Transcriptional instability during evolving sepsis may limit biomarker based risk stratification

ADVANCING THE UNDERSTANDING OF CLINICAL SEPSIS USING GENE EXPRESSION-DRIVEN MACHINE LEARNING TO IMPROVE PATIENT OUTCOMES

ABSTRACT

Sepsis remains a major challenge that necessitates improved approaches to enhance patient outcomes. This study explored the potential of machine learning (ML) techniques to bridge the gap between clinical data and gene expression information to better predict and understand sepsis. We discuss the application of ML algorithms, including neural networks, deep learning, and ensemble methods, to address key evidence gaps and overcome the challenges in sepsis research. The lack of a clear definition of sepsis is highlighted as a major hurdle, but ML models offer a workaround by focusing on endpoint prediction. We emphasize the significance of gene transcript information and its use in ML models to provide insights into sepsis pathophysiology and biomarker identification. Temporal analysis and integration of gene expression data further enhance the accuracy and predictive capabilities of ML models for sepsis. Although challenges such as interpretability and bias exist, ML research offers exciting prospects for addressing critical clinical problems, improving sepsis management, and advancing precision medicine approaches. Collaborative efforts between clinicians and data scientists are essential for the successful implementation and translation of ML models into clinical practice. Machine learning has the potential to revolutionize our understanding of sepsis and significantly improve patient outcomes. Further research and collaboration between clinicians and data scientists are needed to fully understand the potential of ML in sepsis management.

A TRANSCRIPTOMIC APPRECIATION OF CHILDHOOD MENINGOCOCCAL AND POLYMICROBIAL SEPSIS FROM A PRO-INFLAMMATORY AND TRAJECTORIAL PERSPECTIVE, A ROLE FOR VASCULAR ENDOTHELIAL GROWTH FACTOR A AND B MODULATION?

ABSTRACT

This study investigated the temporal dynamics of childhood sepsis by analyzing gene expression changes associated with proinflammatory processes. Five datasets, including four meningococcal sepsis shock (MSS) datasets (two temporal and two longitudinal) and one polymicrobial sepsis dataset, were selected to track temporal changes in gene expression. Hierarchical clustering revealed three temporal phases: early, intermediate, and late, providing a framework for understanding sepsis progression. Principal component analysis supported the identification of gene expression trajectories. Differential gene analysis highlighted consistent upregulation of vascular endothelial growth factor A (VEGF-A) and nuclear factor κB1 (NFKB1), genes involved in inflammation, across the sepsis datasets. NFKB1 gene expression also showed temporal changes in the MSS datasets. In the postmortem dataset comparing MSS cases to controls, VEGF-A was upregulated and VEGF-B downregulated. Renal tissue exhibited higher VEGF-A expression compared with other tissues. Similar VEGF-A upregulation and VEGF-B downregulation patterns were observed in the cross-sectional MSS datasets and the polymicrobial sepsis dataset. Hexagonal plots confirmed VEGF-R (VEGF receptor)-VEGF-R2 signaling pathway enrichment in the MSS cross-sectional studies. The polymicrobial sepsis dataset also showed enrichment of the VEGF pathway in septic shock day 3 and sepsis day 3 samples compared with controls. These findings provide unique insights into the dynamic nature of sepsis from a transcriptomic perspective and suggest potential implications for biomarker development. Future research should focus on larger-scale temporal transcriptomic studies with appropriate control groups and validate the identified gene combination as a potential biomarker panel for sepsis.

Leveraging Data Science and Novel Technologies to Develop and Implement Precision Medicine Strategies in Critical Care

Precision medicine aims to identify treatments that are most likely to result in favorable outcomes for subgroups of patients with similar clinical and biological characteristics. The gaps for the development and implementation of precision medicine strategies in the critical care setting are many, but the advent of data science and multi-omics approaches, combined with the rich data ecosystem in the intensive care unit, offer unprecedented opportunities to realize the promise of precision critical care. In this article, the authors review the data-driven and technology-based approaches being leveraged to discover and implement precision medicine strategies in the critical care setting.

