Skip to main content

Advertisement

Log in

Prognosis of fracture: evaluation of predictive accuracy of the FRAX™ algorithm and Garvan nomogram

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

Abstract

Summary

We evaluated the prognostic accuracy of fracture risk assessment tool (FRAX™) and Garvan algorithms in an independent Australian cohort. The results suggest comparable performance in women but relatively poor fracture risk discrimination in men by FRAX™. These data emphasize the importance of external validation before widespread clinical implementation of prognostic tools in different cohorts.

Introduction

Absolute risk assessment is now recognized as a preferred approach to guide treatment decision. The present study sought to evaluate accuracy of the FRAX™ and Garvan algorithms for predicting absolute risk of osteoporotic fracture (hip, spine, humerus, or wrist), defined as major in FRAX™, in a clinical setting in Australia.

Methods

A retrospective validation study was conducted in 144 women (69 fractures and 75 controls) and 56 men (31 fractures and 25 controls) aged between 60 and 90 years. Relevant clinical data prior to fracture event were ascertained. Based on these variables, predicted 10-year probabilities of major fracture were calculated from the Garvan and FRAX™ algorithms, using US (FRAX-US) and UK databases (FRAX-UK). Area under the receiver operating characteristic curves (AUC) was computed for each model.

Results

In women, the average 10-year probability of major fracture was consistently higher in the fracture than in the nonfracture group: Garvan (0.33 vs. 0.15), FRAX-US (0.30 vs. 0.19), and FRAX-UK (0.17 vs. 0.10). In men, although the Garvan model yielded higher average probability of major fracture in the fracture group (0.32 vs. 0.14), the FRAX™ algorithm did not: FRAX-US (0.17 vs. 0.19) and FRAX-UK (0.09 vs. 0.12). In women, AUC for the Garvan, FRAX-US, and FRAX-UK algorithms were 0.84, 0.77, and 0.78, respectively, vs. 0.76, 0.54, and 0.57, respectively, in men.

Conclusion

In this analysis, although both approaches were reasonably accurate in women, FRAX™ discriminated fracture risk poorly in men. These data support the concept that all algorithms need external validation before clinical implementation.

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

Access this article

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

Similar content being viewed by others

References

  1. Nguyen ND, Ahlborg HG, Center JR, Eisman JA, Nguyen TV (2007) Residual lifetime risk of fractures in women and men. J Bone Miner Res 22:781–788

    Article  PubMed  Google Scholar 

  2. Feuer EJ, Wun LM, Boring CC, Flanders WD, Timmel MJ, Tong T (1993) The lifetime risk of developing breast cancer. J Natl Cancer Inst 85:892–897

    Article  CAS  PubMed  Google Scholar 

  3. Center JR, Nguyen TV, Schneider D, Sambrook PN, Eisman JA (1999) Mortality after all major types of osteoporotic fracture in men and women: an observational study. Lancet 353:878–882

    Article  CAS  PubMed  Google Scholar 

  4. Tosteson AN, Gottlieb DJ, Radley DC, Fisher ES, Melton LJ 3rd (2007) Excess mortality following hip fracture: the role of underlying health status. Osteoporos Int 18:1463–1472

    Article  CAS  PubMed  Google Scholar 

  5. Randell AG, Nguyen TV, Bhalerao N, Silverman SL, Sambrook PN, Eisman JA (2000) Deterioration in quality of life following hip fracture: a prospective study. Osteoporos Int 11:460–466

    Article  CAS  PubMed  Google Scholar 

  6. Nguyen TV, Center JR, Eisman JA (2004) Osteoporosis: underrated, underdiagnosed and undertreated. Med J Aust 180:S18–S22

    PubMed  Google Scholar 

  7. Eisman J, Clapham S, Kehoe L (2004) Osteoporosis prevalence and levels of treatment in primary care: the Australian BoneCare Study. J Bone Miner Res 19:1969–1975

    Article  PubMed  Google Scholar 

  8. Siris ES, Chen YT, Abbott TA, Barrett-Connor E, Miller PD, Wehren LE, Berger ML (2004) Bone mineral density thresholds for pharmacological intervention to prevent fractures. Arch Intern Med 164:1108–1112

    Article  PubMed  Google Scholar 

  9. Nguyen ND, Eisman JA, Center JR, Nguyen TV (2007) Risk factors for fracture in nonosteoporotic men and women. J Clin Endocrinol Metab 92:955–962

    Article  CAS  PubMed  Google Scholar 

  10. Sanders KM, Nicholson GC, Watts JJ, Pasco JA, Henry MJ, Kotowicz MA, Seeman E (2006) Half the burden of fragility fractures in the community occur in women without osteoporosis. When is fracture prevention cost-effective? Bone 38:694–700

    Article  PubMed  Google Scholar 

  11. Cummings SR, Nevitt MC, Browner WS, Stone K, Fox KM, Ensrud KE, Cauley J, Black D, Vogt TM (1995) Risk factors for hip fracture in white women. Study of Osteoporotic Fractures Research Group. N Engl J Med 332:767–773

