Faculty Bibliography

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

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

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 H(2) 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

BACKGROUND AND OBJECTIVE: Various data mining algorithms (DMAs) that perform disporportionality analysis on spontaneous reporting system (SRS) data are being heavily promoted to improve drug safety surveillance. The incremental value of DMAs is ultimately related to their ability to detect truly unexpected associations that would have escaped traditional surveillance and/or their ability to identify the same associations as traditional methods but with greater scientific efficiency. As to the former potential benefit, in the course of evaluating DMAs, we have observed what we call 'surprise reactions'. These adverse reactions may be discounted in manual review of adverse drug reaction (ADR) lists because they are less clinically dramatic, less characteristic of drug effects in general, less serious than the classical type B hypersensitivity reactions or may have subtle pharmacological explanations. Thus these reactions may only become recognised when post hoc explanations are sought based on more refined pharmacological knowledge of the formulation. The objective of this study was to explore notions of 'unexpectedness' as relates to signal detection and data mining by introducing the concept of 'surprise reactions' and to determine if the latter associations, often first reported in the literature, represent a type of ADR amenable to detection with the assistance of adjunctive statistical calculations on SRS data. METHODS: Using commonly cited thresholds, the multi-item gamma Poisson shrinker (MGPS) and proportional reporting ratios (PRRs) were applied to reports in the US FDA Adverse Event Reporting System (AERS) database of well documented 'surprise reactions' compiled by the authors. RESULTS: There were 34 relevant surprise reactions involving 29 separate drugs in 17 different drug classes. Using PRRs (PRR >2, chi(2) >4, N >2), 12 drug-event combinations were signalled before the first ADR citation appeared in MEDLINE, four occurred concurrently and 11 after. With empirical Bayes geometric mean (EBGM) analysis (EBGM >2, N >0), 12 signals occurred before, three concurrently and 11 after publication of the first literature citation. With EB(05) (EB(05)> or =2, N >0), six occurred before, two concurrently and 14 after MEDLINE citation. DISCUSSION: Pharmacovigilance is rather unique in terms of the number and variety of events under surveillance. Some events may be more appropriate targets for statistical approaches than others. The experience of many organisations is that most statistical disproportionalities represent known associations but our findings suggest there could be events that may be discounted on manual review of adverse event lists, which may be usefully highlighted via DMAs. CONCLUSIONS: Identification of surprise reactions may serve as an important niche for DMAs

PURPOSE: To provide commentary and points of caution to consider before incorporating data mining as a routine component of any Pharmacovigilance program, and to stimulate further research aimed at better defining the predictive value of these new tools as well as their incremental value as an adjunct to traditional methods of post-marketing surveillance. METHODS/RESULTS: Commentary includes review of current data mining methodologies employed and their limitations, caveats to consider in the use of spontaneous reporting databases and caution against over-confidence in the results of data mining. CONCLUSIONS: Future research should focus on more clearly delineating the limitations of the various quantitative approaches as well as the incremental value that they bring to traditional methods of pharmacovigilance