Evidence map›Paper›PMID 32381057›Full record

ArticleBMC pulmonary medicine2020

CT differential diagnosis of COVID-19 and non-COVID-19 in symptomatic suspects: a practical scoring method.

Lin Luo, Zhendong Luo, Yizhen Jia, Cuiping Zhou, Jianlong He, Jianxun Lyu, Xinping Shen

Abstract read
In one paragraph

Article in BMC pulmonary medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
27citing papers in PubMed, 2 pooled it
–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

27 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Thoracic imaging tests for the diagnosis of COVID-19.The Cochrane database of systematic reviews · 2022
    Pooled it
  2. Thoracic imaging tests for the diagnosis of COVID-19.The Cochrane database of systematic reviews · 2021
    Pooled it
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  18. Fat Embolism Syndrome Mimicking a COVID-19 Infection.Case reports in critical care · 2021
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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.

Lin LuoDepartment of Radiology, The University of Hong Kong - Shenzhen Hospital, No.1, Haiyuan road Futian District, Shenzhen, 518000, China.
Zhendong LuoDepartment of Radiology, The University of Hong Kong - Shenzhen Hospital, No.1, Haiyuan road Futian District, Shenzhen, 518000, China.
Yizhen JiaDepartment of Core Laboratory, The University of Hong Kong - Shenzhen Hospital, Hospital, No.1, Haiyuan road Futian District, Shenzhen, 518000, China.
Cuiping ZhouDepartment of Radiology, The University of Hong Kong - Shenzhen Hospital, No.1, Haiyuan road Futian District, Shenzhen, 518000, China.
Jianlong HeDepartment of Radiology, The University of Hong Kong - Shenzhen Hospital, No.1, Haiyuan road Futian District, Shenzhen, 518000, China.
Jianxun LyuDepartment of Radiology, The University of Hong Kong - Shenzhen Hospital, No.1, Haiyuan road Futian District, Shenzhen, 518000, China.
Xinping ShenDepartment of Radiology, The University of Hong Kong - Shenzhen Hospital, No.1, Haiyuan road Futian District, Shenzhen, 518000, China. szshenxinping@163.com.

Funding

This study was supported by High Level-Hospital Program, Health Commission of Guangdong Province, China HKUSZH201901026
6 · The paper itself

Abstract

backgroundAlthough typical and atypical CT image findings of COVID-19 are reported in current studies, the CT image features of COVID-19 overlap with those of viral pneumonia and other respiratory diseases. Hence, it is difficult to make an exclusive diagnosis.

methodsThirty confirmed cases of COVID-19 and forty-three cases of other aetiology or clinically confirmed non-COVID-19 in a general hospital were included. The clinical data including age, sex, exposure history, laboratory parameters and aetiological diagnosis of all patients were collected. Seven positive signs (posterior part/lower lobe predilection, bilateral involvement, rounded GGO, subpleural bandlike GGO, crazy-paving pattern, peripheral distribution, and GGO +/- consolidation) from significant COVID-19 CT image features and four negative signs (only one lobe involvement, only central distribution, tree-in-bud sign, and bronchial wall thickening) from other non-COVID-19 pneumonia were used. The scoring analysis of CT features was compared between the two groups (COVID-19 and non-COVID-19).

resultsOlder age, symptoms of diarrhoea, exposure history related to Wuhan, and a lower white blood cell and lymphocyte count were significantly suggestive of COVID-19 rather than non-COVID-19 (p < 0.05). The receiver operating characteristic (ROC) curve of the combined CT image features analysis revealed that the area under the curve (AUC) of the scoring system was 0.854. These cut-off values yielded a sensitivity of 56.67% and a specificity of 95.35% for a score > 4, a sensitivity of 100% and a specificity of 23.26% for a score > 0, and a sensitivity of 86.67% and a specificity of 67.44% for a score >  2.

conclusionsWith a simple and practical scoring system based on CT imaging features, we can make a hierarchical diagnosis of COVID-19 and non-COVID-19 with different management suggestions.

Indexed as

BetacoronavirusAdultCoronavirus InfectionsCOVID-19Diagnosis, DifferentialFemaleHumansMaleMiddle AgedPandemicsPneumonia, ViralSARS-CoV-2Tomography, X-Ray ComputedCoronavirus infectionsLung diseasesPneumoniaTomography, x-ray computed

Identifiers

PMID32381057
PMCPMC7203713

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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.