Acute kidney injury in critical care: complications of hemophagocytic lymphohistiocytosis.

Hemophagocytic lymphohistiocytosis (HLH) is an immune dysfunction characterized by an exaggerated and pathological inflammatory response, potentially leading to systemic inflammatory reactions and multiple-organ failure, including renal involvement. HLH can be classified as primary or secondary, with primary HLH associated with genetic mutations affecting cell degranulation capacity, and secondary HLH often linked to infections, tumors, and autoimmune diseases. The pathogenesis of HLH is not fully understood, but primary HLH is typically driven by genetic defects, whereas secondary HLH involves the activation of CD8+ T cells and macrophages, leading to the release of inflammatory cytokines and systemic inflammatory response syndrome (SIRS). The clinical presentation of HLH includes non-specific manifestations, making it challenging to differentiate from severe sepsis, particularly secondary HLH due to infections. Shared features include prolonged fever, hepatosplenomegaly, hematopenia, hepatic dysfunction, hypertriglyceridemia, and hypofibrinogenemia, along with histiocytosis and hemophagocytosis. However, distinctive markers like dual hemocytopenia, hypertriglyceridemia, hypofibrinogenemia, and elevated sCD25 levels may aid in differentiating HLH from sepsis. Indeed, no singular biomarker effectively distinguishes between hemophagocytic lymphohistiocytosis and infection. However, research on combined biomarkers provides insights into the differential diagnosis. Renal impairment is frequently encountered in both HLH and sepsis. It can result from a systemic inflammatory response triggered by an influx of inflammatory mediators, from direct damage caused by these factors, or as a consequence of the primary disease process. For instance, macrophage infiltration of the kidney can lead to structural damage affecting various renal components, precipitating disease. Presently, tubular necrosis remains the predominant form of renal involvement in HLH-associated acute kidney injury (HLH-AKI). However, histopathological changes may also encompass interstitial inflammation, glomerular abnormalities, microscopic lesions, and thrombotic microangiopathy. Treatment approaches for HLH and sepsis diverge significantly. HLH is primarily managed with repeated chemotherapy to eliminate immune-activating stimuli and suppress hypercellularity. The treatment approach for sepsis primarily focuses on anti-infective therapy and intensive symptomatic supportive care. Renal function significantly influences clinical decision-making, particularly regarding the selection of chemotherapy and antibiotic dosages, which can profoundly impact patient prognosis. Conversely, renal function recovery is a complex process influenced by factors such as disease severity, timely diagnosis, and the intensity of treatment. A crucial aspect in managing HLH-AKI is the timely diagnosis, which plays a pivotal role in reversing renal impairment and creating a therapeutic window for intervention, may have opportunity to improve patient prognosis. Understanding the clinical characteristics, underlying causes, biomarkers, immunopathogenesis, and treatment options for hemophagocytic lymphohistiocytosis associated with acute kidney injury (HLH-AKI) is crucial for improving patient prognosis.

Development and validation of a machine-learning model for predicting the risk of death in sepsis patients with acute kidney injury.

The mortality rate of patients with sepsis-induced acute kidney injury (S-AKI) is notably elevated. The initial categorization of prognostic indicators has a beneficial impact on elucidating and enhancing disease outcomes. This study aimed to predict the mortality risk of S-AKI patients by employing machine learning techniques. The sample size determined by a four-step procedure yielded 1508 samples. The research design necessitated the inclusion of individuals with S-AKI from the Medical Information Mart for Intensive Care (MIMIC)-IV database. The patients were initially admitted to the Intensive Care Unit (ICU) for their hospital stay. Additionally, these patients (aged from 18 to 89 years old) had encountered S-AKI on the day of their admittance. Forty-two predictive factors were analyzed, with hospitalization death as the outcome variable. The training set (4001 cases) consisted of 70 % of the participants, and the remaining (1714 cases) participants were allocated to the validation set. Furthermore, an additional validation set (MIMIC-III) consisted of 1757 patients from the MIMIC-III database. Moreover, an external validation set from the Intensive Care Department of Beijing Friendship Hospital (BFH) comprised 72 patients. Six machine learning models were employed in the prediction, namely the logistic, lasso, rpart, random forest, xgboost, and artificial neural network models. The comparative efficacy of the newly developed model in relation to the APACHE II model for predicting mortality risk was also assessed. The XGBoost model exhibited a superior performance with the training set. With the internal validation set and the two external validation sets (MIMIC-III and BFH), the xgboost algorithm demonstrated the highest performance. Meanwhile, APACHE II performed poorly at predicting the mortality risk with the BFH validation set. The mortality risk was influenced by three primary clinical parameters: urine volume, lactate, and Glasgow Coma Scale (GCS) score. Thus, we developed a prediction model for the risk of death among S-AKI patients that has an improved performance compared to previous models and is a potentially valuable tool for S-AKI prediction and treatment in the clinic.

