Endotoxin-like reactions with intravenous gentamicin: results from pharmacovigilance tools under investigation

Gold standards in pharmacovigilance: the use of definitive anecdotal reports of adverse drug reactions as pure gold and high-grade ore

Anecdotal reports of adverse drug reactions are generally regarded as being of poor evidential quality. This is especially relevant for postmarketing drug safety surveillance, which relies heavily on spontaneous anecdotal reports. The numerous limitations of spontaneous reports cannot be overemphasised, but there is another side to the story: these datasets also contain anecdotal reports that can be considered to describe definitive adverse reactions, without the need for further formal verification. We have previously defined four categories of such adverse reactions: (i) extracellular or intracellular tissue deposition of the drug or a metabolite; (ii) a specific anatomical location or pattern of injury; (iii) physiological dysfunction or direct tissue damage demonstrable by physicochemical testing; and (iv) infection, as a result of the administration of an infective agent as the therapeutic substance or because of demonstrable contamination. In this article, we discuss the implications of these definitive ('between-the-eyes') adverse effects for pharmacovigilance.

Association between gastric acid suppressants and Clostridium difficile colitis and community-acquired pneumonia: analysis using pharmacovigilance tools

OBJECTIVE

Recent epidemiological studies identifying an association between some classes of gastric acid suppressants and Clostridium difficile colitis and community-acquired pneumonia prompted our analysis. Our objective was to retrospectively apply data mining algorithms (DMAs) to the Food and Drug Administration (FDA) drug safety database to see if they might have directed/redirected attention to the reported association of gastric acid suppressive drugs with C. difficile colitis and community-acquired pneumonia, prior to the published epidemiological findings that supported the association.

DESIGN

Two statistical DMAs, proportional reporting ratios (PRRs) and multi-item gamma Poisson shrinker (MGPS), were applied to a spontaneous reporting system (SRS) database to identify signals of disproportionate reporting (SDRs).

RESULTS

SDRs related to community-acquired pneumonia were observed for two proton pump inhibitors (lansoprazole and omeprazole), two H2 antagonists (famotidine and roxatidine), and one antacid (magnesium silicate hydroxide). For C. difficile colitis, an SDR was generated for one proton pump inhibitor (lansoprazole).

CONCLUSIONS

Although our analysis suggests that there may be an association between the SDRs using SRS data and the epidemiological findings, these results may not have alerted public health professionals in advance of published studies to an association between proton pump inhibitors/gastric acid suppressants and C. difficile colitis or community-acquired pneumonia. However, the analysis reveals the potential utility of DMAs to direct attention to more subtle indirect drug adverse effects in SRS databases that as yet are often identified from epidemiological investigations.

Illusions of objectivity and a recommendation for reporting data mining results

OBJECTIVE

Data mining algorithms (DMAs) are being applied to spontaneous reporting system (SRS) databases in the hope of obtaining timely insights into post-licensure safety data. Some DMAs have been characterized as "objective" screening tools. However, there are numerous available modifiable configuration parameters to choose from, including choice of vendor, that may affect results. Our objective is to compare the data mining results on pre-selected drug-event combinations (DECs) between two commonly used software programs using similar protocols.

METHODS

Two DMAs, using three thresholds, were retrospectively applied to the USFDA safety database through Q2 2005 to a set of eight pre-selected DECs.

RESULTS

Differences between the two vendors were found for the number of cases associated with a signal of disproportionate reporting (SDR), first year of SDRs, and the magnitude of the SDR scores for the selected DECs. These were deemed to be potentially significant for 45.8% (11/24) of the data points.

CONCLUSION

The observed differences between vendors could partially be explained by their differing methods of data cleaning and transformation as well as by the specific features of individual algorithms. The choices of vendors and available data mining configurations maximize the exploratory capacity of data mining, but they also raise questions about the claimed objectivity of data mining results and can make data mining exercises susceptible to confirmation bias given the exploratory nature of data mining in pharmacovigilance. When reporting results, the vendor and all data mining configuration details should be specified.

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.