Potential use of data-mining algorithms for the detection of 'surprise' adverse drug reactions.
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.


CONCLUSIONS
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.
Hauben
Manfred
M
Risk Management Strategy, Pfizer Inc., New York, New York, USA.
Horn
Sebastian
S
Reich
Lester
L
D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000465:Algorithms; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D015233:Models, Statistical; D016012:Poisson Distribution; D012107:Research Design; D014481:United States; D014486:United States Food and Drug Administration / Q000706:statistics & numerical data*