Development and validation of novel sepsis subphenotypes using trajectories of vital signs

PURPOSE

Sepsis is a heterogeneous syndrome and identification of sub-phenotypes is essential. This study used trajectories of vital signs to develop and validate sub-phenotypes and investigated the interaction of sub-phenotypes with treatment using randomized controlled trial data.

METHODS

All patients with suspected infection admitted to four academic hospitals in Emory Healthcare between 2014-2017 (training cohort) and 2018-2019 (validation cohort) were included. Group-based trajectory modeling was applied to vital signs from the first 8 h of hospitalization to develop and validate vitals trajectory sub-phenotypes. The associations between sub-phenotypes and outcomes were evaluated in patients with sepsis. The interaction between sub-phenotype and treatment with balanced crystalloids versus saline was tested in a secondary analysis of SMART (Isotonic Solutions and Major Adverse Renal Events Trial).

RESULTS

There were 12,473 patients with suspected infection in training and 8256 patients in validation cohorts, and 4 vitals trajectory sub-phenotypes were found. Group A (N = 3483, 28%) were hyperthermic, tachycardic, tachypneic, and hypotensive. Group B (N = 1578, 13%) were hyperthermic, tachycardic, tachypneic (not as pronounced as Group A) and hypertensive. Groups C (N = 4044, 32%) and D (N = 3368, 27%) had lower temperatures, heart rates, and respiratory rates, with Group C normotensive and Group D hypotensive. In the 6,919 patients with sepsis, Groups A and B were younger while Groups C and D were older. Group A had the lowest prevalence of congestive heart failure, hypertension, diabetes mellitus, and chronic kidney disease, while Group B had the highest prevalence. Groups A and D had the highest vasopressor use (p < 0.001 for all analyses above). In logistic regression, 30-day mortality was significantly higher in Groups A and D (p < 0.001 and p = 0.03, respectively). In the SMART trial, sub-phenotype significantly modified treatment effect (p = 0.03). Group D had significantly lower odds of mortality with balanced crystalloids compared to saline (odds ratio (OR) 0.39, 95% confidence interval (CI) 0.23-0.67, p < 0.001).

CONCLUSION

Sepsis sub-phenotypes based on vital sign trajectory were consistent across cohorts, had distinct outcomes, and different responses to treatment with balanced crystalloids versus saline.

Presymptomatic diagnosis of postoperative infection and sepsis using gene expression signatures

Purpose

Early accurate diagnosis of infection ± organ dysfunction (sepsis) remains a major challenge in clinical practice. Utilizing effective biomarkers to identify infection and impending organ dysfunction before the onset of clinical signs and symptoms would enable earlier investigation and intervention. To our knowledge, no prior study has specifically examined the possibility of pre-symptomatic detection of sepsis.

Methods

Blood samples and clinical/laboratory data were collected daily from 4385 patients undergoing elective surgery. An adjudication panel identified 154 patients with definite postoperative infection, of whom 98 developed sepsis. Transcriptomic profiling and subsequent RT-qPCR were undertaken on sequential blood samples taken postoperatively from these patients in the three days prior to the onset of symptoms. Comparison was made against postoperative day-, age-, sex- and procedure- matched patients who had an uncomplicated recovery (n =151) or postoperative inflammation without infection (n =148).

Results

Specific gene signatures optimized to predict infection or sepsis in the three days prior to clinical presentation were identified in initial discovery cohorts. Subsequent classification using machine learning with cross-validation with separate patient cohorts and their matched controls gave high Area Under the Receiver Operator Curve (AUC) values. These allowed discrimination of infection from uncomplicated recovery (AUC 0.871), infectious from non-infectious systemic inflammation (0.897), sepsis from other postoperative presentations (0.843), and sepsis from uncomplicated infection (0.703).

Conclusion

Host biomarker signatures may be able to identify postoperative infection or sepsis up to three days in advance of clinical recognition. If validated in future studies, these signatures offer potential diagnostic utility for postoperative management of deteriorating or high-risk surgical patients and, potentially, other patient populations.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00134-022-06769-z.

