Skip to main content
SpringerLink
Log in

Vertebral fracture risk (VFR) score for fracture prediction in postmenopausal women

  • Original Article
  • Published:
Osteoporosis International Aims and scope Submit manuscript

Abstract

Summary

Early prognosis of osteoporosis risk is not only important to individual patients but is also a key factor when screening for osteoporosis drug trial populations. We present an osteoporosis fracture risk score based on vertebral heights. The score separated individuals who sustained fractures (by follow-up after 6.3 years) from healthy controls at baseline.

Introduction

This case–control study was designed to assess the ability of three novel fracture risk scoring methods to predict first incident lumbar vertebral fractures in postmenopausal women matched for classical risk factors such as BMD, BMI, and age.

Methods

This was a case–control study of 126 postmenopausal women, 25 of whom sustained at least one incident lumbar fracture and 101 controls that maintained skeletal integrity over a 6.3-year period. Three methods for fracture risk assessment were developed and tested. They are based on anterior, middle, and posterior vertebral heights measured from vertebrae T12-L5 in lumbar radiographs at baseline. Each score’s fracture prediction potential was investigated in two variants using (1) measurements from the single most deformed vertebra or (2) average measurements across vertebrae T12-L5. Emphasis was given to the vertebral fracture risk (VFR) score.

Results

All scoring methods demonstrated significant separation of cases from controls at baseline. Specifically, for the VFR score, cases and controls were significantly different (0.67 ± 0.04 vs. 0.35 ± 0.03, p < 10 −6) with an AUC of 0.82. Dividing the VFR scores into tertiles, the fracture odds ratio for the highest versus lowest tertile was 35 (p < 0.001). Sorting the combined case–control group according to VFR score resulted in 90% of cases in the top half.

Conclusion

At baseline, the three scores separated cases from controls and, especially, the VFR score appears to be predictive of fractures. Control experiments, however also, indicate that VFR-based fracture prediction is operator/annotator dependent and high-quality annotations are needed for good fracture prediction

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  1. Rodan GA, Martin TJ (2000) Therapeutic approaches to bone diseases. Science 289:1508

    Article  PubMed  CAS  Google Scholar 

  2. Watts NB (2001) Osteoporotic vertebral fractures. Neurosurg Focus E12:10

    Google Scholar 

  3. Kanis JA, Borgstrom F, De Laet C, Johansson H, Johnell O, Jonsson B, Oden A, Zethraeus N, Pfleger B, Khaltaev N (2005) Assessment of fracture risk. Osteoporos Int 16:581–589

    Article  PubMed  Google Scholar 

  4. Truumees E (2003) Medical consequences of osteoporotic vertebral compression fractures. Instr Course Lect 52:551–558

    PubMed  Google Scholar 

  5. Jiang G, Eastell R, Barrington NA, Ferrar L (2004) Comparison of methods for the visual identification of prevalent vertebral fracture in osteoporosis. Osteoporos Int 17(11):887–896

    Article  Google Scholar 

  6. Genant HK, Wu CY, van Kuijk C, Nevitt MC (1993) Vertebral fracture assessment using a semiquantitative technique. J Bone Miner Res 8:1137–1148

    Article  PubMed  CAS  Google Scholar 

  7. Ferrar L, Jiang G, Adams J, Eastell R (2005) Identification of vertebral fractures: an update. Osteoporos Int 16:717–728

    Article  PubMed  CAS  Google Scholar 

  8. Grados F, Roux C, de Vernejoul MC, Utard G, Sebert JL, Fardellone P (2001) Comparison of four morphometric definitions and a semiquantitative consensus reading for assessing prevalent vertebral fractures. Osteoporos Int 12:716–722

    Article  PubMed  CAS  Google Scholar 

  9. O’Neill TW, Felsenberg D, Varlow J (2004) Diagnosis of osteoporotic vertebral fractures: importance of recognition and description by radiologists. Am J Roentgenol 183:949–958