Beta 2-microglobulin is an independent risk marker of acute kidney injury in adult patients with hemophagocytic lymphohistiocytosis.

BACKGROUND AND AIMS: The role of beta2-microglobulin (ß2-MG) in predicting acute kidney injury (AKI) in hemophagocytic lymphohistiocytosis patients has been poorly studied. This study aimed to analyze the clinical characteristics of hemophagocytic lymphohistiocytosis patients and identify risk factors that predict AKI development. METHODS: This retrospective observational cohort study conducted at a single-center involved 938 patients diagnosed with hemophagocytic lymphohistiocytosis, who were divided into AKI  group and non-AKI group. Patient data were collected and analyzed using univariate and multivariate binary logistic regression to identify potiential risk factors associated with AKI occurrence.   RESULTS: Among the enrolled patients, 486 were male (51.9%), the median age was 37 years (interquartile range, 28.0, 52.0), 58.4% experienced AKI. Mechanical ventilation (8.0% vs. 0.8%) and vasopressor support (21.7% vs. 4.1%) occurred at significantly higher rates in the AKI group compared to the non-AKI group, with significantly higher in-hospital mortality (5.5% vs. 1.3%) and 28-day mortality (12.8% vs. 5.4%). When ß2-MG was used as a continuous variable, multifactorial analysis showed that ß2-MG, transplantation, and vasopressor support were independently associated with risk for the development of AKI. CONCLUSIONS: The incidence of morbidity and mortality in patients with hemophagocytic lymphohistiocytosis complicated by AKI remains high. Monitoring levels of ß2-MG may provide clinicians with timely indicators of changes in renal function,  facilitating adjustments to treatment strategies.

Protective effects of Dioscin against sepsis-induced cardiomyopathy via regulation of toll-like receptor 4/MyD88/p65 signal pathway.

BACKGROUND: Dioscin has many pharmacological effects; however, its role in sepsis-induced cardiomyopathy (SIC) is unknown. Accordingly, we concentrate on elucidating the mechanism of Dioscin in SIC rat model. METHODS: The SIC rat and H9c2 cell models were established by lipopolysaccharide (LPS) induction. The heart rate (HR), left ventricle ejection fraction (LVEF), mean arterial blood pressure (MAP), and heart weight index (HWI) of rats were evaluated. The myocardial tissue was observed by hematoxylin and eosin staining. 4-Hydroxy-2-nonenal (4-HNE) level in myocardial tissue was detected by immunohistochemistry. Superoxide dismutase (SOD), catalase (CAT), and glutathione (GSH) activities in serum samples of rats and H9c2 cells were determined by colorimetric assay. Bax, B-cell lymphoma-2 (Bcl-2), toll-like receptor 4 (TLR4), myeloid differentiation primary response 88 (MyD88), phosphorylated-p65 (p-p65), and p65 levels in myocardial tissues of rats and treated H9c2 cells were measured by quantitative real-time PCR and Western blot. Viability and reactive oxygen species (ROS) accumulation of treated H9c2 cells were assayed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide and dihydroethidium staining assays. RESULTS: Dioscin decreased HR and HWI, increased LVEF and MAP, alleviated the myocardial tissue damage, and reduced 4-HNE level in SIC rats. Dioscin reversed LPS-induced reduction on SOD, CAT, GSH, and Bcl-2 levels, and increment on Bax and TLR4 levels in rats and H9c2 cells. Overexpressed TLR4 attenuated the effects of Dioscin on promoting viability, as well as dwindling TLR4, ROS and MyD88 levels, and p-p65/p65 value in LPS-induced H9c2 cells. CONCLUSION: Protective effects of Dioscin against LPS-induced SIC are achieved via regulation of TLR4/MyD88/p65 signal pathway.

BAP31 Promotes Adhesion Between Endothelial Cells and Macrophages Through the NF-κB Signaling Pathway in Sepsis.

