Shock : molecular, cellular, and systemic pathobiological aspects and therapeutic approaches : the official journal the Shock Society, the European Shock Society, the Brazilian Shock Society, the International Federation of Shock Societies
-
Background: As an immune marker, serum soluble programmed cell death ligand-1 (sPD-L1) is significantly increased in sepsis and is predictive of mortality. We investigated the prognostic value of sPD-L1 in postseptic immunosuppression and progression to chronic critical illness (CCI). Methods: Adults with sepsis in intensive care units (ICUs) for the first time were screened and assigned to either a CCI group (ICU stay ≥14 days with persistent organ dysfunction) or a rapid recovery (RAP) group based on clinical outcome. ⋯ D 7 -sPD-L1 remained higher in the CCI group, and the area under the curve that predicted the occurrence of CCI was equivalent to the APACHE II score, with areas under the curve of 0.782 and 0.708, respectively. Conclusions: The severity of infection and immunosuppression in sepsis may be linked to serum sPD-L1. D 7 -sPD-L1 is valuable in predicting the progression of CCI in patients.
-
Purpose: To evaluate significant risk variables for sepsis incidence and develop a predictive model for rapid screening and diagnosis of sepsis in patients from the emergency department (ED). Methods: Sepsis-related risk variables were screened based on the PIRO (Predisposition, Insult, Response, Organ dysfunction) system. Training (n = 1,272) and external validation (n = 568) datasets were collected from Peking Union Medical College Hospital (PUMCH) and Beijing Tsinghua Changgung Hospital (BTCH), respectively. ⋯ Both calibration curves of EASE in training and external validation datasets were close to the ideal model and were well-calibrated. Conclusions: The EASE model can predict and screen ED-admitted patients with sepsis. It demonstrated superior diagnostic performance and clinical application promise by external validation and in-parallel comparison with the NEWS scoring system.
-
Key underlying pathological mechanisms contributing to sepsis are hemostatic dysfunction and overwhelming inflammation. Platelet aggregation is required for hemostasis, and platelets are also separately involved in inflammatory responses that require different functional attributes. Nevertheless, P2Y receptor activation of platelets is required for this dichotomy of function. ⋯ However, platelets isolated from patients with sepsis lost the ability to undergo chemotaxis toward N -formylmethionyl-leucyl-phenylalanine, and this suppression was evident at admission through to and including discharge from hospital. Our results suggest that P2Y 1 -dependent inflammatory function in platelets is lost in patients with sepsis resulting from community-acquired pneumonia. Further studies will need to be undertaken to determine whether this is due to localized recruitment to the lungs of a platelet responsive population or loss of function as a result of dysregulation of the immune response.
-
Background: The dysregulation of circular RNAs (circRNAs) is involved in various human diseases, including sepsis-induced acute lung injury (ALI). We aimed to investigate the role of circTDRD9 in the development of sepsis-induced ALI. Methods: Cell models of sepsis-induced ALI were established by treating A549 cells with LPS. ⋯ Importantly, circTDRD9 positively regulated RAB10 expression by binding to miR-223-3p. Conclusion: CircTDRD9 overexpression was closely associated with LPS-induced ALI. CircTDRD9 contributed to LPS-induced ALI partly by upregulating RAB10 via binding to miR-223-3p.
-
Background: Hemorrhage remains the leading cause of death on the battlefield. This study aims to assess the ability of an artificial intelligence triage algorithm to automatically analyze vital-sign data and stratify hemorrhage risk in trauma patients. Methods: Here, we developed the APPRAISE-Hemorrhage Risk Index (HRI) algorithm, which uses three routinely measured vital signs (heart rate and diastolic and systolic blood pressures) to identify trauma patients at greatest risk of hemorrhage. ⋯ The APPRAISE-HRI stratification yielded a hemorrhage likelihood ratio (95% confidence interval) of 0.28 (0.13-0.43) for HRI:I, 1.00 (0.85-1.15) for HRI:II, and 5.75 (3.57-7.93) for HRI:III, suggesting that patients categorized in the low-risk (high-risk) category were at least 3-fold less (more) likely to have hemorrhage than those in the average trauma population. We obtained similar results in a cross-validation analysis. Conclusions: The APPRAISE-HRI algorithm provides a new capability to evaluate routine vital signs and alert medics to specific casualties who have the highest risk of hemorrhage, to optimize decision-making for triage, treatment, and evacuation.