Evidence map›Paper›PMID 34604572›Full record

ReviewJournal of preventive medicine and hygiene2021

Can laboratory tests at the time of admission guide us to the prognosis of patients with COVID-19?

Hamed Akhavizadegan, Hadiseh Hosamirudsari, Mahboobeh Alizadeh, Yousef Alimohamadi, Mojgan Karbakhsh Davari, Samaneh Akbarpour, Amin Nakhostin-Ansari, Alireza Foroughi, Fariba Mansuri, Neda Faraji and 1 more

Open access · greenAbstract readReview
In one paragraph

Review in Journal of preventive medicine and hygiene, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
0.1field-weighted citation impact, top 50% 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, 1 synthesis or guideline pooled it, 3 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. 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

11 authors at 2 institutions in 1 country.

Hamed AkhavizadeganUrology department, Tehran University of Medical Sciences, Tehran, Iran.
Hadiseh HosamirudsariInfectious disease department, Tehran University of Medical Sciences, Tehran, Iran.
Mahboobeh AlizadehInfectious disease department, Baharloo hospital, Tehran University of Medical Sciences, Tehran, Iran.
Yousef AlimohamadiPars Advanced and Minimally Invasive Medical Manners Research Center, Pars Hospital, Iran University of Medical Sciences, Tehran, Iran.
Mojgan Karbakhsh DavariCommunity medicine department, Tehran University of Medical Sciences, Tehran, Iran.
Samaneh AkbarpourDepartment of Epidemiology & Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran.
Amin Nakhostin-AnsariBaharloo hospital, Tehran University of Medical Sciences, Tehran, Iran.
Alireza ForoughiDepartment of Radiology, Tehran University of Medical Sciences, Tehran, Iran.
Fariba MansuriDepartment of Pulmonology and respiratory diseases, Tehran University of Medical Sciences, Tehran, Iran.
Neda FarajiDepartment of Internal medicine, Tehran University of Medical Sciences, Tehran, Iran.
Zohreh NasiriMedical physiology and nursing office, Tehran University of Medical Sciences, Tehran, Iran.
Tehran University of Medical Sciences · IRIran University of Medical Sciences · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionTo enhance the COVID-19 patients' care and to optimize utilizing medical resources during the pandemic, relevant biomarkers are needed for prediction of the disease's progression. The current study was aimed to determine the factors that affect the mortality of COVID-19 patients admitted in Baharloo hospital in Iran.

methodsin the current retrospective study, 56 survived patients and 56 patients who were died (a total of 112 cases) because of COVID-19 infection were randomly selected from those who were admitted to Baharloo hospital. Each patient who was diagnosed with COVID-19 and had recovered from it matched with each non-survived patient in the term of age. Laboratory tests of all these patients at the time of admission were recorded and compared. All analyses performed using spss version 22 by considering α = 0.05 as a significant level.

resultsThere was no statistical difference in the age and gender distribution between the two groups (p > 0.05). The prevalence of diabetes among survived patients was 37.5% and among non-survived patients was 26.8% and there was no statistical difference between two groups regarding this comorbidity (p = 0.22). Also, there was no statistical difference in the prevalence of hypertension and coronary heart diseases between two groups (p > 0.05). Lymphocyte percentage, blood oxygen level, and platelet (PLT) count was significantly higher in patients who had recovered (P < 0.05).

conclusionsLDH level, Lymphocyte percentage, PLT count, and blood Oxygen saturation have associations with severe forms of COVID-19 infection and can be used as predictors to assess the patients who are suspected of infection with COVID-19 at the time of admission.

Indexed as

COVID-19FemaleHumansMalePrognosisRetrospective StudiesRisk FactorsSARS-CoV-2CoronavirusLaboratory testsMortalityPrognosis

Identifiers

PMID34604572
PMCPMC8451338
OpenAlexW3193892276

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.