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BackgroundUnbalanced inflammatory response is a critical feature of sepsis, a life-threatening condition with significant global health burdens. Immune dysfunction, particularly that involving different immune cells in peripheral blood, plays a crucial pathophysiological role and shows early warning signs in sepsis. The objective is to explore the relationship between sepsis and immune subpopulations in peripheral blood, and to identify patients with a higher risk of 28-day mortality based on immunological subtypes with machine-learning (ML) model.MethodsPatients were enrolled according to the sepsis-3 criteria in this retrospective observational study, along with age- and sex-matched healthy controls (HCs). Data on clinical characteristics, laboratory tests, and lymphocyte immunophenotyping were collected. XGBoost and k-means clustering as ML approaches, were employed to analyze the immune profiles and stratify septic patients based on their immunological subtypes. Cox regression survival analysis was used to identify potential biomarkers and to assess their association with 28-day mortality. The accuracy of biomarkers for mortality was determined by the area under the receiver operating characteristic (ROC) curve (AUC) analysis.ResultsThe study enrolled 100 septic patients and 89 HCs, revealing distinct lymphocyte profiles between the two groups. The XGBoost model discriminated sepsis from HCs with an area under the receiver operating characteristic curve of 1.0 and 0.99 in the training and testing set, respectively. Within the model, the top three highest important contributions were the percentage of CD38+CD8+T cells, PD-1+NK cells, HLA-DR+CD8+T cells. Two clusters of peripheral immunophenotyping of septic patients by k-means clustering were conducted. Cluster 1 featured higher proportions of PD1+ NK cells, while cluster 2 featured higher proportions of naïve CD4+T cells. Furthermore, the level of PD-1+NK cells was significantly higher in the non-survivors than the survivors (15.1% vs 8.6%, P

Background: Unbalanced inflammatory response is a critical feature of sepsis, a lifethreatening condition with significant global health burdens. Immune dysfunction, particularly that involving different immune cells in peripheral blood, plays a crucial pathophysiological role and shows early warning signs in sepsis. The objective is to explore the relationship between sepsis and immune subpopulations in peripheral blood, and to identify patients with a higher risk of 28-day mortality based on immunological subtypes with machine-learning (ML) model. Methods: Patients were enrolled according to the sepsis-3 criteria in this retrospective observational study, along with age- and sex-matched healthy controls (HCs). Data on clinical characteristics, laboratory tests, and lymphocyte immunophenotyping were collected. XGBoost and k-means clustering as ML approaches, were employed to analyze the immune profiles and stratify septic patients based on their immunological subtypes. Cox regression survival analysis was used to identify potential biomarkers and to assess their association with 28-day mortality. The accuracy of biomarkers for mortality was determined by the area under the receiver operating characteristic (ROC) curve (AUC) analysis. Results: The study enrolled 100 septic patients and 89 HCs, revealing distinct lymphocyte profiles between the two groups. The XGBoost model discriminated sepsis from HCs with an area under the receiver operating characteristic curve of 1.0 and 0.99 in the training and testing set, respectively. Within the model, the top three highest important contributions were the percentage of CD38+CD8+T cells, PD-1+NK cells, HLA-DR+CD8+T cells. Two clusters of peripheral immunophenotyping of septic patients by k-means clustering were conducted. Cluster 1 featured higher proportions of PD1+ NK cells, while cluster 2 featured higher proportions of naïve CD4+T cells. Furthermore, the level of PD-1+NK cells was significantly higher in the non-survivors than the survivors (15.1% vs 8.6%, P<0.01). Moreover, the levels of PD1+ NK cells combined with SOFA score showed good performance in predicting the 28-day mortality in sepsis (AUC=0.91,95%CI 0.82-0.99), which is superior to PD1+NK cells only(AUC=0.69, sensitivity 0.74, specificity 0.64, cut-off value of 11.25%). In the multivariate Cox regression, high expression of PD1+ NK cells proportion was related to 28-day mortality (aHR=1.34, 95%CI 1.19 to 1.50; P<0.001). Conclusion: The study provides novel insights into the association between PD1+NK cell profiles and prognosis of sepsis. Peripheral immunophenotyping could potentially stratify the septic patients and identify those with a high risk of 28-day mortality. [ABSTRACT FROM AUTHOR]