Development and external validation of prediction models for major osteoporotic fracture and hip fracture in people with intellectual disability

PurposeCompared with the general population, people with intellectual disabilities (ID) have higher incidences of major osteoporotic fracture (MOF) and hip fracture (HF), and osteoporosis develops at a younger age. The rate of HF in those aged 50 years and over is two and four times higher than that in women and men, respectively, without ID. It is essential to identify people with ID at risk of such fractures so that a targeted fracture prevention strategy can be designed. However, current fracture prediction models are derived from the general population and may underestimate risk in the ID population. MethodsPrediction models (IDFracture) for the 10-year risk of HF and MOF were developed and validated in populations of people with ID aged 30-79 years. Models were developed in the CPRD GOLD database and temporally validated in the Aurum database. The predictors included those in current fracture prediction models and ID-specific predictors such as Down syndrome. All the predictors were included in the Cox regression models. Bootstrapping was used to adjust for overfitting. ResultsThe development cohort included 38,665 people with IDs, 1045 with MOFs and 360 with HFs within 10 years. The external validation cohort included 76,385 people, 2420 MOFs and 1001 HFs. Discrimination, as judged by the C statistic, was good: MOF 0.775, HF 0.839. The calibration was also good but tended to overpredict at the highest predicted risks. ConclusionIDFracture has potential as a screening tool in clinical practice to identify people with ID who are at increased risk of MOF and HF. Mini AbstractO_ST_ABSBrief rationaleC_ST_ABSPeople with intellectual disability (ID) have a relatively high incidence of fracture, so current risk prediction models are not appropriate. Main resultA new prediction model for people with ID showed good calibration and discrimination in external validation. Significance of paperThis is the first such prediction model developed for people with ID.