Evidence map›Paper›PMID 38200566›Full record

ArticleRespiratory research2024

Artificial intelligence-based analysis of the spatial distribution of abnormal computed tomography patterns in SARS-CoV-2 pneumonia: association with disease severity.

Yusuke Kataoka, Naoya Tanabe, Masahiro Shirata, Nobuyoshi Hamao, Issei Oi, Tomoki Maetani, Yusuke Shiraishi, Kentaro Hashimoto, Masatoshi Yamazoe, Hiroshi Shima and 13 more

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Respiratory research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

23 authors.

Yusuke KataokaDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
Naoya TanabeDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan. ntana@kuhp.kyoto-u.ac.jp.
Masahiro ShirataDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
Nobuyoshi HamaoDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
Issei OiDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
Tomoki MaetaniDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
Yusuke ShiraishiDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
Kentaro HashimotoDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
Masatoshi YamazoeDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
Hiroshi ShimaDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
Hitomi AjimizuDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
Tsuyoshi OgumaDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
Masahito EmuraDepartment of Respiratory Medicine, Kyoto City Hospital, Kyoto, Japan.
Kazuo EndoDepartment of Respiratory Medicine, Hyogo Prefectural Amagasaki General Medical Center, Amagasaki, Japan.
Yoshinori HasegawaDepartment of Respiratory Medicine, Osaka Saiseikai Nakatsu Hospital, Osaka, Japan.
Tadashi MioDivision of Respiratory Medicine, Center for Respiratory Diseases, National Hospital Organization Kyoto Medical Center, Kyoto, Japan.
Tetsuhiro ShiotaDivision of Respiratory Medicine, Ako City Hospital, Ako, Japan.
Hiroaki YasuiDepartment of Internal Medicine, Horikawa Hospital, Kyoto, Japan.
Hitoshi NakajiDepartment of Respiratory Medicine, Toyooka Hospital, Toyooka, Japan.
Michiko TsuchiyaDepartment of Respiratory Medicine, Rakuwakai Otowa Hospital, Kyoto, Japan.
Keisuke TomiiDepartment of Respiratory Medicine, Kobe City Medical Center General Hospital, Kobe, Japan.
Toyohiro HiraiDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan.
Isao ItoDepartment of Respiratory Medicine, Graduate School of Medicine, Kyoto University, 54 Kawahara-Cho, Shogoin, Sakyo-Ku, Kyoto, 606-8507, Japan. isaoito@kuhp.kyoto-u.ac.jp.

Funding

Japan Agency for Medical Research and Development 19fk0108157h0001
6 · The paper itself

Abstract

backgroundThe substantial heterogeneity of clinical presentations in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia still requires robust chest computed tomography analysis to identify high-risk patients. While extension of ground-glass opacity and consolidation from peripheral to central lung fields on chest computed tomography (CT) might be associated with severely ill conditions, quantification of the central-peripheral distribution of ground glass opacity and consolidation in assessments of SARS-CoV-2 pneumonia remains unestablished. This study aimed to examine whether the central-peripheral distributions of ground glass opacity and consolidation were associated with severe outcomes in patients with SARS-CoV-2 pneumonia independent of the whole-lung extents of these abnormal shadows.

methodsThis multicenter retrospective cohort included hospitalized patients with SARS-CoV-2 pneumonia between January 2020 and August 2021. An artificial intelligence-based image analysis technology was used to segment abnormal shadows, including ground glass opacity and consolidation. The area ratio of ground glass opacity and consolidation to the whole lung (GGO%, CON%) and the ratio of ground glass opacity and consolidation areas in the central lungs to those in the peripheral lungs (GGO(C/P)) and (CON(C/P)) were automatically calculated. Severe outcome was defined as in-hospital death or requirement for endotracheal intubation.

resultsOf 512 enrolled patients, the severe outcome was observed in 77 patients. GGO% and CON% were higher in patients with severe outcomes than in those without. Multivariable logistic models showed that GGO(C/P), but not CON(C/P), was associated with the severe outcome independent of age, sex, comorbidities, GGO%, and CON%.

conclusionIn addition to GGO% and CON% in the whole lung, the higher the ratio of ground glass opacity in the central regions to that in the peripheral regions was, the more severe the outcomes in patients with SARS-CoV-2 pneumonia were. The proposed method might be useful to reproducibly quantify the extension of ground glass opacity from peripheral to central lungs and to estimate prognosis.

Indexed as

COVID-19PneumoniaArtificial IntelligenceFemaleHospital MortalityHumansMalePatient AcuityRetrospective StudiesSARS-CoV-2Central areaCOVID-19Ground glass opacityPeripheral areaPneumoniaQuantitative analysis

Identifiers

PMID38200566
PMCPMC10777587

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