Purpose: To investigate the role of B cell receptor associated protein 31 (BAP31) in the pathogenesis of sepsis. Methods: Cecal ligation and puncture (CLP)-induced C57BL/6J mice, and LPS-challenged endothelial cells (HUVECs) were established to mimic a sepsis animal model and a sepsis cell model, respectively. Cre/LoxP and shRNA methods were used for BAP31 knockdown in vivo and in vitro respectively. Neutrophils/macrophages-endothelial cocultures were used to evaluate neutrophils or macrophages infiltration and adhesion to endothelial cells. Cox proportional hazards model was used to evaluate the survival time of mice. Western blotting (WB) and Quantitative real-time polymerase chain reaction (qRT-PCR) were used to detect toll-like receptor (TLR) signaling pathway, transforming growth factor ß activated kinase 1 (TAK1) signaling pathway and phosphoinositide-3 kinases-protein kinase B (PI3K/AKT) signaling pathway. Results: Deletion of BAP31 reduced CLP-induced mortality of mice, histological damage with less interstitial edema, and neutrophils and macrophages infiltration. IHC and IF showed that BAP31 knockdown significantly decreases the expressions of ICAM1 and VCAM1 both in vivo and in vitro. Coculture showed that LPS-induced neutrophils or macrophages adhesion to endothelial cells was significantly weakened in BAP31 knockdown cells. In addition, BAP31 knockdown of endothelial cells decreased the expression of CD80 and CD86 on the surface of macrophages as well as interleukin 1ß (IL-1ß) and tumor necrosis factor α (TNF-α) during sepsis. Mechanistically, LPS-induced the activation of TLR4, MyD88 and TRAF6, and the phosphorylation of TAK1, PI3K, AKT, IκBα and IKKα/ß, resulting in activation of nuclear factor kappa B (NF-κB) p65 in endothelial cells. However, BAP31 knockdown significantly reversed the expressions of associated proteins. Conclusion: BAP31 up-regulated the expressions of ICAM1 and VCAM1 in endothelial cells leading to sepsis-associated organ injury. This may be involved in activation of TLR signaling pathway, TAK1 pathway, and PI3K-AKT signaling pathway.

Development and validation a nomogram prediction model for early diagnosis of bloodstream infections in the intensive care unit.

Purpose: This study aims to develop and validate a nomogram for predicting the risk of bloodstream infections (BSI) in critically ill patients based on their admission status to the Intensive Care Unit (ICU). Patients and methods: Patients' data were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database (training set), the Beijing Friendship Hospital (BFH) database (validation set) and the eICU Collaborative Research Database (eICU-CRD) (validation set). Univariate logistic regression analyses were used to analyze the influencing factors, and lasso regression was used to select the predictive factors. Model performance was assessed using area under receiver operating characteristic curve (AUROC) and Presented as a Nomogram. Various aspects of the established predictive nomogram were evaluated, including discrimination, calibration, and clinical utility. Results: The model dataset consisted of 14930 patients (1444 BSI patients) from the MIMIC-IV database, divided into the training and internal validation datasets in a 7:3 ratio. The eICU dataset included 2100 patients (100 with BSI) as the eICU validation dataset, and the BFH dataset included 419 patients (21 with BSI) as the BFH validation dataset. The nomogram was constructed based on Glasgow Coma Scale (GCS), sepsis related organ failure assessment (SOFA) score, temperature, heart rate, respiratory rate, white blood cell (WBC), red width of distribution (RDW), renal replacement therapy and presence of liver disease on their admission status to the ICU. The AUROCs were 0.83 (CI 95%:0.81-0.84) in the training dataset, 0.88 (CI 95%:0.88-0.96) in the BFH validation dataset, and 0.75 (95%CI 0.70-0.79) in the eICU validation dataset. The clinical effect curve and decision curve showed that most areas of the decision curve of this model were greater than 0, indicating that this model has a certain clinical effectiveness. Conclusion: The nomogram developed in this study provides a valuable tool for clinicians and nurses to assess individual risk, enabling them to identify patients at a high risk of bloodstream infections in the ICU.

Antibiotic combinations prediction based on machine learning to multicentre clinical data and drug interaction correlation.

BACKGROUND: With increasing antibiotic resistance and regulation, the issue of antibiotic combination has been emphasised. However, antibiotic combination prescribing lacks a rapid identification of feasibility, while its risk of drug interactions is unclear. METHODS: We conducted statistical descriptions on 16 101 antibiotic coprescriptions for inpatients with bacterial infections from 2015 to 2023. By integrating the frequency and effectiveness of prescriptions, we formulated recommendations for the feasibility of antibiotic combinations. Initially, a machine learning algorithm was utilised to optimise grading thresholds and habits for antibiotic combinations. A feedforward neural network (FNN) algorithm was employed to develop antibiotic combination recommendation model (ACRM). To enhance interpretability, we combined sequential methods and DrugBank to explore the correlation between antibiotic combinations and drug interactions. RESULTS: A total of 55 antibiotics, covering 657 empirical clinical antibiotic combinations were used for ACRM construction. Model performance on the test dataset showed AUROCs of 0.589-0.895 for various antibiotic recommendation classes. The ACRM showed satisfactory clinical relevance with 61.54-73.33% prediction accuracy in a new independent retrospective cohort. Antibiotic interaction detection showed that the risk of drug interactions was 29.2% for strongly recommended and 43.5% for not recommended. A positive correlation was identified between the level of clinical recommendation and the risk of drug interactions. CONCLUSIONS: Machine learning modelling of retrospective antibiotic prescriptions habits has the potential to predict antibiotic combination recommendations. The ACRM plays a supporting role in reducing the incidence of drug interactions. Clinicians are encouraged to adopt such systems to improve the management of antibiotic usage and medication safety.

