Evidence map›Paper›PMID 33832444›Full record

ArticleBMC infectious diseases2021

Clinical and CT features of mild-to-moderate COVID-19 cases after two sequential negative nucleic acid testing results: a retrospective analysis.

Yan Rong, Fei Wang, Jinfei Tian, Xinhua Liang, Jing Wang, Xiaoli Li, Dandan Zhang, Jing Liu, Huadong Zeng, Yang Zhou and 1 more

Abstract read
In one paragraph

Article in BMC infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. The efficacy and adverse effects of favipiravir on patients with COVID-19: A systematic review and meta-analysis of published clinical trials and observational studies.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2022
    Pooled it
  2. Article
  3. 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

11 authors.

Yan RongDepartment of Respiratory and Critical Care Medicine, Shenzhen Hospital, Southern Medical University, NO 1333, Xinhu Road, Baoan District, Shenzhen, 518100, China.
Fei WangDepartment of Orthopaedics, The University of Hong Kong - Shenzhen Hospital, Shenzhen, 518053, China.
Jinfei TianDepartment of Intensive Care Unit, Shenzhen Hospital, Southern Medical University, Shenzhen, 518100, China.
Xinhua LiangDepartment of Nephrology, Shenzhen Hospital, Southern Medical University, Shenzhen, 518100, China.
Jing WangDepartment of Radiology, Shenzhen Hospital, Southern Medical University, Shenzhen, 518100, China.
Xiaoli LiDepartment of Respiratory and Critical Care Medicine, Shenzhen Hospital, Southern Medical University, NO 1333, Xinhu Road, Baoan District, Shenzhen, 518100, China.
Dandan ZhangDepartment of Respiratory and Critical Care Medicine, Shenzhen Hospital, Southern Medical University, NO 1333, Xinhu Road, Baoan District, Shenzhen, 518100, China.
Jing LiuDepartment of Health Management Center, Shenzhen Hospital, Southern Medical University, Shenzhen, 518100, China.
Huadong ZengDepartment of Respiratory and Critical Care Medicine, Shenzhen Hospital, Southern Medical University, NO 1333, Xinhu Road, Baoan District, Shenzhen, 518100, China.
Yang ZhouDepartment of Respiratory and Critical Care Medicine, Shenzhen Hospital, Southern Medical University, NO 1333, Xinhu Road, Baoan District, Shenzhen, 518100, China. 309387713@qq.com.
Yi ShiDepartment of Respiratory and Critical Care Medicine, Jinling Hospital, The First School of Clinical Medicine, Southern Medical University, Nanjing, 210002, China. shiyi56@yeah.net.ORCID http://orcid.org/0000-0003-2240-7509

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe clinical and imaging features of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections that progressed to coronavirus disease 2019 (COVID-19) have been explored in numerous studies. However, little is known about these features in patients who received negative respiratory nucleic acid test results after the infections resolved. In this study, we aim to describe these features in a group of Chinese patients.

methodsThis retrospective study includes 51 patients with mild-to-moderate COVID-19 (median age: 34.0 years and 47.1% male) between January 31 and February 28, 2020. Demographic, clinical, laboratory, and computed tomography (CT) imaging data were collected before and after two consecutive negative respiratory SARS-CoV-2 tests.

resultsFollowing a negative test result, the patients' clinical symptoms continued to recover, but abnormal imaging findings were observed in all moderate cases. Specifically, 77.4% of patients with moderate COVID-19 exhibited multi-lobar lung involvement and lesions were more frequently observed in the lower lobes. The most common CT imaging manifestations were ground-glass opacities (51.6%) and fibrous stripes (54.8%%). Twelve of the 31 patients with moderate COVID-19 underwent repeated chest CT scans after a negative SARS-CoV-2 test. Among them, the ground-glass opacities decreased by > 60% within 1 week in seven patients (58.3%), but by < 5% in four patients (13.8%).

conclusionsFollowing a positive and subsequent negative SARS-CoV-2 tests, patients with COVID-19 continued to recover despite exhibiting persistent clinical symptoms and abnormal imaging findings.

Indexed as

Tomography, X-Ray ComputedAdultAsian PeopleCOVID-19COVID-19 Nucleic Acid TestingFemaleHumansLungMaleRetrospective StudiesComputed tomographyCOVID-19Negative nucleic acid testingSARS-CoV-2

Identifiers

PMID33832444
PMCPMC8027977

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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.