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Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks

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Abstract

Summary

We have developed clinical nomograms for predicting 5-year and 10-year fracture risks for any elderly man or woman. The nomograms used age and information concerning fracture history, fall history, and BMD T-score or body weight.

Introduction

Although many fracture risk factors have been identified, the translation of these risk factors into a prognostic model that can be used in primary care setting has not been well realized. The present study sought to develop a nomogram that incorporates non-invasive risk factors to predict 5-year and 10-year absolute fracture risks for an individual man and woman.

Methods

The Dubbo Osteoporosis Epidemiology Study was designed as a community-based prospective study, with 1358 women and 858 men aged 60+ years as at 1989. Baseline measurements included femoral neck bone mineral density (FNBMD), prior fracture, a history of falls and body weight. Between 1989 and 2004, 426 women and 149 men had sustained a low-trauma fracture (not including morphometric vertebral fractures). Two prognostic models based on the Cox’s proportional hazards analysis were considered: model I included age, BMD, prior fracture and falls; and model II included age, weight, prior fracture and fall.

Results

Analysis of the area under the receiver operating characteristic curve (AUC) suggested that model I (AUC = 0.75 for both sexes) performed better than model II (AUC = 0.72 for women and 0.74 for men). Using the models’ estimates, we constructred various nomograms for individualizing the risk of fracture for men and women. If the 5-year risk of 10% or greater is considered “high risk”, then virtually all 80-year-old men with BMD T-scores <-1.0 or 80-year-old women with T-scores <-2.0 were predicted to be in the high risk group. A 60-year-old woman’s risk was considered high risk only if her BMD T-scores ≤-2.5 and with a prior fracture; however, no 60-year-old men would be in the high risk regardless of their BMD and risk profile.

Conclusion

These data suggest that the assessment of fracture risk for an individual cannot be based on BMD alone, since there are clearly various combinations of factors that could substantially elevate an individual’s risk of fracture. The nomograms presented here can be useful for individualizing the short- and intermediate-term risk of fracture and identifying high-risk individuals for intervention to reduce the burden of fracture in the general population.

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References

  1. Cummings SR, Melton LJ (2002) Epidemiology and outcomes of osteoporotic fractures. Lancet 359:1761–1767

    Article  PubMed  Google Scholar 

  2. Raisz LG (2005) Pathogenesis of osteoporosis: concepts, conflicts, and prospects. J Clin Invest 115:3318–3325

    Article  PubMed  CAS  Google Scholar 

  3. Black DM, Steinbuch M, Palermo L, Dargent-Molina P, Lindsay R, Hoseyni MS, Johnell O (2001) An assessment tool for predicting fracture risk in postmenopausal women. Osteoporos Int 12:519–528

    Article  PubMed  CAS  Google Scholar 

  4. van Staa TP, Geusens P, Kanis JA, Leufkens HG, Gehlbach S, Cooper C (2006) A simple clinical score for estimating the long-term risk of fracture in post-menopausal women. QJM 99:673–682

    Article  PubMed  Google Scholar 

  5. Ettinger B, Hillier TA, Pressman A, Che M, Hanley DA (2005) Simple computer model for calculating and reporting 5-year osteoporotic fracture risk in postmenopausal women. J Womens Health (Larchmt) 14:159–171

    Article  Google Scholar 

  6. 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  PubMed  CAS  Google Scholar 

  7. Wyatt JC, Altman DG (1995) Commentary: prognostic models: clinically useful or quickly forgotten? BMJ 311:1539–1541

    Google Scholar 

  8. Nguyen T, Sambrook P, Kelly P, Jones G, Lord S, Freund J, Eisman J (1993) Prediction of osteoporotic fractures by postural instability and bone density. BMJ 307:1111–1115

    Article  PubMed  CAS  Google Scholar 

  9. Marshall D, Johnell O, Wedel H (1996) Meta-analysis of how well measures of bone mineral density predict occurrence of osteoporotic fractures. BMJ 312:1254–1259

