Evidence map›Paper›PMID 36597151›Full record

ArticleEuropean journal of medical research2023

A novel scoring system for early assessment of the risk of the COVID-19-associated mortality in hospitalized patients: COVID-19 BURDEN.

Mohammad Hossein Imanieh, Fatemeh Amirzadehfard, Sina Zoghi, Faezeh Sehatpour, Peyman Jafari, Hamidreza Hassanipour, Maryam Feili, Maryam Mollaie, Pardis Bostanian, Samrad Mehrabi and 2 more

Open access · goldAbstract read
In one paragraph

Article in European journal of medical research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.0field-weighted citation impact, top 28% 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

4 citing papers in PubMed, 5 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Hemodynamic, Oxygenation and Lymphocyte Parameters Predict COVID-19 Mortality.Pathophysiology : the official journal of the International Society for Pathophysiology · 2023
    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

12 authors at 2 institutions in 1 country.

Mohammad Hossein ImaniehGastroenterohepatology Research Center, Shiraz University of Medical Sciences, 9th Floor, Mohammad Rasoul Allah Research Tower, Khalili St, PO Box: 7193635899, Shiraz, Iran.
Fatemeh AmirzadehfardGastroenterohepatology Research Center, Shiraz University of Medical Sciences, 9th Floor, Mohammad Rasoul Allah Research Tower, Khalili St, PO Box: 7193635899, Shiraz, Iran. fatemehamirzadeh75@gmail.com.
Sina ZoghiStudent Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Faezeh SehatpourDepartment of Internal Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
Peyman JafariDepartment of Biostatistics, Shiraz University of Medical Sciences, Shiraz, Iran.
Hamidreza HassanipourStudent Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Maryam FeiliStudent Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Maryam MollaieStudent Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Pardis BostanianStudent Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Samrad MehrabiSleep Disorders Laboratory, Namazi Hospital, Shiraz University of Medical Sciences, Shiraz, Iran.
Reyhaneh DashtianehStudent Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Afrooz FeiliStudent Research Committee, Shiraz University of Medical Sciences, Shiraz, Iran.
Shiraz University of Medical Sciences · IRNamazi Hospital · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCorona Virus Disease 2019 (COVID-19) presentations range from those similar to the common flu to severe pneumonia resulting in hospitalization with significant morbidity and/or mortality. In this study, we made an attempt to develop a predictive scoring model to improve the early detection of high risk COVID-19 patients by analyzing the clinical features and laboratory data available on admission.

methodsWe retrospectively included 480 consecutive adult patients, aged 21-95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were collected from the medical records and analyzed using multiple logistic regression analysis. The final data analysis was utilized to develop a simple scoring model for the early prediction of mortality in COVID-19 patients. The score given to each associated factor was based on the coefficients of the regression analyses.

resultsA novel mortality risk score (COVID-19 BURDEN) was derived, incorporating risk factors identified in this cohort. CRP (> 73.1 mg/L), O

conclusionsUsing this scoring system in COVID-19 patients, the patients with a higher risk of mortality can be identified which will help to reduce hospital care costs and improve its quality and outcome.

Indexed as

COVID-19AdultHospitalizationHospital MortalityHumansPrognosisRetrospective StudiesRisk AssessmentRisk FactorsSARS-CoV-2COVID-19MortalityPrognosisRisk assessmentSARS-CoV-2

Identifiers

PMID36597151
PMCPMC9807969
OpenAlexW4313523889

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.