COMPARISON AMONG PRESEPSIN, PROCALCITONIN, AND C-REACTIVE PROTEIN IN PREDICTING BLOOD CULTURE POSITIVITY AND PATHOGEN IN SEPSIS PATIENTS.

ABSTRACT: Background: Sepsis is caused by the invasion of the bloodstream by microorganisms from local sites of infection, leading to high mortality. This study aimed to compare the predictive ability of the biomarkers presepsin, procalcitonin (PCT), and C-reactive protein for bacteraemia. Methods: In this retrospective, multicentre study, a dataset of patients with sepsis who were prospectively enrolled between November 2017 and June 2021 was analyzed. The performances of the biomarkers for predicting positive blood cultures and infection with specific pathogens were assessed by the areas under the receiver operating characteristic curves (AUCs). The independent effects of the pathogen and foci of infection on presepsin and PCT levels were assessed by linear logistic regression models. Results: A total of 577 patients with 170 positive blood cultures (29.5%) were enrolled. The AUC achieved using PCT levels (0.856) was significantly higher than that achieved using presepsin (0.786, P = 0.0200) and C-reactive protein (0.550, P < 0.0001) levels in predicting bacteraemia. The combined analysis of PCT and presepsin levels led to a significantly higher AUC than the analysis of PCT levels alone for predicting blood culture positivity (0.877 vs. 0.856, P = 0.0344) and gram-negative bacteraemia (0.900 vs. 0.875, P = 0.0216). In a linear regression model, the elevated concentrations of presepsin and PCT were both independently related to Escherichia coli , Klebsiella species, Pseudomonas species, and Streptococcus species infections and Sequential Organ Failure Assessment score. Presepsin levels were also associated with Acinetobacter species and abdominal infection, and PCT levels were positively associated with other Enterobacteriaceae and negatively associated with respiratory infection. Combined analysis of presepsin and PCT levels provided a high sensitivity and specificity in identifying E. coli or Klebsiella species infection. Conclusions: Presepsin and PCT were promising markers for predicting bacteraemia and common pathogens at the time of sepsis onset with a synergistic effect.

Prevalence, Risk Factors and Clinical Outcomes of Acute Kidney Injury after Paediatric Liver Transplantation.

BACKGROUND: Knowledge about acute kidney injury (AKI) in paediatric patients after liver transplantation is limited. This study focused on the prevalence and contributing factors of AKI and its impact on the postoperative outcomes of paediatric recipients. METHODS: The data of 211 paediatric patients (<12 years old) who, from December 2018 to November 2020, received first-time liver transplantation for end-stage liver disease or advanced hepatic failure in our hospital were processed for retrospective analysis. According to the criteria of the Kidney Disease Improving Global Outcomes, all patients were dichotomised into AKI and non-AKI groups. The preoperative characteristics of the patients, laboratory test results, donor type and intraoperative parameters between the two groups were compared. The effects of AKI on the postoperative outcomes of paediatric recipients were analysed. The risk factors for AKI after liver transplantation were analysed by multivariate logistic regression. RESULTS: The incidence of AKI within the first seven days after paediatric liver transplantation was 34.1%. AKI at stages I, II and III accounted for 69.4%, 22.2% and 8.3%, respectively. Compared with non-AKI patients, AKI patients had a higher proportion of hepatic cirrhosis (p = 0.039) and ascites (p = 0.003); Worse hepatic function (prolonged prothrombin time, activated partial thromboplastin time, decreased level of serum albumin and increased international normalised ratio and total bilirubin level); Higher paediatric end-stage liver disease (PELD) score (p = 0.008); And larger amount of intraoperative blood loss (p < 0.001), fluid positive balance (p = 0.035), red blood cells (RBCs) (p < 0.001) and fresh frozen plasma transfusion (p < 0.001). The multivariate logistic regression analysis demonstrated that ascites (odds ratio (OR): 2.273, p = 0.040), PELD (OR: 1.027, p = 0.013) and RBCs transfusion (OR: 1.033, p < 0.001) were independent contributing factors to AKI in paediatric patients who received liver transplantation. AKI contributed to prolonged hospital stays but did not increase hospital mortality. CONCLUSIONS: The onset of AKI can markedly prolong the hospital stay, and is common in paediatric patients undergoing liver transplantation. Poor hepatic function and large RBC transfusion contribute to AKI after liver transplantation.

