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Rhabdomyolysis a result of azithromycin and statins: an unrecognized interaction
The Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden.
The Uppsala Monitoring Centre,WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden and.
Division of Clinical Pharmacology, Sahlgrenska University Hospital, Göteborg, Sweden.
The Uppsala Monitoring Centre,WHO Collaborating Centre for International Drug Monitoring, Uppsala, Sweden.
2009 (English)In: British Journal of Clinical Pharmacology, ISSN 0306-5251, E-ISSN 1365-2125, Vol. 68, no 3, 427-34 p.Article in journal (Refereed) Published
Abstract [en]

AIMS: In a systematic screening of the World Health Organization Adverse Drug Reaction database, VigiBase, in July 2008, a measure of association used to detect interactions (Omega) highlighted azithromycin with the individual statins atorvastatin, lovastatin and simvastatin and rhabdomyolysis. The aim was to examine all reports including rhabdomyolysis-azithromycin and statins in VigiBase to assess if the data were suggestive of an interaction.

METHODS: The individual case reports in VigiBase and the original files were reviewed. In order to investigate the reporting over time for rhabdomyolysis with azithromycin and statins to VigiBase, Omega values were generated retrospectively.

RESULTS: The reporting over time showed that rhabdomyolysis under concomitant use of azithromycin and statins was reported more often than expected from 2000 and onwards in Vigibase. After exclusion of possible duplicates and follow-up reports, 53 cases from five countries remained. Rhabdomyolysis occurred shortly after initiation of azithromycin in 23% of cases. In 11 patients an interaction had been suggested by the reporter. With the exception of one patient, the statin doses reported were within the recommended daily doses.

CONCLUSIONS: Case reports in VigiBase are suggestive that interactions between azithromycin and statins resulting in rhabdomyolysis may occur. This analysis showed the potential of the newly developed disproportionality measure, Omega, which can help to identify drug interactions in VigiBase in the future. The results also showed that reviewing spontaneous reports can add information to drug interactions not established previously.

Place, publisher, year, edition, pages
Wiley-Blackwell, 2009. Vol. 68, no 3, 427-34 p.
Keyword [en]
Adverse drug reaction reporting systems, azithromycin, disproportionality measure, drug interaction, statins, WHO-ADR database
National Category
Medical and Health Sciences
URN: urn:nbn:se:liu:diva-70846DOI: 10.1111/j.1365-2125.2009.03473.xISI: 000269575200015PubMedID: 19740401OAI: diva2:442057
Available from: 2011-09-20 Created: 2011-09-20 Last updated: 2013-10-16Bibliographically approved
In thesis
1. Drug interaction surveillance using individual case safety reports
Open this publication in new window or tab >>Drug interaction surveillance using individual case safety reports
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Background: Drug interactions resulting in adverse drug reactions (ADRs) represent a major health problem both for individuals and society in general. Post-marketing pharmacovigilance reporting databases with compiled individual case safety reports (ICSRs) have been shown to be particularly useful in the detection of novel drug - ADR combinations, though these reports have not been fully used to detect adverse drug interactions.

Aim: To explore the potential to identify drug interactions using ICSRs and to develop a method to facilitate the detection of adverse drug interaction signals in the WHO Global ICSR Database, VigiBase.

Methods: All six studies included in this thesis are based on ICSRs available in VigiBase. Two studies aimed to characterise drug interactions reported in VigiBase. In the first study we examined if contraindicated drug combinations (given in a reference source of drug interactions) were reported on the individual reports in the database, and in the second study we examined the scientific literature for interaction mechanisms for drug combinations most frequently co-reported as interacting in VigiBase. Two studies were case series analyses where the individual reports were manually reviewed. The two remaining studies aimed to develop a method to facilitate detection of novel adverse drug interactions in VigiBase. One examined what information (referred to as indicators) was reported on ICSRs in VigiBase before the interactions became listed in the literature. In the second methodological study, logistic regression was used to set the relative weights of the indicators to form triage algorithms. Three algorithms (one completely data driven, one semi-automated and one based on clinical knowledge) based on pharmacological and reported clinical information and the relative reporting rate of an ADR with a drug combination were developed. The algorithms were then evaluated against a set of 100 randomly selected case series with potential adverse drug interactions. The algorithm’s performances were then evaluated among DDAs with high coefficients.

Results: Drug interactions classified as contraindicated are reported on the individual reports in VigiBase, although they are not necessarily recognised as interactions when reported. The majority (113/123) of drug combinations suspected for being responsible for an ADR were established drug interactions in the literature. Of the 113 drug interactions 46 (41%) were identified as purely pharmacodynamic; 28 (25%) as pharmacokinetic; 18 (16%) were a mix of both types and for 21 (19%) the mechanism have not yet been identified. Suspicions of a drug interaction explicitly noted by the reporter are much more common for known adverse drug interactions than for drugs not known to interact. The clinical evaluation of the triage algorithms showed that 20 were already known in the literature, 30 were classified as signals and 50 as not signals. The performance of the semi-automated and the clinical algorithm were comparable. In the end the clinical algorithm was chosen. At a relevant level, 38% were of the adverse drug interactions were already known in the literature and of the remaining 80% were classified as signals for this algorithm.

Conclusions: This thesis demonstrated that drug interactions can be identified in large post-marketing pharmacovigilance reporting databases. As both pharmacokinetic and pharmacodynamic interactions were reported on ICSRs the surveillance system should aim to detect both. The proposed triage algorithm had a high performance in comparison to the disproportionality measure alone.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 45 p.
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1252
Adverse drug reactions, adverse drug interaction surveillance, drug interactions, individual case safety reports, postmarketing pharmacovigilance, signal detection
National Category
Medical and Health Sciences
urn:nbn:se:liu:diva-70424 (URN)978-91-7393-106-9 (ISBN)
Public defence
2011-10-06, Nils Holger, Campus US, Linköpings universitet, Linköping, 13:00 (English)
Available from: 2011-09-07 Created: 2011-09-07 Last updated: 2011-09-20Bibliographically approved

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Strandell, JohannaHägg, Staffan
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