The prognostic value of platelet aggregation in patients with sepsis.

BACKGROUND: This study aims to investigate the relevance of platelet aggregation markers, specifically arachidonic acid (AA) and adenosine diphosphate (ADP), in relation to the prognosis of sepsis patients. METHODS: A cohort of 40 sepsis patients was included and stratified, based on their 28-day post-treatment prognosis, into two groups: a survival group (n = 31) and a severe sepsis group (n = 9. Then, their various clinical parameters, including patient demographics, platelet counts (PLT), inflammatory markers, and platelet aggregation rates (PAR) induced by AA and ADP between the two groups, were compared. Long-term health implications of sepsis were assessed using the Acute Physiologic Assessment and Chronic Health Evaluation II (APACHE II) score, and logistic regression analysis was conducted to evaluate the prognostic significance of PAR in sepsis patients. RESULTS: Patients with severe sepsis exhibited significantly elevated levels of procalcitonin (PCT), platelet adhesion rates, and PAR induced by ADP (P < 0.05), but having lower PLT (P < 0.05), compared to those in the survival group. Logistic regression analysis demonstrated that PAR induced by ADP was a protective factor in predicting prognosis in sepsis patients (P < 0.01). CONCLUSIONS: Activation of platelets in sepsis intensifies inflammatory response. Patients with sepsis whose ADP-induced PAR was < 60% displayed significant impairment in platelet aggregation function, and had higher mortality rate. Monitoring ADP-induced PAR is crucial for management of sepsis.

The role of platelets in central hubs of inflammation: A literature review.

Platelets are increasingly recognized for their multifaceted roles in inflammation beyond their traditional involvement in haemostasis. This review consolidates knowledge on platelets as critical players in inflammatory responses. This study did an extensive search of electronic databases and identified studies on platelets in inflammation, focusing on molecular mechanisms, cell interactions, and clinical implications, emphasizing recent publications. Platelets contribute to inflammation via surface receptors, release of mediators, and participation in neutrophil extracellular trap formation. They are implicated in diseases like atherosclerosis, rheumatoid arthritis, and sepsis, highlighting their interaction with immune cells as pivotal in the onset and resolution of inflammation. Platelets are central to regulating inflammation, offering new therapeutic targets for inflammatory diseases. Future research should explore specific molecular pathways of platelets in inflammation for therapeutic intervention.

Machine learning derived serum creatinine trajectories in acute kidney injury in critically ill patients with sepsis.

BACKGROUND: Current classification for acute kidney injury (AKI) in critically ill patients with sepsis relies only on its severity-measured by maximum creatinine which overlooks inherent complexities and longitudinal evaluation of this heterogenous syndrome. The role of classification of AKI based on early creatinine trajectories is unclear. METHODS: This retrospective study identified patients with Sepsis-3 who developed AKI within 48-h of intensive care unit admission using Medical Information Mart for Intensive Care-IV database. We used latent class mixed modelling to identify early creatinine trajectory-based classes of AKI in critically ill patients with sepsis. Our primary outcome was development of acute kidney disease (AKD). Secondary outcomes were composite of AKD or all-cause in-hospital mortality by day 7, and AKD or all-cause in-hospital mortality by hospital discharge. We used multivariable regression to assess impact of creatinine trajectory-based classification on outcomes, and eICU database for external validation. RESULTS: Among 4197 patients with AKI in critically ill patients with sepsis, we identified eight creatinine trajectory-based classes with distinct characteristics. Compared to the class with transient AKI, the class that showed severe AKI with mild improvement but persistence had highest adjusted risks for developing AKD (OR 5.16; 95% CI 2.87-9.24) and composite 7-day outcome (HR 4.51; 95% CI 2.69-7.56). The class that demonstrated late mild AKI with persistence and worsening had highest risks for developing composite hospital discharge outcome (HR 2.04; 95% CI 1.41-2.94). These associations were similar on external validation. CONCLUSIONS: These 8 classes of AKI in critically ill patients with sepsis, stratified by early creatinine trajectories, were good predictors for key outcomes in patients with AKI in critically ill patients with sepsis independent of their AKI staging.

