Prehospital identification of community sepsis using biomarkers of host response.
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ABSTRACT:
SUBMITTER: Brant EB
PROVIDER: S-EPMC7901591 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Sepsis is defined as the systemic inflammatory response to infection and is one of the leading causes of mortality in critically ill patients. The goal of the present study is to elucidate the molecular mechanism of sepsis. Transcription profile data (GSE12624) were downloaded that had a total of 70 samples (36 sepsis samples and 34 non-sepsis samples) from the Gene Expression Omnibus database. Protein-protein interaction network analysis was conducted in order to comprehensively understand the interactions of genes in all samples. Hierarchical clustering and analysis of covariance (ANCOVA) global test were performed to identify the differentially expressed clusters in the networks, followed by function and pathway enrichment analyses. Finally, a support vector machine (SVM) was performed to classify the clusters, and 10-fold cross-validation method was performed to evaluate the classification results. A total of 7,672 genes were obtained after preprocessing of the mRNA expression profile data. The PPI network of genes under sepsis and non-sepsis status collected 1,996/2,147 genes and 2,645/2,783 interactions. Moreover, following the ANCOVA global test (P<0.05), 24 differentially expressed clusters with 12 clusters in septic and 12 clusters in non-septic samples were identified. Finally, 207 biomarker genes, including CDC42, CSF3R, GCA, HMGB2, RHOG, SERPINB1, TYROBP SERPINA1, FCER1 G and S100P in the top six clusters, were collected using the SVM method. The SERPINA1, FCER1 G and S100P genes are thought to be potential biomarkers. Furthermore, Gene oncology terms, including the intracellular signaling cascade, regulation of programmed cell death, regulation of cell death, regulation of apoptosis and leukocyte activation may participate in sepsis.
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Project description:Sepsis remains a major global concern and is associated with high mortality and morbidity despite improvements in its management. Markers currently in use have shortcomings such as a lack of specificity and failures in the early detection of sepsis. In this study, we aimed to identify key genes involved in the molecular mechanisms of sepsis and search for potential new biomarkers and treatment targets for sepsis using bioinformatics analyses. Three datasets (GSE95233, GSE57065, and GSE28750) associated with sepsis were downloaded from the public functional genomics data repository Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified using R packages (Affy and limma). Functional enrichment of the DEGs was analyzed with the DAVID database. Protein-protein interaction networks were derived using the STRING database and visualized using Cytoscape software. Potential biomarker genes were analyzed using receiver operating characteristic (ROC) curves in the R package (pROC). The three datasets included 156 whole blood RNA samples from 89 sepsis patients and 67 healthy controls. Between the two groups, 568 DEGs were identified, among which 315 were upregulated and 253 were downregulated in the septic group. These genes were enriched for pathways mainly involved in the innate immune response, T-cell biology, antigen presentation, and natural killer cell function. ROC analyses identified nine genes-LRG1, ELANE, TP53, LCK, TBX21, ZAP70, CD247, ITK, and FYN-as potential new biomarkers for sepsis. Real-time PCR confirmed that the expression of seven of these genes was in accordance with the microarray results. This study revealed imbalanced immune responses at the transcriptomic level during early sepsis and identified nine genes as potential biomarkers for sepsis.
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Project description:BackgroundThe performance of host immune responses biomarkers and clinical scores was compared to identify infection patient populations at risk of progression to sepsis, ICU admission and mortality.MethodsImmune response biomarkers were measured and NEWS, SIRS, and MEWS. Logistic and Cox regression models were employed to evaluate the strength of association.ResultsIL-10 and NEWS had the strongest association with sepsis development, whereas IL-6 and CRP had the strongest association with ICU admission and in-hospital mortality. IL-6 [HR (95%CI) = 2.68 (1.61-4.46)] was associated with 28-day mortality. Patient subgroups with high IL-10 (≥ 5.03 pg/ml) and high NEWS (> 5 points) values had significantly higher rates of sepsis development (88.3% vs 61.1%; p < 0.001), in-hospital mortality (35.0% vs. 16.7%; p < 0.001), 28-day mortality (25.0% vs. 5.6%; p < 0.001), and ICU admission (66.7% vs. 38.9%; p < 0.001).ConclusionsPatients exhibiting low severity signs of infection but high IL-10 levels showed an elevated probability of developing sepsis. Combining IL-10 with the NEWS score provides a reliable tool for predicting the progression from infection to sepsis at an early stage. Utilizing IL-6 in the emergency room can help identify patients with low NEWS or SIRS scores.
