Evidence map›Paper›PMID 34796234›Full record

SynthesisBioMed research international2021

Clinical Features and Laboratory Examination to Identify Severe Patients with COVID-19: A Systematic Review and Meta-Analysis.

Yan Meng, Jinpeng Wang, Kaicheng Wen, Wacili Da, Keda Yang, Siming Zhou, Zhengbo Tao, Hang Liu, Lin Tao

Abstract readMeta-AnalysisSystematic Review
In one paragraph

Synthesis in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Review
  6. 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

9 authors.

Yan MengThe First Hospital of China Medical University, Shenyang, 110001 Liaoning, China.ORCID https://orcid.org/0000-0002-3198-8061
Jinpeng WangThe First Hospital of China Medical University, Shenyang, 110001 Liaoning, China.ORCID https://orcid.org/0000-0003-4379-7403
Kaicheng WenThe First Hospital of China Medical University, Shenyang, 110001 Liaoning, China.ORCID https://orcid.org/0000-0002-5475-2094
Wacili DaThe First Hospital of China Medical University, Shenyang, 110001 Liaoning, China.ORCID https://orcid.org/0000-0002-9857-2394
Keda YangThe First Hospital of China Medical University, Shenyang, 110001 Liaoning, China.ORCID https://orcid.org/0000-0002-3470-0414
Siming ZhouThe First Hospital of China Medical University, Shenyang, 110001 Liaoning, China.ORCID https://orcid.org/0000-0002-0619-5406
Zhengbo TaoThe First Hospital of China Medical University, Shenyang, 110001 Liaoning, China.ORCID https://orcid.org/0000-0002-8133-1156
Hang LiuRagon Institute of MGH, MIT and Harvard, USA.ORCID https://orcid.org/0000-0002-7352-7407
Lin TaoThe First Hospital of China Medical University, Shenyang, 110001 Liaoning, China.ORCID https://orcid.org/0000-0002-1702-9833

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWith the COVID-19 epidemic breakout in China, up to 25% of diagnosed cases are considered to be severe. To effectively predict the progression of COVID-19 via patients' clinical features at an early stage, the prevalence of these clinical factors and their relationships with severe illness were assessed.

methodsIn this study, electronic databases (PubMed, Embase, Web of Science, and Chinese database) were searched to obtain relevant studies, including information on severe patients. Publication bias analysis, sensitivity analysis, prevalence, sensitivity, specificity, likelihood ratio, diagnosis odds ratio calculation, and visualization graphics were achieved through software Review Manager 5.3, Stata 15, Meta-DiSc 1.4, and R.

resultsData of 3.547 patients from 24 studies were included in this study. The results revealed that patients with chronic respiratory system diseases (pooled positive likelihood 6.07, 95% CI: 3.12-11.82), chronic renal disease (4.79, 2.04-11.25), cardiovascular disease (3.45, 2.19-5.44), and symptoms of the onset of chest tightness (3.8, 1.44-10.05), shortness of breath (3.18, 2.24-4.51), and diarrhea (2.04, 1.38-3.04) exhibited increased probability of progressing to severe illness. C-reactive protein, ratio of neutrophils to lymphocytes, and erythrocyte sedimentation rate increased a lot in severe patients compared to nonsevere. Yet, it was found that clinical features including fever, cough, and headache, as well as some comorbidities, have little warning value.

conclusionsThe clinical features and laboratory examination could be used to estimate the process of infection in COVID-19 patients. The findings contribute to the more efficient prediction of serious illness for patients with COVID-19 to reduce mortality.

Indexed as

Cardiovascular DiseasesComorbidityCoughCOVID-19C-Reactive ProteinDiabetes MellitusFemaleFeverHematologic TestsHumansHypertensionMaleSeverity of Illness IndexC-Reactive Protein

Identifiers

PMID34796234
PMCPMC8593588

What OpenQuestion holds

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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.