Evidence map›Paper›PMID 38674248›Full record

ArticleMedicina (Kaunas, Lithuania)2024

Assessing the Diagnostic Values of the Neutrophil-to-Lymphocyte Ratio (NLR) and Systematic Immunoinflammatory Index (SII) as Biomarkers in Predicting COVID-19 Severity: A Multicentre Comparative Study.

Anwar A Sayed

Abstract readMulticenter StudyComparative Study
In one paragraph

Article in Medicina (Kaunas, Lithuania), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
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

9 citing papers in PubMed.

  1. Article
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  3. Observational
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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

1 author.

Anwar A SayedDepartment of Basic Medical Sciences, Taibah University, Madinah 42353, Saudi Arabia.ORCID 0000-0002-2243-0971

Funding

King Salman Center for Disability Research KSRG-2023-460
6 · The paper itself

Abstract

COVID-19 has been notoriously unpredictable in its clinical course. Such unpredictability poses a challenge to clinicians in predicting patients who will develop severe cases and possibly die from the infection. This study aims to assess and compare the diagnostic value of the NLR and SII as biomarkers in predicting COVID-19 severity, represented by mortality, with a multicentre comparative study including 855 patients in Saudi Arabia. Descriptive and analytical statistics were used to compare haematological indices between survivors and non-survivors. The median age of patients included was 41 years old, with an almost equal ratio of men to women. Most participants were Saudis, and the mortality rate in the study cohort was 13.22%. Non-survivors, as compared to survivors, were significantly older, had lower RBC counts, haemoglobin and haematocrit levels, as well as significantly higher WBC and neutrophil counts. Both the NLR and SII were capable of differentiating between survivors and non-survivors, with the latter having significantly higher values. However, the NLR was superior to the SII in such differentiation, as it had a larger area under the curve. This study further confirms the diagnostic values of the NLR and SII as biomarkers in predicting COVID-19 severity and mortality, with the NLR being more sensitive and specific. Clinical guidelines on managing COVID-19 cases should benefit from these findings by harnessing the value of the NLR in COVID-19 management.

Indexed as

BiomarkersCOVID-19LymphocytesNeutrophilsSeverity of Illness IndexAdultAgedFemaleHumansLeukocyte CountLymphocyte CountMaleMiddle AgedSARS-CoV-2Saudi ArabiaBiomarkersbiomarkersCOVID-19diagnosticsmortalitymulticentreNLRSaudi ArabiaSIIsystematic immunoinflammatory index

Identifiers

PMID38674248
PMCPMC11052014

What OpenQuestion holds

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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.