The beneficial effects of neutrophil elastase inhibitor on gastrointestinal dysfunction in sepsis.

To investigate the effects of neutrophil elastase inhibitor (sivelestat sodium) on gastrointestinal function in sepsis. A reanalysis of the data from previous clinical trials conducted at our center was performed. Septic patients were divided into either the sivelestat group or the non-sivelestat group. The gastrointestinal dysfunction score (GIDS), feeding intolerance (FI) incidence, serum levels of intestinal barrier function and inflammatory biomarkers were recorded. The clinical severity and outcome variables were also documented. A total of 163 septic patients were included. The proportion of patients with GIDS ≥2 in the sivelestat group was reduced relative to that in the non-sivelestat group (9.6% vs. 22.5%, p = 0.047) on the 7th day of intensive care unit (ICU) admission. The FI incidence was also remarkably reduced in the sivelestat group in contrast to that in the non-sivelestat group (21.2% vs. 37.8%, p = 0.034). Furthermore, the sivelestat group had fewer days of FI [4 (3, 4) vs. 5 (4-6), p = 0.008]. The serum levels of d-lactate (p = 0.033), intestinal fatty acid-binding protein (p = 0.005), interleukin-6 (p = 0.001), white blood cells (p = 0.007), C-reactive protein (p = 0.001), and procalcitonin (p < 0.001) of the sivelestat group were lower than those of the non-sivelestat group. The sivelestat group also demonstrated longer ICU-free days [18 (0-22) vs. 13 (0-17), p = 0.004] and ventilator-free days [22 (1-24) vs. 16 (1-19), p = 0.002] compared with the non-sivelestat group. In conclusion, sivelestat sodium administration appears to improve gastrointestinal dysfunction, mitigate dysregulated inflammation, and reduce disease severity in septic patients.

Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning.

BACKGROUND: Sepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological and metabolic changes in response to the disease. Previous studies have indicated that the stress hyperglycemia ratio (SHR) can serve as a reliable predictor of adverse outcomes in various cardiovascular and cerebrovascular diseases. However, there is limited research on the relationship between the SHR and adverse outcomes in patients with infectious diseases, particularly in critically ill patients with sepsis. Therefore, this study aimed to explore the association between the SHR and adverse outcomes in critically ill patients with sepsis. METHODS: Clinical data from 2312 critically ill patients with sepsis were extracted from the MIMIC-IV (2.2) database. Based on the quartiles of the SHR, the study population was divided into four groups. The primary outcome was 28-day all-cause mortality, and the secondary outcome was in-hospital mortality. The relationship between the SHR and adverse outcomes was explored using restricted cubic splines, Cox proportional hazard regression, and Kaplan‒Meier curves. The predictive ability of the SHR was assessed using the Boruta algorithm, and a prediction model was established using machine learning algorithms. RESULTS: Data from 2312 patients who were diagnosed with sepsis were analyzed. Restricted cubic splines demonstrated a "U-shaped" association between the SHR and survival rate, indicating that an increase in the SHR is related to an increased risk of adverse events. A higher SHR was significantly associated with an increased risk of 28-day mortality and in-hospital mortality in patients with sepsis (HR > 1, P < 0.05) compared to a lower SHR. Boruta feature selection showed that SHR had a higher Z score, and the model built using the rsf algorithm showed the best performance (AUC = 0.8322). CONCLUSION: The SHR exhibited a U-shaped relationship with 28-day all-cause mortality and in-hospital mortality in critically ill patients with sepsis. A high SHR is significantly correlated with an increased risk of adverse events, thus indicating that is a potential predictor of adverse outcomes in patients with sepsis.