Perspectives on the Immune System in Sepsis

Beyond the modifications shown by the biochemistry labs, profound and ample modifications are seen in septic patients at a molecular level stemming from DNA translation and gene expression, manifested as unique profiles of mRNA (messenger), as well as non-coding, functional RNAs: miRNA (micro) and lncRNAs (long non-coding). Counteracting these modifications requires treatment with pleiotropic molecules and/or combination of molecules and opens the possibility of future treatments with arrays of siRNAs and/or specific panels of small molecules tailored for each patient subpopulation.

Network pharmacology prediction and molecular docking-based strategy to explore the potential mechanism of Huanglian Jiedu Decoction against sepsis

BACKGROUND

Huanglian Jiedu Decoction (HLJDD) is a classical herbal formula with potential efficacy in the treatment of sepsis. However, the main components and potential mechanisms of HLJDD remain unclear. This study aims to initially clarify the potential mechanism of HLJDD in the treatment of sepsis based on network pharmacology and molecular docking techniques.

METHODS

The principal components and corresponding protein targets of HLJDD were searched on TCMSP, BATMAN-TCM and ETCM and the compound-target network was constructed by Cytoscape3.8.2. Sepsis targets were searched on OMIM and DisGeNET databases. The intersection of compound target and disease target was obtained and the coincidence target was imported into STRING database to construct a PPI network. We further performed GO and KEGG enrichment analysis on the targets. Finally, molecular docking study was approved for the core target and the active compound.

RESULTS

There are 257 nodes and 792 edges in the component target network. The compounds with a higher degree value are quercetin, kaempferol, and wogonin. The protein with a higher degree in the PPI network is JUN, RELA, TNF. GO and KEGG analysis showed that HLJDD treatment of sepsis mainly involves positive regulation of transcription from RNA polymerase II promoter, negative regulation of apoptosis process, response to hypoxia and other biological processes. The signaling pathways mainly include PI3K-AKT, MAPK, TNF signaling pathway. The molecular docking results showed that quercetin, kaempferol and wogonin have higher affinity with JUN, RELA and TNF.

CONCLUSION

This study reveals the active ingredients and potential molecular mechanism of HLJDD in the treatment of sepsis, and provides a reference for subsequent basic research.

Dosing of Extracorporeal Cytokine Removal In Septic Shock (DECRISS): protocol of a prospective, randomised, adaptive, multicentre clinical trial

INTRODUCTION

Sepsis and septic shock have mortality rates between 20% and 50%. In sepsis, the immune response becomes dysregulated, which leads to an imbalance between proinflammatory and anti-inflammatory mediators. When standard therapeutic measures fail to improve patients' condition, additional therapeutic alternatives are applied to reduce morbidity and mortality. One of the most recent alternatives is extracorporeal cytokine adsorption with a device called CytoSorb. This study aims to compare the efficacy of standard medical therapy and continuous extracorporeal cytokine removal with CytoSorb therapy in patients with early refractory septic shock. Furthermore, we compare the dosing of CytoSorb adsorber device changed every 12 or 24 hours.

METHODS AND ANALYSIS

It is a prospective, randomised, controlled, open-label, international, multicentre, phase III study. Patients fulfilling the inclusion criteria will be randomly assigned to receive standard medical therapy (group A) or-in addition to standard treatment-CytoSorb therapy. CytoSorb treatment will be continuous and last for at least 24 hours, CytoSorb adsorber device will be changed every 12 (group B) or 24 hours (group C). Our primary outcome is shock reversal (no further need or a reduced (≤10% of the maximum dose) vasopressor requirement for 3 hours) and time to shock reversal (number of hours elapsed from the start of the treatment to shock reversal).Based on sample size calculation, 135 patients (1:1:1) will need to be enrolled in the study. A predefined interim analysis will be performed after reaching 50% of the planned sample size, therefore, the corrected level of significance (p value) will be 0.0294.

ETHICS AND DISSEMINATION

Ethics approval was obtained from the Scientific and Research Ethics Committee of the Hungarian Medical Research Council (OGYÉI/65049/2020). Results will be submitted for publication in a peer-reviewed journal.

TRIAL REGISTRATION NUMBER

NCT04742764; Pre-results.

