Evidence map›Paper›PMID 29621976›Full record

ArticleBMC bioinformatics2018

A simple method for exploring adverse drug events in patients with different primary diseases using spontaneous reporting system.

Yoshihiro Noguchi, Anri Ueno, Manami Otsubo, Hayato Katsuno, Ikuto Sugita, Yuta Kanematsu, Aki Yoshida, Hiroki Esaki, Tomoya Tachi, Hitomi Teramachi

Open access · goldAbstract read
In one paragraph

Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
1.5field-weighted citation impact, top 24% 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

8 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
  2. Article
  3. Article
  4. Drug fever: a narrative review.Annals of clinical epidemiology · 2023
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  8. SAEgnal: A Predictive Assessment Framework for Optimizing Safety Profiles in Immuno-Oncology Combination Trials.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science
    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

10 authors at 1 institution in 1 country.

Yoshihiro NoguchiLaboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan. noguchiy@gifu-pu.ac.jp.
Anri UenoLaboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.
Manami OtsuboLaboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.
Hayato KatsunoLaboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.
Ikuto SugitaLaboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.
Yuta KanematsuLaboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.
Aki YoshidaLaboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.
Hiroki EsakiLaboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.
Tomoya TachiLaboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan.
Hitomi TeramachiLaboratory of Clinical Pharmacy, Gifu Pharmaceutical University, 1-25-4,Daigakunishi, Gifu, 501-1196, Japan. teramachih@gifu-pu.ac.jp.
Gifu Pharmaceutical University · JP

Funding

Japan Society for the Promotion of Science 16K19175
6 · The paper itself

Abstract

backgroundPatient background (e.g. age, sex, and primary disease) is an important factor to consider when monitoring adverse drug events (ADEs) for the purpose of pharmacovigilance. However, in disproportionality methods, when additional factors are considered, the number of combinations that have to be computed increases, and it becomes very difficult to explore the whole spontaneous reporting system (SRS). Since the signals need to be detected quickly in pharmacovigilance, a simple exploration method is required. Although association rule mining (AR) is commonly used for the analysis of large data, its application to pharmacovigilance is rare and there are almost no studies comparing AR with conventional signal detection methods.

methodsIn this study, in order to establish a simple method to explore ADEs in patients with kidney or liver injury as a background disease, the AR and proportional reporting ratio (PRR) signal detection methods were compared. We used oral medicine SRS data from the Japanese Adverse Drug Event Report database (JADER), and used AR as the proposed search method and PRR as the conventional method for comparison. "Rule count ≥ 3", "min lift value > 1", and "min conviction value > 1" were used as the AR detection criteria, and the PRR detection criteria were "Rule count ≥3", "PRR ≥ 2", and "χ

resultsIn patients with kidney injury, the AR method had a sensitivity of 99.58%, specificity of 94.99%, and Youden's index of 0.946, while in patients with liver injury, the sensitivity, specificity, and Youden's index were 99.57%, 94.87%, and 0.944, respectively. Additionally, the lift value and the strength of the signal were positively correlated.

conclusionsIt was suggested that computation using AR might be simple with the detection power equivalent to that of the conventional signal detection method as PRR. In addition, AR can theoretically be applicable to SRS other than JADER. Therefore, complicated conditions (patient's background etc.) that must take factors other than the ADE into consideration can be easily explored by selecting the AR as the first screening for ADE exploration in pharmacovigilance using SRS.

Indexed as

Adverse Drug Reaction Reporting SystemsArea Under CurveChemical and Drug Induced Liver InjuryDatabases, FactualHumansJapanKidney DiseasesPharmacovigilanceROC Curve

Identifiers

PMID29621976
PMCPMC5887208
OpenAlexW2801744654

What OpenQuestion holds

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LicenceCC BY
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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.