Evidence map›Paper›PMID 34351940›Full record

ArticlePloS one2021

A new haematocytometric index: Predicting severity and mortality risk value in COVID-19 patients.

Meltem Kilercik, Özlem Demirelce, Muhittin Abdulkadir Serdar, Parvana Mikailova, Mustafa Serteser

Abstract read
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed, 2 pooled it
–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

34 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  12. Machine Learning-Based Prediction of In-Hospital Mortality in Severe COVID-19 Patients Using Hematological Markers.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2025
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  17. The Role of Hemogram-derived Ratios in COVID-19 Severity Stratification in a Primary Healthcare Facility.Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH · 2023
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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

5 authors.

Meltem KilercikAcibadem Labmed Clinical Laboratories, İstanbul, Turkey.
Özlem DemirelceAcibadem Labmed Clinical Laboratories, İstanbul, Turkey.ORCID 0000-0003-3688-7451
Muhittin Abdulkadir SerdarAcibadem Labmed Clinical Laboratories, İstanbul, Turkey.
Parvana MikailovaAcibadem Labmed Clinical Laboratories, İstanbul, Turkey.
Mustafa SerteserAcibadem Labmed Clinical Laboratories, İstanbul, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionCoronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 virus, is a major public health concern spanning from healthy carriers to patients with life-threatening conditions. Although most of COVID-19 patients have mild-to-moderate clinical symptoms, some patients have severe pneumonia leading to death. Therefore, the early prediction of disease prognosis and severity is crucial in COVID-19 patients. The main objective of this study is to evaluate the haemocytometric parameters and identify severity score associated with SARS-CoV-2 infection.

methodsClinical and laboratory records were retrospectively reviewed from 97 cases of COVID-19 admitted to hospitals in Istanbul, Turkey. The patient groups were subdivided into three major groups: Group 1 (Non-critical): 59 patients, Group 2 (Critical-Survivors): 23 patients and Group 3 (Critical-Non-survivors):15 patients. These data was tested for correlation, including with derived haemocytometric parameters. The blood analyses were performed the Sysmex XN-series automated hematology analyser using standard laboratory protocols. All statistical testing was undertaken using Analyse-it software.

results97 patients with COVID-19 disease and 935 sequential complete blood count (CBC-Diff) measurements (days 0-30) were included in the final analyses. Multivariate analysis demonstrated that red cell distribution width (RDW) (>13.7), neutrophil to lymphocyte ratio (NLR) (4.4), Hemoglobin (Hgb) (<11.4 gr/dL) and monocyte to neutrophil ratio (MNR) (0.084) had the highest area under curve (AUC) values, respectively in discrimination critical patients than non-critical patients. In determining Group 3, MNR (<0.095), NLR (>5.2), Plateletcount (PLT) (>142 x103/L) and RDW (>14) were important haemocytometric parameters, and the mortality risk value created by their combination had the highest AUC value (AUC = 0.911, 95% CI, 0886-0.931). Trend analysis of CBC-Diff parameters over 30 days of hospitalization, NLR on day 2, MNR on day 4, RDW on day 6 and PLT on day 7 of admission were found to be the best time related parameters in discrimination non-critical (mild-moderate) patient group from critical (severe and non-survivor) patient group.

conclusionNLR is a strong predictor for the prognosis for severe COVID-19 patients when the cut-off chosen was 4.4, the combined mortality risk factor COVID-19 disease generated from RDW-CV, NLR, MNR and PLT is best as a mortality haematocytometric index.

Indexed as

AdultAgedArea Under CurveBlood Cell CountCOVID-19FemaleHemoglobinsHumansLaboratoriesLymphocytesMaleMiddle AgedMonocytesNeutrophilsPlatelet CountPrognosisHemoglobins

Identifiers

PMID34351940
PMCPMC8341498

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.