Machine Learning Identifies Complicated Sepsis Course and Subsequent Mortality Based on 20 Genes in Peripheral Blood Immune Cells at 24 H Post-ICU Admission

A complicated clinical course for critically ill patients admitted to the intensive care unit (ICU) usually includes multiorgan dysfunction and subsequent death. Owing to the heterogeneity, complexity, and unpredictability of the disease progression, ICU patient care is challenging. Identifying the predictors of complicated courses and subsequent mortality at the early stages of the disease and recognizing the trajectory of the disease from the vast array of longitudinal quantitative clinical data is difficult. Therefore, we attempted to perform a meta-analysis of previously published gene expression datasets to identify novel early biomarkers and train the artificial intelligence systems to recognize the disease trajectories and subsequent clinical outcomes. Using the gene expression profile of peripheral blood cells obtained within 24 h of pediatric ICU (PICU) admission and numerous clinical data from 228 septic patients from pediatric ICU, we identified 20 differentially expressed genes predictive of complicated course outcomes and developed a new machine learning model. After 5-fold cross-validation with 10 iterations, the overall mean area under the curve reached 0.82. Using a subset of the same set of genes, we further achieved an overall area under the curve of 0.72, 0.96, 0.83, and 0.82, respectively, on four independent external validation sets. This model was highly effective in identifying the clinical trajectories of the patients and mortality. Artificial intelligence systems identified eight out of twenty novel genetic markers (SDC4, CLEC5A, TCN1, MS4A3, HCAR3, OLAH, PLCB1, and NLRP1) that help predict sepsis severity or mortality. While these genes have been previously associated with sepsis mortality, in this work, we show that these genes are also implicated in complex disease courses, even among survivors. The discovery of eight novel genetic biomarkers related to the overactive innate immune system, including neutrophil function, and a new predictive machine learning method provides options to effectively recognize sepsis trajectories, modify real-time treatment options, improve prognosis, and patient survival.

CX3CR1 Depletion Promotes the Formation of Platelet-Neutrophil Complexes and Aggravates Acute Peritonitis

BACKGROUND

Peritonitis is a life-threatening condition on intensive care units. Inflammatory cytokines and their receptors drive inflammation, cause the formation of platelet-neutrophil complexes (PNCs) and therefore the migration of polymorphonuclear neutrophils (PMNs) into the inflamed tissue. CX3CL1 and its receptor CX3CR1 are expressed in various cells, and promote inflammation. The shedding of CX3CL1 is mediated by a disintegrin and metalloprotease (ADAM) 17. The role of the CX3CL1-CX3CR1 axis in acute peritonitis remains elusive.

METHODS

In zymosan-induced peritonitis, we determined the formation of PNCs in the blood and the expression of PNC-related molecules on PNCs. PMN migration into the peritoneal lavage was evaluated in wild-type (WT) and CX3CR1-/- animals by flow cytometry. CX3CL1, ADAM17, and the expression of various inflammatory cytokines were detected. Further, we determined the inflammation-associated activation of the intracellular transcription factor extracellular signal-regulated kinase 1/2 (ERK1/2) by Western blot.

RESULTS

The PMN accumulation in the peritoneal lavage and the PNC formation in the circulation were significantly raised in CX3CR1-/- compared with WT animals. The expression of PNC-related selectins on PNCs was significantly increased in the blood of CX3CR1-/- animals, as well as cytokine levels. Further, we observed an increased activation of ERK1/2 and elevated ADAM17 expression in CX3CR1-/- during acute inflammation. Selective ERK1/2 inhibition ameliorated inflammation-related increased ADAM17 expression.

CONCLUSIONS

A CX3CR1 deficiency raised the release of inflammatory cytokines and increased the PNC formation respectively PMN migration via an elevated ERK1/2 activation during acute peritonitis. Further, we observed a link between the ERK1/2 activation and an elevated ADAM17 expression on PNC-related platelets and PMNs during inflammation. Our data thus illustrate a crucial role of CX3CR1 on the formation of PNCs and regulating inflammation in acute peritonitis.

Data Heterogeneity: The Enzyme to Catalyze Translational Bioinformatics?

