Evidence map›Paper›PMID 36873473›Full record

ArticleHeliyon2023

Evaluation of risk factors for COVID-19 severity or death and their relationship to metabolic pathways.

F C Marhuenda-Egea, J Narro-Serrano

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.6field-weighted citation impact, top 37% 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

2 citing papers in PubMed, 3 citations in OpenAlex.

  1. Review
  2. 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

2 authors at 1 institution in 1 country.

F C Marhuenda-EgeaDepartamento de Agroquímica y Bioquímica, Universidad de Alicante, Spain.
J Narro-SerranoDepartamento de Química Física, Universidad de Alicante, Spain.
University of Alicante · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Since the state of alarm was declared due to the COVID-19 pandemic, hospitals have been the main ones in charge of registering the therapeutic follow-up of affected people. The analysis of these data has allowed those different biochemical markers have been identified as predictors of the severity of the disease, but most of the published studies tend to be eminently descriptive and do not propose a biochemical hypothesis to explain the alteration of the results they are showing. Our objective is to recognize the main metabolic processes that are occurring in COVID-19 patients, as well as the identification of clinical parameters that are decisive to predict the severity of the disease. Methods: A multivariate analysis was carried out from the clinical parameters collected in the database of the HM hospitals in Madrid, to determine the most relevant variables to predict the severity of the disease. Chemometric methods allow these variables to be obtained by applying a classification strategy with PLS-LDA. Findings and interpretation: The variables that most contribute to separation are age in men and, in both sexes, the concentration of lactate dehydrogenase, urea and C-reactive protein.Oxygen deficiency in the tissues, due to the loss of functionality of the lungs, could be affecting the muscle tissue with special severity. Inflammation and tissue damage is related to increased LDH and CRP. The loss of muscle mass and the increase in the concentration of urea and LDH is explained by the adaptation of muscle metabolism to this oxygen deficiency. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profits sectors.

Indexed as

BiochemistryChemometricCOVID-19MetabolomicsPrognosis

Identifiers

PMID36873473
PMCPMC9972677
OpenAlexW4322628312

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.