Background: Severe SARS-CoV-2 infection results in lymphopenia and impaired function of T, B, and NK (TBNK-dominant) lymphocytes. Mitochondria are essential targets of SARS-CoV-2 and the efficacy of lymphocyte mitochondrial function for immunosurveillance in COVID-19 patients has not been evaluated.
Methods: Multi-parametric flow cytometry was used to characterize mitochondrial function, including mitochondrial mass (MM) and low mitochondrial membrane potential (MMPlow), in TBNK-dominant lymphocytes from severe (n = 93) and moderate (n = 77) hospitalized COVID-19 patients. We compared the role of novel lymphocyte mitochondrial indicators and routine infection biomarkers as early predictors of severity and death in COVID-19 patients. We then developed a mortality decision tree prediction model based on immunosurveillance indicators through machine learning.
Results: At admission, the MM of circulating NK cells (NK-MM) was the best discriminator of severe/moderate disease (AUC = 0.8067) compared with the routine infection biomarkers. The NK cell count and NK-MM displayed superior diagnostic effects to distinguish patients with non-fatal or fatal outcomes. Interestingly, NK-MM was significantly polarized in non-survivors, with some patients showing a decrease and others showing an abnormal increase. Kaplan-Meier analysis showed that NK-MM had the optimal predictive efficacy (hazard ratio = 11.66). The decision tree model has the highest proportion of importance for NK-MM, which is superior to the single diagnostic effect of the above indicators (AUC = 0.8900).
Conclusion: NK-MM was not only associated with disease severity, its abnormal increases or decreases also predicted mortality risk. The resulting decision tree prediction model is the first to focus on immune monitoring indicators to provide decision-making clues for COVID-19 clinical management.
Keywords: COVID-19; Immunosurveillance; Mitochondria; Natural killer cell; Severity.
Copyright © 2023. Published by Elsevier B.V.