Evidence map›Paper›PMID 33585111›Full record

ArticleCureus2021

Neutrophil-to-Lymphocyte, Lymphocyte-to-Monocyte, and Platelet-to-Lymphocyte Ratios: Prognostic Significance in COVID-19.

Shivakumar Bg, Siddharth Gosavi, Amogh Ananda Rao, Shashank Shastry, Sharanya C Raj, Anirudha Sharma, Ashutosh Suresh, Rakesh Noubade

Open access · diamondAbstract read
In one paragraph

Article in Cureus, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
20citing papers in PubMed, 3 pooled it
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

20 citing papers in PubMed, 3 syntheses or guidelines pooled it, 43 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

8 authors.

Shivakumar BgInternal Medicine, Jagadguru Jayadeva Murugarajendra Medical College, Davangere, IND.
Siddharth GosaviInternal Medicine, Jagadguru Jayadeva Murugarajendra Medical College, Davangere, IND.
Amogh Ananda RaoInternal Medicine, Jagadguru Jayadeva Murugarajendra Medical College, Davangere, IND.
Shashank ShastryInternal Medicine, Jagadguru Jayadeva Murugarajendra Medical College, Davangere, IND.
Sharanya C RajInternal Medicine, Jagadguru Jayadeva Murugarajendra Medical College, Davangere, IND.
Anirudha SharmaInternal Medicine, Jagadguru Jayadeva Murugarajendra Medical College, Davangere, IND.
Ashutosh SureshInternal Medicine, Jagadguru Jayadeva Murugarajendra Medical College, Davangere, IND.
Rakesh NoubadeInternal Medicine, Jagadguru Jayadeva Murugarajendra Medical College, Davangere, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and objective Neutrophils are primarily responsible for activating the immune system, and systemic inflammation destroys CD4+ T lymphocytes and increases suppressor CD8+ T lymphocytes, thereby leading to an increased neutrophil-to-lymphocyte ratio (NLR). An increase in the apoptosis of lymphocytes leads to lymphopenia and elevated thrombopoietin (THPO) promotes megakaryocyte production. The reflections of these inflammatory changes can be vital in gauging the progression of the disease. This study aimed at examining the prognostic value of normal and derived neutrophil-to-lymphocyte, lymphocyte-to-monocyte, platelet-to-lymphocyte, and mean platelet volume (MPV)-to-platelet count ratios in patients with coronavirus disease 2019 (COVID-19). Methodology This was a retrospective cross-sectional study conducted in the wards of Chigateri General Hospital, Davangere for a period of two months. Complete blood count was ordered for all patients at the time of admission along with confirmation of the disease by reverse transcription-polymerase chain reaction (RT-PCR). Results The final study population consisted of 100 patients. The mean age of patients who survived (43 years) was significantly lower than the mean age of non-survivors (59.1 years), with a p-value of <0.001. NLR was raised in 60% of the population and was significantly higher in patients who survived the disease, with a p-value of 0.004. The platelet-to-lymphocyte ratio (PLR) also followed a similar trend with a p-value of 0.017. Even though the lymphocyte-to-monocyte ratio (LMR) also mimicked the trend, the statistical association was not significant (p-value: 0.09). The derived NLR and MPV-to-platelets ratios were not found to be significantly associated with mortality in this study. Discussion Younger patients had better clinical outcomes in our study population compared to the geriatric age group. A significant correlation between LMR and mortality was observed when a cut-off of 2.5 was considered as a differentiating benchmark. Conflicting trends were observed in NLR and PLR in our study; however, LMR was in accordance with most other studies. The phase that a patient is in with regard to the natural history of the disease also influences the blood cell ratios. Nonetheless, all three ratios can be used as crucial screening and prognostic tools as they are readily available with the help of a complete hemogram. This is an investigation modality that is widely accessible even in remote areas and resource-limited settings. Conclusion These hematological ratios can facilitate in categorizing the disease severity and progression in patients, thereby enabling us to make appropriate and informed clinical decisions. Since the second wave of the novel coronavirus is on the verge of arrival, it is imperative to channel resources for the patients early in their disease course to ultimately prevent complications and reduce mortality.

Indexed as

complete blood countcovid-19prognostic markers

Identifiers

PMID33585111
PMCPMC7875321
OpenAlexW3119046568

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.