Objectives To evaluate the utility of applying the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) across multiple observational databases within an organization and to apply standardized analytics tools for conducting observational research.Materials and methods Six deidentified patient-level datasets were transformed to the OMOP CDM. We evaluated the extent of information loss that occurred through the standardization process. We developed a standardized analytic tool to replicate the cohort construction process from a published epidemiology protocol and applied the analysis to all 6 databases to assess time-to-execution and comparability of results.Results Transformation to the CDM resulted in minimal information loss across all 6 databases. Patients and observations excluded were due to identified data quality issues in the source system, 96% to 99% of condition records and 90% to 99% of drug records were successfully mapped into the CDM using the standard vocabulary. The full cohort replication and descriptive baseline summary was executed for 2 cohorts in 6 databases in less than 1 hour.Discussion The standardization process improved data quality, increased efficiency, and facilitated cross-database comparisons to support a more systematic approach to observational research. Comparisons across data sources showed consistency in the impact of inclusion criteria, using the protocol and identified differences in patient characteristics and coding practices across databases.Conclusion Standardizing data structure (through a CDM), content (through a standard vocabulary with source code mappings), and analytics can enable an institution to apply a network-based approach to observational research across multiple, disparate observational health databases.

Administrative claims and electronic health records are valuable resources for evaluating pharmaceutical effects during pregnancy. However, direct measures of gestational age are generally not available. Establishing a reliable approach to infer the duration and outcome of a pregnancy could improve pharmacovigilance activities. We developed and applied an algorithm to define pregnancy episodes in four observational databases: three US-based claims databases: Truven MarketScan® Commercial Claims and Encounters (CCAE), Truven MarketScan® Multi-state Medicaid (MDCD), and the Optum ClinFormatics® (Optum) database and one non-US database, the United Kingdom (UK) based Clinical Practice Research Datalink (CPRD). Pregnancy outcomes were classified as live births, stillbirths, abortions and ectopic pregnancies. Start dates were estimated using a derived hierarchy of available pregnancy markers, including records such as last menstrual period and nuchal ultrasound dates. Validation included clinical adjudication of 700 electronic Optum and CPRD pregnancy episode profiles to assess the operating characteristics of the algorithm, and a comparison of the algorithm’s Optum pregnancy start estimates to starts based on dates of assisted conception procedures. Distributions of pregnancy outcome types were similar across all four data sources and pregnancy episode lengths found were as expected for all outcomes, excepting term lengths in episodes that used amenorrhea and urine pregnancy tests for start estimation. Validation survey results found highest agreement between reviewer chosen and algorithm operating characteristics for questions assessing pregnancy status and accuracy of outcome category with 99–100% agreement for Optum and CPRD. Outcome date agreement within seven days in either direction ranged from 95–100%, while start date agreement within seven days in either direction ranged from 90–97%. In Optum validation sensitivity analysis, a total of 73% of algorithm estimated starts for live births were in agreement with fertility procedure estimated starts within two weeks in either direction; ectopic pregnancy 77%, stillbirth 47%, and abortion 36%. An algorithm to infer live birth and ectopic pregnancy episodes and outcomes can be applied to multiple observational databases with acceptable accuracy for further epidemiologic research. Less accuracy was found for start date estimations in stillbirth and abortion outcomes in our sensitivity analysis, which may be expected given the nature of the outcomes.

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BackgroundThe unique structure and coding of the Clinical Practice Research Datalink (CPRD) presents challenges for epidemiologic analysis and for comparisons with other databases. To address this limitation we sought to transform CPRD into the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).MethodsAn extraction, transformation and loading process was developed, which detailed source code mappings, Read code domain classification, an imputation algorithm for drug duration and special handling of lifestyle/clinical data. Completeness and accuracy of the above elements were assessed. A final validation exercise involved replication of a published case–control study that examined use of nonsteroidal anti-inflammatory drugs (NSAIDs) and the risk of first-time acute myocardial infarction (AMI) in raw CPRD data and the CPRD CDM.FindingsAll elements of the CPRD CDM transformation were assessed to be of high quality. 99.9 % of database condition records and 89.7 % of database drug records were mapped (majority unmapped drugs were devices and over-the-counter products); 3.1 % of duration imputations were deemed possibly erroneous and prevalences for selected conditions and drugs across CPRD raw and CDM data were equivalent. Results between the replication raw data and CDM study agreed for conditions, demographics and lifestyle data with slight NSAID exposure data loss owing to unmapped drugs.ConclusionCPRD can be accurately transformed into the OMOP CDM with acceptable information loss across drugs, conditions and observations. We determined that for a particular use, case CDM structure was adequate and mappings could be improved but did not substantially change the results of our analysis.Electronic supplementary materialThe online version of this article (doi:10.1007/s40264-014-0214-3) contains supplementary material, which is available to authorized users.

