Evidence map›Paper›PMID 24454585›Full record

ArticleApplied clinical informatics2013

Lessons learned from developing a drug evidence base to support pharmacovigilance.

J C Smith, J C Denny, Q Chen, H Nian, A Spickard, S T Rosenbloom, R A Miller

Abstract read
In one paragraph

Article in Applied clinical informatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
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

6 citing papers in PubMed.

  1. PVLens: Enhancing Pharmacovigilance Through Automated Label Extraction.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024
    Article
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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.

J C SmithDepartment of Biomedical Informatics, Vanderbilt University School of Medicine , Nashville, Tennessee, USA.
J C Denny
Q Chen
H NianDepartment of Biostatistics, Vanderbilt University School of Medicine, Nashville, Tennessee, USA; 4School of Nursing, Vanderbilt University , Nashville, Tennessee, USA.
A Spickard
S T Rosenbloom
R A Miller

Funding

Vanderbilt Biomedical Informatics Training ProgramT15LM007450 · NLM · VANDERBILT UNIVERSITY · PI Jessica S. Ancker, Bradley A. Malin · 2002 to 2026
$19.7M
Pharmacogenomics of Arrhythmia TherapyU19HL065962 · NHLBI · VANDERBILT UNIVERSITY MEDICAL CENTER · PI RODEN, DAN M · 2010 to 2014
$17.4M
From GWAS to PheWAS: Scanning the EMR Phenome for Gene-disease AssociationsR01LM010685 · NLM · VANDERBILT UNIVERSITY MEDICAL CENTER · PI BASTARACHE, LISA · 2011 to 2022
$6.3M
National Infrastructure for Standardized and Portable EHR Phenotyping AlgorithmsR01GM105688 · NIGMS · WEILL MEDICAL COLL OF CORNELL UNIV · PI LUO, YUAN, PATHAK, JYOTISHMAN · 2013 to 2020
$5.7M
TIME:(Tools for Inpatient Monitoring using Evidence)for Safe &AppropriateTestingR01LM007995 · NLM · VANDERBILT UNIVERSITY · PI MILLER, RANDOLPH A · 2004 to 2009
$2.1M
Evidence-based Diagnostic Tools for Translational and Clinical Research (eTfor2)R01LM010828 · NLM · VANDERBILT UNIVERSITY · PI MILLER, RANDOLPH A · 2010 to 2012
$1.1M
A New Approach to Correct Verification Bias Using Auxiliary InformationR21HL097334 · NHLBI · VANDERBILT UNIVERSITY · PI CHEN, QINGXIA · 2011 to 2012
$428k
NHLBI NIH HHS R21 HL097334NHLBI NIH HHS R21HL097334-01A2NHLBI NIH HHS U19 HL065962NIGMS NIH HHS R01 GM105688NLM NIH HHS 2T15LM007450NLM NIH HHS R01 LM007995NLM NIH HHS R01LM007995NLM NIH HHS R01 LM010685NLM NIH HHS R01LM010685NLM NIH HHS R01 LM010828NLM NIH HHS R01LM010828NLM NIH HHS T15 LM007450
6 · The paper itself

Abstract

objectiveThis work identified challenges associated with extraction and representation of medication-related information from publicly available electronic sources.

methodsWe gained direct observational experience through creating and evaluating the Drug Evidence Base (DEB), a repository of drug indications and adverse effects (ADEs), and supplemented this through literature review. We extracted DEB content from the National Drug File Reference Terminology, from aggregated MEDLINE co-occurrence data, and from the National Library of Medicine's DailyMed. To understand better the similarities, differences and problems with the content of DEB and the SIDER Side Effect Resource, and Vanderbilt's MEDI Indication Resource, we carried out statistical evaluations and human expert reviews.

resultsWhile DEB, SIDER, and MEDI often agreed on medication indications and side effects, cross-system shortcomings limit their current utility. The drug information resources we evaluated frequently employed multiple, disparate vaguely related UMLS concepts to represent a single specific clinical drug indication or adverse effect. Thus, evaluations comparing drug-indication and drug-ADE coverage for such resources will encounter substantial numbers of false negative and false positive matches. Furthermore, our review found that many indication and ADE relationships are too complex - logically and temporally - to represent within existing systems.

conclusionTo enhance applicability and utility, future drug information systems deriving indications and ADEs from public resources must represent clinical concepts uniformly and as precisely as possible. Future systems must also better represent the inherent complexity of indications and ADEs.

Indexed as

PharmacovigilanceAdverse Drug Reaction Reporting SystemsDrug LabelingEvidence-Based MedicineHumansMEDLINEReproducibility of ResultsUnited StatesUnited States Food and Drug Administrationadverse effectsdrug product labelingDrug therapyknowledge basesunified medical language system

Identifiers

PMID24454585
PMCPMC3885918

What OpenQuestion holds

Textmetadata
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.