Existing fracture risk assessment tools are not designed to predict fracture-associated consequences, possibly contributing to the current undermanagement of fragility fractures worldwide. We aimed to develop a risk assessment tool for predicting the conceptual risk of fragility fractures and its consequences. The study involved 8965 people aged ≥60 years from the Dubbo Osteoporosis Epidemiology Study and the Canadian Multicentre Osteoporosis Study. Incident fracture was identified from X-ray reports and questionnaires, and death was ascertained though contact with a family member or obituary review. We used a multistate model to quantify the effects of the predictors on the transition risks to an initial and subsequent incident fracture and mortality, accounting for their complex interrelationships, confounding effects, and death as a competing risk. There were 2364 initial fractures, 755 subsequent fractures, and 3300 deaths during a median follow-up of 13 years (interquartile range [IQR] 7–15). The prediction model included sex, age, bone mineral density, history of falls within 12 previous months, prior fracture after the age of 50 years, cardiovascular diseases, diabetes mellitus, chronic pulmonary diseases, hypertension, and cancer. The model accurately predicted fragility fractures up to 11 years of follow-up and post-fracture mortality up to 9 years, ranging from 7 years after hip fractures to 15 years after non-hip fractures. For example, a 70-year-old woman with a T-score of −1.5 and without other risk factors would have 10% chance of sustaining a fracture and an 8% risk of dying in 5 years. However, after an initial fracture, her risk of sustaining another fracture or dying doubles to 33%, ranging from 26% after a distal to 42% post hip fracture. A robust statistical technique was used to develop a prediction model for individualization of progression to fracture and its consequences, facilitating informed decision making about risk and thus treatment for individuals with different risk profiles.
Bibliographical noteFunding Information:
We thank Professor Chris Jackson (MRC Biostatistics Unit, Cambridge, UK), the author of the msm and fic R packages, for his valuable help for data analyses. This work was supported by the National Health Medical Research Council Australia Projects 1070187 (to TT, DA, JAE, TVN, and JRC), 1008219 (to JRC), and 1073430 (to DB). DB was supported by a fellowship from Australian and New Zealand Bone and Mineral Society. Other funding bodies were an Osteoporosis Australia-Amgen grant and the Mrs Gibson and Ernst Heine Family Foundation. The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; or preparation, review, or approval of the manuscript. The Canadian Multicentre Osteoporosis Study was funded by the Canadian Institutes of Health Research (CIHR); Merck Frosst Canada Ltd.; Eli Lilly Canada Inc.; Novartis Pharmaceuticals Inc.; The Alliance: sanofi-aventis & Procter and Gamble Pharmaceuticals Canada Inc.; Servier Canada Inc.; Amgen Canada Inc.; The Dairy Farmers of Canada; and The Arthritis Society. Authors? roles: Study design: TT, DB, HMP, TVN, and JRC. Data analysis: TT, HMP, TVN, and JRC. Data interpretation: all authors. Drafting the manuscript: TT, TVN, and JRC. Revising the manuscript contents and approving the final version of the manuscript: all authors.
- FRAGILITY FRACTURE
- MULTISTATE PREDICTION MODEL
- SUBSEQUENT FRACTURE