Comparison between XGboost model and logistic regression model for predicting sepsis after extremely severe burns.

OBJECTIVE: To compare an Extreme Gradient Boosting (XGboost) model with a multivariable logistic regression (LR) model for their ability to predict sepsis after extremely severe burns. METHODS: For this observational study, patient demographic and clinical information were collected from medical records. The two models were evaluated using area under curve (AUC) of the receiver operating characteristic (ROC) curve. RESULTS: Of the 103 eligible patients with extremely severe burns, 20 (19%) were in the sepsis group, and 83 (81%) in the non-sepsis group. The LR model showed that age, admission time, body index (BI), fibrinogen, and neutrophil to lymphocyte ratio (NLR) were risk factors for sepsis. Comparing AUC of the ROC curves, the XGboost model had a higher predictive performance (0.91) than the LR model (0.88). The SHAP visualization tool indicated fibrinogen, NLR, BI, and age were important features of sepsis in patients with extremely severe burns. CONCLUSIONS: The XGboost model was superior to the LR model in predictive efficacy. Results suggest that, fibrinogen, NLR, BI, and age were correlated with sepsis after extremely severe burns.

Prussian Blue Analogues-Derived Molecularly Imprinted Nanozyme Array for Septicemia Detection.

Septicemia, a severe bacterial infection, poses significant risks to human health. Early detection of septicemia by tracking specific biomarkers is crucial for a timely intervention. Herein, we developed a molecularly imprinted (MI) TiO2-Fe-CeO2 nanozyme array derived from Ce[Fe(CN)6] Prussian blue analogues (PBA), specifically targeting valine, leucine, and isoleucine, as potential indicators of septicemia. The synthesized nanozyme arrays were thoroughly characterized using various analytical techniques, including Fourier transform infrared spectroscopy, X-ray diffraction, field-emission scanning electron microscope, and energy-dispersive X-ray. The results confirmed their desirable physical and chemical properties, indicating their suitability for the oxidation of 3,3',5,5'-tetramethylbenzidine serving as a colorimetric probe in the presence of a persulfate oxidizing agent, further highlighting the potential of these arrays for sensitive and accurate detection applications. The MITiO2 shell selectively captures valine, leucine, and isoleucine, partially blocking the cavities for substrate access and thereby hindering the catalyzed TMB chromogenic reaction. The nanozyme array demonstrated excellent performance with linear detection ranges of 5 µM to 1 mM, 10-450 µM, and 10-450 µM for valine, leucine, and isoleucine, respectively. Notably, the corresponding limit of detection values were 0.69, 1.46, and 2.76 µM, respectively. The colorimetric assay exhibited outstanding selectivity, reproducibility, and performance in the detection of analytes in blood samples, including C-reactive protein at a concentration of 61 mg/L, procalcitonin at 870 ng/dL, and the presence of Pseudomonas aeruginosa bacteria. The utilization of Ce[Fe(CN)6]-derived MITiO2-Fe-CeO2 nanozyme arrays holds considerable potential in the field of septicemia detection. This approach offers a sensitive and specific method for early diagnosis and intervention, thereby contributing to improved patient outcomes.

Lactate dynamics in paediatric patients with severe sepsis: insights from a prospective cohort study.

