Evidence map›Paper›PMID 30009034›Full record

ArticlePharmacology research & perspectives2018

Screening of anticancer drugs to detect drug-induced interstitial pneumonia using the accumulated data in the electronic medical record.

Yoshie Shimai, Toshihiro Takeda, Katsuki Okada, Shirou Manabe, Kei Teramoto, Naoki Mihara, Yasushi Matsumura

Open access · goldAbstract read
In one paragraph

Article in Pharmacology research & perspectives, 2018. 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
0.5field-weighted citation impact, top 43% 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, 8 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

7 authors at 3 institutions in 2 countries.

Yoshie ShimaiDepartment of Medical Informatics Osaka University Graduate School of Medicine Suita Japan.
Toshihiro TakedaDepartment of Medical Informatics Osaka University Graduate School of Medicine Suita Japan.ORCID 0000-0003-3073-7823
Katsuki OkadaDepartment of Medical Informatics Osaka University Graduate School of Medicine Suita Japan.
Shirou ManabeDepartment of Medical Informatics Osaka University Graduate School of Medicine Suita Japan.
Kei TeramotoDepartment of Medical Informatics Osaka University Graduate School of Medicine Suita Japan.
Naoki MiharaDepartment of Medical Informatics National Cancer Center Hospital Suita Japan.
Yasushi MatsumuraDepartment of Medical Informatics Osaka University Graduate School of Medicine Suita Japan.
The University of Osaka · JPNational Cancer Center · USTottori University Hospital · JP

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Because drug-induced interstitial pneumonia (DIP) is a serious adverse drug reaction, its quantitative risk with individual medications should be taken into due consideration when selecting a medicine. We developed an algorithm to detect DIP using medical record data accumulated in a hospital. Chest computed tomography (CT) is mainly used for the diagnosis of IP, and chest X-ray reports, KL-6, and SP-D values are used to support the diagnosis. The presence of IP in the reports was assessed by a method using natural language-processing, in which IP was estimated according to the product of the likelihood ratio of characteristic keywords in each report. The sensitivity and the specificity of the method for chest CT reports were 0.92 and 0.97, while those for chest X-ray reports were 0.83 and 1, respectively. The occurrence of DIP was estimated by the patterns of presence of IP before, during, and after the administration of the target medicine. The occurrence rate of DIP in cases administered Gefitinib; Methotrexate (MTX); Tegafur, Gimeracil, and Oteracil potassium (TS-1); and Tegafur and Uracil (UTF) was 6.0%, 2.3%, 1.4%, and 0.7%, respectively. The estimated DIP cases were checked by having the medical records independently reviewed by medical doctors. By chart review, the positive predictive values of DIP against Gefitinib, MTX, TS-1, and UFT were 69.2%, 44.4%, 58.6%, and 77.8%, respectively. Although the cases extracted by this method included some that did not have DIP, this method can estimate the relative risk of DIP between medicines.

Indexed as

AlgorithmsAntineoplastic AgentsElectronic Health RecordsHumansLung Diseases, InterstitialRadiography, ThoracicTomography, X-Ray ComputedAntineoplastic Agentsadverse drug reactionelectronic medical recordimaging reportinterstitial pneumonianatural language processing

Identifiers

PMID30009034
PMCPMC6043691
OpenAlexW2883655066

What OpenQuestion holds

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LicenceCC BY
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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.