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.
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.
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.
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?
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.
This is a preview of subscription content, access via your institution.
Buy single article
Instant access to the full article PDF.
Tax calculation will be finalised during checkout.
Aspden P, Institute of Medicine (U.S.). Committee on identifying and preventing medication errors. Preventing medication errors. Washington, DC: National Academies Press; 2007.
Magro L, Moretti U, Leone R. Epidemiology and characteristics of adverse drug reactions caused by drug–drug interactions. Expert Opin Drug Saf. 2012;11(1):83–94.
Centers for Disease Control and Prevention FASTSTATS—Emergency Department Visits. 2012. US Department of Health and Human Services. [cited 12/06/2013]; Available from: http://www.cdc.gov/nchs/fastats/ervisits.htm.
Centers for Disease Control and Prevention FASTSTATS—Hospital Utilization. 2010. US Department of Health and Human Services. [cited 12/06/2013]; Available from: http://www.cdc.gov/nchs/fastats/hospital.htm.
Centers for Medicare and Medicaid Services. Eligible Professional Meaningful Use Core Measures: Measure 2 of 15. 2010. US Department of Health and Human Services. [cited 01/09/2013]; Available from: http://www.cms.gov/Regulations-and-Guidance/Legislation/EHRIncentivePrograms/downloads/2_Drug_Interaction_ChecksEP.pdf.
Hatton RC, Rosenberg AF, Morris CT, McKelvey RP, Lewis JR. Evaluation of contraindicated drug–drug interaction alerts in a hospital setting. Ann Pharmacother. 2011;45(3):297–308.
Shah VS, Weber RJ, Nahata MC. Contradictions in contraindications for drug–drug interactions. Ann Pharmacother. 2011;45(3):409–11.
Horn JR, Hansten PD. Comment: evaluation of contraindicated drug–drug interaction alerts in a hospital setting. Ann Pharmacother. 2011;45(6):826 (author reply-7).
Kesselheim AS, Cresswell K, Phansalkar S, Bates DW, Sheikh A. Clinical decision support systems could be modified to reduce ‘alert fatigue’ while still minimizing the risk of litigation. Health Aff (Millwood). 2011;30(12):2310–7.
Saverno KR, Hines LE, Warholak TL, Grizzle AJ, Babits L, Clark C, et al. Ability of pharmacy clinical decision-support software to alert users about clinically important drug–drug interactions. J Am Med Inform Assoc. 2011;18(1):32–7.
Metzger J, Welebob E, Bates DW, Lipsitz S, Classen DC. Mixed results in the safety performance of computerized physician order entry. Health Aff (Millwood). 2010;29(4):655–63.
Abarca J, Colon LR, Wang VS, Malone DC, Murphy JE, Armstrong EP. Evaluation of the performance of drug–drug interaction screening software in community and hospital pharmacies. J Manag Care Pharm. 2006;12(5):383–9.
van der Sijs H, Aarts J, Vulto A, Berg M. Overriding of drug safety alerts in computerized physician order entry. J Am Med Inform Assoc. 2006;13(2):138–47.
Miller AM, Boro MS, Korman NE, Davoren JB. Provider and pharmacist responses to warfarin drug–drug interaction alerts: a study of healthcare downstream of CPOE alerts. J Am Med Inform Assoc. 2011;18(Suppl 1):i45–50.
Weingart SN, Toth M, Sands DZ, Aronson MD, Davis RB, Phillips RS. Physicians’ decisions to override computerized drug alerts in primary care. Arch Intern Med. 2003;163(21):2625–31.
Grizzle AJ, Mahmood MH, Ko Y, Murphy JE, Armstrong EP, Skrepnek GH, et al. Reasons provided by prescribers when overriding drug–drug interaction alerts. Am J Manag Care. 2007. p. 573–8 (United States).
Caccia S, Garattini S, Pasina L, Nobili A. Predicting the clinical relevance of drug interactions from pre-approval studies. Drug Saf. 2009;32(11):1017–39.
Holbrook AM, Pereira JA, Labiris R, McDonald H, Douketis JD, Crowther M, et al. Systematic overview of warfarin and its drug and food interactions. Arch Intern Med. 2005;165(10):1095–106
Huang SM, Strong JM, Zhang L, Reynolds KS, Nallani S, Temple R, et al. New era in drug interaction evaluation: US Food and Drug Administration update on CYP enzymes, transporters, and the guidance process. J Clin Pharmacol. 2008;48(6):662–70.
Horn JR, Hansten PD, Chan LN. Proposal for a new tool to evaluate drug interaction cases. Ann Pharmacother. 2007;41(4):674–80.
Hines LE, Malone DC, Murphy JE. Recommendations for generating, evaluating, and implementing drug–drug interaction evidence. Pharmacotherapy. 2012;32(4):304–13.