BACKGROUND: Sepsis is an infection-related systemic inflammatory response that often leads to elevated lactate levels. Monitoring lactate levels during severe sepsis is vital for influencing clinical outcomes. The aim of this study was to assess the association between plasma lactate levels and mortality in children with severe sepsis or septic shock. METHODS: The current prospective study was conducted in the PICU of University Children's Hospital. The International Paediatric Sepsis Consensus Conference criteria for Definitions of Sepsis and Organ Failure in 2005 were used to diagnose patients with sepsis. We measured plasma lactate levels upon admission (Lac H0) and 6 h later (Lac H6). The static indices included the absolute lactate values (Lac H0 and Lac H6), while the dynamic indices included the delta-lactate level (ΔLac) and the 6-hour lactate clearance. The 6-hour lactate clearance was calculated using the following formula: [(Lac H0-Lac H6)100/Lac H0]. ΔLac was calculated as the difference between the Lac H0 and Lac H6 levels. Patient survival or death after a PICU stay was the primary outcome. RESULTS: A total of 46 patients were included in this study: 25 had septic shock, and 21 had severe sepsis. The mortality rate was 54.3%. The Lac H0 did not significantly differ between survivors and nonsurvivors. In contrast, the survivors had significantly lower Lac H6 levels, higher ΔLac levels, and higher 6-hour lactate clearance rates than nonsurvivors. Lactate clearance rates below 10%, 20%, and 30% were significantly associated with mortality. The best cut-off values for the lactate clearance rate and Lac H6 for the prediction of mortality in the PICU were < 10% and ≥ 4 mmol/L, respectively. Patients with higher Lac H6 levels and lower lactate clearance rates had significantly higher PICU mortality based on Kaplan-Meier survival curve analysis. CONCLUSIONS: This study highlights the significance of lactate level trends over time for the prediction of mortality in the PICU in patients with severe sepsis or septic shock. Elevated lactate levels and decreased lactate clearance six hours after hospitalisation are associated with a higher mortality rate.

Identification of sepsis-associated mitochondrial genes through RNA and single-cell sequencing approaches.

BACKGROUND: Sepsis ranks among the most formidable clinical challenges, characterized by exorbitant treatment costs and substantial demands on healthcare resources. Mitochondrial dysfunction emerges as a pivotal risk factor in the pathogenesis of sepsis, underscoring the imperative to identify mitochondrial-related biomarkers. Such biomarkers are crucial for enhancing the accuracy of sepsis diagnostics and prognostication. METHODS: In this study, adhering to the SEPSIS 3.0 criteria, we collected peripheral blood within 24 h of admission from 20 sepsis patients at the ICU of the Southwest Medical University Affiliated Hospital and 10 healthy volunteers as a control group for RNA-seq. The RNA-seq data were utilized to identify differentially expressed RNAs. Concurrently, mitochondrial-associated genes (MiAGs) were retrieved from the MitoCarta3.0 database. The differentially expressed genes were intersected with MiAGs. The intersected genes were then subjected to GO (Gene Ontology), and KEGG (Kyoto Encyclopedia of Genes and Genomes) analyses and core genes were filtered using the PPI (Protein-Protein Interaction) network. Subsequently, relevant sepsis datasets (GSE65682, GSE28750, GSE54514, GSE67652, GSE69528, GSE95233) were downloaded from the GEO (Gene Expression Omnibus) database to perform bioinformatic validation of these core genes. Survival analysis was conducted to assess the prognostic value of the core genes, while ROC (Receiver Operating Characteristic) curves determined their diagnostic value, and a meta-analysis confirmed the accuracy of the RNA-seq data. Finally, we collected 5 blood samples (2 normal controls (NC); 2 sepsis; 1 SIRS (Systemic Inflammatory Response Syndrome), and used single-cell sequencing to assess the expression levels of the core genes in the different blood cell types. RESULTS: Integrating high-throughput sequencing with bioinformatics, this study identified two mitochondrial genes (COX7B, NDUFA4) closely linked with sepsis prognosis. Survival analysis demonstrated that patients with lower expression levels of COX7B and NDUFA4 exhibited a higher day survival rate over 28 days, inversely correlating with sepsis mortality. ROC curves highlighted the significant sensitivity and specificity of both genes, with AUC values of 0.985 for COX7B and 0.988 for NDUFA4, respectively. Meta-analysis indicated significant overexpression of COX7B and NDUFA4 in the sepsis group in contrast to the normal group (P < 0.01). Additionally, single-cell RNA sequencing revealed predominant expression of these core genes in monocytes-macrophages, T cells, and B cells. CONCLUSION: The mitochondrial-associated genes (MiAGs) COX7B and NDUFA4 are intimately linked with the prognosis of sepsis, offering potential guidance for research into the mechanisms underlying sepsis.