IntroductionOver-the-counter analgesics such as paracetamol and ibuprofen are among the most widely used, and having a good understanding of their safety profile is important to public health. Prior observational studies estimating the risks associated with paracetamol use acknowledge the inherent limitations of these studies. One threat to the validity of observational studies is channeling bias, i.e. the notion that patients are systematically exposed to one drug or the other, based on current and past comorbidities, in a manner that affects estimated relative risk.ObjectivesThe aim of this study was to examine whether evidence of channeling bias exists in observational studies that compare paracetamol with ibuprofen, and, if so, the extent to which confounding adjustment can mitigate this bias.Study Design and SettingIn a cohort of 140,770 patients, we examined whether those who received any paracetamol (including concomitant users) were more likely to have prior diagnoses of gastrointestinal (GI) bleeding, myocardial infarction (MI), stroke, or renal disease than those who received ibuprofen alone. We compared propensity score distributions between drugs, and examined the degree to which channeling bias could be controlled using a combination of negative control disease outcome models and large-scale propensity score matching. Analyses were conducted using the Clinical Practice Research Datalink.ResultsThe proportions of prior MI, GI bleeding, renal disease, and stroke were significantly higher in those prescribed any paracetamol versus ibuprofen alone, after adjusting for sex and age. We were not able to adequately remove selection bias using a selected set of covariates for propensity score adjustment; however, when we fit the propensity score model using a substantially larger number of covariates, evidence of residual bias was attenuated.ConclusionsAlthough using selected covariates for propensity score adjustment may not sufficiently reduce bias, large-scale propensity score matching offers a novel approach to consider to mitigate the effects of channeling bias.Electronic supplementary materialThe online version of this article (doi:10.1007/s40264-017-0581-7) contains supplementary material, which is available to authorized users.

BackgroundLittle data exist on how opioid doses vary with the length of exposure among chronic opioid users.MethodsTo characterize the change in the dosage of opioids over time, a retrospective cohort study using the PharMetrics database for the years 1999 through 2008 was conducted. Individuals exposed to opioids in 2000 who had 2 opioid dispensings at least 6 months apart and were opioid naive (did not receive any opioid 6 month before their exposure in 2000) were included. The date of the first dispensing in 2000 was defined as the index date and the dispensing had to be for a strong and full agonist opioid. All opioid doses were converted to oral morphine equivalent doses. Exposure was classified as continuous or intermittent. Mean, median, interquartile range, and 95th percentile of opioid dose over 6-month periods, as well as the percentage of subjects who ever received a high or very high opioid dose, were calculated.ResultsAmong the 48,986 subjects, the mean age was 44.5 years and 54.5% were women. Intermittent exposure was observed in 99% of subjects; continuous exposure was observed in 1% of subjects. The mean duration of exposure for the subjects who were continuously exposed to opioids was 477 days. In subjects with no cancer diagnosis who were continuously exposed to opioids, the mean, 25th, 50th, and 75th percentile of dose was stable during the first 2 years of use, but the 95th percentile increased. Seven percent of them were exposed to doses of 180 mg or more of morphine at some point.ConclusionsDose escalation is uncommon in subjects with intermittent exposure to opioids. For subjects with continuous exposure to opioids who have cancer, doses rise substantially with time. For those without cancer, doses remain relatively stable for the first 2 years of use, but subsequently increase. Seven percent of subjects with no cancer diagnosis will be exposed to daily doses of 180 mg or more of morphine equivalent at some point.

Introduction: Patients with B cell malignancies have an inherent increased risk of bleeding. However, the incidence of major hemorrhage among patients with MCL and CLL has not been described. The objective of this study is to evaluate the risk of major hemorrhage in a real world setting by using a population-based data source. Methods: The SEER-Medicare linked database, a database of SEER cancer registry data linked to individual Medicare administrative claims, was utilized to follow a cohort of persons newly treated for CLL or MCL to estimate the incidence of major hemorrhage (CNS and non-CNS). Major hemorrhage was defined as having at least one code for hemorrhage in a critical area or organ or having another bleeding code with a transfusion within 14 days of the event. Patients with a cancer diagnosis on or after 1/1/2000 were followed through disenrollment from the database, death, the occurrence of major hemorrhage, or the end of the study period (12/31/2011), whichever came first. Incidence rates (IR) of major hemorrhage were characterized in terms of incidence per person-years (pys) of follow-up with 95% confidence intervals calculated according to a Poisson distribution. Rates in the CLL and MCL populations were compared to those in the age and gender-matched general population of a sample of non-cancer Medicare patients using Cox proportional hazards models. Results: A total of 1,587 treated MCL patients, 6,717 treated CLL/SLL patients, and 14,816 age and gender-matched non-cancer patients were identified in the database. Median age among all three cohorts was approximately 75 years. Among patients treated for MCL, 287 (18%) had at least one major hemorrhage, corresponding to an incidence of 5.8 per 100 pys. Among 6,717 CLL patients, 1,211 (18%) had at least one major hemorrhage (IR: 6.0 per 100 pys). In the age and gender-matched non-cancer population, incidence of major hemorrhage was 1.6 per 100 pys. The hazard ratio for development of any major hemorrhage among CLL patients compared to the non-cancer cohort was 8.3 (95% CI: 7.5-9.2), and for MCL compared to the non-cancer cohort was 8.8 (95% CI: 7.6-10.2). IR of CNS hemorrhage was also higher among MCL and CLL patients (0.9 and 1.2 per 100 pys, respectively) compared to the non-cancer cohort (0.04 per 100 pys). Gastrointestinal hemorrhage was the most frequent site of occurrence. Conclusions: Among persons newly initiating treatment for CLL and MCL, incidence of major hemorrhage was found to be over 8 times higher than that of the age- and gender-matched general population. Additional analyses to establish whether this increased risk is attributable to the disease itself, comorbid conditions, choice of cancer therapy, or concomitant medications in the patient population and/or other risk factors are planned. Baseline risks among CLL and MCL patients should be considered when establishing risk/benefit profiles of a particular treatment. Disclosures Gifkins: Johnson and Johnson: Employment. Matcho:Johnson and Johnson: Employment. Yang:Pharmacyclics, Inc: Employment. Xu:Johnson and Johnson: Employment. Gooden:Pharmacyclics, Inc.: Employment. Wildgust:Janssen Pharmaceuticals, Inc.: Employment.