A Retrospective Evaluation of a Data Mining Approach to Aid Finding New Adverse Drug Reaction Signals in the WHO International Database

Lindquist, Marie; Ståhl, Malin; Bate, Andrew; Edwards, I. Ralph; Meyboom, R.H.B.

doi:10.2165/00002018-200023060-00004

Cited by 191 publications

(101 citation statements)

References 7 publications

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“…As the reports were received from a variety of countries, this reduces the possibility that the volume of reports was purely due to selective reporting due to country-or region-specific publication bias. The raw number of reports per combination cannot be interpreted as an estimate of incidence, while the raw number is higher than expected based on general reporting of the drug and adverse reactions.This leads to positive disproportionality scores that have proved predictive of emerging but currently recognized ADR issues [27].…”

Section: Discussionmentioning

confidence: 99%

Rhabdomyolysis a result of azithromycin and statins: an unrecognized interaction

Strandell

Bate

Hägg

et al. 2009

Brit J Clinical Pharma

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WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT • Rhabdomyolysis is a serious but rare adverse effect of statins. • The mechanism of rhabdomyolysis with statins is poorly defined, but the occurrence is known to increase with dose/ concentration. • Some macrolides are known to interact with statins; however, azithromycin has not been described to interact with statins with the exception of two literature case reports. WHAT THIS STUDY ADDS • A case series in the World Health Organization Adverse Drug Reaction (WHO‐ADR) database was suggestive of a possible drug interaction between statins and azithromycin with rhabdomyolysis. • A disproportionality measure, Omega, was shown to identify previously not recognized suspected drug interactions within the WHO‐ADR dataset. 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.

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Section: Discussionmentioning

confidence: 99%

Rhabdomyolysis a result of azithromycin and statins: an unrecognized interaction

Strandell

Bate

Hägg

et al. 2009

Brit J Clinical Pharma

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“…However, they can be found in the Combinations Database, which includes all new combinations, statistically prominent or not. Although the results of the BCPNN signalling system of the UMC [7] and of systems in place at other centres show reasonable sensitivity and specificity, they should not be regarded as a panacea.…”

Section: Signals Not Found By Disproportionalitymentioning

confidence: 87%

“…As a rule, further study, using the most appropriate (and usually different) method, is needed to put the hypothesis to the test. [7] Often this is a formal pharmacoepidemiological study, but also pharmacological or pathological studies may give support. Nowadays, on a worldwide basis there are far more signals found than can reasonably be tested.…”

Section: Signal Testingmentioning

confidence: 99%

Signal Selection and Follow-Up in Pharmacovigilance

et al. 2002

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The detection of unknown and unexpected connections between drug exposure and adverse events is one of the major challenges of pharmacovigilance. For the identification of possible connections in large databases, automated statistical systems have been introduced with promising results. From the large numbers of associations so produced, the human mind has to identify signals that are likely to be important, in need of further assessment and follow-up and that may require regulatory action. Such decisions are based on a variety of clinical, epidemiological, pharmacological and regulatory criteria. Likewise, there are a number of criteria that underlie the subsequent evaluation of such signals. A good understanding of the logic underlying these processes fosters rational pharmacovigilance and efficient drug regulation. In the future a combination of quantitative and qualitative criteria may be incorporated in automated signal detection.

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“…2. An early example of this was the retrospective analysis of VigiBase Ò by Lindquist et al [20], whereas more recent examples include the studies by Alvarez et al [14] and Strandell et al [21]. The reference set proposed by Alvarez et al [14] is particularly interesting in that it provides dates not just for the regulatory action associated with each signal, but also the first dates that each signal was first discussed by the EMA's signal management team.…”

Section: Related Workmentioning

confidence: 99%

Zoo or Savannah? Choice of Training Ground for Evidence-Based Pharmacovigilance

et al. 2014

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Pharmacovigilance seeks to detect and describe adverse drug reactions early. Ideally, we would like to see objective evidence that a chosen signal detection approach can be expected to be effective. The development and evaluation of evidence-based methods require benchmarks for signal detection performance, and recent years have seen unprecedented efforts to build such reference sets. Here, we argue that evaluation should be made against emerging and not established adverse drug reactions, and we present real-world examples that illustrate the relevance of this to pharmacovigilance methods development for both individual case reports and longitudinal health records. The establishment of broader reference sets of emerging safety signals must be made a top priority to achieve more effective pharmacovigilance methods development and evaluation.

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A Retrospective Evaluation of a Data Mining Approach to Aid Finding New Adverse Drug Reaction Signals in the WHO International Database

Abstract: Our evaluation showed that the BCPNN approach had a high and promising predictive value in identifying early signals of new adverse drug reactions.

Cited by 191 publications

References 7 publications

Rhabdomyolysis a result of azithromycin and statins: an unrecognized interaction

Rhabdomyolysis a result of azithromycin and statins: an unrecognized interaction

Signal Selection and Follow-Up in Pharmacovigilance

Zoo or Savannah? Choice of Training Ground for Evidence-Based Pharmacovigilance

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