Up to 95% of novel interventions demonstrating significant effects at the bench fail to translate to the bedside. In recent years, the windfalls of “big data” have afforded investigators more substrate for research than ever before. However, issues with translation have persisted: although countless biomarkers for diagnostic and therapeutic targeting have been proposed, few of these generalize effectively. We assert that inadequate heterogeneity in datasets used for discovery and validation causes their nonrepresentativeness of the diversity observed in real-world patient populations. This nonrepresentativeness is contrasted with advantages rendered by the solicitation and utilization of data heterogeneity for multisystemic disease modeling. Accordingly, we propose the potential benefits of models premised on heterogeneity to promote the Institute for Healthcare Improvement’s Triple Aim. In an era of personalized medicine, these models can confer higher quality clinical care for individuals, increased access to effective care across all populations, and lower costs for the health care system.

Sepsis: Symptoms, Assessment, Diagnosis, and the Hour-1 Bundle in Patients With Cancer

Sepsis has a higher incidence of hospital stays and poorer morbidity and mortality outcomes in patients with cancer. The development of infection in weakened immune systems and prolonged treatment courses increase the risk for sepsis in patients with cancer. The causes of infection that can lead to sepsis in patients with cancer are further complicated by disease- or therapy-related neutropenia. Early recognition of sepsis is critical for prompt treatment to prevent tissue damage, organ failure, and mortality. The Surviving Sepsis Campaign recommends the Hour-1 bundle as best practice for sepsis management.

Unsupervised Analysis of Transcriptomics in Bacterial Sepsis Across Multiple Datasets Reveals Three Robust Clusters

OBJECTIVES

To find and validate generalizable sepsis subtypes using data-driven clustering.

DESIGN

We used advanced informatics techniques to pool data from 14 bacterial sepsis transcriptomic datasets from eight different countries (n = 700).

SETTING

Retrospective analysis.

SUBJECTS

Persons admitted to the hospital with bacterial sepsis.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A unified clustering analysis across 14 discovery datasets revealed three subtypes, which, based on functional analysis, we termed "Inflammopathic, Adaptive, and Coagulopathic." We then validated these subtypes in nine independent datasets from five different countries (n = 600). In both discovery and validation data, the Adaptive subtype is associated with a lower clinical severity and lower mortality rate, and the Coagulopathic subtype is associated with higher mortality and clinical coagulopathy. Further, these clusters are statistically associated with clusters derived by others in independent single sepsis cohorts.

CONCLUSIONS

The three sepsis subtypes may represent a unifying framework for understanding the molecular heterogeneity of the sepsis syndrome. Further study could potentially enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit.

Prognostic and predictive enrichment in sepsis

Sepsis is a heterogeneous disease state that is both common and consequential in critically ill patients. Unfortunately, the heterogeneity of sepsis at the individual patient level has hindered advances in the field beyond the current therapeutic standards, which consist of supportive care and antibiotics. This complexity has prompted attempts to develop a precision medicine approach, with research aimed towards stratifying patients into more homogeneous cohorts with shared biological features, potentially facilitating the identification of new therapies. Several investigators have successfully utilized leukocyte-derived mRNA and discovery-based approaches to subgroup patients on the basis of biological similarities defined by transcriptomic signatures. A critical next step is to develop a consensus sepsis subclassification system, which includes transcriptomic signatures as well as other biological and clinical data. This goal will require collaboration among various investigative groups, and validation in both existing data sets and prospective studies. Such studies are required to bring precision medicine to the bedside of critically ill patients with sepsis.

Endotype Transitions During the Acute Phase of Pediatric Septic Shock Reflect Changing Risk and Treatment Response

OBJECTIVE

We previously identified septic shock endotypes A and B based on 100 genes reflecting adaptive immunity and glucocorticoid receptor signaling. The endotypes differ with respect to outcome and corticosteroid responsiveness. We determined whether endotypes change during the initial 3 days of illness, and whether changes are associated with outcomes.

DESIGN

Observational cohort study including existing and newly enrolled participants.

SETTING

Multiple PICUs.