Blood calprotectin as a biomarker for infection and sepsis - the prospective CASCADE trial.

BACKGROUND: Early in the host-response to infection, neutrophils release calprotectin, triggering several immune signalling cascades. In acute infection management, identifying infected patients and stratifying these by risk of deterioration into sepsis, are crucial tasks. Recruiting a heterogenous population of patients with suspected infections from the emergency department, early in the care-path, the CASCADE trial aimed to evaluate the accuracy of blood calprotectin for detecting bacterial infections, estimating disease severity, and predicting clinical deterioration. METHODS: In a prospective, observational trial from February 2021 to August 2022, 395 patients (n = 194 clinically suspected infection; n = 201 controls) were enrolled. Blood samples were collected at enrolment. The accuracy of calprotectin to identify bacterial infections, and to predict and identify sepsis and mortality was analysed. These endpoints were determined by a panel of experts. RESULTS: The Area Under the Receiver Operating Characteristic (AUROC) of calprotectin for detecting bacterial infections was 0.90. For sepsis within 72 h, calprotectin's AUROC was 0.83. For 30-day mortality it was 0.78. In patients with diabetes, calprotectin had an AUROC of 0.94 for identifying bacterial infection. CONCLUSIONS: Calprotectin showed notable accuracy for all endpoints. Using calprotectin in the emergency department could improve diagnosis and management of severe infections, in combination with current biomarkers. CLINICAL TRIAL REGISTRATION NUMBER: DRKS00020521.

Predictive value of laboratory indicators for in-hospital death in children with community-onset sepsis: a prospective observational study of 266 patients.

BACKGROUND AND OBJECTIVES: This study aimed to identify predictors of sepsis-associated in-hospital mortality from readily available laboratory biomarkers at onset of illness that include haematological, coagulation, liver and kidney function, blood lipid, cardiac enzymes and arterial blood gas. METHODS: Children with sepsis were enrolled consecutively in a prospective observational study involving paediatric intensive care units (PICUs) of two hospitals in Beijing, between November 2016 and January 2020. The data on demographics, laboratory examinations during the first 24 hours after PICU admission, complications and outcomes were collected. We screened baseline laboratory indicators using the Least Absolute Shrinkage and Selection Operator (LASSO) analysis, then we constructed a mortality risk model using Cox proportional hazards regression analysis. The ability of risk factors to predict in-hospital mortality was evaluated by receiver operating characteristic (ROC) curves. RESULTS: A total of 266 subjects were enrolled including 44 (16.5%) deaths and 222 (83.5%) survivors. Those who died showed a shorter length of hospitalisation, and a higher proportion of mechanical ventilation, complications and organ failure (p<0.05). LASSO analysis identified 13 clinical parameters related to prognosis, which were included in the final Cox model. An elevated triglyceride (TG) remained the most significant risk factor of death (HR=1.469, 95% CI: 1.010 to 2.136, p=0.044), followed by base excess (BE) (HR=1.131, 95% CI: 1.046 to 1.223, p=0.002) and pH (HR=0.95, 95% CI: 0.93 to 0.97, p<0.001). The results of the ROC curve showed that combined diagnosis of the three indicators-TG+BE+pH-has the best area under the curve (AUC) (AUC=0.77, 95% CI: 0.69 to 0.85, p<0.001), with a 68% sensitivity and 80% specificity. CONCLUSION: Laboratory factors of TG, BE and pH during the first 24 hours after intensive care unit admission are associated with in-hospital mortality in PICU patients with sepsis. The combination of the three indices has high diagnostic value.

A machine learning model for the early diagnosis of bloodstream infection in patients admitted to the pediatric intensive care unit.