Therapeutic Drug Monitoring-Guided Vancomycin Therapy of a Pediatric Patient after Liver Transplantation: a Case Report.

BACKGROUND: Vancomycin administration is challenging in critically ill patients because of pharmacokinetic changes and requires careful therapeutic drug monitoring (TDM) to guide the appropriate dosing for an effective serum concentration and to avoid toxicity. METHODS: We reported a one-year-old female pediatric patient with a body mass index of 15.4 had successful TDM-guided vancomycin therapy after a living donor liver transplantation for biliary atresia. RESULTS: The patient was admitted to the Intensive Care Unit for sepsis after her second liver transplantation. Even with the administration of the maximum approved vancomycin dosage (40 mg/kg/day), the serum trough levels were less than the recommended therapeutic level. After several adjustments based on TDM, a continuous pump infusion of up to 800 mg/day was needed to reach the desired serum trough concentration of > 10 µg/mL. Sepsis was controlled, and the patient was transferred from the Intensive Care Unit to the general ward and finally discharged home on a regular follow-up plan. CONCLUSIONS: TDM-guided vancomycin continuous infusion may be an effective therapeutic option for pediatric patients after liver transplantation.

Prevalence, Risk Factors and Clinical Outcomes of Acute Kidney Injury after Paediatric Liver Transplantation

Background: Knowledge about acute kidney injury (AKI) in paediatric patients after liver transplantation is limited. This study focused on the prevalence and contributing factors of AKI and its impact on the postoperative outcomes of paediatric recipients. Methods: The data of 211 paediatric patients (<12 years old) who, from December 2018 to November 2020, received first-time liver transplantation for end-stage liver disease or advanced hepatic failure in our hospital were processed for retrospective analysis. According to the criteria of the Kidney Disease Improving Global Outcomes, all patients were dichotomised into AKI and non-AKI groups. The preoperative characteristics of the patients, laboratory test results, donor type and intraoperative parameters between the two groups were compared. The effects of AKI on the postoperative outcomes of paediatric recipients were analysed. The risk factors for AKI after liver transplantation were analysed by multivariate logistic regression. Results: The incidence of AKI within the first seven days after paediatric liver transplantation was 34.1%. AKI at stages I, II and III accounted for 69.4%, 22.2% and 8.3%, respectively. Compared with non-AKI patients, AKI patients had a higher proportion of hepatic cirrhosis (p = 0.039) and ascites (p = 0.003); Worse hepatic function (prolonged prothrombin time, activated partial thromboplastin time, decreased level of serum albumin and increased international normalised ratio and total bilirubin level); Higher paediatric end-stage liver disease (PELD) score (p = 0.008); And larger amount of intraoperative blood loss (p < 0.001), fluid positive balance (p = 0.035), red blood cells (RBCs) (p < 0.001) and fresh frozen plasma transfusion (p < 0.001) (AU)

CLINICAL CHARACTERISTICS AND PREDICTORS OF MORTALITY DIFFER BETWEEN PULMONARY AND ABDOMINAL SEPSIS.

ABSTRACT: Background: Pulmonary sepsis and abdominal sepsis have pathophysiologically distinct phenotypes. This study aimed to compare their clinical characteristics and predictors of mortality. Methods: In this multicenter retrospective trial, 1,359 adult patients who fulfilled the Sepsis-3 criteria were enrolled and classified into the pulmonary sepsis or abdominal sepsis groups. Plasma presepsin was measured, and the scores of Acute Physiology and Chronic Health Evaluation (APACHE) II, Mortality in Emergency Department Sepsis (MEDS), and Simplified Acute Physiology Score (SAPS) II were calculated at enrollment. Data on 28-day mortality were collected for all patients. Results: Compared with patients with abdominal sepsis (n = 464), patients with pulmonary sepsis (n = 895) had higher 28-day mortality rate, illness severity scores, incidence of shock and acute kidney injury, and hospitalization costs. Lactate level and APACHE II and MEDS scores were independently associated with 28-day mortality in both sepsis types. Independent predictors of 28-day mortality included Pa o2 /F io2 ratio (hazard ratio [HR], 0.998; P < 0.001) and acute kidney injury (HR, 1.312; P = 0.039) in pulmonary sepsis, and SAPS II (HR, 1.037; P = 0.017) in abdominal sepsis. A model that combined APACHE II score, lactate, and MEDS score or SAPS II score had the best area under the receiver operating characteristic curve in predicting mortality in patients with pulmonary sepsis or abdominal sepsis, respectively. Interaction term analysis confirmed the association between 28-day mortality and lactate, APACHE II score, MEDS score, SAPS II score, and shock according to the sepsis subgroups. The mortality of patients with pulmonary sepsis was higher than that of patients with abdominal sepsis among patients without shock (32.9% vs. 8.8%; P < 0.001) but not among patients with shock (63.7 vs. 48.4%; P = 0.118). Conclusions: Patients with pulmonary sepsis had higher 28-day mortality than patients with abdominal sepsis. The study identified sepsis subgroup-specific mortality predictors. Shock had a larger effect on mortality in patients with abdominal sepsis than in those with pulmonary sepsis.

