LAUSR

BACKGROUND In aging societies osteoporotic fractures are a major health problem with high economic costs. Targeting prevention at individuals at high risk is important to reduce the future burden of fractures. Available risk assessment tools (e.g., FRAX®, QFracture, the algorithm provided by the German Osteology Society (DVO-Tool)) rely on self-reported patient information to predict fracture risk. Time and resource constraints, limited access to clinical data, and (un)willingness to participate may hamper the use of these tools. To overcome such obstacles, the aim is to develop a fracture risk assessment tool based on claims data that may be directly used on an institutional level. METHODS Administrative claims data of an elderly (≥65years) population (N=298,530) for the period from 2006 through 2014 was used. Major osteoporotic fractures (MOF) were identified based on hospital diagnoses. We applied Cox proportional hazard regression to determine the association of individual risk factors and fracture risk. Hazard ratios were used to construct a risk score. The discriminative ability of the score was evaluated using C-statistics. RESULTS We identified 7864 MOF during follow-up. The median time to first fracture during follow-up was 371.5days. Individuals with a MOF during follow-up had a higher mean and median risk score (mean: 4.53; median: 4) than individuals without MOF (mean: 3.07; median: 3). Adding drug-related risk factors slightly improved discrimination compared to a simple model with age, gender, and prior fracture. CONCLUSION We developed a fracture risk score model based on in-hospital treated subjects to predict MOF that can be used on an institutional level. The score included age, sex and prior fracture as risk factors. Adding other risk factors involved very small improvement in discrimination.