Xi Jia,1,2,* Xiaojing Li,3,* Linzi Miao,1 Rong Bao,4 Hui Xiong,3 Ran You,1 Yao Lu,1 Xiaoning Gui,1 Chenxue Qu1 

1Department of Clinical Laboratory, Peking University First Hospital, Beijing, People’s Republic of China; 2Department of Clinical Laboratory, Baoding No.1 Central Hospital, Baoding, Hebei Province, People’s Republic of China; 3Emergency Department, Peking University First Hospital, Beijing, People’s Republic of China; 4Department of Clinical Laboratory, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Chenxue Qu, Department of Clinical Laboratory, Peking University First Hospital, No. 8 Xishiku Street Xicheng District, Beijing, People’s Republic of China, 100034, Tel +86-13661375860, Email qucx2012@163.com

Background: Sepsis causes substantial morbidity and mortality and constitutes a major public health problem. In patients with sepsis, immunosuppression is associated with poor prognosis, and immune monitoring during the early stages has prognostic value. This study aims to explore immunologic parameters associated with sepsis prognosis, potentially identifying patients who may benefit from immunotherapy, improving intensive care survival.
Methods: A total of 65 patients with sepsis from the Department of Emergency Medicine were divided based on survival at 28 days (47 in the survival group, 18 in the non-survival group). Peripheral blood was collected to measure absolute lymphocyte count and T lymphocyte subpopulations, including the percentage and absolute count of total T cells, CD4+ T, CD8+ T, and NK cells, and the percentages of naïve CD4+ T, central memory CD4+ T, effector CD4+ T, effector memory CD4+ T, naïve CD8+ T, central memory CD8+ T, effector CD8+ T, effector memory CD8+ T, CD4+HLA-DR+ T, and CD8+HLA-DR+ T cells, and Tregs. The differences in these parameters between the two groups were compared and a regression model was constructed to identify possible risk factors for death in patients with sepsis.
Results: The absolute lymphocyte count, absolute T cell count (CD3+, CD4+, and CD8+) and naïve CD4+ T cell percentage were significantly lower in the non-survival group. Conversely, Tregs were higher in patients who did not survive sepsis. In regression analysis, the absolute lymphocyte count and naïve CD4+ T cell percentage remained statistically significant. The receiver operating characteristic curve showed that a model based on the absolute lymphocyte count (435 cells/μL) and naïve CD4+ T cell percentage (20.25%) performed best in predicting sepsis prognosis.
Conclusion: Monitoring of absolute lymphocyte count and analysis of T cell subtypes in the early phase of sepsis is predictive of outcome and may help identify those patients who would benefit from immunotherapy, improving survival.

Keywords: sepsis, absolute lymphocyte count, T cell subpopulations, immune function