SAT0591 A CLINICAL TOOL FOR AUTOMATED PREDICTION OF HIP AND MAJOR OSTEOPOROTIC FRACTURES USING ELECTRONIC MEDICAL RECORDS DATA: THE EPIC STUDY
Authors: Cristian Tebé, Natalia Pallarés, Cristina Carbonell-Abella, Carlen Reyes, Xavier Nogues, Adolfo Diez-Perez, Daniel Martinez-Laguna, Daniel Prieto-Alhambra
Journal: Annals of the Rheumatic Diseases (2019)

<h3>Background</h3> With increasing availability of patient data in healthcare, there is an unprecedented opportunity for prediction tools that can be automatically implemented in electronic medical records system. <h3>Objectives</h3> We aimed to develop and validate a fracture prediction tool that leverages patient data as routinely available in primary care computerized records. <h3>Methods</h3> We conducted a population-based cohort study. Data was extracted from all subjects registered in the SIDIAP database on 1/1/2012, with data for 1+ years, and aged 50 years or older on that date. SIDIAP contains primary care records linked to pharmacy dispensations for &gt;6 million people, equivalent to &gt;80% of the population of Catalonia. Participants were followed up until the earliest of death, transfer out/migration, or end of 2017. Two models were developed to predict hip fracture (main outcome) and major osteoporotic (hip, clinical vertebral, wrist/forearm, and proximal humerus) fracture risk over 5 years. Potential predictors were pre-specified based on previous literature and combined in Cox models to derive prediction tools. Internal validation was performed using c-statistic for discrimination, and observed vs predicted plots for calibration. Multiple imputation with chained equations was used to minimize the impact of missing data on body mass index, smoking, and alcohol drinking. Bootstrapping methods were used to select key predictors to be combined in the final resulting models. <h3>Results</h3> A total of 1.76 million people (9.76 million person-years) were included, 50.7% women, of average age 65.4 years old. A 10.1% and 7.4% were lost to follow-up over 5 years due to mortality and migration respectively. Fracture rates were 3.57/1,000 person-years [95%CI 3.53-3.60] for hip and 11.61 [11.54-11.68] for major fracture. Key predictors of increased fracture risk included age, female gender, history of falls or previous fractures, specific medication/s use (insulin, GnRH inhibitors, anticonvulsants, sedatives, SSRI, antipsychotics), and a history of diabetes mellitus (type 1&gt;type 2), cerebrovascular disease, ischemic heart disease, COPD and anorexia nervosa. Variables associated with lower fracture risk included use of statins, thiazidic diuretics, and overweight/obesity. Combined, these resulted in a c-statistic of 84.9% for hip and 72.9% for major fracture. Calibration was excellent for both outcomes. <h3>Conclusion</h3> We have developed and validated a clinical prediction tool for 5-year hip and major osteoporotic fracture risks. The algorithm has excellent performance and can be installed in electronic primary care records systems for automated risk calculations at the population level. More research is needed on the transportability and external validity of this prediction tool. <h3>Acknowledgement</h3> Research grant from the Carlos III Institute of Health, Ministry of Economy and Competitiveness (Spain), awarded on the 2017, with reference PI17/00471, co-funded with European Union ERDF funds (European Regional Development Fund) <h3>Disclosure of Interests</h3> Cristian Tebé: None declared, Natalia Pallarés: None declared, Cristina Carbonell-Abella Speakers bureau: AMGEN, LILLY, Carlen Reyes: None declared, Xavier Nogues Speakers bureau: Amgen and Eli Lilly, Adolfo Diez-Perez Speakers bureau: Lilly, Amgen, GSK and UCB, Daniel Martinez-Laguna Speakers bureau: Eli Lilly, Amgen, Ferrer, Rubió and Novartis., Daniel Prieto-Alhambra Grant/research support from: Grants from Amgen, UCB Biopharma and Servier outside the submitted work, Consultant for: UCB Biopharma, Speakers bureau: Amgen