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Consensus Recommendations for Systematic Evaluation of Drug–Drug Interaction Evidence for Clinical Decision Support

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Abstract

Background

Healthcare organizations, compendia, and drug knowledgebase vendors use varying methods to evaluate and synthesize evidence on drug–drug interactions (DDIs). This situation has a negative effect on electronic prescribing and medication information systems that warn clinicians of potentially harmful medication combinations.

Objective

The aim of this study was to provide recommendations for systematic evaluation of evidence for DDIs from the scientific literature, drug product labeling, and regulatory documents.

Methods

A conference series was conducted to develop a structured process to improve the quality of DDI alerting systems. Three expert workgroups were assembled to address the goals of the conference. The Evidence Workgroup consisted of 18 individuals with expertise in pharmacology, drug information, biomedical informatics, and clinical decision support. Workgroup members met via webinar 12 times from January 2013 to February 2014. Two in-person meetings were conducted in May and September 2013 to reach consensus on recommendations.

Results

We developed expert consensus answers to the following three key questions. (i) What is the best approach to evaluate DDI evidence? (ii) What evidence is required for a DDI to be applicable to an entire class of drugs? (iii) How should a structured evaluation process be vetted and validated?

Conclusion

Evidence-based decision support for DDIs requires consistent application of transparent and systematic methods to evaluate the evidence. Drug compendia and clinical decision support systems in which these recommendations are implemented should be able to provide higher-quality information about DDIs.

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Acknowledgments

This project was funded by the Agency for Healthcare Research and Quality (AHRQ) Grant No. 1R13HS021826-01 (Malone DC-PI). RDB received support to contribute to this project from AHRQ Grant No. K12 HS019461-01, National Library of Medicine Grant No. R01 LM011838-01, and National Institute of Aging Grant No. K01 AG044433-01. Additional support was provided by Cerner, Elsevier Clinical Solutions, Epocrates, athenahealth, Inc., FDB (First Databank, Inc.), Truven Health Analytics, and Wolters Kluwer. The authors would like to thank Alissa Rich for her participation in workgroup discussions and Loretta Peters for her editorial assistance in preparing this manuscript. We would like to acknowledge that John Horn developed the Drug Interaction Probability Scale that is recommended for use in evaluating drug interaction case reports.

Conflicts of interest

Lisa E. Hines, Mary Brown, and Amy J. Grizzle have no conflicts of interest to report except for grant support and travel support from the University of Arizona. Richard T. Scheife, Richard D. Boyce, John Horn and Jeremiah D. Momper have no conflicts of interest to report except for travel support from the University of Arizona to attend workgroup meetings. Christine D. Sommer is an employee of FDB, which supported the travel to workgroup meetings and reviewed the manuscript during business hours. Stephen J. Sklar is an employee of Wolters Kluwer, which provided a portion of financial support for this project and provided support for his travel to meetings. Sophie P. Chung and Susan Comes are employees of Epocrates, athenahealth, Inc., which paid for their travel to workgroup meetings. Gretchen Jones was employed by Epocrates, athenahealth, Inc., which paid for the travel to meetings, and she holds stock in that company. Clarissa Borst is an employee of Elsevier, which provided support to the conference. Daniel C. Malone received support from the University of Arizona to conduct this project, which was partially funded by a conference grant from the Agency for Healthcare Research and Quality. Michael A. Wittie, Darrell R. Abernethy, Samantha K. Wong and Tricia Lee Wilkins have no conflicts of interest that are directly relevant to the content of this study.

Author information

Authors and Affiliations

  1. Tufts University School of Medicine, Boston, MA, USA

    Richard T. Scheife

  2. University of Arizona College of Pharmacy, 1295 N Martin Ave, PO Box 210202, Tucson, AZ, 85721-0202, USA

    Lisa E. Hines, Mary L. Brown, Amy J. Grizzle & Daniel C. Malone

  3. Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA

    Richard D. Boyce

  4. Epocrates, athenahealth, Inc., San Francisco, CA, USA

    Sophie P. Chung, Gretchen Jones & Susan Comes

  5. University of California, San Diego, Skaggs School of Pharmacy and Pharmaceutical Sciences, La Jolla, CA, USA

    Jeremiah D. Momper

  6. FDB (First Databank, Inc.), South San Francisco, CA, USA

    Christine D. Sommer

  7. Office of Clinical Pharmacology, U.S. Food and Drug Administration, Silver Springs, MD, USA

    Darrell R. Abernethy

  8. University of Washington School of Pharmacy, Seattle, WA, USA

    John R. Horn

  9. Wolters Kluwer Health, Indianapolis, IN, USA

    Stephen J. Sklar

  10. Cerner Multum, Denver, CO, USA

    Samantha K. Wong

  11. U.S. Office of the National Coordinator for Health Information Technology, Washington, DC, USA

    Tricia Lee Wilkins & Michael A. Wittie

  12. Elsevier Clinical Solutions, Tampa, FL, USA

    Clarissa Borst

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  1. Richard T. Scheife
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  2. Lisa E. Hines
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  3. Richard D. Boyce
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  4. Sophie P. Chung
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  8. John R. Horn
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  9. Stephen J. Sklar
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  11. Gretchen Jones
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  12. Mary L. Brown
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  13. Amy J. Grizzle
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  14. Susan Comes
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  15. Tricia Lee Wilkins
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  16. Clarissa Borst
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  17. Michael A. Wittie
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  18. Daniel C. Malone
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Correspondence to Daniel C. Malone.

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Scheife, R.T., Hines, L.E., Boyce, R.D. et al. Consensus Recommendations for Systematic Evaluation of Drug–Drug Interaction Evidence for Clinical Decision Support. Drug Saf 38, 197–206 (2015). https://doi.org/10.1007/s40264-014-0262-8

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