This study investigates the prognostic value of immune cell subsets in assessing the risk of death in patients with sepsis. This retrospective study collected 169 patients from March 2020 to February 2021 at our hospital. Baseline data were collected from patients. The absolute values (Abs) and percentages (%) of immune cell subsets for lymphocytes, T cells, CD4+ cells, CD8+, B cells, NK cells, and NKT cells were measured using flow Cytometry. Among the included patients, 43 patients were in the nonsurvivor group and 126 patients were in the survivor group. The age of patients in the nonsurvivor survivor was higher than that of survivor group patients ( P = .020). SOFA, APACHE II, C-reactive protein, and procalcitonin were higher in the nonsurvivor group than in the survivor group (all P values < .05). Multivariate regression analysis showed that lymphocytes (%) and SOFA were independent risk factors affecting patients’ prognosis. Lymphocytes (%) have the highest area under the receiver operating characteristic (ROC) curve (0.812). The model area under the ROC curve for immune cell subsets was 0.800, with a sensitivity of 72.09%, and specificity of 79.27% ( z = 7.796, P <  .001). Analysis of patient prognosis by immune cell subsets diagnostic showed statistically significant differences in the grouping of cut-off values for all 5 indicators (all P < .05). The lymphocytes (%) and SOFA score are independent risk factors affecting the prognosis of patients. A moderate predictive power for mortality in sepsis patients by immune cell subsets model.

Purpose To determine the utility of a novel serum biomarker for the outcome prediction of critically ill patients with pneumonia. Patients and Methods A retrospective analysis of critically ill patients was performed at an emergency department. The expression and prediction value of parameters were assessed. Binary logistic regression analysis was utilized to determine the indicators associated with in-hospital mortality of pneumonia patients. The Last Absolute Shrinkage and Selection Operator was used to further determine the independent predictors, which were validated by multiple logistic regression. The receiver operator characteristic curve was performed to assess their prediction values. A prognostic nomogram model was finally established for the outcome prediction for critically ill patients with pneumonia. Results Retinol-binding protein (RBP) was significantly reduced in non-survived and pneumonia patients. CURB-65 score, levels of RBP, and blood urea nitrogen (BUN) were associated with in-hospital mortality of critically ill patients with pneumonia. Their combination was determined to be an ideal prognostic predictor (area under the curve of 0.762) and further developed into a nomogram prediction model (c-index 0.764). Conclusion RBP is a novel in-hospital mortality predictor, which well supplements the CURB-65 score for critical pneumonia patients.

In this study, we want to investigate the clinical value of each index of thromboelastography (TEG) on the prognosis of infected patients. The clinical baseline data and TEG test results of 431 infected patients in our hospital’s emergency department between January 2018 and December 2018 were selected. And the patients were divided into death and survival groups to analyze the predictive value of each index of TEG and the joint model on the death of infected patients. In the correlation study of C-reactive protein (CRP) and procalcitonin (PCT) with each TEG parameter, CRP was positively correlated with maximum amplitude (MA, r  =  0.145, P  =  .003) and elasticity constants (E, r  =  0.098, P  =  .043), respectively. PCT was positively correlated with coagulation reaction time (R, r  =  0.124, P  =  .010) and time to MA (TMA) ( r  =  0.165, P  =  .001), respectively; PCT was negatively correlated with α-Angle ( r  =  0.124, P  =  .010) and coagulation index (CI, r  =  −0.108, P  =  .026), respectively. Multifactorial regression analysis showed that granulocytes, thrombocytes, platelet distribution width (PDW), and infection site were independent influences on infected patients’ death. Diagnostic data showed that all eight TEG indicators had good specificity for predicting death, but all had poor sensitivity; thrombodynamic potential index (TPI) had the best diagnostic value (area under the curve, AUC  =  0.609, P  =  .002). The eight-indicator modeling of TEG showed that the TEG model combined with PCT and CRP, respectively, had lower diagnostic efficacy than PCT (AUC  =  0.756, P < .001); however, TEG had better specificity (82.73%) when diagnosed independently. The granulocytes, thrombocytes, PDW, and infection site are independent influencing factors of death in infected patients. Each index of TEG has better specificity in the diagnosis of death in infected patients.