Hansten PD, Horn JR, Hazlet TK. ORCA: OpeRational ClassificAtion of drug interactions. J Am Pharm Assoc. 2001;41(2):161–5.
Ridgeley MS, Greenberg MD. Too many alerts, too much liability: sorting through the malpractice implications of drug–drug interaction clinical decision support. Saint Louis Univ J Health Law Policy. 2012;5(257):257–96.
Oates JA. Chapter 5. The science of drug therapy. In: Brunton LL, editor. Goodman & Gilman’s The Pharmacological Basis of Therapeutics. 11th ed. McGraw-Hill, Medical Publishing Division; 2006.
Hines LE, Murphy JE. Potentially harmful drug–drug interactions in the elderly: a review. Am J Geriatr Pharmacother. 2011;9(6):364–77.
Talbot JCC, Aronson JK, Stephens MDB. Stephens’ detection and evaluation of adverse drug reactions : principles and practice. 6th ed. Chichester: Wiley; 2011.
Food and Drug Administration. Title 21–Food and Drugs Chapter I–Food and Drug Administration; US Department of Health and Human Services Subchapter D–Drugs for Human Use. 2013. [cited 7/28/14]; Available from: http://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=320.33.
Naranjo CA, Busto U, Sellers EM, Sandor P, Ruiz I, Roberts EA, et al. A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther. 1981;30(2):239–45.
Hutchinson TA, Lane DA. Assessing methods for causality assessment of suspected adverse drug reactions. J Clin Epidemiol. 1989;42(1):5–16.
Böttiger Y, Laine K, Andersson ML, Korhonen T, Molin B, Ovesjö ML, et al. SFINX-a drug–drug interaction database designed for clinical decision support systems. Eur J Clin Pharmacol. 2009;65(6):627–33.
van Roon EN, Flikweert S, le Comte M, Langendijk PN, Kwee-Zuiderwijk WJ, Smits P, et al. Clinical relevance of drug–drug interactions : a structured assessment procedure. Drug Saf. 2005;28(12):1131–9.
Boyce R, Collins C, Horn J, Kalet I. Computing with evidence Part II: an evidential approach to predicting metabolic drug–drug interactions. J Biomed Inform. 2009;42(6):990–1003.
Boyce RD, Handler SM, Karp JF, Hanlon JT. Age-related changes in antidepressant pharmacokinetics and potential drug–drug interactions: a comparison of evidence-based literature and package insert information. Am J Geriatr Pharmacother. 2012;10(2):139–50.
Boyce RD, Collins C, Clayton M, Kloke J, Horn JR. Inhibitory metabolic drug interactions with newer psychotropic drugs: inclusion in package inserts and influences of concurrence in drug interaction screening software. Ann Pharmacother. 2012;46(10):1287–98.
McDonagh MS, Peterson K, Balshem H, Helfand M. US Food and Drug Administration documents can provide unpublished evidence relevant to systematic reviews. J Clin Epidemiol. 2013;66(10):1071–81.
Turner EH. How to access and process FDA drug approval packages for use in research. BMJ. 2013;347:f5992.
O’Connor AB. The need for improved access to FDA reviews. JAMA. 2009;302(2):191–3.
DiNicolantonio JJ, Serebruany VL. Challenging the FDA black box warning for high aspirin dose with ticagrelor in patients with diabetes. Diabetes. 2013;62(3):669–71.
Anthony M, Romero K, Malone DC, Hines LE, Higgins L, Woosley RL. Warfarin interactions with substances listed in drug information compendia and in the FDA-approved label for warfarin sodium. Clin Pharmacol Ther. 2009;86(4):425–9.
Chao SD, Maibach HI. Lack of drug interaction conformity in commonly used drug compendia for selected at-risk dermatologic drugs. Am J Clin Dermatol. 2005;6(2):105–11.
Hines LE, Ceron-Cabrera D, Romero K, Anthony M, Woosley RL, Armstrong EP, et al. Evaluation of warfarin drug interaction listings in US product information for warfarin and interacting drugs. Clin Ther. 2011;33(1):36–45.
Food and Drug Administration. Guidance for industry: drug interaction studies—study design, data analysis, and implications for dosing and labeling recommendations: draft guidance. 2012. US Department of Health and Human Services. [cited 12/1/2014]; Available from: http://www.fda.gov/downloads/drugs/guidancecomplianceregulatoryinformation/guidances/ucm292362.pdf.
Food and Drug Administration. Information for Healthcare Professionals on FDA’s New Prescribing Information for Drugs. US Department of Health and Human Services. [cited 10/29/10]; Available from: http://www.fda.gov/Drugs/GuidanceComplianceRegulatoryInformation/LawsActsandRules/ucm084189.htm.
Food and Drug Administration. Indexing structured product labeling for human prescription drug and biologic products. Docket No. FDA-2010-N-0256. Federal Register. 2010;2010(75):33312–3.