PATIENTS

Children with septic shock.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We measured the 100 endotyping genes at day 1 and day 3 of illness in 375 patients. We determined if endotype assignment changes over time, and whether changing endotype is associated with corticosteroid response and outcomes. We used multivariable logistic regression to adjust for illness severity, age, and comorbidity burden. Among the 132 subjects assigned to endotype A on day 1, 56 (42%) transitioned to endotype B by day 3. Among 243 subjects assigned to endotype B on day 1, 77 (32%) transitioned to endotype A by day 3. Assignment to endotype A on day 1 was associated with increased odds of mortality. This risk was modified by the subsequent day 3 endotype assignment. Corticosteroids were associated with increased risk of mortality among subjects who persisted as endotype A.

CONCLUSIONS

A substantial proportion of children with septic shock transition endotypes during the acute phase of illness. The risk of poor outcome and the response to corticosteroids change with changes in endotype assignment. Patients persisting as endotype A are at highest risk of poor outcomes.

Extracorporeal cytokine adsorption in septic shock: A proof of concept randomized, controlled pilot study

BACKGROUND

The aim of this proof of concept, prospective, randomized pilot trial was to investigate the effects of extracorporeal cytokine removal (CytoSorb®) applied as a standalone treatment in patients with septic shock.

METHODS

20 patients with early (<24 h) onset of septic shock of medical origin, on mechanical ventilation, norepinephrine>10 μg/min, procalcitonin (PCT) > 3 ng/mL without the need for renal replacement therapy were randomized into CytoSorb (n = 10) and Control groups (n = 10). CytoSorb therapy lasted for 24 h. Clinical and laboratory data were recorded at baseline (T₀), T₁₂, T₂₄, and T₄₈ hours.

RESULTS

Overall SOFA scores did not differ between the groups. In the CytoSorb-group norepinephrine requirements and PCT concentration decreased significantly (norepinephrine: CytoSorb: T₀ = 0.54[IQR:0.20-1.22], T₄₈ = 0.16[IQR:0.07-0.48], p = .016; Controls: T₀ = 0.43[IQR:0.19-0.64], T₄₈ = 0.25[IQR:0.08-0.65] μg/kg/min; PCT: CytoSorb: T₀ median = 20.6[IQR: 6.5-144.5], T₄₈ = 5.6[1.9-54.4], p = .004; Control: T₀ = 13.2[7.6-47.8], T₄₈ = 9.2[3.8-44.2]ng/mL). Big-endothelin-1 concentrations were also significantly lower in the CytoSorb group (CytoSorb: T₀ = 1.3 ± 0.6, *T₂₄ = 1.0 ± 0.4, T₄₈ = 1.4 ± 0.8, *p = .003; Control: T₀ = 1.1 ± 0.7, T₂₄ = 1.1 ± 0.6, T₄₈ = 1.2 ± 0.6 pmol/L, p = .115). There were no CytoSorb therapy-related adverse events.

CONCLUSIONS

This is the first trial to investigate the effects of early extracorporeal cytokine adsorption treatment in septic shock applied without renal replacement therapy. It was found to be safe with significant effects on norepinephrine requirements, PCT and Big-endothelin-1 concentrations compared to controls.

TRIAL REGISTRATION

The study has been registered on ClinicalTrials.gov, under the registration number of NCT02288975, registered 13 November 2014.

A community approach to mortality prediction in sepsis via gene expression analysis

Improved risk stratification and prognosis prediction in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here, we present prognostic models for 30-day mortality generated independently by three scientific groups by using 12 discovery cohorts containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance is validated in five cohorts of community-onset sepsis patients in which the models show summary AUROCs ranging from 0.765-0.89. Similar performance is observed in four cohorts of hospital-acquired sepsis. Combining the new gene-expression-based prognostic models with prior clinical severity scores leads to significant improvement in prediction of 30-day mortality as measured via AUROC and net reclassification improvement index These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis.

