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  • Title: Reports of hyperkalemia after publication of RALES--a pharmacovigilance study.
    Author: Hauben M, Reich L, Gerrits CM.
    Journal: Pharmacoepidemiol Drug Saf; 2006 Nov; 15(11):775-83. PubMed ID: 16804951.
    Abstract:
    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.
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