Data mining in pharmacovigilance: lessons from phantom ships.
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Adverse Drug Reaction Reporting Systems↗
Biomedical subjects
Charles M Gerrits
Publications and source records attributed to Charles M Gerrits.
Reports of hyperkalemia after publication of RALES--a pharmacovigilance study.
PURPOSE: A population-based study and anecdotal reports have indicated that the publication of the Randomized Aldactone Evaluation Study (RALES) was associated with not merely a broader use of spironolactone in the treatment of heart failure, but also with a coinciding sharp increase in hyperkalemia-associated morbidity/mortality in patients also being treated with ACE-inhibitors. Data mining algorithms (DMAs) are being applied to spontaneous reporting system (SRS) databases in hopes of obtaining early warnings/additional insights into post-licensure safety data. We applied two DMAs (i.e. multi-item gamma Poisson shrinker [MGPS] and proportional reporting ratios [PRRs]) to spontaneous reporting system (SRS) data to determine if these DMAs could have provided an earlier indication of a possible hyperkalemia safety issue. METHODS: MGPS and PRRs were retrospectively applied to US FDA-AERS, an SRS database. Year-by-year analysis and analysis of increasing cumulative time intervals were performed on cases in which both spironolactone and hyperkalemia and possibly related cardiac events had been reported. RESULTS: Neither of the DMAs initially provided a compelling signal of disproportionate reporting (SDR) for hyperkalemia after publication of RALES. However, using events consistent with clinical sequelae of hyperkalemia (e.g,. sudden death), SDRs were identified with PRRs. CONCLUSIONS: The quality and usefulness of data mining analysis is highly situation dependent and may vary with the knowledge and experience of the drug safety reviewer. Our analysis suggests that contemporary DMAs may have significant limitations in detecting increased frequency of labeled events in real-life prospective pharmacovigilance. There is a paucity of research in this area and we recommend further research for new approaches to detecting increased frequency of labeled events.
Adverse Drug Reaction Reporting Systems↗
The role of data mining in pharmacovigilance.
A principle concern of pharmacovigilance is the timely detection of adverse drug reactions that are novel by virtue of their clinical nature, severity and/or frequency. The cornerstone of this process is the scientific acumen of the pharmacovigilance domain expert. There is understandably an interest in developing database screening tools to assist human reviewers in identifying associations worthy of further investigation (i.e., signals) embedded within a database consisting largely of background 'noise' containing reports of no substantial public health significance. Data mining algorithms are, therefore, being developed, tested and/or used by health authorities, pharmaceutical companies and academic researchers. After a focused review of postapproval drug safety signal detection, the authors explain how the currently used algorithms work and address key questions related to their validation, comparative performance, deployment in naturalistic pharmacovigilance settings, limitations and potential for misuse. Suggestions for further research and development are offered.
Adverse Drug Reaction Reporting Systems↗