Independent external validation of nomograms for predicting risk of low-trauma fracture and hip fracture

Background A set of nomograms based on the Dubbo Osteoporosis Epidemiology Study predicts the five- and ten-year absolute risk of fracture using age, bone mineral density and history of falls and low-trauma fracture. We assessed the discrimination and calibration of these nomograms among participants in the Canadian Multicentre Osteoporosis Study. Methods We included participants aged 55–95 years for whom bone mineral density measurement data and at least one year of follow-up data were available. Self-reported incident fractures were identified by yearly postal questionnaire or interview (years 3, 5 and 10). We included low-trauma fractures before year 10, except those of the skull, face, hands, ankles and feet. We used a Cox proportional hazards model. Results Among 4152 women, there were 583 fractures, with a mean follow-up time of 8.6 years. Among 1606 men, there were 116 fractures, with a mean follow-up time of 8.3 years. Increasing age, lower bone mineral density, prior fracture and prior falls were associated with increased risk of fracture. For low-trauma fractures, the concordance between predicted risk and fracture events (Harrell C) was 0.69 among women and 0.70 among men. For hip fractures, the concordance was 0.80 among women and 0.85 among men. The observed fracture risk was similar to the predicted risk in all quintiles of risk except the highest quintile of women, where it was lower. The net reclassification index (19.2%, 95% confidence interval [CI] 6.3% to 32.2%), favours the Dubbo nomogram over the current Canadian guidelines for men. Interpretation The published nomograms provide good fracture-risk discrimination in a representative sample of the Canadian population.

[1]  N. D. Nguyen,et al.  Development of a nomogram for individualizing hip fracture risk in men and women , 2007, Osteoporosis International.

[2]  Terry Heazlewood,et al.  The Foundation , 1997 .

[3]  J. Pasco,et al.  Bone mineral density reference ranges for Australian men: Geelong Osteoporosis Study , 2010, Osteoporosis International.

[4]  A. Tenenhouse,et al.  Change in bone mineral density as a function of age in women and men and association with the use of antiresorptive agents , 2008, Canadian Medical Association Journal.

[5]  S. Cummings,et al.  A comparison of prediction models for fractures in older women: is more better? , 2009, Archives of internal medicine.

[6]  Jacques P. Brown,et al.  Research Notes: The Canadian Multicentre Osteoporosis Study (CaMos): Background, Rationale, Methods , 1999, Canadian Journal on Aging / La Revue canadienne du vieillissement.

[7]  Yvonne Vergouwe,et al.  Prognosis and prognostic research: validating a prognostic model , 2009, BMJ : British Medical Journal.

[8]  Sundeep Khosla,et al.  Clinical practice. Osteopenia. , 2007, The New England journal of medicine.

[9]  J. Pasco,et al.  The population burden of fractures originates in women with osteopenia, not osteoporosis , 2006, Osteoporosis International.

[10]  Daniel B. Mark,et al.  TUTORIAL IN BIOSTATISTICS MULTIVARIABLE PROGNOSTIC MODELS: ISSUES IN DEVELOPING MODELS, EVALUATING ASSUMPTIONS AND ADEQUACY, AND MEASURING AND REDUCING ERRORS , 1996 .

[11]  Eugene McCloskey,et al.  Independent clinical validation of a Canadian FRAX tool: Fracture prediction and model calibration , 2010, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[12]  D L McGee,et al.  How generalizable are coronary risk prediction models? Comparison of Framingham and two national cohorts. , 1999, American heart journal.

[13]  A. Tenenhouse,et al.  Estimation of the Prevalence of Low Bone Density in Canadian Women and Men Using a Population-Specific DXA Reference Standard: The Canadian Multicentre Osteoporosis Study (CaMos) , 2000, Osteoporosis International.

[14]  A. Silman,et al.  Incidence of Limb Fracture across Europe: Results from the European Prospective Osteoporosis Study (EPOS) , 2002, Osteoporosis International.

[15]  E. Barrett-Connor,et al.  Bone mineral density thresholds for pharmacological intervention to prevent fractures. , 2004, Archives of internal medicine.

[16]  Jacques P. Brown,et al.  Recommendations for bone mineral density reporting in Canada. , 2005, Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes.

[17]  J. Pasco,et al.  Prevalence of osteoporosis in Australian women: Geelong Osteoporosis Study. , 2000, Journal of clinical densitometry : the official journal of the International Society for Clinical Densitometry.

[18]  F. Harrell,et al.  Prognostic/Clinical Prediction Models: Multivariable Prognostic Models: Issues in Developing Models, Evaluating Assumptions and Adequacy, and Measuring and Reducing Errors , 2005 .

[19]  A. LaCroix,et al.  Validity of self-report for fractures among a multiethnic cohort of postmenopausal women: results from the Women's Health Initiative observational study and clinical trials , 2004, Menopause.

[20]  W. Leslie,et al.  Trends in hip fracture rates in Canada. , 2009, JAMA.

[21]  L. Lix,et al.  Simplified System for Absolute Fracture Risk Assessment: Clinical Validation in Canadian Women , 2009, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[22]  N. D. Nguyen,et al.  Development of prognostic nomograms for individualizing 5-year and 10-year fracture risks , 2008, Osteoporosis International.

[23]  M. Aickin Maximum likelihood estimation of agreement in the constant predictive probability model, and its relation to Cohen's kappa. , 1990, Biometrics.

[24]  W. Leslie,et al.  Population-based Canadian hip fracture rates with international comparisons , 2010, Osteoporosis International.

[25]  Lawrence Joseph,et al.  Multiple Imputation to Account for Missing Data in a Survey: Estimating the Prevalence of Osteoporosis , 2002, Epidemiology.

[26]  Jacques P. Brown,et al.  The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women , 2007, Osteoporosis International.

[27]  Klaus Engelke,et al.  Universal standardization for dual X‐ray absorptiometry: Patient and phantom cross‐calibration results , 1994, Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research.

[28]  N. Cook Statistical evaluation of prognostic versus diagnostic models: beyond the ROC curve. , 2008, Clinical chemistry.

[29]  J. Eisman,et al.  Prognosis of fracture: evaluation of predictive accuracy of the FRAX™ algorithm and Garvan nomogram , 2010, Osteoporosis International.

[30]  S. Lord,et al.  Prediction of osteoporotic fractures by postural instability and bone density. , 1993, BMJ.

[31]  M. Pencina,et al.  Evaluating the added predictive ability of a new marker: From area under the ROC curve to reclassification and beyond , 2008, Statistics in medicine.

[32]  H. Wahner,et al.  Updated Data on Proximal Femur Bone Mineral Levels of US Adults , 1998, Osteoporosis International.