    PubMed  CAS  Google Scholar 

  10. Johnell O, Kanis JA, Oden A, Johansson H, De Laet C, Delmas P, Eisman JA, Fujiwara S, Kroger H, Mellstrom D, Meunier PJ, Melton LJ 3rd, O, Neill T, Pols H, Reeve J, Silman A, Tenenhouse A (2005) Predictive value of BMD for hip and other fractures. J Bone Miner Res 20:1185–1194

    Article  PubMed  Google Scholar 

  11. Kanis JA, Johnell O, De Laet C, Johansson H, Oden A, Delmas P, Eisman J, Fujiwara S, Garnero P, Kroger H, McCloskey EV, Mellstrom D, Melton LJ, Pols H, Reeve J, Silman A, Tenenhouse A (2004) A meta-analysis of previous fracture and subsequent fracture risk. Bone 35:375–382

    Article  PubMed  CAS  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. Jones G, Nguyen T, Sambrook PN, Kelly PJ, Gilbert C, Eisman JA (1994) Symptomatic fracture incidence in elderly men and women: the Dubbo Osteoporosis Epidemiology Study (DOES). Osteoporos Int 4:277–282

    Article  PubMed  CAS  Google Scholar 

  14. Simons LA, McCallum J, Simons J, Powell I, Ruys J, Heller R, Lerba C (1990) The Dubbo study: an Australian prospective community study of the health of elderly. Aust N Z J Med 20:783–789

    PubMed  CAS  Google Scholar 

  15. Nguyen TV, Center JR, Sambrook PN, Eisman JA (2001) Risk factors for proximal humerus, forearm, and wrist fractures in elderly men and women: the Dubbo Osteoporosis Epidemiology Study. Am J Epidemiol 153:587–595

    Article  PubMed  CAS  Google Scholar 

  16. National Osteoporosis Society (1998) Guidance on the prevention and management of corticosteroid induced osteoporosis. National Osteoporosis Society, Camerton, Bath, UK

    Google Scholar 

  17. Angus RM, Sambrook PN, Pocock NA, Eisman JA (1989) A simple method for assessing calcium intake in Caucasian women. J Am Diet Assoc 89:209–214

    PubMed  CAS  Google Scholar 

  18. Nguyen TV, Sambrook PN, Eisman JA (1997) Sources of variability in bone mineral density measurements: implications for study design and analysis of bone loss. J Bone Miner Res 12:124–135

    Article  PubMed  CAS  Google Scholar 

  19. Henry MJ, Pasco JA, Pocock NA, Nicholson GC, Kotowicz MA (2004) Reference ranges for bone densitometers adopted Australia-wide: Geelong osteoporosis study. Australas Radiol 48:473–475

    Article  PubMed  CAS  Google Scholar 

  20. Pocock NA, Sambrook PN, Nguyen T, Kelly P, Freund J, Eisman JA (1992) Assessment of spinal and femoral bone density by dual X-ray absorptiometry: comparison of lunar and hologic instruments. J Bone Miner Res 7:1081–1084

    Article  PubMed  CAS  Google Scholar 

  21. Hoeting JA (1999) Bayesian model averaging: a tutorial. Stat Sci 14:382–147

    Article  Google Scholar 

  22. Raftery AE, Madigan D, Hoeting JA (1997) Bayesian model averaging dor linear regression models. J Am Stat A 92:179–191

    Article  Google Scholar 

  23. Volinsky CT, Madigan D, Raftery AE, Kronmal RA (1997) Bayesian model averaging in proportional hazard models: assessing the risk of a stroke. Appl Statist 48:433–448

    Google Scholar 

  24. Raftery AE (1995) Bayesian model selection in social research. In: Marsden PV (ed) Sociological methodology. Blackwell, Cambridge, MA

    Google Scholar 

  25. Swets JA (1986) Form of empirical ROCs in discrimination and diagnostic tasks: implications for theory and measurement of performance. Psychol Bull 99:181–198

    Article  PubMed  CAS  Google Scholar 

  26. Swets JA (1986) Indices of discrimination or diagnostic accuracy: their ROCs and implied models. Psychol Bull 99:100–117

