Sepsis is one of the most significant causes of mortality in intensive care units. It indicates crosstalk between inflammation and coagulation. In this study, we aimed to identify prognostic markers among sepsis biomarkers and coagulation/fibrinolysis markers. Patients with sepsis according to the Sepsis-3 criteria were enrolled from January 2013 to September 2015. Univariate and multivariate logistic regression analyses were performed to identify an independent predictive marker of 28-day mortality among sepsis biomarkers and coagulation/fibrinolysis markers on ICU admission. Receiver operating characteristic analysis was performed; the optimal cutoff value of 28-day mortality was calculated using the predictive marker. Patients were classified into two groups according to the cutoff level of the predictive marker. Patient characteristics were compared between the groups. A total of 186 patients were enrolled in this study; the 28-day mortality was 19.4% (36/186). PAI-1 was identified as the only independent predictive marker of 28-day mortality by univariate and multivariate logistic regression. The area under the curve was 0.72; the optimal cutoff level was 83 ng/ml (sensitivity, 75%; specificity, 61%). Patients were classified into a higher group (PAI-1 level ≥83 ng/ml; n = 85) and a lower group (PAI-1 level <83 ng/ml; n = 101). All disseminated intravascular coagulation (DIC) scores and Sequential Organ Failure Assessment score were significantly higher in the higher group than in the lower group. PAI-1 can predict prognosis in sepsis patients. PAI-1 reflects DIC with suppressed fibrinolysis and organ failure, with microthrombi leading to microcirculatory dysfunction.

Introduction: Inflammation and coagulation are closely interrelated pathophysiologic processes in the pathogenesis of sepsis. However, the diagnostic criteria of sepsis and disseminated intravascular coagulation (DIC) are different. This study aimed to define a biomarker panel to predict sepsis-induced DIC in emergency department patients. Methods: Eighty-two patients who were admitted to the emergency department of a tertiary university hospital were included in this study. The inclusion criteria were as follows: (1) age >18 years; (2) ≥1 systemic inflammatory response syndrome (SIRS) criteria. Patients were excluded if they lacked biomarker data or apparent clinical manifestations. Eleven biomarkers were assayed from blood drawn on ED admission. Receiver operating curve (ROC) analysis including the area under the ROC and multivariable logistic regression were used to identify an optimal combination of biomarkers to create a diagnostic panel. The derived formula for weighting biomarker values was used to determine the severity of sepsis-induced DIC, which was divided into three categories: mild, moderate, and severe. We also investigated the ability of this classification to predict secondary outcome measures of rates of sepsis and DIC, DIC score, acute physiology and chronic health evaluation (APACHE) II score, sequential organ failure score (SOFA) score, and 28-day all-cause mortality. Results: Among the 11 biomarkers tested, the optimal 2-marker panel comprised presepsin and protein C. The area under the curve for the accuracies of predicting sepsis and DIC from these two biomarkers were 0.913 and 0.880, respectively. When patients were divided according to the severity of sepsis-induced DIC, all secondary outcomes except for mortality were significantly higher depending on the severity (P < .0001). The overall mortality rates of mild, moderate, and severe sepsis-induced DIC were 7.14%, 15.4%, and 28.6%, respectively (P = .0994). Conclusions: A biomarker panel of presepsin and protein C is predictive of the severity of sepsis-induced DIC in suspected ED patients. These criteria for sepsis-induced DIC are very simple, easy to implement, and can be used in intensive care units as a point-of-care test.

Introduction Sepsis is not only the leading cause of death in the intensive care unit (ICU) but also a major risk factor for physical and cognitive impairment and mental disorders, known as postintensive care syndrome (PICS), reduced health-related quality of life (HRQoL) and even mental health disorders in patient families (PICS-family; PICS-F). The ABCDEF bundle is strongly recommended to overcome them, while the association between implementing the bundle and the long-term outcomes is also unknown. Methods and analysis This is a multicentre prospective observational study at 26 ICUs. All consecutive patients between 1 November 2020 and 30 April 2022, who are 18 years old or older and expected to stay in an ICU for more than 48 hours due to sepsis or septic shock, are enrolled. Follow-up to evaluate survival and PICS/ PICS-F will be performed at 3, 6 and 12 months and additionally every 6 months up to 5 years after hospital discharge. Primary outcomes include survival at 12 months, which is the primary outcome, and the incidence of PICS defined as the presence of any physical impairment, cognitive impairment or mental disorders. PICS assessment scores, HRQoL and employment status are evaluated. The association between the implementation rate for the ABCDEF bundle and for each of the individual elements and long-term outcomes will be evaluated. The PICS-F, defined as the presence of mental disorders, and HRQoL of the family is also assessed. Additional analyses with data up to 5 years follow-up are planned. Ethics and dissemination This study received ethics approvals from Saiseikai Utsunomiya Hospital (2020-42) and all other participating institutions and was registered in the University Hospital Medical Information Network Clinical Trials Registry. Informed consent will be obtained from all patients. The findings will be published in peer-reviewed journals and presented at scientific conferences. Trial registration number UMIN000041433.

This study aimed to clarify whether the glial fibrillary acidic protein (GFAP) and soluble protein-100β (S100β) can predict severe traumatic brain injury (TBI) in patients with severe multiple trauma. This is a single-center retrospective observational study of 179 patients with severe multiple trauma. The GFAP and S100β were measured upon patient arrival at the hospital. We divided the patients into the severe TBI group (with a Traumatic Coma Data Bank classification of ≥III), the non-severe TBI group (non-TBI group [absence of abnormality on the computed tomography scan and extracranial injury], and the mild to moderate TBI group [TCDB classification I and II]). We compared biomarker levels between the two groups and then evaluated the accuracy of predicting severe TBI using a receiver operating characteristic curve. A total of 41 patients had severe TBI, and 138 had non-severe TBI. Mean GFAP levels were significantly higher in the severe TBI group (median, 6000 pg/mL; interquartile range [IQR], 651–15,548 pg/mL) than in the non-severe TBI group (median, 149 pg/mL; IQR, 0–695 pg/mL) (p < 0.0001). In contrast, there was no significant difference in S100β levels between the severe TBI group (median, 64 pg/mL; IQR, 0–536 pg/mL) and non-severe TBI group (median, 117 pg/mL; IQR, 0–403 pg/mL) (p = 0.637). The area under the receiver operating characteristic curve was 0.810 (p < 0.0001) for GFAP and 0.476 (p = 0.908) for S100β. For the GFAP, the optimal cutoff value for detecting severe TBI was 947 pg/mL (sensitivity, 75.6%; specificity, 78.3%). In patients with severe multiple trauma, the GFAP level at hospital arrival could predict severe TBI, whereas the S100β level was not a useful predictor.