Horn JR, Hansten PD. Predicting the magnitude of drug interactions: the final frontier. Pharmacy Times. 2006;72.
de Leon J, Spina E, Diaz FJ. Pharmacokinetic drug interaction studies must consider pharmacological heterogeneity, use of repeated dosing, and translation into a message understandable to practicing clinicians. J Clin Psychopharmacol. 2009;29(3):201–5.
Anglin R, Yuan Y, Moayyedi P, Tse F, Armstrong D, Leontiadis GI. Risk of upper gastrointestinal bleeding with selective serotonin reuptake inhibitors with or without concurrent nonsteroidal anti-inflammatory use: a systematic review and meta-analysis. Am J Gastroenterol. 2014;109(6):811–9.
Floor-Schreudering A, Geerts AF, Aronson JK, Bouvy ML, Ferner RE, De Smet PA. Checklist for standardized reporting of drug–drug interaction management guidelines. Eur J Clin Pharmacol. 2014;70(3):313–8.
Verbeurgt P, Mamiya T, Oesterheld J. How common are drug and gene interactions? Prevalence in a sample of 1143 patients with CYP2C9, CYP2C19 and CYP2D6 genotyping. Pharmacogenomics. 2014;15(5):655–65.
Tamblyn R, Eguale T, Buckeridge DL, Huang A, Hanley J, Reidel K, et al. The effectiveness of a new generation of computerized drug alerts in reducing the risk of injury from drug side effects: a cluster randomized trial. J Am Med Inform Assoc. 2012;19(4):635–43.
Seidling HM, Klein U, Schaier M, Czock D, Theile D, Pruszydlo MG, et al. What, if all alerts were specific—estimating the potential impact on drug interaction alert burden. Int J Med Inform. 2014;83(4):285–91.
Miura M, Tada H, Yasui-Furukori N, Uno T, Sugawara K, Tateishi T, et al. Effect of clarithromycin on the enantioselective disposition of lansoprazole in relation to CYP2C19 genotypes. Chirality. 2005;17(6):338–44.
Guyatt GH, Oxman AD, Kunz R, Vist GE, Falck-Ytter Y, Schunemann HJ. What is “quality of evidence” and why is it important to clinicians? BMJ. 2008;336(7651):995–8.
Guyatt GH, Oxman AD, Vist GE, Kunz R, Falck-Ytter Y, Alonso-Coello P, et al. GRADE: an emerging consensus on rating quality of evidence and strength of recommendations. BMJ. 2008;336(7650):924–6.
Atkins D, Best D, Briss PA, Eccles M, Falck-Ytter Y, Flottorp S, et al. Grading quality of evidence and strength of recommendations. BMJ. 2004;328(7454):1490 (2004-06-17 21:56:41).
Grade Working Group. [cited 7/28/14]; Available from: http://www.gradeworkinggroup.org/index.htm.
The University of Liverpool. HEP-Drug Interactions. [cited 12/1/14]; Available from: http://www.hep-druginteractions.org/.
The University of Liverpool. HIV-Drug Interactions. [cited 12/1/14]; Available from: http://www.hiv-druginteractions.org.
Horn JR, Hansten, Philip D. “Classy” drug interactions. Pharmacy Times. 2005. [cited 12/1/14]; Available at: http://www.pharmacytimes.com/publications/issue/2005/2005-06/2005-06-9585.
Varhe A, Olkkola KT, Neuvonen PJ. Oral triazolam is potentially hazardous to patients receiving systemic antimycotics ketoconazole or itraconazole. Clin Pharmacol Ther. 1994;56(6 Pt 1):601–7.
Varhe A, Olkkola KT, Neuvonen PJ. Effect of fluconazole dose on the extent of fluconazole-triazolam interaction. Br J Clin Pharmacol. 1996;42(4):465–70.
Hughes BM, Small RE, Brink D, McKenzie ND. The effect of flurbiprofen on steady-state plasma lithium levels. Pharmacotherapy. 1997;17(1):113–20.
Phansalkar S, Desai AA, Bell D, Yoshida E, Doole J, Czochanski M, et al. High-priority drug–drug interactions for use in electronic health records. J Am Med Inform Assoc. 2012;19(5):735–43.
Abarca J, Malone DC, Armstrong EP, Grizzle AJ, Hansten PD, Van Bergen RC, et al. Concordance of severity ratings provided in four drug interaction compendia. J Am Pharm Assoc (2003). 2004;44(2):136–41.
Murphy MK, Black NA, Lamping DL, McKee CM, Sanderson CF, Askham J, et al. Consensus development methods, and their use in clinical guideline development. Health Technol Assess. 1998;2(3):i–iv (1–88).
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.
About this article
Cite this article
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
- Clinical Decision Support
- Clinical Decision Support System
- Narrow Therapeutic Index Drug
- Computerize Clinical Decision Support System
- Drug Interaction Probability Scale