The Antibody-Secreting Cell Response to Infection: Kinetics and Clinical Applications

Despite the availability of advances in molecular diagnostic testing for infectious disease, there is still a need for tools that advance clinical care and public health. Current methods focus on pathogen detection with unprecedented precision, but often lack specificity. In contrast, the host immune response is highly specific for the infecting pathogen. Serological studies are rarely helpful in clinical settings, as they require acute and convalescent antibody testing. However, the B cell response is much more rapid and short-lived, making it an optimal target for determining disease aetiology in patients with infections. The performance of tests that aim to detect circulating antigen-specific antibody-secreting cells (ASCs) has previously been unclear. Test performance is reliant on detecting the presence of ASCs in the peripheral blood. As such, the kinetics of the ASC response to infection, the antigen specificity of the ASC response, and the methods of ASC detection are all critical. In this review, we summarize previous studies that have used techniques to enumerate ASCs during infection. We describe the emergence, peak, and waning of these cells in peripheral blood during infection with a number of bacterial and viral pathogens, as well as malaria infection. We find that the timing of antigen-specific ASC appearance and disappearance is highly conserved across pathogens, with a peak response between day 7 and day 8 of illness and largely absent following day 14 since onset of symptoms. Data show a sensitivity of ~90% and specificity >80% for pathogen detection using ASC-based methods. Overall, the summarised work indicates that ASC-based methods may be very sensitive and highly specific for determining the etiology of infection and have some advantages over current methods. Important areas of research remain, including more accurate definition of the timing of the ASC response to infection, the biological mechanisms underlying variability in its magnitude and the evolution and the B cell receptor in response to immune challenge. Nonetheless, there is potential of the ASC response to infection to be exploited as the basis for novel diagnostic tests to inform clinical care and public health priorities.

Risk Stratification and Prognosis in Sepsis: What Have We Learned from Microarrays?

Sepsis mortality rates have decreased in recent years but remain unacceptably high. Risk stratification and prognostication is of particular importance because high-risk patients may benefit from earlier clinical interventions, whereas low-risk patients may benefit from not undergoing unnecessary procedures. Prognostication is currently done mostly via clinical criteria and blood lactate levels. This article summarizes the literature on the complexity of changes at the molecular level for the casual reader.

The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)

IMPORTANCE

Definitions of sepsis and septic shock were last revised in 2001. Considerable advances have since been made into the pathobiology (changes in organ function, morphology, cell biology, biochemistry, immunology, and circulation), management, and epidemiology of sepsis, suggesting the need for reexamination.

OBJECTIVE

To evaluate and, as needed, update definitions for sepsis and septic shock.

PROCESS

A task force (n = 19) with expertise in sepsis pathobiology, clinical trials, and epidemiology was convened by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine. Definitions and clinical criteria were generated through meetings, Delphi processes, analysis of electronic health record databases, and voting, followed by circulation to international professional societies, requesting peer review and endorsement (by 31 societies listed in the Acknowledgment).

KEY FINDINGS FROM EVIDENCE SYNTHESIS

Limitations of previous definitions included an excessive focus on inflammation, the misleading model that sepsis follows a continuum through severe sepsis to shock, and inadequate specificity and sensitivity of the systemic inflammatory response syndrome (SIRS) criteria. Multiple definitions and terminologies are currently in use for sepsis, septic shock, and organ dysfunction, leading to discrepancies in reported incidence and observed mortality. The task force concluded the term severe sepsis was redundant.

RECOMMENDATIONS

Sepsis should be defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. For clinical operationalization, organ dysfunction can be represented by an increase in the Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score of 2 points or more, which is associated with an in-hospital mortality greater than 10%. Septic shock should be defined as a subset of sepsis in which particularly profound circulatory, cellular, and metabolic abnormalities are associated with a greater risk of mortality than with sepsis alone. Patients with septic shock can be clinically identified by a vasopressor requirement to maintain a mean arterial pressure of 65 mm Hg or greater and serum lactate level greater than 2 mmol/L (>18 mg/dL) in the absence of hypovolemia. This combination is associated with hospital mortality rates greater than 40%. In out-of-hospital, emergency department, or general hospital ward settings, adult patients with suspected infection can be rapidly identified as being more likely to have poor outcomes typical of sepsis if they have at least 2 of the following clinical criteria that together constitute a new bedside clinical score termed quickSOFA (qSOFA): respiratory rate of 22/min or greater, altered mentation, or systolic blood pressure of 100 mm Hg or less.

