Evidence map›Paper›PMID 35346080›Full record

ArticleBMC medical imaging2022

Performances of clinical characteristics and radiological findings in identifying COVID-19 from suspected cases.

Xuanxuan Li, Yajing Zhao, Yiping Lu, Yingyan Zheng, Nan Mei, Qiuyue Han, Zhuoying Ruan, Anling Xiao, Xiaohui Qiu, Dongdong Wang and 1 more

Abstract read
In one paragraph

Article in BMC medical imaging, 2022. 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
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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

11 authors.

Xuanxuan Li *Department of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing'an District, Shanghai, 200040, China.
Yajing Zhao *Department of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing'an District, Shanghai, 200040, China.
Yiping LuDepartment of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing'an District, Shanghai, 200040, China.
Yingyan ZhengDepartment of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing'an District, Shanghai, 200040, China.
Nan MeiDepartment of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing'an District, Shanghai, 200040, China.
Qiuyue HanDepartment of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing'an District, Shanghai, 200040, China.
Zhuoying RuanDepartment of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing'an District, Shanghai, 200040, China.
Anling XiaoDepartment of Radiology, Fu Yang No. 2 People's Hospital, 450 Linquan Road, Fuyang, Anhui Province, China.
Xiaohui QiuDepartment of Radiology, Bozhou People's Hospital, 616, Duzhong Road, Bozhou, Anhui Province, China.
Dongdong WangDepartment of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing'an District, Shanghai, 200040, China. wangddfsk@126.com.
Bo YinDepartment of Radiology, Huashan Hospital, Fudan University, 12, Middle Wulumuqi Rd., Jing'an District, Shanghai, 200040, China. yinbo@fudan.edu.cn.

Funding

Medical Engineering Fund of Fudan University yg2021-029Science and Technology Commission of Shanghai Municipality 21YF1404800Shanghai Hospital Development Center SHDC2020CR4069Youth Medical Talents -Medical Imaging Practitioner No.3030256001Youth Program of Special Project for Clinical Research of Shanghai Municipal Health Commission Health industry 20204Y0421
6 · The paper itself

Abstract

backgroundTo identify effective factors and establish a model to distinguish COVID-19 patients from suspected cases.

methodsThe clinical characteristics, laboratory results and initial chest CT findings of suspected COVID-19 patients in 3 institutions were retrospectively reviewed. Univariate and multivariate logistic regression were performed to identify significant features. A nomogram was constructed, with calibration validated internally and externally.

results239 patients from 2 institutions were enrolled in the primary cohort including 157 COVID-19 and 82 non-COVID-19 patients. 11 features were selected by LASSO selection, and 8 features were found significant using multivariate logistic regression analysis. We found that the COVID-19 group are more likely to have fever (OR 4.22), contact history (OR 284.73), lower WBC count (OR 0.63), left lower lobe involvement (OR 9.42), multifocal lesions (OR 8.98), pleural thickening (OR 5.59), peripheral distribution (OR 0.09), and less mediastinal lymphadenopathy (OR 0.037). The nomogram developed accordingly for clinical practice showed satisfactory internal and external validation.

conclusionsIn conclusion, fever, contact history, decreased WBC count, left lower lobe involvement, pleural thickening, multifocal lesions, peripheral distribution, and absence of mediastinal lymphadenopathy are able to distinguish COVID-19 patients from other suspected patients. The corresponding nomogram is a useful tool in clinical practice.

Indexed as

COVID-19HumansLogistic ModelsNomogramsRetrospective StudiesTomography, X-Ray ComputedCOVID-19Differential diagnosisLogistic modelsNomogramsX-ray computed tomography

Identifiers

PMID35346080
PMCPMC8960213

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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.