    Google Scholar 

  10. Stone J, Gurrin LC, Byrnes GB, Schroen CJ, Treloar SA, Padilla EJ, Dite GS, Southey MC, Hayes VM, Hopper JL (2007) Mammographic density and candidate gene variants: a twins and sisters study. Cancer Epidemiol Biomark Prev 16:1479–1484

    Article  CAS  Google Scholar 

  11. Nielsen VAH, Pødenphant J, Martens S, Gotfredsen A, Riis BJ (1991) Precision in assessment of osteoporosis from spine radiographs. Euro K Radiol 13:11–14

    Article  Google Scholar 

  12. Delmas PD, Langerijt L, Watts NB, Eastell R, Genant H, Grauer A, Cahall DL (2005) Underdiagnosis of vertebral fractures is a worldwide problem: the IMPACT study. J Bone Miner Res 20:557–563

    Article  PubMed  Google Scholar 

  13. Eastell R, Cedel SL, Wahner HW, Riggs BL, Melton LJ (1991) Classification of vertebral fractures. J Bone miner Res (Print) 6:207–215

    Article  CAS  Google Scholar 

  14. Black DM, Palermo L, Nevitt MC, Genant HK, Epstein R, San Valentin R, Cummings SR (1995) Comparison of methods for defining prevalent vertebral deformities: the Study of Osteoporotic Fractures. J Bone Miner Res 10:890–902

    Article  PubMed  CAS  Google Scholar 

  15. Davies KM, Recker RR, Heaney RP (1989) Normal vertebral dimensions and normal variation in serial measurements of vertebrae. J Bone Miner Res 4:341–349

    Article  PubMed  CAS  Google Scholar 

  16. Jensen KK, Tougaard L (1981) A simple X-ray method for monitoring progress of osteoporosis. Lancet 2:19–20

    Article  PubMed  CAS  Google Scholar 

  17. Kleerekoper M, Parfitt AM, Ellis BI (1984) Measurement of vertebral fracture rates in osteoporosis. Copenhagen Int Symp Osteoporos 1:103–108

    Google Scholar 

  18. Melton LJ, Kan SH, Frye MA, Wahner HW, O’Fallon WM, Riggs BL (2005) Epidemiology of vertebral fractures in women. Am J Epidemiol 129:1000–1011

    Google Scholar 

  19. Minne HW, Leidig G, Wuster C, Siromachkostov L, Baldauf G, Bickel R, Sauer P, Lojen M, Ziegler R (1988) A newly developed spine deformity index (SDI) to quantitate vertebral crush fractures in patients with osteoporosis. Bone Miner 3:335–349

    PubMed  CAS  Google Scholar 

  20. Reshef A, Schwartz A, Ben Menachem Y, Menczel J, Guggenheim K (1971) Radiological osteoporosis: correlation with dietary and biochemical findings. J Am Geriatr Soc 19:391–402

    PubMed  CAS  Google Scholar 

  21. Ross PD, Yhee YK, He YF, Davis JW, Kamimoto C, Epstein RS, Wasnich RD (1993) A new method for vertebral fracture diagnosis. J Bone Miner Res 8:167–174

    Article  PubMed  CAS  Google Scholar 

  22. Smith-Bindman R, Steiger P, Cummings SR, Genant HK (1991) The index of radiographic area (IRA): a new approach to estimating the severity of vertebral deformity. Bone Miner 15:137–149

    Article  PubMed  CAS  Google Scholar 

  23. McCloskey EV, Spector TD, Eyres KS, Fern ED, O’Rourke N, Vasikaran S, Kanis JA (1993) The assessment of vertebral deformity: a method for use in population studies and clinical trials. Osteoporos Int 3(3):138–147

    Article  PubMed  CAS  Google Scholar 

  24. Bagger YZ, Tanko LB, Alexandersen P, Hansen HB, Qin G, Christiansen C (2006) The long-term predictive value of bone mineral density measurements for fracture risk is independent of the site of measurement and the age at diagnosis: results from the prospective epidemiological risk factors study. Osteoporos Int 17:471–477

