Evaluation of adverse drug reaction formatting in drug information databases
DOI:
https://doi.org/10.5195/jmla.2020.983Keywords:
Adverse Drug Reaction, Clinical Pharmacy Information Systems, Drug Information ServiceAbstract
Objective: The research evaluated the differences in formatting of adverse drug reaction (ADR) information in drug monographs in commonly used drug information (DI) databases.
Methods: A cross-sectional analysis of formatting of ADR information for twenty commonly prescribed oral medications in seven commonly used DI databases was performed. Databases were assessed for presentation of ADR information, including presence of placebo comparisons, severity of ADR, onset of ADR, formatting of ADRs in percentile (quantitative) format or qualitative format, whether references were used to cite information, whether ADRs are grouped by organ system, and word count of the ADR section. Data were collected by two study investigators and discrepancies were resolved via consensus. Chi-square analyses and one-way analysis of variance (ANOVA) were used to evaluate for mean group differences in categorical and continuous data, respectively.
Results: The seven DI databases varied significantly on each analyzed ADR variable, including variables known to impact interpretation such as placebo comparisons and qualitative versus quantitative formatting. Placebo comparisons were most common among monographs in Micromedex In-Depth Answers (70%) but were absent among monographs in Epocrates, Lexicomp, and Micromedex. Quantitative information was commonly used in most databases but was absent in Epocrates. Average word counts were higher in Clinical Pharmacology and Micromedex In-Depth answers compared to other databases.
Conclusion: Substantial variation in ADR formatting exists between the most common DI databases. These differences may translate into alternative interpretations of medical information and, thus, impact clinical judgment. Further studies are needed to assess whether these differences impact clinical practice.References
Kongkaew C, Noyce PR, Ashcroft DM. Hospital admissions associated with adverse drug reactions: a systematic review of prospective observational studies. Ann Pharmacother. 2008 Jul;42(7):1017–25.
Miguel A, Azevedo LF, Araujo M, Pereira AC. Frequency of adverse drug reactions in hospitalized patients: a systematic review and meta-analysis. Pharmacoepidemiol Drug Saf. 2012 Nov;21(11):1139–54. DOI: http://dx.doi.org/10.1002/pds.3309.
World Health Organization (WHO). WHO draft guidelines for adverse event reporting and learning systems. Geneva, Switzerland: The Organization; 2005.
Institute for Safe Medication Practices. Baxter and the Institute for Safe Medication Practices (ISMP) address global medication error prevention [Internet]. The Institute; 2017 [cited 21 May 2019]. <https://www.ismp.org/news/baxter-and-institute-safe-medication-practices-ismp-address-global-medication-error-prevention>.
Dempsey JT, Matta LS, Carter DM, Stevens CA, Stevenson LW, Desai AS, Cheng JW. Assessment of drug therapy-related issues in an outpatient heart failure population and the potential impact of pharmacist-driven intervention. J Pharm Pract. 2017 Jun;30(3):318–23. DOI: http://dx.doi.org/10.1177/0897190016641491.
Hamblin S, Rumbaugh K, Miller R. Prevention of adverse drug events and cost savings associated with PharmD interventions in an academic level I trauma center: an evidence-based approach. J Trauma Acute Care Surg. 2012 Dec;73(6):1484–90. DOI: http://dx.doi.org/10.1097/TA.0b013e318267cd80.
Schnipper JL, Kirwin JL, Cotugno MC, Wahlstrom SA, Brown BA, Tarvin E, Kachalia A, Horng M, Roy CL, McKean SC, Bates DW. Role of pharmacist counseling in preventing adverse drug reactions after hospitalization. Arch Int Med. 2006 Mar 13;166(5):565–71.
Belgado BS, Hatton RC, Doering PL. Evaluation of electronic drug information resources for answering questions received by decentralized pharmacists. Am J Health-Syst Pharm. 1997 Nov 15;54(22):2592–6.
Clauson KA, Marsh WA, Polen HH, Seamon MJ, Ortiz BI. Clinical decision support tools: analysis of online drug information databases. BMC Med Inform Decis Mak. 2007 Mar 8;7:7. DOI: http://dx.doi.org/10.1186/1472-6947-7-7.
Moutford CM, Lee T, De Lemos J, Loewen PS. Quality and usability of common drug information databases. Can J Hosp Pharm. 2010 Mar;63(2):130–7.
Rambaran KA, Huynh HA, Zhang Z, Robles J. The gap in electronic drug information resources: a systematic review. Cureus. 2018 Jun 22;10(6):e2860. DOI: http://dx.doi.org/10.7759/cureus.2860.
Bui TC, Krieger HA, Blumenthal-Barby JA. Framing effects on physicians’ judgement and decision making. Psychol Rep. 2015 Oct;117(2):508–22. DOI: http://dx.doi.org/10.2466/13.PR0.117c20z0.
Gong J, Zhang Y, Yang Z, Huang Y, Feng J, Zhang W. The framing effect in medical decision-making: a review of the literature. Psychol Health Med. 2013;18(6):645–53. DOI: http://dx.doi.org/10.1080/13548506.2013.766352.
McGettigan P, Sly K, O’Connell D, Hill S, Henry D. The effects of information framing on the practices of physicians. J Gen Intern Med. 1999 Oct;14(10):633–42.
O’Donohughe AC, Sullivan HW, Aikin KJ. Randomized study of placebo and framing information in direct-to-consumer print advertisements for prescription drugs. Ann Behav Med. 2014 Dec;48(3):311–22. DOI: http://dx.doi.org/10.1007/s12160-014-9603-1.
