Evidence map›Paper›PMID 38753223›Full record

ArticleJournal of medical systems2024

An innovative method to strengthen evidence for potential drug safety signals using Electronic Health Records.

H Abedian Kalkhoran, J Zwaveling, F van Hunsel, A Kant

Abstract read
In one paragraph

Article in Journal of medical systems, 2024. 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
–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

7 citing papers in PubMed.

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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

4 authors.

H Abedian KalkhoranDepartment of Clinical Pharmacology and Toxicology, Leiden University Medical Centre, Leiden, the Netherlands. h.abedian_kalkhoran@lumc.nl.ORCID http://orcid.org/0000-0002-6279-6310
J ZwavelingDepartment of Clinical Pharmacology and Toxicology, Leiden University Medical Centre, Leiden, the Netherlands.ORCID http://orcid.org/0000-0002-6080-6183
F van HunselThe Netherlands Pharmacovigilance Centre Lareb, 's-Hertogenbosch, the Netherlands.ORCID http://orcid.org/0000-0001-8965-3224
A KantDepartment of Clinical Pharmacology and Toxicology, Leiden University Medical Centre, Leiden, the Netherlands.ORCID http://orcid.org/0000-0003-3767-110X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Reports from spontaneous reporting systems (SRS) are hypothesis generating. Additional evidence such as more reports is required to determine whether the generated drug-event associations are in fact safety signals. However, underreporting of adverse drug reactions (ADRs) delays signal detection. Through the use of natural language processing, different sources of real-world data can be used to proactively collect additional evidence for potential safety signals. This study aims to explore the feasibility of using Electronic Health Records (EHRs) to identify additional cases based on initial indications from spontaneous ADR reports, with the goal of strengthening the evidence base for potential safety signals. For two confirmed and two potential signals generated by the SRS of the Netherlands Pharmacovigilance Centre Lareb, targeted searches in the EHR of the Leiden University Medical Centre were performed using a text-mining based tool, CTcue. The search for additional cases was done by constructing and running queries in the structured and free-text fields of the EHRs. We identified at least five additional cases for the confirmed signals and one additional case for each potential safety signal. The majority of the identified cases for the confirmed signals were documented in the EHRs before signal detection by the Dutch Medicines Evaluation Board. The identified cases for the potential signals were reported to Lareb as further evidence for signal detection. Our findings highlight the feasibility of performing targeted searches in the EHR based on an underlying hypothesis to provide further evidence for signal generation.

Indexed as

Adverse Drug Reaction Reporting SystemsElectronic Health RecordsPharmacovigilanceData MiningDrug-Related Side Effects and Adverse ReactionsHumansNatural Language ProcessingNetherlandsDrug Safety SurveillanceElectronic Health RecordsPharmacovigilanceReal World DataText-mining

Identifiers

PMID38753223
PMCPMC11098892

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Registered trials

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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.