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Validation of Neutrophil-to-Lymphocyte Ratio Cut-off Value Associated with High In-Hospital Mortality in COVID-19 Patients.
Yildiz, Halil; Castanares-Zapatero, Diego; Pierman, Guillaume; Pothen, Lucie; De Greef, Julien; Aboubakar Nana, Frank; Rodriguez-Villalobos, Hector; Belkhir, Leila; Yombi, Jean Cyr.
  • Yildiz H; Department of Internal Medicine and Infectious Diseases, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium.
  • Castanares-Zapatero D; Department of Intensive Care Unit, Cliniques Universitaires Saint-Luc, UCLouvain, Brussels, Belgium.
  • Pierman G; Department of Internal Medicine and Infectious Diseases, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium.
  • Pothen L; Department of Internal Medicine and Infectious Diseases, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium.
  • De Greef J; Department of Internal Medicine and Infectious Diseases, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium.
  • Aboubakar Nana F; Department of Pneumology, Cliniques Universitaires Saint-Luc, UCLouvain, Brussels, Belgium.
  • Rodriguez-Villalobos H; Department of Microbiology, Cliniques Universitaires Saint-Luc, UCLouvain, Brussels, Belgium.
  • Belkhir L; Department of Internal Medicine and Infectious Diseases, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium.
  • Yombi JC; Department of Internal Medicine and Infectious Diseases, Cliniques Universitaires Saint-Luc, Université Catholique de Louvain (UCLouvain), Brussels, Belgium.
Int J Gen Med ; 14: 5111-5117, 2021.
Article in English | MEDLINE | ID: covidwho-1817643
ABSTRACT

INTRODUCTION:

The neutrophil-to-lymphocyte ratio (NLR) could be a predictive factor of severe COVID-19. However, most relevant studies are retrospective, and the optimal NLR cut-off point has not been determined. The objective of our research was identification and validation of the best NLR cut-off value on admission that could predict high in-hospital mortality in COVID-19 patients.

METHODS:

Medical files of all patients admitted for COVID-19 pneumonia in our dedicated COVID-units between March and April 2020 (derivation cohort) and between October and December 2020 (validation cohort) were reviewed.

RESULTS:

Two hundred ninety-nine patients were included in the study (198 in the derivation and 101 in the validation cohort, respectively). Youden's J statistic in the derivation cohort determined the optimal cut-off value for the performance of NLR at admission to predict mortality in hospitalized patients with COVID-19. The NLR cut-off value of 5.94 had a sensitivity of 62% and specificity of 64%. In ROC curve analysis, the AUC was 0.665 [95% CI 0.530-0.801, p= 0.025]. In the validation cohort, the best predictive cut-off value of NLR was 6.4, which corresponded to a sensitivity of 63% and a specificity of 64% with AUC 0.766 [95% CI 0.651-0.881, p <0.001]. When the NLR cut-off value of 5.94 was applied in the validation cohort, there was no significant difference in death and survival in comparison with the derivation NLR cut-off. Net reclassification improvement (NRI) analysis showed no significant classification change in outcome between both NLR cut-off values (NRI0.012, p=0.31).

CONCLUSION:

In prospective analysis, an NLR value of 5.94 predicted high in-hospital mortality upon admission in patients hospitalized for COVID-19 pneumonia.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study Language: English Journal: Int J Gen Med Year: 2021 Document Type: Article Affiliation country: IJGM.S326666

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Cohort study / Observational study / Prognostic study Language: English Journal: Int J Gen Med Year: 2021 Document Type: Article Affiliation country: IJGM.S326666