    Article  PubMed  CAS  Google Scholar 

  27. Swets JA (1988) Measuring the accuracy of diagnostic systems. Science 240:1285–1293

    Article  PubMed  CAS  Google Scholar 

  28. Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143:29–36

    PubMed  CAS  Google Scholar 

  29. Cox DR (1972) Regression models and life tables. J R Stat Soc (B) 34:187–220

    Google Scholar 

  30. Cox DR, Snell EJ (1989) Analysis of binary data. Chapman and Hall, London, U.K

    Google Scholar 

  31. Harrell FE Jr, Lee KL, Mark DB (1996) Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med 15:361–387

    Article  PubMed  Google Scholar 

  32. R Development Core Team (2007) R: a language and environment for statistical computing. In. R Foundation for Statistical Computing, Vienna, Austria

  33. Harrell FE Jr, Margolis PA, Gove S, Mason KE, Mulholland EK, Lehmann D, Muhe L, Gatchalian S, Eichenwald HF (1998) Development of a clinical prediction model for an ordinal outcome: the World Health Organization Multicentre Study of Clinical Signs and Etiological agents of Pneumonia, Sepsis and Meningitis in Young Infants. WHO/ARI young infant multicentre study group. Stat Med 17:909–944

    Article  PubMed  Google Scholar 

  34. Ensrud KE, Lipschutz RC, Cauley JA, Seeley D, Nevitt MC, Scott J, Orwoll ES, Genant HK, Cummings SR (1997) Body size and hip fracture risk in older women: a prospective study. Study of osteoporotic fractures research group. Am J Med 103:274–280

    Article  PubMed  CAS  Google Scholar 

  35. Henry MJ, Pasco JA, Sanders KM, Nicholson GC, Kotowicz MA (2006) Fracture Risk (FRISK) Score: Geelong osteoporosis study. Radiology 241:190–196

    Article  PubMed  Google Scholar 

  36. Cummings SR (2006) A 55-year-old woman with osteopenia. JAMA 296:2601–2610

    Article  PubMed  CAS  Google Scholar 

  37. Sambrook PN, Eisman JA (2000) Osteoporosis prevention and treatment. Med J Aust 172:226–229

    PubMed  CAS  Google Scholar 

  38. Dargent-Molina P, Douchin MN, Cormier C, Meunier PJ, Breart G (2002) Use of clinical risk factors in elderly women with low bone mineral density to identify women at higher risk of hip fracture: The EPIDOS prospective study. Osteoporos Int 13:593–599

    Article  PubMed  CAS  Google Scholar 

  39. Cranney A, Guyatt G, Griffith L, Wells G, Tugwell P, Rosen C (2002) Meta-analyses of therapies for postmenopausal osteoporosis. IX: summary of meta-analyses of therapies for postmenopausal osteoporosis. Endocr Rev 23:570–578

    Article  PubMed  CAS  Google Scholar 

  40. Nguyen ND, Eisman JA, Nguyen TV (2006) Anti-hip fracture efficacy of bisphosphonates: a bayesian analysis of clinical trials. J Bone Miner Res 21:340–349

    Article  PubMed  CAS  Google Scholar 

  41. Vestergaard P, Jorgensen NR, Mosekilde L, Schwarz P (2007) Effects of parathyroid hormone alone or in combination with antiresorptive therapy on bone mineral density and fracture risk-a meta-analysis. Osteoporos Int 18:45–57

    Article  PubMed  CAS  Google Scholar 

  42. 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 

  43. Kanis JA, Johansson H, Oden A, Johnell O, De Laet C, Eisman JA, McCloskey EV, Mellstrom D, Melton LJ 3rd, Pols HA, Reeve J, Silman AJ, Tenenhouse A (2004) A family history of fracture and fracture risk: a meta-analysis. Bone 35:1029–1037

    Article  PubMed  CAS  Google Scholar 

  44. Kanis JA, Johansson H, Oden A, Johnell O, de Laet C, Melton IL, Tenenhouse A, Reeve J, Silman AJ, Pols HA, Eisman JA, McCloskey EV, Mellstrom D (2004) A meta-analysis of prior corticosteroid use and fracture risk. J Bone Miner Res 19:893–899

