Evidence map›Paper›PMID 32528682›Full record

ArticleSAGE open medicine2020

Analysis of drug-induced interstitial lung disease using the Japanese Adverse Drug Event Report database.

Kiyoka Matsumoto, Satoshi Nakao, Shiori Hasegawa, Toshinobu Matsui, Kazuyo Shimada, Ririka Mukai, Mizuki Tanaka, Hiroaki Uranishi, Mitsuhiro Nakamura

Open access · goldAbstract read
In one paragraph

Article in SAGE open medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed, 2 pooled it
0.1field-weighted citation impact, top 50% 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

21 citing papers in PubMed, 2 syntheses or guidelines pooled it, 1 citations in OpenAlex.

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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

9 authors at 1 institution in 1 country.

Kiyoka MatsumotoLaboratory of Drug Informatics, Gifu Pharmaceutical University, Gifu, Japan.
Satoshi NakaoLaboratory of Drug Informatics, Gifu Pharmaceutical University, Gifu, Japan.
Shiori HasegawaLaboratory of Drug Informatics, Gifu Pharmaceutical University, Gifu, Japan.
Toshinobu MatsuiLaboratory of Drug Informatics, Gifu Pharmaceutical University, Gifu, Japan.
Kazuyo ShimadaLaboratory of Drug Informatics, Gifu Pharmaceutical University, Gifu, Japan.
Ririka MukaiLaboratory of Drug Informatics, Gifu Pharmaceutical University, Gifu, Japan.
Mizuki TanakaLaboratory of Drug Informatics, Gifu Pharmaceutical University, Gifu, Japan.
Hiroaki UranishiLaboratory of Drug Informatics, Gifu Pharmaceutical University, Gifu, Japan.
Mitsuhiro NakamuraLaboratory of Drug Informatics, Gifu Pharmaceutical University, Gifu, Japan.ORCID https://orcid.org/0000-0002-5062-5522
Gifu Pharmaceutical University · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesDrug-induced interstitial lung disease occurs when exposure to a drug causes inflammation and, eventually, fibrosis of the lung interstitium. Drug-induced interstitial lung disease is associated with substantial morbidity and mortality. The aim of this retrospective study was to obtain new information on the time-to-onset profiles of drug-induced interstitial lung disease by consideration of other associated clinical factors using the Japanese Adverse Drug Event Report database.

methodsWe identified and analyzed reports of drug-induced interstitial lung disease between 2004 and 2018 from the Japanese Adverse Drug Event Report database. The reporting odds ratio and 95% confidence interval was used to detect the signal for each drug-induced interstitial lung disease incidence. We evaluated the time-to-onset profile of drug-induced interstitial lung disease and used the applied association rule mining technique to uncover undetected relationships, such as possible risk factors.

resultsThe reporting odds ratios (95% confidence intervals) of drug-induced interstitial lung disease due to temsirolimus, gefitinib, sho-saiko-to, sai-rei-to, osimertinib, amiodarone, alectinib, erlotinib, everolimus, and bicalutamide were 18.3 (15.6-21.3), 17.8 (16.5-19.2), 16.3 (11.8-22.4), 14.5 (11.7-18.2), 12.5 (10.7-14.7), 10.9 (9.9-11.9), 10.6 (8.1-13.9), 9.6 (8.8-10.4), 9.4 (8.7-10.0), and 9.2 (7.9-10.6), respectively. The median durations (day (interquartile range)) for drug-induced interstitial lung disease were as follows: amiodarone (123.0 (27.0-400.5)), methotrexate (145.5 (67.8-475.8)), fluorouracil (86.0 (35.5-181.3)), gemcitabine (53.0 (20.0-83.0)), paclitaxel (52.0 (28.5-77.5)), docetaxel (47.0 (18.8-78.3)), bleomycin (92.0 (38.0-130.5)), oxaliplatin (45.0 (11.0-180.0)), nivolumab (56.0 (21.0-135.0)), gefitinib (24.0 (11.0-55.0)), erlotinib (21.0 (9.0-49.0)), temsirolimus (38.0 (14.0-68.5)), everolimus (56.0 (35.0-90.0)), osimertinib (51.5 (21.0-84.8)), alectinib (78.5 (44.3-145.8)), bicalutamide (50.0 (28.0-147.0)), pegylated interferon-2α (140.0 (75.8-233.0)), sai-rei-to (35.0 (20.0-54.5)), and sho-saiko-to (33.0 (13.5-74.0)) days. Association rule mining suggested that the risk of drug-induced interstitial lung disease was increased by a combination of amiodarone or sho-saiko-to and aging.

conclusionOur results showed that patients who receive gefitinib or erlotinib should be closely monitored for the development of drug-induced interstitial lung disease within a short duration (4 weeks). In addition, elderly people who receive amiodarone or sho-saiko-to should be carefully monitored for the development of drug-induced interstitial lung disease.

Indexed as

Drug-induced interstitial lung diseasepharmacovigilancethe Japanese Adverse Drug Event Report databasetime-to-onset profile

Identifiers

PMID32528682
PMCPMC7262990
OpenAlexW3014059182

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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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.