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.

INTRODUCTION Drug safety researchers seek to know the degree of certainty with which a particular drug is associated with an adverse drug reaction. There are different sources of information used in pharmacovigilance to identify, evaluate, and disseminate medical product safety evidence including spontaneous reports, published peer-reviewed literature, and product labels. Automated data processing and classification using these evidence sources can greatly reduce the manual curation currently required to develop reference sets of positive and negative controls (i.e. drugs that cause adverse drug events and those that do not) to be used in drug safety research. METHODS In this paper we explore a method for automatically aggregating disparate sources of information together into a single repository, developing a predictive model to classify drug-adverse event relationships, and applying those predictions to a real world problem of identifying negative controls for statistical method calibration. RESULTS Our results showed high predictive accuracy for the models combining all available evidence, with an area under the receiver-operator curve of ≥ 0.92 when tested on three manually generated lists of drugs and conditions that are known to either have or not have an association with an adverse drug event. CONCLUSIONS Results from a pilot implementation of the method suggests that it is feasible to develop a scalable alternative to the time-and-resource-intensive, manual curation exercise previously applied to develop reference sets of positive and negative controls to be used in drug safety research.

The entire drug safety enterprise has a need to search, retrieve, evaluate, and synthesize scientific evidence more efficiently. This discovery and synthesis process would be greatly accelerated through access to a common framework that brings all relevant information sources together within a standardized structure. This presents an opportunity to establish an open-source community effort to develop a global knowledge base, one that brings together and standardizes all available information for all drugs and all health outcomes of interest (HOIs) from all electronic sources pertinent to drug safety. To make this vision a reality, we have established a workgroup within the Observational Health Data Sciences and Informatics (OHDSI, http://ohdsi.org) collaborative. The workgroup’s mission is to develop an open-source standardized knowledge base for the effects of medical products and an efficient procedure for maintaining and expanding it. The knowledge base will make it simpler for practitioners to access, retrieve, and synthesize evidence so that they can reach a rigorous and accurate assessment of causal relationships between a given drug and HOI. Development of the knowledge base will proceed with the measureable goal of supporting an efficient and thorough evidence-based assessment of the effects of 1,000 active ingredients across 100 HOIs. This non-trivial task will result in a high-quality and generally applicable drug safety knowledge base. It will also yield a reference standard of drug–HOI pairs that will enable more advanced methodological research that empirically evaluates the performance of drug safety analysis methods.

BackgroundA recent Canadian case–control study reported a 4.5-fold increased risk of retinal detachment (RD) during oral fluoroquinolone use. Of the fluoroquinolone-exposed cases, 83 % were exposed to ciprofloxacin. We sought to replicate this finding, and assess whether it applied to all fluoroquinolones.MethodsIn two large US healthcare databases, we performed three case–control analyses: one replicating the recent study; one addressing additional potential confounders; and one that increased sample size by dropping the Canadian study’s requirement for a prior ophthalmologist visit. We also performed a self-controlled case-series (SCCS) analysis in which each subject served as his or her own comparator.ResultsIn the replication case–control analyses, the adjusted odds ratios (ORs) for any exposure to fluoroquinolones or ciprofloxacin were approximately 1.2 in both databases, and were statistically significant, and the ORs for current exposure were modestly above 1 in one database, modestly below 1 in the other, and not statistically significant. In the other case–control analyses, the ORs were close to 1. In a post hoc age-stratified case–control analysis, we observed an association of RD with fluoroquinolone exposure among older subjects in one of the two databases. All estimates from the SCCS analyses were below 1.2 and none was statistically significant.ConclusionThe present study does not confirm the recent Canadian study’s finding of a strong relationship between RD and current exposure to fluoroquinolones. Instead, it found a modest association between RD and current or any exposure to fluoroquinolones in the case–control analyses, and no association in the SCCS analyses.