Effect of an Herbal-Based Injection on 28-Day Mortality in Patients With Sepsis: The EXIT-SEP Randomized Clinical Trial.

Importance: Previous research has suggested that Xuebijing injection (XBJ), an herbal-based intravenous preparation, may reduce mortality among patients with sepsis. Objective: To determine the effect of XBJ vs placebo on 28-day mortality among patients with sepsis. Design, Setting, and Participants: The Efficacy of Xuebijing Injection in Patients With Sepsis (EXIT-SEP) trial was a multicenter, randomized double-blind, placebo-controlled trial conducted in intensive care units at 45 sites and included 1817 randomized patients with sepsis (sepsis 3.0) present for less than 48 hours. Patients aged 18 to 75 years with a Sequential Organ Failure Assessment score of 2 to 13 were enrolled. The study was conducted from October 2017 to June 2019. The final date of follow-up was July 26, 2019. Data analysis was performed from January 2020 to August 2022. Interventions: The patients were randomized to receive either intravenous infusion of XBJ (100 mL, n = 911) or volume-matched saline placebo (n = 906) every 12 hours for 5 days. Main Outcomes and Measures: The primary outcome was 28-day mortality. Results: Among the 1817 patients who were randomized (mean [SD] age, 56.5 [13.5] years; 1199 [66.0%] men), 1760 (96.9%) completed the trial. In these patients, the 28-day mortality rate was significantly different between the placebo group and the XBJ group (230 of 882 patients [26.1%] vs 165 of 878 patients [18.8%], respectively; P < .001). The absolute risk difference was 7.3 (95% CI, 3.4-11.2) percentage points. The incidence of adverse events was 222 of 878 patients (25.3%) in the placebo group and 200 of 872 patients (22.9%) in the XBJ group. Conclusions and Relevance: In this randomized clinical trial among patients with sepsis, the administration of XBJ reduced 28-day mortality compared with placebo. Trial Registration: ClinicalTrials.gov Identifier: NCT03238742.

Over-expression of programmed death-ligand 1 and programmed death-1 on antigen-presenting cells as a predictor of organ dysfunction and mortality during early sepsis: a prospective cohort study.

BACKGROUND: This study aimed to explore the changes of programmed death-ligand 1 (PD-L1) and programmed death-1 (PD-1) expression on antigen-presenting cells (APCs) and evaluate their association with organ failure and mortality during early sepsis. METHODS: In total, 40 healthy controls and 198 patients with sepsis were included in this study. Peripheral blood was collected within the first 24 h after the diagnosis of sepsis. The expression of PD-L1 and PD-1 was determined on APCs, such as B cells, monocytes, and dendritic cells (DCs), by flow cytometry. Cytokines in plasma, such as interferon-γ (IFN-γ), tumor necrosis factor-α (TNF-α), interleukin-4 (IL-4), IL-6, IL-10, and IL-17A were determined by Luminex assay. RESULTS: PD-1 expression decreased significantly on B cells, monocytes, myeloid DCs (mDCs), and plasmacytoid DCs (pDCs) as the severity of sepsis increased. PD-1 expression was also markedly decreased in non-survivors compared with survivors. In contrast, PD-L1 expression was markedly higher on mDCs, pDCs, and monocytes in patients with sepsis than in healthy controls and in non-survivors than in survivors. The PD-L1 expression on APCs (monocytes and DCs) was weakly related to organ dysfunction and inflammation. The area under the receiver operating characteristic curve (AUC) of the PD-1 percentage of monocytes (monocyte PD-1%)+APACHE II model (0.823) and monocyte PD-1%+SOFA model (0.816) had higher prognostic value than other parameters alone. Monocyte PD-1% was an independent risk factor for 28-day mortality. CONCLUSION: The severity of sepsis was correlated with PD-L1 or PD-1 over-expression on APCs. PD-L1 in monocytes and DCs was weakly correlated with inflammation and organ dysfunction during early sepsis. The combination of SOFA or APACHE II scores with monocyte PD-1% could improve the prediction ability for mortality.

The study on role of endothelial cell autophagy in rats with sepsis-induced acute kidney injury.

