Skip to main content
Log in

A Retrospective Evaluation of a Data Mining Approach to Aid Finding New Adverse Drug Reaction Signals in the WHO International Database

  • Original Research Article
  • Published:
Drug Safety Aims and scope Submit manuscript

Abstract

Background: The detection of new drug safety signals is of growing importance with ever more new drugs becoming available and exposure to medicines increasing. The task of evaluating information relating to safety lies with national agencies and, for international data, with the World Health Organization Programme for International Drug Monitoring.

Rationale: An established approach for identifying new drug safety signals from the international database of more than 2 million case reports depends upon clinical experts from around the world. With a very large amount of information to evaluate, such an approach is open to human error. To aid the clinical review, we have developed a new signalling process using Bayesian logic, applied to data mining, within a confidence propagation neural network (Bayesian Confidence Propagation Neural Network; BCPNN). Ultimately, this will also allow the evaluation of complex variables.

Methods: The first part of this study tested the predictive value of the BCPNN in new signal detection as compared with reference literature sources (Martindale’s Extra Pharmacopoeia in 1993 and July 2000, and the Physicians Desk Reference in July 2000). In the second part of the study, results with the BCPNN method were compared with those of the former signalling procedure.

Results: In the study period (the first quarter of 1993) 107 drug—adverse reaction combinations were highlighted as new positive associations by the BCPNN, and referred to new drugs. 15 drug—adverse reaction combinations on new drugs became negative BCPNN associations in the study period. The BCPNN method detected signals with a positive predictive value of 44% and the negative predictive value was 85%. 17 as yet unconfirmed positive associations could not be dismissed with certainty as false positive signals.

Of the 10 drug—adverse reaction signals produced by the former signal detection system from data sent out for review during the study period, 6 were also identified by the BCPNN. These 6 associations have all had a more than 10-fold increase of reports and 4 of them have been included in the reference sources. The remaining 4 signals that were not identified by the BCPNN had a small, or no, increase in the number of reports, and are not listed in the reference sources.

Conclusion: Our evaluation showed that the BCPNN approach had a high and promising predictive value in identifying early signals of new adverse drug reactions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Table I
Table II
Table III
Table IV
Table V

Similar content being viewed by others

References

  1. Lindquist M, Edwards IR, Bate A, et al. From association to alert - a revised approach to International Signal analysis. Pharmacoepidemiol Drug Saf 1999; 8: S15–25

    Article  PubMed  Google Scholar 

  2. Bate A, Lindquist M, Edwards IR, et al. A Bayesian neural network method for adverse drug reaction signal generation. Eur J Clin Pharmacol 1998; 54: 315–21

    Article  PubMed  CAS  Google Scholar 

  3. Orre R, Lansner A, Bate A, et al. Bayesian neural networks with confidence estimations applied to data mining. Computational Statistics and Data Analysis 2000; 34 (4): 473–93

    Article  Google Scholar 

  4. Edwards IR, Biriell C. Harmonisation in pharmacovigilance. Drug Saf 1994; 10: 93–102

    Article  PubMed  CAS  Google Scholar 

  5. Reynolds JEF. 30th ed. Martindale: the extra pharmacopoeia. London: The Pharmaceutical Press, 1993

    Google Scholar 

  6. Micromedex vol 105. Micromedex® healthcare series vol.105 [online]. Available from URL: http://mdx.com. [Accessed 2000 July]

  7. Micromedex vol 100. Micromedex® healthcare series vol.100 [online]. Available from URL: http://mdx.com. [Accessed 1999 June]

  8. Finney DJ. Systematic signalling of adverse reactions to drugs. Methods Inf Med 1974; 13 (1): 1–10

    PubMed  CAS  Google Scholar 

  9. DuMouchel W. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system. Am Stat 1999; 53 (3): 177–90

    Google Scholar 

  10. Meyboom RH, Egberts AC, Edwards IR, et al. Principles of signal detection in pharmacovigilance. Drug Saf 1997; 16 (6): 355–65

    Article  PubMed  CAS  Google Scholar 

  11. Yokotsuka M, Aoyama M, Kutoba K. The use of a medical dictionary for regulatory activities terminology (MedRA) in prescription-event monitoring in Japan (J-PEM). Int J Med Inf 2000; 57 (2-3): 139–53

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors are indebted to national centres contributing data to the WHO International Drug Monitoring Programme. The opinions and conclusions, however, are not necessarily those of the various centres nor of the WHO.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Marie Lindquist.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lindquist, M., Ståhl, M., Bate, A. et al. A Retrospective Evaluation of a Data Mining Approach to Aid Finding New Adverse Drug Reaction Signals in the WHO International Database. Drug-Safety 23, 533–542 (2000). https://doi.org/10.2165/00002018-200023060-00004

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.2165/00002018-200023060-00004

Keywords

Navigation