Evidence map›Paper›PMID 35283588›Full record

ArticleBioinformation2021

Hematological profiles of COVID-19 patients at the Ratlam district, Madhya Pradesh State, India.

Reetesh Kumar Gujar, Anil Meena, Shailendra Singh Chouhan, K S Likhar

Open access · hybridAbstract read
In one paragraph

Article in Bioinformation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed, 1 pooled it
0.8field-weighted citation impact, top 25% 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

12 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

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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

4 authors at 1 institution in 1 country.

Reetesh Kumar GujarDepartment of Pathology, Government Medical College Ratlam, Madhya Pradesh 457001, India.
Anil MeenaDepartment of Pathology, Government Medical College Ratlam, Madhya Pradesh 457001, India.
Shailendra Singh ChouhanDepartment of Pathology, Government Medical College Ratlam, Madhya Pradesh 457001, India.
K S LikharDepartment of Pathology, Government Medical College Ratlam, Madhya Pradesh 457001, India.
Government Medical College · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

It is of interest to compare the hematological profile (using Complete blood count) of COVID patients admitted in ICU, private ward, and isolation ward with varying severity. This data will help predict the severity of infection at peripheries and rural areas.  Detailed history and CBC was performed for all the cases. Different ratios and indexes such as systemic inflammatory index (SII), Neutrophil lymphocyte ratio (NLR), platelet lymphocyte ratio (PLR) were assessed. A total of 862 cases with a mean age of 49.9 ±17.4 years were enrolled. Hemoglobin level, lymphocyte (count per liter and percentage) were significantly lower in patients admitted in ICU as compared to patients admitted in the isolation ward and private ward (p <0.05). However, TLC, neutrophils, platelet count were higher in patients admitted to ICU (p <0.05). The Various ratios such as SII, NLR, and PLR showed significantly higher value in cases admitted in ICU (p <0.05). The TLC, neutrophil count, neutrophil percentage, SII, NLR, and PLR were significant predictors of severe disease (admission in ICU) with high diagnostic accuracy. We show that complete blood count method is a simple, readily available, rapid, and inexpensive tool that can be utilized for diagnosis and can predicting the severity of COVID 19 where RTPCR or trained staff is not available. Thus, NLR (%), SII, PLR, and TLC can predict severe illness with high accuracy.

Indexed as

COVIDHematologyNLRROC curveSII

Identifiers

PMID35283588
PMCPMC8882073
OpenAlexW3194710688

What OpenQuestion holds

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LicenceCC BY
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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.