Evidence map›Paper›PMID 33762928›Full record

ArticleFrontiers in pharmacology2020

A Minimal Information Model for Potential Drug-Drug Interactions.

Harry Hochheiser, Xia Jing, Elizabeth A Garcia, Serkan Ayvaz, Ratnesh Sahay, Michel Dumontier, Juan M Banda, Oya Beyan, Mathias Brochhausen, Evan Draper and 13 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
0.8field-weighted citation impact, top 30% 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

5 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
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  4. Review
  5. Article
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

23 authors at 16 institutions in 6 countries.

Harry HochheiserDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
Xia JingDepartment of Public Health Sciences, Clemson University, Clemson, SC, United States.
Elizabeth A GarciaPharmacy Consulting International (PCI), New York City, NY, United States.
Serkan AyvazDepartment of Software Engineering, Bahçeşehir University, Istanbul, Turkey.
Ratnesh SahayClinical Data Science, AstraZeneca, Cambridge, United Kingdom.
Michel DumontierInstitute of Data Science, Maastricht University, Maastricht, Netherlands.
Juan M BandaDepartment of Computer Science, Georgia State University, Atlanta, GA, United States.
Oya BeyanFraunhofer Institute for Applied Information Technology, RWTH Aachen University, Aachen, Germany.
Mathias BrochhausenDepartment of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States.
Evan DraperMayo Clinic, Rochester, MN, United States.
Sam HabielOpen Source Electronic Health Record Alliance, Washington, DC, United States.
Oktie HassanzadehIBM Research, Yorktown Heights, NY, United States.
Maria Herrero-ZazoThe European Bioinformatics Institute, Birney Research Group, London, United Kingdom.
Brian HocumGenelex Corporation, Seattle, WA, United States.
John HornSchool of Pharmacy, University of Washington, Seattle, WA, United States.
Brian LeBaronSoutheast Louisiana Veterans Health Care System, New Orleans, LA, United States.
Daniel C MaloneDepartment of Pharmacotherapy, University of Utah, Salt Lake City, UT, United States.
Øystein NytrøDepartment of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway.
Thomas ReeseDepartment of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States.
Katrina RomagnoliDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
Jodi SchneiderSchool of Information Science, University of Illinois, Champaign, IL, United States.
Louisa Yu ZhangDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
Richard D BoyceDepartment of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, United States.
University of Pittsburgh · USUniversity of Utah · USAstraZeneca (United Kingdom) · GBBahçeşehir University · TRClemson University · USEuropean Bioinformatics Institute · GBFraunhofer Institute for Applied Information Technology · DEGeorgia State University · USMaastricht University · NLMayo Clinic in Arizona · USNorwegian University of Science and Technology · NOOpen Source Science Project · USSoutheast Louisiana Veterans Health Care System · USUniversity of Florida Health · USUniversity of Illinois Urbana-Champaign · USUniversity of Washington · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Despite the significant health impacts of adverse events associated with drug-drug interactions, no standard models exist for managing and sharing evidence describing potential interactions between medications. Minimal information models have been used in other communities to establish community consensus around simple models capable of communicating useful information. This paper reports on a new minimal information model for describing potential drug-drug interactions. A task force of the Semantic Web in Health Care and Life Sciences Community Group of the World-Wide Web consortium engaged informaticians and drug-drug interaction experts in in-depth examination of recent literature and specific potential interactions. A consensus set of information items was identified, along with example descriptions of selected potential drug-drug interactions (PDDIs). User profiles and use cases were developed to demonstrate the applicability of the model. Ten core information items were identified: drugs involved, clinical consequences, seriousness, operational classification statement, recommended action, mechanism of interaction, contextual information/modifying factors, evidence about a suspected drug-drug interaction, frequency of exposure, and frequency of harm to exposed persons. Eight best practice recommendations suggest how PDDI knowledge artifact creators can best use the 10 information items when synthesizing drug interaction evidence into artifacts intended to aid clinicians. This model has been included in a proposed implementation guide developed by the HL7 Clinical Decision Support Workgroup and in PDDIs published in the CDS Connect repository. The complete description of the model can be found at https://w3id.org/hclscg/pddi.

Indexed as

adverse drug eventsclinical informaticsdrug-drug interactionknowledge representationminimal information model

Identifiers

PMID33762928
PMCPMC7982727
OpenAlexW3135719128

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.