Perneger TV, Agoritsas T. Doctors and patients’ susceptibility to framing bias: a randomized trial. J Gen Intern Med. 2011 Dec;26(12):1411–7. DOI: http://dx.doi.org/10.1007/s11606-011-1810-x.
McConachie SM, Giuliano CA, Mohammad I, Kale-Pradhan PB. Adverse drug reactions in drug information databases: does presentation affect interpretation? J Med Libr Assoc. 2020 Jan;108(1):76–83. DOI: http://dx.doi.org/10.5195/jmla.2020.748.
McNeil BJ, Pauker SG, Sox HC Jr., Tversky A. On the elicitation of preferences for alternative therapies. N Engl J Med. 1982 May 27;306(21):1259–62. DOI: http://dx.doi.org/10.1056/nejm198205273062103.
O’Connor AM, Boyd NF, Tritchler DL, Kriukov Y, Sutherland H, Till JE. Eliciting preferences for alternative cancer drug treatments. the influence of framing, medium, and rater variables. Med Decis Making. 1985 Winter;5(4):453–63. DOI: http://dx.doi.org/10.1177/0272989x8500500408.
Medical Expenditure Panel Survey (MEPS). The top 200 of 2020: provided by the ClinCalc DrugStats Database [Internet]. Version 20.0. Rockville, MD: Agency for Healthcare Research and Quality (AHRQ); 2007–2017 [cited 1 Aug 2019]. <https://clincalc.com/DrugStats/Top200Drugs.aspx>.
Food Drug Administration Center for Drugs Evaluation Research. Guidance for industry: adverse reactions section of labeling for human prescription drug and biological products—content and format [Internet]. The Administration; 2006 [cited 12 May 2020]. <https://www.fda.gov/media/72139/download>.
Perez-Ricart A, Gea-Rodriguez E, Roca-Montanana A, Gil-Manez E, Perez-Feliu A. Integrating pharmacovigilance into the routine of pharmacy department: experience of nine years. Farm Hosp. 2019 Jul 1;43(4):128–33. DOI: http://dx.doi.org/10.7399/fh.11169.
Leufkens HG. Pharmacy-led pharmacovigilance: ready for use or missed opportunity? Pharmacoepidemiol Drug Saf. 2019 Dec;28(12):1562. DOI: http://dx.doi.org/10.1002/pds.4901.
Carvajal MJ, Clauson KA, Gershman J, Polen HH. Associations of gender and age groups on the knowledge and use of drug information resources by American pharmacists. Pharm Pract (Granada). 2013 Apr;11(2):71–80. DOI: http://dx.doi.org/10.4321/s1886-36552013000200003.
Naranjo CA, Busto U, Sellers EM, Sandor P, Ruiz I, Roberts EA, Janecek E, Domecq C, Greenblatt DJ. A method for estimating the probability of adverse drug reactions. Clin Pharmacol Ther. 1981 Aug;30(2):239–45. DOI: http://dx.doi.org/10.1038/clpt.1981.154.
Arimone Y, Bégaud B, Miremont-Salamé G, Fourrier-Réglat A, Moore N, Molimard M, Haramburu F. Agreement of expert judgement in causality assessment of adverse drug reactions. Eur J Clin Pharmacol. 2005 May;61(3):169–73. DOI: http://dx.doi.org/10.1007/s00228-004-0869-2.
Arimone Y, Miremont-Salamé G, Haramburu F, Molimard M, Moore N, Fourrier-Réglat A, Bégaud B. Inter-expert agreement of seven criteria in causality assessment of adverse drug reactions. Br J Clin Pharmacol. 2007 Oct;64(4):482–8. DOI: http://dx.doi.org/10.1111/j.1365-2125.2007.02937.x.
Behera SK, Das S, Xavier AS, Velupula S, Sandhiya S. Comparison of different methods for causality assessment of adverse drug reactions. Int J Clin Pharm. 2018 Aug;40(4):903–10. DOI: http://dx.doi.org/10.1007/s11096-018-0694-9.
Tangiisuran B, Auyeung V, Cheek L, Rajkumar C, Davies G. Interrater reliability of the assessment of adverse drug reactions in the hospitalised elderly. J Nutr Health Aging. 2013;17(8):700–5.
Sinayev A, Peters E, Tusler M, Fraenkel L. Presenting numeric information with percentages and descriptive risk labels: a randomized trial. Med Decis Making. 2015 Nov;35(8):937–47. DOI: http://dx.doi.org/10.1177/0272989X15584922.
Peters E, Hart S, Fraenkel L. Informing patients: the influence of numeracy, framing, and format of side effect information on risk perceptions. Med Decis Making. 2011 May–Jun;31(3):432–6. DOI: http://dx.doi.org/10.1177/0272989X10391672.
Lipkus IM. Numeric, verbal, and visual formats of conveying health risks: suggested best practices and future recommendations. Med Decis Making. 2007 Sep–Oct;27(5):696–713. DOI: http://dx.doi.org/10.1177/0272989X07307271.
US Food and Drug Administration. Part III: drugs and foods under the 1938 act and its amendments [Internet]. The Administration [cited 28 Jan 2020]. <https://www.fda.gov/about-fda/fdas-evolving-regulatory-powers/part-iii-drugs-and-foods-under-1938-act-and-its-amendments>.