    Article  PubMed  Google Scholar 

  45. Pongchaiyakul C, Nguyen ND, Jones G, Center JR, Eisman JA, Nguyen TV (2005) Asymptomatic vertebral deformity as a major risk factor for subsequent fractures and mortality: a long-term prospective study. J Bone Miner Res 20:1349–1355

    Article  PubMed  Google Scholar 

  46. Harrel FE (2001) Regression modeling strategies with applications to linear models, logistic regression, and survival analysis. Springer, New York, NY

    Google Scholar 

  47. Royston P, Altman DG, Sauerbrei W (2006) Dichotomizing continuous predictors in multiple regression: a bad idea. Stat Med 25:127–141

    Article  PubMed  Google Scholar 

  48. Bianco FJ Jr (2006) Nomograms and medicine. Eur Urol 50:884–886

    Article  PubMed  Google Scholar 

  49. Kattan MW (2003) Nomograms are superior to staging and risk grouping systems for identifying high-risk patients: preoperative application in prostate cancer. Curr Opin Urol 13:111–116

    Article  PubMed  Google Scholar 

  50. Wong SL, Kattan MW, McMasters KM, Coit DG (2005) A nomogram that predicts the presence of sentinel node metastasis in melanoma with better discrimination than the American Joint Committee on Cancer staging system. Ann Surg Oncol 12:282–288

    Article  PubMed  Google Scholar 

  51. Ross PL, Gerigk C, Gonen M, Yossepowitch O, Cagiannos I, Sogani PC, Scardino PT, Kattan MW (2002) Comparisons of nomograms and urologists’ predictions in prostate cancer. Semin Urol Oncol 20:82–88

    Article  PubMed  Google Scholar 

  52. Hosmer DW, Lemeshow S (1999) Assessing the fit of the model. In Apllied logistic regression. Wiley J, New York, pp 135–175

  53. Kattan M (2002) Statistical prediction models, artificial neural networks, and the sophism “I am a patient, not a statistic”. J Clin Oncol 20:885–887

    Article  PubMed  Google Scholar 

  54. Ensrud KE, Black DM, Palermo L, Bauer DC, Barrett-Connor E, Quandt SA, Thompson DE, Karpf DB (1997) Treatment with alendronate prevents fractures in women at highest risk: results from the Fracture Intervention Trial. Arch Intern Med 157:2617–2624

    Article  PubMed  CAS  Google Scholar 

  55. 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  PubMed  CAS  Google Scholar 

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Acknowledgments

We gratefully acknowledge the assistance of Janet Watters, Donna Reeves, Shaye Field and Jodie Ratleg for the interview, data collection and measurement bone mineral density. We also appreciate the invaluable help of the staff of Dubbo Base Hospital. We thank David Hayes and the IT group of the Garvan Institute of Medical Research for the management of the database.

Conflict of interest statement

Dr. John Eisman serves as a consultant and receives corporate appointment from Amgen, deCode, Eli Lilly and Company, GE-Lunar, Merck Sharp & Dohme Ltd., Novartis, Organon, Roche-GSK, sanofi-aventis and Servier. All other authors have no conflict of interest.

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Authors and Affiliations

  1. Bone and Mineral Research Program, Garvan Institute of Medical Research, St Vincent’s Hospital, 384 Victoria Street, Darlinghurst, Sydney, NSW, 2010, Australia

    N. D. Nguyen, S. A. Frost, J. R. Center, J. A. Eisman & T. V. Nguyen

  2. Faculty of Medicine, The University of New South Wales, Sydney, NSW, Australia

    J. A. Eisman & T. V. Nguyen

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  1. N. D. Nguyen
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  2. S. A. Frost
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  3. J. R. Center
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  4. J. A. Eisman
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  5. T. V. Nguyen
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Correspondence to T. V. Nguyen.

Additional information

Source of financial support: This works has been supported by the National Health and Medical Research Council of Australia, and untied educational grants from GE-Lunar, Merck Australia, Eli Lilly International and sanofi-aventis Australia.

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Nguyen, N.D., Frost, S.A., Center, J.R. et al. Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks. Osteoporos Int 19, 1431–1444 (2008). https://doi.org/10.1007/s00198-008-0588-0

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