    Article  CAS  PubMed  Google Scholar 

  12. Nguyen ND, Pongchaiyakul C, Center JR, Eisman JA, Nguyen TV (2005) Identification of high-risk individuals for hip fracture: a 14-year prospective study. J Bone Miner Res 20:1921–1928

    Article  PubMed  Google Scholar 

  13. 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 

  14. Nguyen ND, Frost SA, Center JR, Eisman JA, Nguyen TV (2007) Development of a nomogram for individualizing hip fracture risk in men and women. Osteoporos Int 18:1109–1117

    Article  CAS  PubMed  Google Scholar 

  15. Nguyen ND, Frost SA, Center JR, Eisman JA, Nguyen TV (2008) Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks. Osteoporos Int 19:1431–1444

    Article  CAS  PubMed  Google Scholar 

  16. Kanis JA, Johnell O, Oden A, Johansson H, McCloskey E (2008) FRAX and the assessment of fracture probability in men and women from the UK. Osteoporos Int 19:385–397

    Article  CAS  PubMed  Google Scholar 

  17. Obuchowski NA, Zhou XH (2002) Prospective studies of diagnostic test accuracy when disease prevalence is low. Biostatistics 3:477–492

    Article  PubMed  Google Scholar 

  18. R Development Core Team (2008) A language and environment for statistical computing, version 2.7.0. R Foundation for Statistical Computing, Vienna http://www.R-project.org

    Google Scholar 

  19. Nguyen TV, Center JR, Eisman JA (2005) Femoral neck bone loss predicts fracture risk independent of baseline BMD. J Bone Miner Res 20:1195–1201

    Article  PubMed  Google Scholar 

  20. Borgstrom F, Johnell O, Kanis JA, Jonsson B, Rehnberg C (2006) At what hip fracture risk is it cost-effective to treat? International intervention thresholds for the treatment of osteoporosis. Osteoporos Int 17:1459–1471

    Article  CAS  PubMed  Google Scholar 

  21. Torgerson DJ, Kanis JA (1995) Cost-effectiveness of preventing hip fractures in the elderly population using vitamin D and calcium. OJM 88:135–139

    CAS  Google Scholar 

  22. Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (2001) Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 285:2486–97

    Article  Google Scholar 

  23. Gobbi PG, Baldini L, Broglia C, Goldaniga M, Comelli M, Morel P, Morra E, Cortelazzo S, Bettini R, Merlini G (2005) Prognostic validation of the international classification of immunoglobulin M gammopathies: a survival advantage for patients with immunoglobulin M monoclonal gammopathy of undetermined significance? Clin Cancer Res 11:1786–1790

    Article  CAS  PubMed  Google Scholar 

  24. Wong GL, Hui AY, Wong VW, Chan FK, Sung JJ, Chan HL (2005) A retrospective study on clinical features and prognostic factors of biopsy-proven primary biliary cirrhosis in Chinese patients. Am J Gastroenterol 100:2205–2211

    Article  PubMed  Google Scholar 

  25. Bartley AN, Ross DW (2002) Validation of p53 immunohistochemistry as a prognostic factor in breast cancer in clinical practice. Arch Pathol Lab Med 126:456–458

    PubMed  Google Scholar 

  26. Smeenk JM, Stolwijk AM, Kremer JA, Braat DD (2000) External validation of the Templeton model for predicting success after IVF. Hum Reprod 15:1065–1068

    Article  CAS  PubMed  Google Scholar 

  27. Schindl M, Wigmore SJ, Currie EJ, Laengle F, Garden OJ (2005) Prognostic scoring in colorectal cancer liver metastases: development and validation. Arch Surg 140:183–189

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The authors would like to thank the staff at St. Vincent’s Hospital Outpatient Clinics and Medical Records for their assistance with data collection. We are also grateful for the untied financial support by educational grants from Merck Sharp and Dohme, Sanofi-Aventis, Procter & Gamble Australia, Novartis and St. Vincent’s Hospital Department of Nuclear Medicine.

Conflicts of interest

John A. Eisman’s research, including the Dubbo Osteoporosis Epidemiology Study, has been supported by and/or he has provided consultation to Amgen, deCode, Eli Lilly, GE-Lunar, Merck Sharp and Dohme, Novartis, Roche-GSK, Sanofi-Aventis, Servier and Wyeth Australia. He was the editor-in-chief for the Journal of Bone and Mineral Research between 2003 and 2007 and serves in the following committees: Department of Health and Aging, Australian Government and Royal Australasian College of General Practitioners.

Jacqueline R Center has been supported by and/or given educational talks for Eli Lilly, Merck Sharp and Dohme, and Sanofi-Aventis.

Other authors have no conflicts of interest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to T. V. Nguyen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sandhu, S.K., Nguyen, N.D., Center, J.R. et al. Prognosis of fracture: evaluation of predictive accuracy of the FRAX™ algorithm and Garvan nomogram. Osteoporos Int 21, 863–871 (2010). https://doi.org/10.1007/s00198-009-1026-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00198-009-1026-7

Keywords

Navigation