Defining a Reference Set to Support Methodological Research in Drug Safety

Ryan, Patrick B.; Schuemie, Martijn J.; Welebob, Emily; Duke, Jon; Valentine, Sarah E.; Hartzema, Abraham G.

doi:10.1007/s40264-013-0097-8

Cited by 108 publications

(121 citation statements)

References 74 publications

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“…Although actual incidence cannot be inferred from spontaneous reporting systems, the fact that antimycotics are reported in approx 3% of the total cases in FAERS suggests that the risk could be common. Our study also confirmed that miconazole and griseofulvin are not associated with clinically significant risk of DILI, a finding in line with recent data from EU-ADR and OMOP Consortia (Europe and United States projects, respectively), which labeled these drugs as "negative controls" in drug safety studies investigating DILI, based on the existing scientific literature and expert opinion [48,49] . As a matter of fact, miconazole is mainly used as topical preparation, whereas griseofulvin has specific tropism for keratin tissues with only low plasma concentrations.…”

Section: Discussionsupporting

confidence: 90%

Assessing liver injury associated with antimycotics: Concise literature review and clues from data mining of the FAERS database

Raschi¹,

Poluzzi²,

Koci³

et al. 2014

WJH

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AIM: To inform clinicians on the level of hepatotoxic risk among antimycotics in the post-marketing setting, following the marketing suspension of oral ketoconazole for drug-induced liver injury (DILI). METHODS:The publicly available international FAERS database (2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011) was used to extract DILI cases (including acute liver failure events), where antimycotics with systemic use or potential systemic absorption were reported as suspect or interacting agents. The reporting pattern was analyzed by calculating the reporting odds ratio and corresponding 95%CI, a measure of disproportionality, with time-trend analysis where appropriate. RESULTS:From 1687284 reports submitted over the 8-year period, 68115 regarded liver injury. Of these, 2.9% are related to antimycotics (1964 cases, of which 112 of acute liver failure). Eleven systemic antimycotics (including ketoconazole and the newer triazole derivatives voriconazole and posaconazole) and terbinafine (used systemically to treat onychomicosis) generated a significant disproportionality, indicating a post-marketing signal of risk. CONCLUSION:Virtually all antimycotics with systemic action or absorption are commonly reported in clinically significant cases of DILI. Clinicians must be aware of this aspect and monitor patients in case switch is considered, especially in critical poly-treated patients under chronic treatment.

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Section: Discussionsupporting

confidence: 90%

Assessing liver injury associated with antimycotics: Concise literature review and clues from data mining of the FAERS database

Raschi¹,

Poluzzi²,

Koci³

et al. 2014

WJH

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“…Event frequency and other qualifiers are also extracted. SPLICER demonstrated high accuracy in AE extraction, with a sensitivity of 93% and PPV of 95%, and was used in various projects including the formation of the OMOP gold standard[49, 50] and in assessing the labeling consistency of bio-equivalent drugs[51]. While not yet publicly available, the tool as well as the resulting knowledgebase can be obtained by contacting the authors.…”

Section: Product Labelingmentioning

confidence: 99%

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

et al. 2014

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Text mining is the computational process of extracting meaningful information from large amounts of unstructured text. Text mining is emerging as a tool to leverage underutilized data sources that can improve pharmacovigilance, including the objective of adverse drug event detection and assessment. This article provides an overview of recent advances in pharmacovigilance driven by the application of text mining, and discusses several data sources—such as biomedical literature, clinical narratives, product labeling, social media, and Web search logs—that are amenable to text-mining for pharmacovigilance. Given the state of the art, it appears text mining can be applied to extract useful ADE-related information from multiple textual sources. Nonetheless, further research is required to address remaining technical challenges associated with the text mining methodologies, and to conclusively determine the relative contribution of each textual source to improving pharmacovigilance.

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“…Even with only four adverse event outcomes under investigation, an extensive literature search and evaluation was required to identify the 165 drug-event pairs in which the drug was clearly implicated [10]. The search had to be repeated to document the 234 cases where no association was suspected; proving a negative is always an uncertain task because undocumented adverse effects could well exist.…”

Section: The Observational Medical Outcomes Partnership (Omop) Experimentioning

confidence: 99%

Electronic Health Data for Postmarket Surveillance: A Vision Not Realized

Moore

Furberg

2015

Drug Saf

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What has been learned about electronic health data as a primary data source for regulatory decisions regarding the harms of drugs? Observational studies with electronic health data for postmarket risk assessment can now be conducted in Europe and the US in patient populations numbering in the tens of millions compared with a few hundred patients in a typical clinical trial. With standard protocols, results can be obtained in a few months; however, extensive research published by scores of investigators has illuminated the many obstacles that prevent obtaining robust, reproducible results that are reliable enough to be a primary source for drug safety decisions involving the health and safety of millions of patients. The most widely used terminology for coding patient interactions with medical providers for payment has proved illsuited to identifying the adverse effects of drugs. Directly conflicting results were reported in otherwise similar patient health databases, even using identical event definitions and research methods. Evaluation of some accepted statistical methods revealed systematic bias, while others appeared to be unreliable. When electronic health data studies detected no drug risk, there were no robust and accepted standards to judge whether the drug was unlikely to cause the adverse effect or whether the study was incapable of detecting it. Substantial investment and careful thinking is needed to improve the reliability of risk assessments based on electronic health data, and current limitations need to be fully understood. Key PointsElectronic health data for postmarket surveillance became a key element in the new paradigm for drug regulation, which involved fewer and smaller clinical trials prior to marketing approval.The research programs and pilot systems created to study harms of licensed drugs proved largely unable to provide credible evidence of new, unsuspected drug adverse effects, and conflicting and contradictory results when seeking to confirm known harms.Major problems included a limited underlying terminology, few validation studies, and the need for additional statistical standards for these complex data.

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Defining a Reference Set to Support Methodological Research in Drug Safety

Cited by 108 publications

References 74 publications

Assessing liver injury associated with antimycotics: Concise literature review and clues from data mining of the FAERS database

Assessing liver injury associated with antimycotics: Concise literature review and clues from data mining of the FAERS database

Text Mining for Adverse Drug Events: the Promise, Challenges, and State of the Art

Electronic Health Data for Postmarket Surveillance: A Vision Not Realized

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