Prediction Model Based on the Combination of Cytokines and Lymphocyte Subsets for Prognosis of SARS-CoV-2 Infection
Ying Luo, Liyan Mao, Yuan Xu, Ying Xue, Qun Lin, Guoxing Tang, Huijuan Song, Feng Wang, Ziyong Sun
Abstract
Abstract Background There are currently rare satisfactory markers for predicting the death of patients with coronavirus disease 2019 (COVID-19). The aim of this study is to establish a model based on the combination of serum cytokines and lymphocyte subsets for predicting the prognosis of the disease. Methods A total of 739 participants with COVID-19 were enrolled at Tongji Hospital from February to April 2020 and classified into fatal ( n = 51) and survived ( n = 688) groups according to the patient’s outcome. Cytokine profile and lymphocyte subset analysis was performed simultaneously. Results The fatal patients exhibited a significant lower number of lymphocytes including B cells, CD4 + T cells, CD8 + T cells, and NK cells and remarkably higher concentrations of cytokines including interleukin-2 receptor, interleukin-6, interleukin-8, and tumor necrosis factor-α on admission compared with the survived subjects. A model based on the combination of interleukin-8 and the numbers of CD4 + T cells and NK cells showed a good performance in predicting the death of patients with COVID-19. When the threshold of 0.075 was used, the sensitivity and specificity of the prediction model were 90.20% and 90.26%, respectively. Meanwhile, interleukin-8 was found to have a potential value in predicting the length of hospital stay until death. Conclusions Significant increase of cytokines and decrease of lymphocyte subsets are found positively correlated with in-hospital death. A model based on the combination of three markers provides an attractive approach to predict the prognosis of COVID-19.