    Article  PubMed  Google Scholar 

  25. Pettersen PC, de Bruijne M, Chen J, He Q, Christiansen C, Tanko LB (2007) A computer-based measure of irregularity in vertebral alignment is a BMD-independent predictor of fracture risk in postmenopausal women. Osteoporos Int 18(11):1525–1530

    Article  PubMed  CAS  Google Scholar 

  26. DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845

    Article  PubMed  CAS  Google Scholar 

  27. Tarone RE (1985) On heterogeneity tests based on efficient scores. Biometrika 72(1):91–95

    Article  Google Scholar 

  28. Zebaze RMD, Maalouf G, Maalouf N, Seeman E (2004) Loss of regularity in the curvature of the thoracolumbar spine: a measure of structural failure. J Bone Miner Res 19:1099–1104

    Article  PubMed  Google Scholar 

  29. Duan Y, Seeman E, Turner CH (2001) The biomechanical basis of vertebral body fragility in men and women. J Bone Miner Res 16:2276–2283

    Article  PubMed  CAS  Google Scholar 

  30. Hasserius R, Karlsson MK, Nilsson BE, Johnell O (2003) Prevalent vertebral deformities predict increased mortality and increased fracture rate in both men and women: a 10-year population-based study of 598 individuals from the Swedish cohort in the European Vertebral Osteoporosis Study. Osteoporos Int 14:61–68

    Article  PubMed  CAS  Google Scholar 

  31. Lunt M, O’Neill TW, Felsenberg D, Reeve J, Kanis JA, Cooper C, Silman AJ (2003) Characteristics of a prevalent vertebral deformity predict subsequent vertebral fracture: results from the European Prospective Osteoporosis Study (EPOS). Bone 33:505–513

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the funding from the Danish Research Foundation (Den Danske Forskningsfond) supporting this work. The authors thank Jane Petersen and Annette Olesen for the repeat annotations.

Conflicts of interest

The VFR-methodology is part of a pending patent. Martin Lillholm is an employee of Synarc Imaging Techonologies/Nordic Bioscience Imaging (SIT/NBI). Anarta Ghosh is a former employee of SIT/NBI. Paola C Pettersen is an employee of Center for Clinical and Basic Research (CCBR). Erik B Dam is an employee of SIT/NBI. Morten A Karsdal is an employee and shareholder of Nordic Bioscience (NB). Claus Christiansen is an employee and shareholder of NB and CCBR. Harry K Genant is an employee and shareholder of Synarc. Mads Nielsen is partly funded by SIT/NBI. Marleen de Bruijne was previously funded by Nordic Bioscience.

Author information

Authors and Affiliations

  1. Synarc Imaging Technologies A/S, Herlev, Denmark

    M. Lillholm, A. Ghosh, E. B. Dam & M. Nielsen

  2. Center for Clinical and Basic Research, Ballerup, Denmark

    P. C. Pettersen

  3. University of Copenhagen, Copenhagen, Denmark

    M. de Bruijne & M. Nielsen

  4. Biomedical Imaging Group Rotterdam, Erasmus MC-University Medical Center Rotterdam, Rotterdam, The Netherlands

    M. de Bruijne

  5. Nordic Bioscience A/S, Herlev, Denmark

    M. A. Karsdal & C. Christiansen

  6. Department of Radiology, University of California at San Francisco, San Francisco, CA, USA

    H. K. Genant

  7. Synarc, San Francisco, CA, USA

    H. K. Genant

Authors
  1. M. Lillholm
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. A. Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. P. C. Pettersen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. de Bruijne
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. E. B. Dam
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. M. A. Karsdal
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. C. Christiansen
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. H. K. Genant
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. M. Nielsen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Lillholm.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lillholm, M., Ghosh, A., Pettersen, P.C. et al. Vertebral fracture risk (VFR) score for fracture prediction in postmenopausal women. Osteoporos Int 22, 2119–2128 (2011). https://doi.org/10.1007/s00198-010-1436-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00198-010-1436-6

Keywords

Search

Navigation