Whom to treat? The contribution of vertebral X-rays to risk-based algorithms for fracture prediction. Results from the European Prospective Osteoporosis Study

S. Kaptoge, G. Armbrecht, D. Felsenberg, M. Lunt, K. Weber, S. Boonen, I. Jajic, J. J. Stepan, D. Banzer, W. Reisinger, J. Janott, G. Kragl, C. Scheidt-Nave, B. Felsch, C. Matthis, H. H. Raspe, G. Lyritis, G. Poor, R. Nuti, T. MiazgowskiK. Hoszowski, J. B. Armas, A. L. Vaz, L. I. Benevolenskaya, P. Masaryk, J. B. Cannata, O. Johnell, David M Reid, A. Bhalla, A. D. Woolf, C. J. Todd, C. Cooper, R. Eastell, J. A. Kanis, T. W. O'Neill, A. J. Silman, J. Reeve

Research output: Contribution to journalArticle

35 Citations (Scopus)


Introduction: Vertebral fracture is a strong risk factor for future spine and hip fractures; yet recent data suggest that only 5-20% of subjects with a spine fracture are identified in primary care. We aimed to develop easily applicable algorithms predicting a high risk of future spine fracture in men and women over 50 years of age. Methods: Data was analysed from 5,561 men and women aged 50+ years participating in the European Prospective Osteoporosis Study (EPOS). Lateral thoracic and lumbar spine radiographs were taken at baseline and at an average of 3.8 years later. These were evaluated by an experienced radiologist. The risk of a new (incident) vertebral fracture was modelled as a function of age, number of prevalent vertebral fractures, height loss, sex and other fracture history reported by the subject, including limb fractures occurring between X-rays. Receiver Operating Characteristic (ROC) curves were used to compare the predictive ability of models. Results: In a negative binomial regression model without baseline X-ray data, the risk of incident vertebral fracture significantly increased with age [RR 1.74, 95% CI (1.44, 2.10) per decade], height loss [1.08 (1.04, 1.12) per cm decrease], female sex [1.48 (1.05, 2.09)], and recalled fracture history; [1.65 (1.15, 2.38) to 3.03 (1.66, 5.54)] according to fracture site. Baseline radiological assessment of prevalent vertebral fracture significantly improved the areas subtended by ROC curves from 0.71 (0.67, 0.74) to 0.74 (0.70, 0.77) P=0.013 for predicting 1+ incident fracture; and from 0.74 (0.67, 0.81) to 0.83 (0.76, 0.90) P=0.001 for 2+ incident fractures. Age, sex and height loss remained independently predictive. The relative risk of a new vertebral fracture increased with the number of prevalent vertebral fractures present from 3.08 (2.10, 4.52) for 1 fracture to 9.36 (5.72, 15.32) for 3+. At a specificity of 90%, the model including X-ray data improved the sensitivity for predicting 2+ and 1+ incident fractures by 6 and 4 fold respectively compared with random guessing. At 75% specificity the improvements were 3.2 and 2.4 fold respectively. With the modelling restricted to the subjects who had BMD measurements (n=2,409), the AUC for predicting 1+ vs. 0 incident vertebral fractures improved from 0.72 (0.66, 0.79) to 0.76 (0.71, 0.82) upon adding femoral neck BMD (P=0.010). Conclusion: We conclude that for those with existing vertebral fractures, an accurately read spine X-ray will form a central component in future algorithms for targeting treatment, especially to the most vulnerable. The sensitivity of this approach to identifying vertebral fracture cases requiring anti-osteoporosis treatment, even when X-rays are ordered highly selectively, exceeds by a large margin the current standard of practice as recorded anywhere in the world.

Original languageEnglish
Pages (from-to)1369-1381
Number of pages13
JournalOsteoporosis International
Issue number9
Publication statusPublished - 2006


  • algorithm
  • osteoporosis diagnosis
  • osteoporosis treatment
  • radiograph
  • spine X-ray
  • vertebral fracture
  • women
  • EPOS
  • bone
  • prevalence
  • density
  • spine
  • men
  • determinants
  • metaanalysis
  • deformities


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