Evidence map›Paper›PMID 34546617›Full record

SynthesisInternational journal of clinical practice2021

Exploration of severe Covid-19 associated risk factor in China: Meta-analysis of current evidence.

Yun-Jing Zhang, Xi-Feng Sun, Bing Xie, Wen-Juan Feng, Shi-Liang Han

Open access · greenAbstract readMeta-Analysis
In one paragraph

Synthesis in International journal of clinical practice, 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
0.5field-weighted citation impact, top 34% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Review
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 2 institutions in 1 country.

Yun-Jing ZhangDepartment of Nephrology, Zibo Central Hospital, Zibo, China.
Xi-Feng SunDepartment of Nephrology, Zibo Central Hospital, Zibo, China.
Bing XieDepartment of Orthopaedics, Zibo Central Hospital, Zibo, China.
Wen-Juan FengDepartment of Reproductive Medicine, Jinan Central Hospital Affiliated to Shandong University, Jinan, China.
Shi-Liang HanDepartment of Orthopaedics, Zibo Central Hospital, Zibo, China.
Central Hospital of Zibo · CNJinan Central Hospital · CN

Funding

Zibo research and development project 2019ZC010099
6 · The paper itself

Abstract

aimThis meta-analysis aimed to explore potential risk factors for severe Covid-19.

methodsWe systemically and comprehensively retrieved the eligible study evaluating clinical differences between severe vs non-severe Covid-19. Main effect sizes were demographic characteristics, comorbidities, signs and symptoms, laboratory findings as well as radiological features of chest CT.

resultsA total of 2566 Covid-19 people (771 in the severe group and 1795 in the non-severe group) from 14 studies were eligible for this meta-analysis. It was demonstrated that older age and males were more likely to have severe Covid-19. Patients with underlying comorbidities, such as hypertension, diabetes, heart disease and COPD were significantly more susceptible to severe Covid-19. Patients with dyspnoea were more likely to be severely ill. Depressed total lymphocytes were observed in this article. Meanwhile, although reticulation (30.8%), intrathoracic lymph node enlargement (20.5%) and pleural effusions (30.8%) were relatively infrequent, meta-analysis revealed that patients with these presentations in chest CT were associated with increased risk of severe Covid-19.

conclusionsThere are significant differences in clinical characteristics between the severe and non-severe Covid-19 patients. Many factors are related to the severity of the disease, which can help clinicians to differentiate severe patients from non-severe patients.

Indexed as

COVID-19AgedChinaComorbidityHumansMaleRetrospective StudiesRisk FactorsSARS-CoV-2Tomography, X-Ray Computed

Identifiers

PMID34546617
PMCPMC8646532
OpenAlexW3201214680

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.