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Project description:Sepsis after trauma increases the risk of mortality rate for patients in intensive care unit (ICUs). Currently, it is difficult to predict outcomes in individual patients with sepsis due to the complexity of causative pathogens and the lack of specific treatment. This study aimed to identify metabolomic biomarkers in patients with multiple trauma and those with multiple trauma accompanied with sepsis. Therefore, the metabolic profiles of healthy persons designated as normal controls (NC), multiple trauma patients (MT), and multiple trauma complicated with sepsis (MTS) (30 cases in each group) were analyzed with ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS)-based untargeted plasma metabolomics using collected plasma samples. The differential metabolites were enriched in amino acid metabolism, lipid metabolism, glycometabolism and nucleotide metabolism. Then, nine potential biomarkers, namely, acrylic acid, 5-amino-3-oxohexanoate, 3b-hydroxy-5-cholenoic acid, cytidine, succinic acid semialdehyde, PE [P-18:1(9Z)/16:1(9Z)], sphinganine, uracil, and uridine, were found to be correlated with clinical variables and validated using receiver operating characteristic (ROC) curves. Finally, the three potential biomarkers succinic acid semialdehyde, uracil and uridine were validated and can be applied in the clinical diagnosis of multiple traumas complicated with sepsis.
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Project description:Long non-coding RNAs (lncRNAs) has been proven by many to play a crucial part in the process of sepsis. To obtain a better understanding of sepsis, the molecular biomarkers associated with it, and its possible pathogenesis, we obtained data from RNA-sequencing analysis using serum from three sepsis patients and three healthy controls (HCs). Using edgeR (one of the Bioconductor software package), we identified 1118 differentially expressed mRNAs (DEmRNAs) and 1394 differentially expressed long noncoding RNAs (DElncRNAs) between sepsis patients and HCs. We identified the biological functions of these disordered genes using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analyses. The GO analysis showed that the homophilic cell adhesion via plasma membrane adhesion molecules was the most significantly enriched category. The KEGG signaling pathway analysis indicated that the differentially expressed genes (DEGs) were most significantly enriched in retrograde endocannabinoid signaling. Using STRING, a protein-protein interaction network was also created, and Cytohubba was used to determine the top 10 hub genes. To examine the relationship between the hub genes and sepsis, we examined three datasets relevant to sepsis that were found in the gene expression omnibus (GEO) database. PTEN and HIST2H2BE were recognized as hub gene in both GSE4607, GSE26378, and GSE9692 datasets. The receiver operating characteristic (ROC) curves indicate that PTEN and HIST2H2BE have good diagnostic value for sepsis. In conclusion, this two hub genes may be biomarkers for the early diagnosis of sepsis, our findings should deepen our understanding of the pathogenesis of sepsis.
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Project description:Background: There is wide heterogeneity in sepsis in causative pathogens, host response, organ dysfunction, and outcomes. Clinical and biologic phenotypes of sepsis are proposed, but the role of pathogen data on sepsis classification is unknown. Methods: We conducted a secondary analysis of the Recombinant Human Activated Protein C (rhAPC) Worldwide Evaluation in Severe Sepsis (PROWESS) Study. We used latent class analysis (LCA) to identify sepsis phenotypes using, (i) only clinical variables ("host model") and, (ii) combining clinical with microbiology variables (e.g., site of infection, culture-derived pathogen type, and anti-microbial resistance characteristics, "host-pathogen model"). We describe clinical characteristics, serum biomarkers, and outcomes of host and host-pathogen models. We tested the treatment effects of rhAPC by phenotype using Kaplan-Meier curves. Results: Among 1,690 subjects with severe sepsis, latent class modeling derived a 4-class host model and a 4-class host-pathogen model. In the host model, alpha type (N = 327, 19%) was younger and had less shock; beta type (N=518, 31%) was older with more comorbidities; gamma type (N = 532, 32%) had more pulmonary dysfunction; delta type (N = 313, 19%) had more liver, renal and hematologic dysfunction and shock. After the addition of microbiologic variables, 772 (46%) patients changed phenotype membership, and the median probability of phenotype membership increased from 0.95 to 0.97 (P < 0.01). When microbiology data were added, the contribution of individual variables to phenotypes showed greater change for beta and gamma types. In beta type, the proportion of abdominal infections (from 20 to 40%) increased, while gamma type patients had an increased rate of lung infections (from 50 to 78%) with worsening pulmonary function. Markers of coagulation such as d-dimer and plasminogen activator inhibitor (PAI)-1 were greater in the beta type and lower in the gamma type. The 28 day mortality was significantly different for individual phenotypes in host and host-pathogen models (both P < 0.01). The treatment effect of rhAPC obviously changed in gamma type when microbiology data were added (P-values of log rank test changed from 0.047 to 0.780). Conclusions: Sepsis host phenotype assignment was significantly modified when microbiology data were added to clinical variables, increasing cluster cohesiveness and homogeneity.
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Project description:Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response. Herein, we used a machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurements from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10 and TRAIL where the test dataset was 70%. Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed good accuracy of 96.64% and 94.64%. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making.
