http://repub.eur.nl/res/aut/53313/ List of Publications en http://repub.eur.nl/static-eur/img/logo.png http://repub.eur.nl http://repub.eur.nl/res/pub/37403/ 2012-10-01T00:00:00Z BACKGROUND: Drug safety monitoring relies primarily on spontaneous reporting, but electronic health care record databases offer a possible alternative for the detection of adverse drug reactions (ADRs). OBJECTIVES: To evaluate the relative performance of different statistical methods for detecting drug-adverse event associations in electronic health care record data representing potential ADRs. RESEARCH DESIGN: Data from 7 databases across 3 countries in Europe comprising over 20 million subjects were used to compute the relative risk estimates for drug-event pairs using 10 different methods, including those developed for spontaneous reporting systems, cohort methods such as the longitudinal gamma poisson shrinker, and case-based methods such as case-control. The newly developed method "longitudinal evaluation of observational profiles of adverse events related to drugs" (LEOPARD) was used to remove associations likely caused by protopathic bias. Data from the different databases were combined by pooling of data, and by meta-analysis for random effects. A reference standard of known ADRs and negative controls was created to evaluate the performance of the method. MEASURES: The area under the curve of the receiver operator characteristic curve was calculated for each method, both with and without LEOPARD filtering. RESULTS: The highest area under the curve (0.83) was achieved by the combination of either longitudinal gamma poisson shrinker or case-control with LEOPARD filtering, but the performance between methods differed little. LEOPARD increased the overall performance, but flagged several known ADRs as caused by protopathic bias. CONCLUSIONS: Combinations of methods demonstrate good performance in distinguishing known ADRs from negative controls, and we assume that these could also be used to detect new drug safety signals. Copyright http://repub.eur.nl/res/pub/34257/ 2011-01-01T00:00:00Z Purpose: In this proof-of-concept paper we describe the framework, process, and preliminary results of combining data from European electronic healthcare record (EHR) databases for large-scale monitoring of drug safety. Methods: Aggregated demographic, clinical, and prescription data from eight databases in four countries (Denmark, Italy, Netherlands, the UK) were pooled using a distributed network approach by generation of common input data followed by local aggregation through custom-built software, Jerboa©. Comparison of incidence rates of upper gastrointestinal bleeding (UGIB) and nonsteroidal anti-inflammatory drug (NSAID) utilization patterns were used to evaluate data harmonization and quality across databases. The known association of NSAIDs and UGIB was employed to demonstrate sensitivity of the system by comparing incidence rate ratios (IRRs) of UGIB during NSAID use to UGIB during all other person-time. Results: The study population for this analysis comprised 19 647 445 individuals corresponding to 59 929 690 person-years of follow-up. 39 967 incident cases of UGIB were identified during the study period. Crude incidence rates varied between 38.8 and 109.5/100 000 person-years, depending on country and type of database, while age-standardized rates ranged from 25.1 to 65.4/100 000 person-years. NSAID use patterns were similar for databases within the same country but heterogeneous among different countries. A statistically significant age- and gender-adjusted association between use of any NSAID and increased risk for UGIB was confirmed in all databases, IRR from 2.0 (95%CI:1.7-2.2) to 4.3 (95%CI: 4.1-4.5). Conclusions: Combining data from EHR databases of different countries to identify drug-adverse event associations is feasible and can set the stage for changing and enlarging the scale for drug safety monitoring.