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Whom to treat? The contribution of vertebral X-rays to risk-based algorithms for fracture prediction. Results from the European Prospective Osteoporosis Study
Authors: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 Póor  R. Nuti  T. Miazgowski  K. Hoszowski  J. Bruges Armas  A. Lopes Vaz  L. I. Benevolenskaya  P. Masaryk  J. B. Cannata  O. Johnell  D. 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
Affiliation:1. Department of Medicine & Institute of Public Health, University of Cambridge, Cambridge, UK
32. Strangeways Research Laboratory, Worts Causeway, Cambridge, CB1 8RN, UK
2. Department of Radiology Charite, University Medicine Berlin Campus Benjamin Franklin, Berlin, Germany
3. ARC Epidemiology Unit, University of Manchester, Manchester, UK
4. University Hospital, Graz, Austria
5. University Hospital, Leuven, Belgium
6. Clinical Hospital, Zagreb, Croatia
7. Charles University, Prague, Czech-Republic
8. Behring Hospital, Berlin, Germany
9. Humboldt University, Berlin, Germany
10. Ruhr University, Bochum, Germany
11. Medical Academy, Erfurt, Germany
12. University of Heidelberg, Heidelberg, Germany
13. Clinic for Internal Medicine, Jena, Germany
14. Institute of Social Medicine, Lubeck, Germany
15. University of Athens, Athens, Greece
16. National Institute of Rheumatology and Physiotherapy, Budapest, Hungary
17. University of Siena, Siena, Italy
18. University School of Medicine, Szczecin, Poland
19. PKP Hospital, Warsaw, Poland
20. Hospital de Angra do Herismo, SEEBMO, Azores, Portugal
21. Hospital de San Joao, Oporto, Portugal
22. Institute of Rheumatology, Moscow, Russia
23. Institute of Rheumatic Diseases, Piestany, Slovakia
24. Asturia General Hospital, Oviedo, Spain
25. Lund University, Malm?, Sweden
26. University of Aberdeen, Aberdeen, UK
27. Royal National Hospital for Rheumatic Diseases, Bath, UK
28. Royal Cornwall Hospital, Truro, UK
29. School of Nursing, Midwifery and Social Work, University of Manchester, Manchester, UK
30. University of Southampton, Southampton, UK
31. University of Sheffield, Sheffield, UK
Abstract: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.This work was presented in part at the 30th European Symposium on Calcified Tissues, 8–12 May 2003, Rome, Italy.A.J. Silman and J. Reeve are the EU Grant holders and Project Leaders.
Keywords:Algorithm  Osteoporosis diagnosis  Osteoporosis treatment  Radiograph  Spine X-ray  Vertebral fracture
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