Evidence map›Paper›PMID 32474631›Full record

ArticleEuropean radiology2020

Relationship between clinical types and radiological subgroups defined by latent class analysis in 2019 novel coronavirus pneumonia caused by SARS-CoV-2.

Xu Fang, Xiao Li, Yun Bian, Xiang Ji, Jianping Lu

Open access · bronzeAbstract read
In one paragraph

Article in European radiology, 2020. 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
0.2field-weighted citation impact, top 33% 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, 11 citations in OpenAlex.

  1. Article
  2. Article
  3. Quantitative Analysis for Lung Disease on Thin-Section CT.Diagnostics (Basel, Switzerland) · 2023
    Review
  4. Article
  5. Article
  6. Article
  7. Review
  8. 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

5 authors at 3 institutions in 1 country.

Xu FangDepartment of Radiology, Changhai Hospital, The Navy Military Medical University, Changhai road 168, Shanghai, 200434, China.
Xiao LiDepartment of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, 210002, Jiangsu, China.
Yun BianDepartment of Radiology, Changhai Hospital, The Navy Military Medical University, Changhai road 168, Shanghai, 200434, China. bianyun2012@foxmail.com.ORCID http://orcid.org/0000-0002-4863-4956
Xiang JiShanghai United Imaging Intelligence Healthcare, Shanghai, China.
Jianping LuDepartment of Radiology, Changhai Hospital, The Navy Military Medical University, Changhai road 168, Shanghai, 200434, China.
Second Military Medical University · CNNanjing General Hospital of Nanjing Military Command · CNUnited Imaging Healthcare (China) · CN

Funding

National Science Foundation for Scientists of China 81871352National Science Foundation for Young Scientists of China 81701689
6 · The paper itself

Abstract

objectivesTo investigate whether meaningful subgroups sharing the CT features of patients with COVID-19 pneumonia could be identified using latent class analysis (LCA) and explore the relationship between the LCA-derived subgroups and clinical types.

methodsThis retrospective review included 499 patients with confirmed COVID-19 pneumonia between February 11 and March 8, 2020. Subgroups sharing the CT features were identified using LCA. Univariate and multivariate logistic regression models were utilized to analyze the association between clinical types and the LCA-derived subgroups.

resultsTwo radiological subgroups were identified using LCA. There were 228 subjects (45.69%) in class 1 and 271 subjects (54.31%) in class 2. The CT findings of class 1 were smaller pulmonary infection volume, more peripheral distribution, more GGO, more maximum lesion range ≤ 5 cm, a smaller number of lesions, less involvement of lobes, less air bronchogram, less dilatation of vessels, less hilar and mediastinal lymph node enlargement, and less pleural effusion than the CT findings of class 2. Univariate analysis demonstrated that older age, therapy, presence of fever, presence of hypertension, decreased lymphocyte count, and increased CRP levels were significant parameters associated with an increased risk for class 2. Multivariate analyses revealed that the patients with clinically severe type disease had a 1.97-fold risk of class 2 than the patients with clinically moderate-type disease.

conclusionsThe demographic and clinical differences between the two radiological subgroups based on the LCA were significantly different. Two radiological subgroups were significantly associated with clinical moderate and severe types. KEY POINTS: • Two radiological subgroups were identified using LCA. • Older age, therapy, presence of fever, presence of hypertension, decreased lymphocyte count, and increased CRP levels were significant parameters with an increased risk for class 2 defined by LCA. • Patients with clinically severe type had a 1.97-fold higher risk of class 2 defined by LCA in comparison with patients showing clinically moderate-type disease.

Indexed as

BetacoronavirusLatent Class AnalysisCoronavirus InfectionsCOVID-19Cross-Sectional StudiesFemaleHumansLungMaleMiddle AgedPandemicsPneumonia, ViralRetrospective StudiesSARS-CoV-2Severity of Illness IndexTomography, X-Ray ComputedCoronavirus infectionsPneumonia, Latent class analysisTomography, X-ray computed

Identifiers

PMID32474631
PMCPMC7261041
OpenAlexW3029925950

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.