Sepsis often causes acute kidney injury (AKI). Autophagy of renal tubular epithelial cells is considered a cytoprotective mechanism in septic AKI; however, the role of autophagy of renal endothelial cells is uninvestigated. The current study examined whether autophagy was induced by sepsis in renal endothelial cells and whether induction of autophagy in these cells attenuated the degree of AKI. Cecal ligation and puncture (CLP) was used as a model of sepsis in rats. Four experimental groups included: sham, CLP alone, CLP + rapamycin (RAPA), and CLP + dimethyl sulfoxide (DMSO), where RAPA was used as an activator of autophagy. CLP increased renal LC3-II protein levels with an additional transient increase by RAPA at 18 h. In addition, CLP induced autophagosome formation in renal endothelial cells had an additional increase induced by RAPA. Interestingly, the levels of bone morphogenetic protein and activin membrane-bound inhibitor (BAMBI), an endothelial cell-specific protein in the kidney, were also increased by CLP, albeit it was transiently downregulated by RAPA at 18 h. Serum thrombomodulin increased and renal vascular endothelial (VE)-cadherin decreased following CLP, and these changes were attenuated by RAPA. The renal cortex exhibited and inflammatory tissue damage after CLP, and RAPA alleviated these histopathological injuries. The current findings indicate that autophagy was induced by sepsis in renal endothelial cells, and upregulation of autophagy in these cells alleviated endothelial injury and AKI. In addition, BAMBI was induced by sepsis in the kidney, which may play a role in regulating endothelial stability in septic AKI.

Population pharmacokinetics and dosing optimisation of imipenem in critically ill patients.

OBJECTIVE: The objective of this study was to explore factors that affect the clearance of imipenem in critically ill patients and to provide a dosing regimen for such patients. METHODS: A prospective open-label study enrolled 51 critically ill patients with sepsis. Patients were between the ages of 18 and 96. Blood samples were collected in duplicate before (0 hour) and at 0.5, 1, 1.5, 2, 3, 4, 6, and 8 hours after imipenem administration. The plasma imipenem concentration was determined by the high-performance liquid chromatography-ultraviolet detection (HPLC-UV) method. A population pharmacokinetic (PPK) model was developed using nonlinear mixed-effects modelling methods to identify covariates. Monte Carlo simulations were performed using the final PPK model to explore the effect of different dosing regimens on the probability of target attainment (PTA). RESULTS: The imipenem concentration data were best described by a two-compartment model. Creatinine clearance (CrCl, mL/min) was a covariate that affected central clearance (CLc). Patients were divided into four subgroups based on different CrCl rates. Monte Carlo simulations were performed to assess the PTA differences between empirical dosing regimens (0.5 g every 6 hours (q6h), 0.5 g every 8 hours (q8h), 0.5 g every 12 hours (q12h), 1 g every 6 hours (q6h), 1 g every 8 hours (q8h), and 1 g every 12 hours (q12h)) and to determine the target achievement rate covariate. CONCLUSION: This study identified covariates for CLc, and the proposed final model can be used to guide clinicians administering imipenem in this particular patient population.

[Incidence of maternal sepsis in ICUs of hospitals in Beijing: a multicenter cohort study].

