Managing Drug-Risk Information - What to Do with All Those New Numbers

Harvard University, Cambridge, Massachusetts, United States
New England Journal of Medicine (Impact Factor: 54.42). 08/2009; 361(7):647-9. DOI: 10.1056/NEJMp0905466
Source: PubMed
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    ABSTRACT: Background: Drug adverse event (AE) signal detection using the Gamma Poisson Shrinker (GPS) is commonly applied in spontaneous reporting. AE signal detection using large observational health plan databases can expand medication safety surveillance. Methods: Using data from nine health plans, we conducted a pilot study to evaluate the implementation and findings of the GPS approach for two antifungal drugs, terbinafine and itraconazole, and two diabetes drugs, pioglitazone and rosiglitazone. We evaluated 1676 diagnosis codes grouped into 183 different clinical concepts and four levels of granularity. Several signaling thresholds were assessed. GPS results were compared to findings from a companion study using the identical analytic dataset but an alternative statistical method-the tree-based scan statistic (TreeScan). Results: We identified 71 statistical signals across two signaling thresholds and two methods, including closely-related signals of overlapping diagnosis definitions. Initial review found that most signals represented known adverse drug reactions or confounding. About 31% of signals met the highest signaling threshold. Conclusions: The GPS method was successfully applied to observational health plan data in a distributed data environment as a drug safety data mining method. There was substantial concordance between the GPS and TreeScan approaches. Key method implementation decisions relate to defining exposures and outcomes and informed choice of signaling thresholds.
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    ABSTRACT: The promise of augmenting pharmacovigilance with patient-generated data drawn from the Internet was called out by a scientific committee charged with conducting a review of FDA's current and planned pharmacovigilance practices. To this end, we present a study on harnessing behavioral data drawn from Internet search logs to detect adverse drug reactions (ADRs). By analyzing search queries collected from 80 million consenting users and by using a widely recognized benchmark of ADRs, we find that the performance of ADR detection via search logs is comparable and complementary to detection based on FDA's adverse event reporting system (AERS). We show that by jointly leveraging data from AERS and search logs, the accuracy of ADR detection can be improved by 19% over the use of each data source independently. The results suggest that leveraging nontraditional sources, such as online search logs, could supplement existing pharmacovigilance approaches.Clinical Pharmacology & Therapeutics (2014); Accepted article preview online 8 April 2014 doi:10.1038/clpt.2014.77.
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