Bloodstream infection (BSI) is associated with increased morbidity and mortality in the pediatric intensive care unit (PICU) and high healthcare costs. Early detection and appropriate treatment of BSI may improve patient's outcome. Data on machine-learning models to predict BSI in pediatric patients are limited and neither study included time series data. We aimed to develop a machine learning model to predict an early diagnosis of BSI in patients admitted to the PICU. This was a retrospective cohort study of patients who had at least one positive blood culture result during stay at a PICU of a tertiary-care university hospital, from January 1st to December 31st 2019. Patients with positive blood culture results with growth of contaminants and those with incomplete data were excluded. Models were developed using demographic, clinical and laboratory data collected from the electronic medical record. Laboratory data (complete blood cell counts with differential and C-reactive protein) and vital signs (heart rate, respiratory rate, blood pressure, temperature, oxygen saturation) were obtained 72 hours before and on the day of blood culture collection. A total of 8816 data from 76 patients were processed by the models. The machine committee was the best-performing model, showing accuracy of 99.33%, precision of 98.89%, sensitivity of 100% and specificity of 98.46%. Hence, we developed a model using demographic, clinical and laboratory data collected on a routine basis that was able to detect BSI with excellent accuracy and precision, and high sensitivity and specificity. The inclusion of vital signs and laboratory data variation over time allowed the model to identify temporal changes that could be suggestive of the diagnosis of BSI. Our model might help the medical team in clinical-decision making by creating an alert in the electronic medical record, which may allow early antimicrobial initiation and better outcomes.

Mendelian randomization analysis reveals causal associations of serum metabolites with sepsis and 28-day mortality.

Metabolic disorder has been found to be an important factor in the pathogenesis and progression of sepsis. However, the causation of such an association between serum metabolites and sepsis has not been established. We conducted a two-sample Mendelian randomization (MR) study. A genome-wide association study of 486 human serum metabolites was used as the exposure, whereas sepsis and sepsis mortality within 28 days were set as the outcomes. In MR analysis, 6 serum metabolites were identified to be associated with an increased risk of sepsis, and 6 serum metabolites were found to be related to a reduced risk of sepsis. Furthermore, there were 9 metabolites positively associated with sepsis-related mortality, and 8 metabolites were negatively correlated with sepsis mortality. In addition, "glycolysis/gluconeogenesis" (p = 0.001), and "pyruvate metabolism" (p = 0.042) two metabolic pathways were associated with the incidence of sepsis. This MR study suggested that serum metabolites played significant roles in the pathogenesis of sepsis, which may provide helpful biomarkers for early disease diagnosis, therapeutic interventions, and prognostic assessments for sepsis.

Lactate to Albumin Ratio Is Not Predictive of Outcome in Septic Dogs: A Retrospective Case-Control Study.

The objective of this study was to investigate the value of the lactate to albumin ratio (L:A) as a prognostic marker for mortality in septic dogs. A single-center retrospective case-control study based on clinical record review was conducted at an academic teaching hospital. All records were extracted for diagnoses of bacterial sepsis, septic peritonitis, septic shock, or septicemia between February 2012 and October 2021. The study included 143 dogs. The most commonly identified sepsis diagnoses in dogs were septic peritonitis (55%; 78/143), unclassified sepsis (20%), and sepsis secondary to wounds or dermatological conditions (10%; 15/143). Median lactate and albumin for all dogs at presentation were 2.80 mmol/L and 2.6 g/dL, respectively; the median L:A ratio was 1.22. No clinically or statistically significant differences in lactate (P = 0.631), albumin (P = 0.695), or L:A (P = 0.908) were found between survivors and nonsurvivors.

Free Radical-Associated Gene Signature Predicts Survival in Sepsis Patients.

