21% of the population.
Conclusion: We conclude that the use of clinical risk factors can identify elderly women at high fracture risk and that such patients have a low average BMD. BMD testing is required, however, in a minority of women—a fraction that depends on the probabilities accepted for classification and the thresholds of risk chosen. These findings need to be validated in other cohorts at different ages and from different regions of the world.