Evidence map›Paper›PMID 35421918›Full record

ArticleEuropean journal of translational myology2022

Clinical features predicting COVID-19 mortality risk.

Hamidreza Kouhpayeh

Open access · goldAbstract read
In one paragraph

Article in European journal of translational myology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed
3.7field-weighted citation impact, top 5% 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

28 citing papers in PubMed, 39 citations in OpenAlex.

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  14. Effect of physical activity on long COVID fatigue: an unsolved enigma.European journal of translational myology · 2023
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  19. Consequences of COVID-19 and Its Variants: Understanding the Physical, Oral, and Psychological Impact.International journal of environmental research and public health · 2023
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  20. 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

1 author at 1 institution in 1 country.

Hamidreza KouhpayehTropical and Infectious Diseases Department, Zahedan University of Medical Sciences, Zahedan, Iran; Zahedan University of Medical Sciences Research Center, Emam Ali Hospital, Zahedan. hkouhpayeh@yahoo.com.
Zahedan University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Currently, the world is involved by a pandemic of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2), which has been responsible for the deaths of hundreds of thousands of people so far. The consequences of infection with SARS-CoV-2 vary widely from asymptomatic to severe. Considering the increasing prevalence of different types of virus and acute infection with this disease, strategies to prevent mortality from COVID-19 should be seriously analyzed. In this study, the epidemiological, clinical and laboratory characteristics of patients with COVID-19 were investigated in order to identify risk factors for mortality. Chronic diseases such as chronic kidney disease (CKD), COPD, diabetes, hypertension, cardiovascular disease (CVD), cancer, increased D-dimer, male gender, old age, smoking and obesity are among the deadly risk factors associated COVID-19. Furthermore, lymphopenia and neutrophilia are often present in patients with SARS-CoV-2, and the ratio of absolute neutrophils to lymphocytes (NLR) was significantly increased in patients without bacterial infection. These findings could be used in the future to control and prevent disease, because timely identification of patients with risk of COVID-19 is important to provide better treatment strategies for reduction of mortality.

Identifiers

PMID35421918
PMCPMC9295175
OpenAlexW4223532749

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.