Sepsis continues to overwhelm hospital systems with its high mortality rate and prevalence. A strategy to reduce the strain of sepsis on hospital systems is to develop a diagnostic/prognostic measure that identifies patients who are more susceptible to septic death. Current biomarkers fail to achieve this outcome, as they only have moderate diagnostic power and limited prognostic capabilities. Sepsis disrupts a multitude of pathways in many different organ systems, making the identification of a single powerful biomarker difficult to achieve. However, a common feature of many of these perturbed pathways is the increased generation of reactive oxygen species (ROS), which can alter gene expression, changes in which may precede the clinical manifestation of severe sepsis. Therefore, the aim of this study was to evaluate whether ROS-related circulating molecular signature can be used as a tool to predict sepsis survival. Here we created a ROS-related gene signature and used two Gene Expression Omnibus datasets from whole blood samples of septic patients to generate a 37-gene molecular signature that can predict survival of sepsis patients. Our results indicate that peripheral blood gene expression data can be used to predict the survival of sepsis patients by assessing the gene expression pattern of free radical-associated -related genes in patients, warranting further exploration.

Integrative analysis of metabolomics and transcriptomics to uncover biomarkers in sepsis.

To utilize metabolomics in conjunction with RNA sequencing to identify biomarkers in the blood of sepsis patients and discover novel targets for diagnosing and treating sepsis. In January 2019 and December 2020, blood samples were collected from a cohort of 16 patients diagnosed with sepsis and 11 patients diagnosed with systemic inflammatory response syndrome (SIRS). Non-targeted metabolomics analysis was conducted using liquid chromatography coupled with mass spectrometry (LC-MS/MS technology), while gene sequencing was performed using RNA sequencing. Afterward, the metabolite data and sequencing data underwent quality control and difference analysis, with a fold change (FC) greater than or equal to 2 and a false discovery rate (FDR) less than 0.05.Co-analysis was then performed to identify differential factors with consistent expression trends based on the metabolic pathway context; KEGG enrichment analysis was performed on the crossover factors, and Meta-analysis of the targets was performed at the transcriptome level using the public dataset. In the end, a total of five samples of single nucleated cells from peripheral blood (two normal controls, one with systemic inflammatory response syndrome, and two with sepsis) were collected and examined to determine the cellular location of the essential genes using 10× single cell RNA sequencing (scRNA-seq). A total of 485 genes and 1083 metabolites were found to be differentially expressed in the sepsis group compared to the SIRS group. Among these, 40 genes were found to be differentially expressed in both the metabolome and transcriptome. Functional enrichment analysis revealed that these genes were primarily involved in biological processes related to inflammatory response, immune regulation, and amino acid metabolism. Furthermore, a meta-analysis identified four genes, namely ITGAM, CD44, C3AR1, and IL2RG, which were highly expressed in the sepsis group compared to the normal group (P < 0.05). Additionally, scRNA-seq analysis revealed that the core genes ITGAM and C3AR1 were predominantly localized within the macrophage lineage. The primary genes ITGAM and C3AR1 exhibit predominant expression in macrophages, which play a significant role in inflammatory and immune responses. Moreover, these genes show elevated expression levels in the plasma of individuals with sepsis, indicating their potential as valuable subjects for further research in sepsis.

Association of copeptin levels with patient prognosis and survival in sepsis syndromes: a meta-analysis.

