Characteristics and Predictive Value of T-lymphocyte Subset Absolute Counts in Patients with COVID-19-associated Acute Respiratory Failure: A Retrospective Study

Ashwini Pednekar; Durga Suthar; Neha Sanwalka; Kiran Ghodke; Nikhil Rabade

doi:10.5005/jp-journals-10071-24352

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Characteristics and Predictive Value of T-lymphocyte Subset Absolute Counts in Patients with COVID-19-associated Acute Respiratory Failure: A Retrospective Study

Ashwini Pednekar, Durga Suthar, Neha Sanwalka, Kiran Ghodke, Nikhil Rabade

Keywords : Acute respiratory failure, Coronavirus disease-2019, Disease severity, Severe acute respiratory syndrome CoV-2 infection, T-lymphocyte subsets

Citation Information : Pednekar A, Suthar D, Sanwalka N, Ghodke K, Rabade N. Characteristics and Predictive Value of T-lymphocyte Subset Absolute Counts in Patients with COVID-19-associated Acute Respiratory Failure: A Retrospective Study. Indian J Crit Care Med 2022; 26 (11):1198-1203.

DOI: 10.5005/jp-journals-10071-24352

License: CC BY-NC 4.0

Published Online: 31-10-2022

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Abstract

Background: Of the factors influencing severity and outcomes following coronavirus disease-2019 (COVID-19), cellular immune response has a strong impact. The spectrum of response varies from over-activation to hypo-functioning. The severe infection leads to reduction in numbers and dysfunction of T-lymphocytes/subsets. Patients and methods: This retrospective, single-center study aimed to analyze the expression of T-lymphocyte/subsets by flow cytometry and inflammation-related biomarker, serum ferritin in real-time polymerase chain reaction (RT-PCR) positive patients. According to oxygen requirements, patients were stratified into nonsevere (room air, nasal prongs, and face mask) and severe [nonrebreather mask (NRBM), noninvasive ventilation (NIV), high-flow nasal oxygen (HFNO), and invasive mechanical ventilation (IMV)] subgroups for analysis. Patients were classified into survivors and nonsurvivors. Mann–Whitney U test was used to analyze differences in T-lymphocyte and subset values when classified according to gender, the severity of COVID, outcome, and prevalence of diabetes mellitus (DM). Cross tabulations were computed for categorical data and compared using Fisher's exact test. Spearman correlation was used to analyze the correlation of T-lymphocyte and subset values with age or serum ferritin levels. p <0.05 values were considered to be statistically significant. Results: A total of 379 patients were analyzed. Significantly higher percentage of patients with DM were aged ≥61 years in both nonsevere and severe COVID groups. A significant negative correlation of CD3+, CD4+, and CD8+ was found with age. CD3+ and CD4+ absolute counts were significantly higher in females as compared to males. Patients with severe COVID had significantly lesser total lymphocyte (%), CD3+, CD4+, and CD8+ counts as compared to those with nonsevere COVID (p <0.05). T-lymphocyte subsets were reduced in patients with severe disease. A significant negative correlation of total lymphocyte (%), CD3+, CD4+, and CD8+ counts was found with serum ferritin levels. Conclusions: T-lymphocyte/subset trends are an independent risk factor for clinical prognosis. Monitoring may help in intervening in patients with disease progression.

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