OBJECTIVE: To investigate the epidemiological data of maternal sepsis in intensive care unit (ICU), analyze the common causes, outcomes of maternal sepsis, and the risk factors of multi-drug resistant (MDR) bacteria. METHODS: A retrospective cohort study. Maternal sepsis cases admitted to ICUs of Peking University Third Hospital, Beijing Chao-Yang Hospital Affiliated to Capital Medical University, and Beijing Friendship Hospital Affiliated to Capital Medical University from January 2008 to September 2022 were enrolled. The following data were recorded: demographic characteristics, sequential organ failure assessment (SOFA) during infection, infection time, infection sites, invasive intervention measures before infection, microbial culture results, blood routine test during infection, body temperature, and clinical outcomes caused by infection. According to the time of sepsis occurrence, the patients were divided into pre-ICU sepsis group and ICU sepsis group, and the causes of sepsis in the two groups were analyzed. According to whether MDR occurred, the patients were divided into MDR group and non-MDR group, and clinical outcomes were analyzed. Multivariate Logistic regression was used to analyze the risk factors of MDR bacteria infection in obstetrics with sepsis. RESULTS: 160 patients were enrolled, among which 104 cases of sepsis happened before ICU and 56 cases of sepsis happened during ICU, 53 cases were with MDR bacteria and 107 cases were without MDR bacteria. The median age of the patients was 30.5 (28.0, 34.0) years old, the median temperature was 38.8 (38.2, 39.5) centigrade, and the median white blood cell count (WBC) was 17.2 (13.2, 21.3)×109/L, the median SOFA score was 5.0 (3.0, 8.0), and 130 cases (81.2%) were referred from other hospitals. The main infection sites were uterine cavity in 64 cases (40.0%), lung in 48 cases (30.0%), abdominal and pelvic cavity in 30 cases (18.8%), urinary system in 27 cases (16.9%). Sepsis led to hysterectomy in 6 cases (3.8%), stillbirth in 8 cases (5.0%), and neonatal death in 2 cases (1.3%). The main surgical intervention measures were cesarean section (44 cases, accounting for 27.5%), followed by exploratory laparotomy (19 cases, 11.9%). The median length of ICU stay was 5.0 (3.0, 10.0) days, and the median hospital length was 14.0 (10.0, 20.8) days. Intrauterine infection was the primary cause of sepsis happened during ICU, accounting for 50.0% (28/56), of which postpartum hemorrhage accounted for 85.7% (24/28). The proportion of diabetes [28.3% (15/53) vs. 14.0% (15/107)], intrauterine operation [41.5% (22/53) vs. 23.4% (25/107)], intrauterine infection [50.9% (27/53) vs. 34.6% (37/107)] and bacteremia [18.9% (10/53) vs. 2.8% (3/107)] in the MDR group were significantly higher than those in the non-MDR group (all P < 0.05). Multivariate Logistic regression analysis showed that diabetes [odds ratio (OR) = 2.348, 95% confidence interval (95%CI) was 1.006-5.480, P = 0.048] and intrauterine operation (OR = 2.541, 95%CI was 1.137-5.678, P = 0.023) were independent risk factors for MDR bacterial infection in obstetrics with sepsis. CONCLUSIONS: Intrauterine infection is the common cause of maternal sepsis in ICU, and postpartum hemorrhage is the common cause of secondary intrauterine infection in ICU. MDR bacteria can lead to serious clinical outcomes. Diabetes and intrauterine operation are independent risk factors for MDR bacteria' infection.

The Response of Macrophages in Sepsis-Induced Acute Kidney Injury.

Sepsis-induced acute kidney injury (SAKI) is common in critically ill patients and often leads to poor prognosis. At present, the pathogenesis of SAKI has not been fully clarified, and there is no effective treatment. Macrophages are immune cells that play an important role in the pathogenesis of SAKI. The phenotype and role of macrophages can vary from early to later stages of SAKI. Elucidating the role of macrophages in SAKI will be beneficial to its diagnosis and treatment. This article reviews past studies describing the role of macrophages in SAKI, with the aim of identifying novel therapeutic targets.

Nomogram Prediction Model of Hypernatremia on Mortality in Critically Ill Patients.

Objective: To investigate the value of hypernatremia in the intensive care unit (ICU) for the risk prediction of mortality in severe patients. Methods: Clinical data of critically ill patients admitted to the ICU of Beijing Friendship Hospital, were collected for retrospective analysis. Univariate and multivariate logistic regression analyses were employed to analyze the influencing factors. Nomograms predicting the mortality were constructed with R software and validated with repeated sampling. Results: A total of 442 cases were eligible for this study. Hypernatremia within 48 hours of ICU admission, change in sodium concentration (CNa+) within 48 hours, septic shock, APACHE II score, hyperlactatemia within 48 hours, use of continuous renal replacement therapy (CRRT) within 48 hours, and the use of mechanical ventilation (MV) within 48 hours of ICU admission were all identified as independent risk factors for death within 28 days of ICU admission. These predictors were included in a nomogram of 28-day mortality in severe patients, which was constructed using R software. Conclusion: The nomogram could predict the individualized risk of 28-day mortality based on the above factors. The model has better discrimination and accuracy and has high clinical application value.

Chinese experts' consensus on the application of intensive care big data.

The development of intensive care medicine is inseparable from the diversified monitoring data. Intensive care medicine has been closely integrated with data since its birth. Critical care research requires an integrative approach that embraces the complexity of critical illness and the computational technology and algorithms that can make it possible. Considering the need of standardization of application of big data in intensive care, Intensive Care Medicine Branch of China Health Information and Health Care Big Data Society, Standard Committee has convened expert group, secretary group and the external audit expert group to formulate Chinese Experts' Consensus on the Application of Intensive Care Big Data (2022). This consensus makes 29 recommendations on the following five parts: Concept of intensive care big data, Important scientific issues, Standards and principles of database, Methodology in solving big data problems, Clinical application and safety consideration of intensive care big data. The consensus group believes this consensus is the starting step of application big data in the field of intensive care. More explorations and big data based retrospective research should be carried out in order to enhance safety and reliability of big data based models of critical care field.