BACKGROUND: Sepsis syndromes are a major burden in the ICU with very high mortality. Vasopressin and copeptin are released in response to hypovolemia and have shown potential significance in diagnosing sepsis. OBJECTIVE: To investigate the levels of copeptin in patients with sepsis syndromes and evaluate its relation with patient prognosis and mortality. METHODS: Four databases were searched for literature published from inception to the 8th of November 2022. Original research articles where copeptin was measured in sepsis patients and compared with controls were included. Data extraction and synthesis: study characteristics, levels of copeptin in the participants, and copeptin assay description were extracted. Levels of copeptin in patients were pooled and compared with controls in terms of the standard mean difference (SMD) generated using a random-effects model. RESULTS: Fifteen studies met the selection criteria. Copeptin levels were significantly higher in patients with sepsis, severe sepsis, and septic shock as compared to controls [(SMD: 1.49, 95% CI: 0.81-2.16, P<0.0001), (SMD: 1.94, 95% CI: 0.34-3.54, P=0.02), and (SMD: 2.17, 95% CI: 0.68-3.66, P=0.004), respectively]. The highest copeptin levels were noted in septic shock patients. The admission copeptin levels were significantly lower in survivors as compared to nonsurvivors (SMD: -1.73; 95% CI: -2.41 to -1.06, P<0.001). CONCLUSION AND RELEVANCE: Copeptin was significantly elevated in sepsis, severe sepsis, and septic shock. Survivors had a significantly lower copeptin during admission. Copeptin offered an excellent predictability to predict 1-month mortality. Measuring the copeptin in sepsis patients can aid treating physicians to foresee patients' prognosis.

Heart rate and oxygen saturation patterns in very low birth weight infants with early onset sepsis and histologic chorioamnionitis.

BACKGROUND: Chorioamnionitis and early onset sepsis (EOS) in very low birth weight (VLBW,< 1500 g) infants may cause a systemic inflammatory response reflected in patterns of heart rate (HR) and oxygenation measured by pulse oximetry (SpO2). Identification of these patterns might inform decisions about duration of antibiotic therapy after birth. OBJECTIVE: Compare early HR and SpO2 patterns in VLBW infants with or without early onset sepsis (EOS) or histologic chorioamnionitis (HC). STUDY DESIGN: Retrospective study of placental pathology and HR and SpO2 in the first 72 h from birth in relation to EOS status for inborn VLBW NICU patients 2012-2019. RESULT: Among 362 VLBW infants with HR and SpO2 data available, clinical, or culture-positive EOS occurred in 91/362 (25%) and HC in 81/355 (22%). In univariate analysis, EOS was associated with higher mean HR, lower mean SpO2, and less negative skewness of HR in the first 3 days after birth. HC was associated with higher standard deviation and skewness of HR but no difference in SpO2. In multivariable modeling, significant risk factors for EOS were mean HR, gestational age, HC, mean SpO2, and skewness of SpO2. CONCLUSION: HR and SpO2 patterns differ shortly after birth in VLBW infants exposed to HC or with EOS, likely reflecting a systemic inflammatory response.

Distinct patterns of vital sign and inflammatory marker responses in adults with suspected bloodstream infection.

OBJECTIVES: To identify patterns in inflammatory marker and vital sign responses in adult with suspected bloodstream infection (BSI) and define expected trends in normal recovery. METHODS: We included patients ≥16 y from Oxford University Hospitals with a blood culture taken between 1-January-2016 and 28-June-2021. We used linear and latent class mixed models to estimate trajectories in C-reactive protein (CRP), white blood count, heart rate, respiratory rate and temperature and identify CRP response subgroups. Centile charts for expected CRP responses were constructed via the lambda-mu-sigma method. RESULTS: In 88,348 suspected BSI episodes; 6908 (7.8%) were culture-positive with a probable pathogen, 4309 (4.9%) contained potential contaminants, and 77,131(87.3%) were culture-negative. CRP levels generally peaked 1-2 days after blood culture collection, with varying responses for different pathogens and infection sources (p < 0.0001). We identified five CRP trajectory subgroups: peak on day 1 (36,091; 46.3%) or 2 (4529; 5.8%), slow recovery (10,666; 13.7%), peak on day 6 (743; 1.0%), and low response (25,928; 33.3%). Centile reference charts tracking normal responses were constructed from those peaking on day 1/2. CONCLUSIONS: CRP and other infection response markers rise and recover differently depending on clinical syndrome and pathogen involved. However, centile reference charts, that account for these differences, can be used to track if patients are recovering line as expected and to help personalise infection.