Positive predictive value (PPV) associated with statins from EHR prescribing data relative to pharmacy dispensing data in the OptumLabs™ Data Warehouse Objective Background Methods 1 Partnership for Health Analytic Research (PHAR), Beverly Hills, CA USA 2 Rutgers Biomedical and Health Sciences, Newark, NJ, USA 3 American Medical Group Association, Alexandria, VA, USA 4 Weill Cornell Medical College, Healthcare Policy and Research, NY, NY, USA 5 University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA 6 Visiting Fellow at OptumLabs, Cambridge, MA, USA Electronic health records (EHRs) permit observational research with clinical and laboratory data more granular than administrative claims data. However, there is little published data regarding the relationship between EHR prescribing (Rx) data and pharmacy dispensing claims used to classify longitudinal medication exposure. In this study, positive predictive value (PPV) is the proportion of patients who have a statin EHR prescription record (Rx) who also have a statin pharmacy dispensing claim (A) for the same medication out of all patients who have a statin EHR Rx (A + B). PPV is an important validity metric for classifying medication exposure. The study population was extracted from the OptumLabs Data Warehouse (OLDW), which contains de-identified administrative claims and EHR data. We included patients who had both (linked) claims (with a pharmacy benefit) and EHR data in We compared ambulatory statin EHR Rxs (atorvastatin, simvastatin, and rosuvastatin) with pharmacy dispensing claims for each statin (in independent analyses) in 1, 3, 6, and 12 month exposure periods. We classified statin exposure by ≥ 1 Rx in EHR data and by ≥ 1 pharmacy claim – in each peirod. We stratified these analysis by patients with an incident vs. prevalent statin EHR Rx, age, and Medicare supplemental insurance. Christopher G Rowan, PhD 1,2,6, Nikita Stempniewicz, BS 3,6, James Flory, MD, MSCE 4, Tobias Gerhard, PhD 2, James D Lewis, MD, MSCE 5, John Cuddeback, MD, PhD 3,6 and Sean Hennessy, PharmD, PhD 5 Affiliations This study aimed to assess the PPV of classifying statin exposure using EHR Rx data, compared to pharmacy claims data. As medication prescribing data often lack important details regarding medication fills and days supplied, the goal of this research initiative was to provide guidance to researchers using EHR Rx data to classify medication exposure. Results 16795, 25309, and 6744 patients had an EHR Rx for atorvastatin, simvastatin, and rosuvastatin, respectively. Among patients with an EHR Rx, approximately 60% of patients had an EHR Rx for each statin in the prior year (prevalent cohort). Patients with a prevalent statin EHR Rx were 2-15% more likely to have a pharmacy claim than those with an incident EHR Rx. For all exposure periods and all statins, patients with an prevalent statin EHR Rx had a higher PPV, than patients with an incident EHR Rx. The magnitude of the difference (incident vs. prevalent EHR Rx) was less for atorvastatin and simvastatin than rosuvastatin. For all statins, older patients (age >= 65) with a statin EHR Rx had a lower probability of having a statin dispensing than younger patients. Likewise, patients with Medicare supplemental insurance had a lower PPV for all statins. Importantly, in all strata, the PPV stabilized when exposure windows of >= 3 months were used. Overall, rosuvastatin had a slightly lower PPV than atorvastatin and simvastatin. Conclusion For all statins, PPV increases with the duration of the exposure period, but stabilizes for exposure periods >=3 months. In order to increase the validity of classifying statin exposure using EHR Rx data, researchers should use at least a 3 month period. Additionally, researchers should be cognizant of differences between classifying medication exposure for patients with an incident (new users) vs. prevalent EHR Rx. Patients with a prevalent statin EHR Rx were more likely to have a statin dispensing claim. Researchers using HER Rx data should also be aware of differences in PPV for older vs. younger patients. This may be the result of insurance coverage and/or fixed income. Further research is warranted to elucidate this finding.