Evidence map›Paper›PMID 30609981›Full record

ArticleJournal of medical Internet research2019

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

Amy J Grizzle, John Horn, Carol Collins, Jodi Schneider, Daniel C Malone, Britney Stottlemyer, Richard David Boyce

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 22 citations in OpenAlex.

  1. Observational
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 4 institutions in 1 country.

Amy J GrizzleCenter for Health Outcomes & PharmacoEconomic Research, College of Pharmacy, University of Arizona, Tucson, AZ, United States.ORCID 0000-0003-4485-1459
John HornSchool of Pharmacy, University of Washington, Seattle, WA, United States.ORCID 0000-0003-4525-4250
Carol CollinsSchool of Pharmacy, University of Washington, Seattle, WA, United States.ORCID 0000-0003-0191-2549
Jodi SchneiderSchool of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, United States.ORCID 0000-0002-5098-5667
Daniel C MaloneCenter for Health Outcomes & PharmacoEconomic Research, College of Pharmacy Department of Pharmacy Practice and Science, University of Arizona, Tucson, AZ, United States.ORCID 0000-0002-5006-9394
Britney StottlemyerDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.ORCID 0000-0002-6318-3938
Richard David BoyceDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.ORCID 0000-0002-2993-2085
University of Arizona · USUniversity of Pittsburgh · USUniversity of Washington · USUniversity of Illinois Urbana-Champaign · US

Funding

Addressing gaps in clinically useful evidence on drug-drug interactionsR01LM011838 · NLM · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI BOYCE, RICHARD DAVID · 2014 to 2017
$1.6M
NLM NIH HHS R01 LM011838
6 · The paper itself

Abstract

backgroundPreventing drug interactions is an important goal to maximize patient benefit from medications. Summarizing potential drug-drug interactions (PDDIs) for clinical decision support is challenging, and there is no single repository for PDDI evidence. Additionally, inconsistencies across compendia and other sources have been well documented. Standard search strategies for complete and current evidence about PDDIs have not heretofore been developed or validated.

objectiveThis study aimed to identify common methods for conducting PDDI literature searches used by experts who routinely evaluate such evidence.

methodsWe invited a convenience sample of 70 drug information experts, including compendia editors, knowledge-base vendors, and clinicians, via emails to complete a survey on identifying PDDI evidence. We created a Web-based survey that included questions regarding the (1) development and conduct of searches; (2) resources used, for example, databases, compendia, search engines, etc; (3) types of keywords used to search for the specific PDDI information; (4) study types included and excluded in searches; and (5) search terms used. Search strategy questions focused on 6 topics of the PDDI information-(1) that a PDDI exists; (2) seriousness; (3) clinical consequences; (4) management options; (5) mechanism; and (6) health outcomes.

resultsTwenty participants (response rate, 20/70, 29%) completed the survey. The majority (17/20, 85%) were drug information specialists, drug interaction researchers, compendia editors, or clinical pharmacists, with 60% (12/20) having >10 years' experience. Over half (11/20, 55%) worked for clinical solutions vendors or knowledge-base vendors. Most participants developed (18/20, 90%) and conducted (19/20, 95%) search strategies without librarian assistance. PubMed (20/20, 100%) and Google Scholar (11/20, 55%) were most commonly searched for papers, followed by Google Web Search (7/20, 35%) and EMBASE (3/20, 15%). No respondents reported using Scopus. A variety of subscription and open-access databases were used, most commonly Lexicomp (9/20, 45%), Micromedex (8/20, 40%), Drugs@FDA (17/20, 85%), and DailyMed (13/20, 65%). Facts and Comparisons was the most commonly used compendia (8/20, 40%). Across the 6 attributes of interest, generic drug name was the most common keyword used. Respondents reported using more types of keywords when searching to identify the existence of PDDIs and determine their mechanism than when searching for the other 4 attributes (seriousness, consequences, management, and health outcomes). Regarding the types of evidence useful for evaluating a PDDI, clinical trials, case reports, and systematic reviews were considered relevant, while animal and in vitro data studies were not.

conclusionsThis study suggests that drug interaction experts use various keyword strategies and various database and Web resources depending on the PDDI evidence they are seeking. Greater automation and standardization across search strategies could improve one's ability to identify PDDI evidence. Hence, future research focused on enhancing the existing search tools and designing recommended standards is needed.

Indexed as

Drug InteractionsHumansInternetSurveys and Questionnairesdrug interaction expertsdrug interactionspotential drug-drug interactionssurveys

Identifiers

PMID30609981
PMCPMC6682289
OpenAlexW2896026924

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.