Literature DB >> 31838211

Testing the face validity and inter-rater agreement of a simple approach to drug-drug interaction evidence assessment.

Amy J Grizzle1, Lisa E Hines2, Daniel C Malone3, Olga Kravchenko4, Harry Hochheiser5, Richard D Boyce6.   

Abstract

Low concordance between drug-drug interaction (DDI) knowledge bases is a well-documented concern. One potential cause of inconsistency is variability between drug experts in approach to assessing evidence about potential DDIs. In this study, we examined the face validity and inter-rater reliability of a novel DDI evidence evaluation instrument designed to be simple and easy to use.
METHODS: A convenience sample of participants with professional experience evaluating DDI evidence was recruited. Participants independently evaluated pre-selected evidence items for 5 drug pairs using the new instrument. For each drug pair, participants labeled each evidence item as sufficient or insufficient to establish the existence of a DDI based on the evidence categories provided by the instrument. Participants also decided if the overall body of evidence supported a DDI involving the drug pair. Agreement was computed both at the evidence item and drug pair levels. A cut-off of ≥ 70% was chosen as the agreement threshold for percent agreement, while a coefficient > 0.6 was used as the cut-off for chance-corrected agreement. Open ended comments were collected and coded to identify themes related to the participants' experience using the novel approach.
RESULTS: The face validity of the new instrument was established by two rounds of evaluation involving a total of 6 experts. Fifteen experts agreed to participate in the reliability assessment, and 14 completed the study. Participant agreement on the sufficiency of 22 of the 34 evidence items (65%) did not exceed the a priori agreement threshold. Similarly, agreement on the sufficiency of evidence for 3 of the 5 drug pairs (60%) was poor. Chance-corrected agreement at the drug pair level further confirmed the poor interrater reliability of the instrument (Gwet's AC1 = 0.24, Conger's Kappa = 0.24). Participant comments suggested several possible reasons for the disagreements including unaddressed subjectivity in assessing an evidence item's type and study design, an infeasible separation of evidence evaluation from the consideration of clinical relevance, and potential issues related to the evaluation of DDI case reports.
CONCLUSIONS: Even though the key findings were negative, the study's results shed light on how experts approach DDI evidence assessment, including the importance situating evidence assessment within the context of consideration of clinical relevance. Analysis of participant comments within the context of the negative findings identified several promising future research directions including: novel computer-based support for evidence assessment; formal evaluation of a more comprehensive evidence assessment approach that requires consideration of specific, explicitly stated, clinical consequences; and more formal investigation of DDI case report assessment instruments.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2019        PMID: 31838211      PMCID: PMC7537787          DOI: 10.1016/j.jbi.2019.103355

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  16 in total

1.  Clinical relevance of drug-drug interactions : a structured assessment procedure.

Authors:  Eric N van Roon; Sander Flikweert; Marianne le Comte; Pim N J Langendijk; Wilma J M Kwee-Zuiderwijk; Paul Smits; Jacobus R B J Brouwers
Journal:  Drug Saf       Date:  2005       Impact factor: 5.606

2.  SFINX-a drug-drug interaction database designed for clinical decision support systems.

Authors:  Ylva Böttiger; Kari Laine; Marine L Andersson; Tuomas Korhonen; Björn Molin; Marie-Louise Ovesjö; Tuire Tirkkonen; Anders Rane; Lars L Gustafsson; Birgit Eiermann
Journal:  Eur J Clin Pharmacol       Date:  2009-02-11       Impact factor: 2.953

3.  Turning off frequently overridden drug alerts: limited opportunities for doing it safely.

Authors:  Heleen van der Sijs; Jos Aarts; Teun van Gelder; Marc Berg; Arnold Vulto
Journal:  J Am Med Inform Assoc       Date:  2008-04-24       Impact factor: 4.497

4.  The measurement of observer agreement for categorical data.

Authors:  J R Landis; G G Koch
Journal:  Biometrics       Date:  1977-03       Impact factor: 2.571

5.  Consensus recommendations for systematic evaluation of drug-drug interaction evidence for clinical decision support.

Authors:  Richard T Scheife; Lisa E Hines; Richard D Boyce; Sophie P Chung; Jeremiah D Momper; Christine D Sommer; Darrell R Abernethy; John R Horn; Stephen J Sklar; Samantha K Wong; Gretchen Jones; Mary L Brown; Amy J Grizzle; Susan Comes; Tricia Lee Wilkins; Clarissa Borst; Michael A Wittie; Daniel C Malone
Journal:  Drug Saf       Date:  2015-02       Impact factor: 5.606

6.  Toward a complete dataset of drug-drug interaction information from publicly available sources.

Authors:  Serkan Ayvaz; John Horn; Oktie Hassanzadeh; Qian Zhu; Johann Stan; Nicholas P Tatonetti; Santiago Vilar; Mathias Brochhausen; Matthias Samwald; Majid Rastegar-Mojarad; Michel Dumontier; Richard D Boyce
Journal:  J Biomed Inform       Date:  2015-04-24       Impact factor: 6.317

7.  Formalizing Evidence Type Definitions for Drug-Drug Interaction Studies to Improve Evidence Base Curation.

Authors:  Joseph Utecht; Mathias Brochhausen; John Judkins; Jodi Schneider; Richard D Boyce
Journal:  Stud Health Technol Inform       Date:  2017

Review 8.  Information needs for making clinical recommendations about potential drug-drug interactions: a synthesis of literature review and interviews.

Authors:  Katrina M Romagnoli; Scott D Nelson; Lisa Hines; Philip Empey; Richard D Boyce; Harry Hochheiser
Journal:  BMC Med Inform Decis Mak       Date:  2017-02-22       Impact factor: 2.796

9.  Development of an evidence evaluation and synthesis system for drug-drug interactions, and its application to a systematic review of HIV and malaria co-infection.

Authors:  Kay Seden; Sara Gibbons; Catia Marzolini; Jonathan M Schapiro; David M Burger; David J Back; Saye H Khoo
Journal:  PLoS One       Date:  2017-03-23       Impact factor: 3.240

10.  Identifying Common Methods Used by Drug Interaction Experts for Finding Evidence About Potential Drug-Drug Interactions: Web-Based Survey.

Authors:  Amy J Grizzle; John Horn; Carol Collins; Jodi Schneider; Daniel C Malone; Britney Stottlemyer; Richard David Boyce
Journal:  J Med Internet Res       Date:  2019-01-04       Impact factor: 5.428

View more
  2 in total

1.  Automatically classifying the evidence type of drug-drug interaction research papers as a step toward computer supported evidence curation.

Authors:  Linh Hoang; Richard D Boyce; Nigel Bosch; Britney Stottlemyer; Mathias Brochhausen; Jodi Schneider
Journal:  AMIA Annu Symp Proc       Date:  2021-01-25

2.  DDIWAS: High-throughput electronic health record-based screening of drug-drug interactions.

Authors:  Patrick Wu; Scott D Nelson; Juan Zhao; Cosby A Stone; QiPing Feng; Qingxia Chen; Eric A Larson; Bingshan Li; Nancy J Cox; C Michael Stein; Elizabeth J Phillips; Dan M Roden; Joshua C Denny; Wei-Qi Wei
Journal:  J Am Med Inform Assoc       Date:  2021-07-14       Impact factor: 7.942

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.