Potential Utility of Data‐Mining Algorithms for Early Detection of Potentially Fatal/Disabling Adverse Drug Reactions: A Retrospective Evaluation

The objective of this study was to apply 2 data‐mining algorithms to a drug safety database to determine if these methods would have flagged potentially fatal/disabling adverse drug reactions that triggered black box warnings/drug withdrawals in advance of initial identification via “traditional” methods. Relevant drug‐event combinations were identified from a journal publication. Data‐mining algorithms using commonly cited disproportionality thresholds were then applied to the US Food and Drug Administration database. Seventy drug‐event combinations were considered sufficiently specific for retrospective data mining. In a minority of instances, potential signals of disproportionate reporting were provided clearly in advance of initial identification via traditional pharmacovigilance methods. Data‐mining algorithms have the potential to improve pharmacovigilance screening; however, for the majority of drug‐event combinations, there was no substantial benefit of either over traditional methods. They should be considered as potential supplements to, and not substitutes for, traditional pharmacovigilance strategies. More research and experience will be needed to optimize deployment of data‐mining algorithms in pharmacovigilance.

[1]  R. O’Neill,et al.  Use of Screening Algorithms and Computer Systems to Efficiently Signal Higher-Than-Expected Combinations of Drugs and Events in the US FDA’s Spontaneous Reports Database , 2002, Drug safety.

[2]  Manfred Hauben,et al.  A Brief Primer on Automated Signal Detection , 2003, The Annals of pharmacotherapy.

[3]  William DuMouchel,et al.  Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System , 1999 .

[4]  S. Evans,et al.  Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports , 2001, Pharmacoepidemiology and drug safety.

[5]  Manfred Hauben,et al.  Trimethoprim-induced hyperkalaemia -- lessons in data mining. , 2004, British journal of clinical pharmacology.

[6]  M. Lindquist,et al.  A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions , 2002, Pharmacoepidemiology and drug safety.

[7]  A. Trontell.  Expecting the unexpected--drug safety, pharmacovigilance, and the prepared mind. , 2004, The New England journal of medicine.

[8]  M. Hauben.  Application of an Empiric Bayesian Data Mining Algorithm to Reports of Pancreatitis Associated with Atypical Antipsychotics , 2004, Pharmacotherapy.

[9]  E. Puijenbroek,et al.  Application of Quantitative Signal Detection in the Dutch Spontaneous Reporting System for Adverse Drug Reactions , 2003, Drug safety.

[10]  B Begaud,et al.  False-positives in spontaneous reporting: should we worry about them? , 1994, British journal of clinical pharmacology.

[11]  D. J. Finney,et al.  The detection of adverse reactions to therapeutic drugs. , 1982, Statistics in medicine.

[12]  A. Bate,et al.  A Bayesian neural network method for adverse drug reaction signal generation , 1998, European Journal of Clinical Pharmacology.

[13]  W. DuMouchel,et al.  Disproportionality analysis using empirical Bayes data mining: a tool for the evaluation of drug interactions in the post‐marketing setting , 2003, Pharmacoepidemiology and drug safety.

[14]  S. Wolfe,et al.  Timing of new black box warnings and withdrawals for prescription medications. , 2002, JAMA.

[15]  A Lawrence Gould,et al.  Practical pharmacovigilance analysis strategies. , 2003, Pharmacoepidemiology and drug safety.

[16]  M. Lindquist,et al.  A Retrospective Evaluation of a Data Mining Approach to Aid Finding New Adverse Drug Reaction Signals in the WHO International Database , 2000, Drug safety.