Evidence map›Paper›PMID 37198387›Full record

ArticleInfectious diseases and therapy2023

Dynamic NLR and PLR in Predicting COVID-19 Severity: A Retrospective Cohort Study.

Erika Asperges, Giuseppe Albi, Valentina Zuccaro, Margherita Sambo, Teresa C Pieri, Matteo Calia, Marta Colaneri, Laura Maiocchi, Federica Melazzini, Angioletta Lasagna and 4 more

Abstract read
In one paragraph

Article in Infectious diseases and therapy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.

0numbers the graph read from it
0cells of the map it votes in
30citing 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

30 citing papers in PubMed.

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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

14 authors.

Erika Asperges *U.O.C. Malattie Infettive I Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.ORCID http://orcid.org/0000-0002-1618-8867
Giuseppe Albi *Department of Electrical, Computer and Biomedical Engineering, University of Pavia, 27100, Pavia, Italy.
Valentina ZuccaroU.O.C. Malattie Infettive I Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Margherita SamboU.O.C. Malattie Infettive I Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Teresa C PieriU.O.C. Malattie Infettive I Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Matteo CaliaU.O.C. Malattie Infettive I Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Marta ColaneriU.O.C. Malattie Infettive I Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Laura MaiocchiU.O.C. Malattie Infettive I Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Federica MelazziniU.O.C. Medicina Interna Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Angioletta LasagnaU.O.C. Oncologia Medica Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Andrea PeriDipartimento di Chirurgia Fondazione, IRCCS Policlinico San Matteo, Pavia, Italy.
Francesco MojoliU.O.C. Anestesia e Rianimazione Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Paolo SacchiU.O.C. Malattie Infettive I Fondazione IRCCS Policlinico San Matteo, Pavia, Italy.
Raffaele BrunoU.O.C. Malattie Infettive I Fondazione IRCCS Policlinico San Matteo, Pavia, Italy. raffaele.bruno@unipv.it.

Funding

Pan-European Response to the ImpactS of COVID-19 and future Pandemics and Epidemics 101016233
6 · The paper itself

Abstract

introductionThe hyperinflammation phase of severe SARS-CoV-2 is characterised by complete blood count alterations. In this context, the neutrophil-to-lymphocyte ratio (NLR) and the platelet-to-lymphocyte ratio (PLR) can be used as prognostic factors. We studied NLR and PLR trends at different timepoints and computed optimal cutoffs to predict four outcomes: use of continuous positive airways pressure (CPAP), intensive care unit (ICU) admission, invasive ventilation and death.

methodsWe retrospectively included all adult patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia admitted from 23 January 2020 to 18 May 2021. Analyses included non-parametric tests to study the ability of NLR and PLR to distinguish the patients' outcomes at each timepoint. Receiver operating characteristic (ROC) curves were built for NLR and PLR at each timepoint (minus discharge) to identify cutoffs to distinguish severe and non-severe disease. Their statistical significance was assessed with the chi-square test. Collection of data under the SMACORE database was approved with protocol number 20200046877.

resultsWe included 2169 patients. NLR and PLR were higher in severe coronavirus disease 2019 (COVID-19). Both ratios were able to distinguish the outcomes at each timepoint. For NLR, the areas under the receiver operating characteristic curve (AUROC) ranged between 0.59 and 0.81, and for PLR between 0.53 and 0.67. From each ROC curve we computed an optimal cutoff value.

conclusionNLR and PLR cutoffs are able to distinguish severity grades and mortality at different timepoints during the course of disease, and, as such, they allow a tailored approach. Future prospects include validating our cutoffs in a prospective cohort and comparing their performance against other COVID-19 scores.

Indexed as

COVID-19CutoffICUMortalityNLRPLR

Identifiers

PMID37198387
PMCPMC10191087

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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.