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Project description:BACKGROUND:Hypothermia is associated with adverse outcome in patients with sepsis. The objective of this study was to characterize the host immune response in patients with hypothermic sepsis in order to determine if an excessive anti-inflammatory response could explain immunosuppression and adverse outcome. Markers of endothelial activation and integrity were also measured to explore potential alternative mechanisms of hypothermia. Finally we studied risk factors for hypothermia in an attempt to find new clues to the etiology of hypothermia in sepsis. METHODS:Consecutive patients diagnosed with sepsis within 24 hours after admission to ICUs in two tertiary hospitals in the Netherlands were included in the study (n = 525). Hypothermia was defined as body temperature below 36 °C in the first 24 h of ICU admission. RESULTS:Hypothermia was identified in 186 patients and was independently associated with mortality. Levels of proinflammatory and anti-inflammatory cytokines were not different between groups. Hypothermia was also not associated with an altered response to ex vivo stimulation with lipopolysaccharide in a subset of 15 patients. Risk factors for hypothermia included low body mass index, hypertension and chronic cardiovascular insufficiency. Levels of the endothelial activation marker fractalkine were increased during the first 4 days of ICU stay. CONCLUSIONS:Hypothermia during sepsis is independently associated with mortality, which cannot be attributed to alterations in the host immune responses that were measured in this study. Given that risk factors for hypothermic sepsis are mainly cardiovascular and that the endothelial activation marker fractalkine increased in hypothermia, these findings may suggest that vascular dysfunction plays a role in hypothermic sepsis.
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Project description:BackgroundPrehospital recognition of sepsis may inform case management by ambulance clinicians, as well as inform transport decisions. The objective of this study was to develop a prehospital sepsis screening tool for use by ambulance clinicians.MethodsWe derived and validated a sepsis screening tool, utilising univariable logistic regression models to identify predictors for inclusion, and multivariable logistic regression to generate the SEPSIS score. We utilised a retrospective cohort of adult patients transported by ambulance (n?=?38483) to hospital between 01 July 2013 and 30 June 2014. Records were linked using LinkPlus® software. Successful linkage was achieved in 33289 cases (86%). Eligible patients included adult, non-trauma, non-mental health, non-cardiac arrest cases. Of 33289 linked cases, 22945 cases were eligible. Eligible cases were divided into derivation (n?=?16063, 70%) and validation (n?=?6882, 30%) cohorts. The primary outcome measure was high risk of severe illness or death from sepsis, as defined by the National Institute for Health and Care Excellence Sepsis guideline.Results'High risk of severe illness or death from sepsis' was present in 3.7% of derivation (n?=?593) and validation (n?=?254) cohorts. The SEPSIS score comprises the following variables: age, respiratory rate, peripheral oxygen saturations, heart rate, systolic blood pressure, temperature and level of consciousness (p?<?0.001 for all variables). Area under the curve was 0.87 (95%CI 0.85-0.88) for the derivation cohort, and 0.86 (95%CI 0.84-0.88) for the validation cohort. In an undifferentiated adult medical population, for a SEPSIS score???5, sensitivity was 0.37 (0.31-0.44), specificity was 0.96 (0.96-0.97), positive predictive value was 0.27 (0.23-0.32), negative predictive value was 0.97 (0.96-0.97), positive likelihood value was 13.5 (9.7-18.73) and the negative likelihood value was 0.83 (0.78-0.88).ConclusionThis is the first screening tool developed to identify NICE high risk of severe illness or death from sepsis. The SEPSIS score is significantly associated with high risk of severe illness or death from sepsis on arrival at the Emergency Department. It may assist ambulance clinicians to identify those patients with sepsis in need of antibiotic therapy. However, it requires external validation, in clinical practice by ambulance clinicians, in an independent population.
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Project description:BackgroundSepsis is a common cause of morbidity and mortality in the ICU patients. Early diagnosis and appropriate patient management is the key to improve the patient survival and to limit disabilities in sepsis patients. This study was aimed to find new diagnostic biomarkers of sepsis.MethodsIn this study, serum proteomic profiles in sepsis patients by iTRAQ2D-LC-MS/MS. Thirty seven differentially expressed proteins were identified in patients with sepsis, and six proteins including ApoC3, SERPINA1, VCAM1, B2M, GPX3, and ApoE were selected for further verification by ELISA and immunoturbidimetry in 53 patients of non-sepsis, 37 patients of sepsis, and 35 patients of septic shock. Descriptive statistics, functional enrichment analysis, and ROC curve analysis were conducted.ResultsThe level of ApoC3 was gradually decreased among non-sepsis, sepsis, and septic shock groups (p = 0.049). The levels of VCAM1 (p = 0.010), B2M (p = 0.004), and ApoE (p = 0.039) were showing an increased tread in three groups, with the peak values of B2M and ApoE in the sepsis group. ROC curve analysis for septic diagnosis showed that the areas under ROC curve (AUC) of ApoC3, VCAM1, B2M, and ApoE were 0.625, 0.679, 0.581, and 0.619, respectively, which were lower than that of PCT (AUC 0.717) and CRP (AUC 0.706), but there were no significant differences between each index and PCT or CRP. The combination including four validated indexes and two classical infection indexes for septic diagnosis had the highest AUC-ROC of 0.772.ConclusionProteins of ApoC3, VCAM1, B2M, and ApoE provide a supplement to classical biomarkers for septic diagnosis.
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