CONCLUSIONS AND RELEVANCE

These updated definitions and clinical criteria should replace previous definitions, offer greater consistency for epidemiologic studies and clinical trials, and facilitate earlier recognition and more timely management of patients with sepsis or at risk of developing sepsis.

The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)

IMPORTANCE

Definitions of sepsis and septic shock were last revised in 2001. Considerable advances have since been made into the pathobiology (changes in organ function, morphology, cell biology, biochemistry, immunology, and circulation), management, and epidemiology of sepsis, suggesting the need for reexamination.

OBJECTIVE

To evaluate and, as needed, update definitions for sepsis and septic shock.

PROCESS

A task force (n = 19) with expertise in sepsis pathobiology, clinical trials, and epidemiology was convened by the Society of Critical Care Medicine and the European Society of Intensive Care Medicine. Definitions and clinical criteria were generated through meetings, Delphi processes, analysis of electronic health record databases, and voting, followed by circulation to international professional societies, requesting peer review and endorsement (by 31 societies listed in the Acknowledgment).

KEY FINDINGS FROM EVIDENCE SYNTHESIS

Limitations of previous definitions included an excessive focus on inflammation, the misleading model that sepsis follows a continuum through severe sepsis to shock, and inadequate specificity and sensitivity of the systemic inflammatory response syndrome (SIRS) criteria. Multiple definitions and terminologies are currently in use for sepsis, septic shock, and organ dysfunction, leading to discrepancies in reported incidence and observed mortality. The task force concluded the term severe sepsis was redundant.

RECOMMENDATIONS

Sepsis should be defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. For clinical operationalization, organ dysfunction can be represented by an increase in the Sequential [Sepsis-related] Organ Failure Assessment (SOFA) score of 2 points or more, which is associated with an in-hospital mortality greater than 10%. Septic shock should be defined as a subset of sepsis in which particularly profound circulatory, cellular, and metabolic abnormalities are associated with a greater risk of mortality than with sepsis alone. Patients with septic shock can be clinically identified by a vasopressor requirement to maintain a mean arterial pressure of 65 mm Hg or greater and serum lactate level greater than 2 mmol/L (>18 mg/dL) in the absence of hypovolemia. This combination is associated with hospital mortality rates greater than 40%. In out-of-hospital, emergency department, or general hospital ward settings, adult patients with suspected infection can be rapidly identified as being more likely to have poor outcomes typical of sepsis if they have at least 2 of the following clinical criteria that together constitute a new bedside clinical score termed quickSOFA (qSOFA): respiratory rate of 22/min or greater, altered mentation, or systolic blood pressure of 100 mm Hg or less.

CONCLUSIONS AND RELEVANCE

These updated definitions and clinical criteria should replace previous definitions, offer greater consistency for epidemiologic studies and clinical trials, and facilitate earlier recognition and more timely management of patients with sepsis or at risk of developing sepsis.

A comprehensive time-course-based multicohort analysis of sepsis and sterile inflammation reveals a robust diagnostic gene set

Although several dozen studies of gene expression in sepsis have been published, distinguishing sepsis from a sterile systemic inflammatory response syndrome (SIRS) is still largely up to clinical suspicion. We hypothesized that a multicohort analysis of the publicly available sepsis gene expression data sets would yield a robust set of genes for distinguishing patients with sepsis from patients with sterile inflammation. A comprehensive search for gene expression data sets in sepsis identified 27 data sets matching our inclusion criteria. Five data sets (n = 663 samples) compared patients with sterile inflammation (SIRS/trauma) to time-matched patients with infections. We applied our multicohort analysis framework that uses both effect sizes and P values in a leave-one-data set-out fashion to these data sets. We identified 11 genes that were differentially expressed (false discovery rate ≤1%, inter-data set heterogeneity P > 0.01, summary effect size >1.5-fold) across all discovery cohorts with excellent diagnostic power [mean area under the receiver operating characteristic curve (AUC), 0.87; range, 0.7 to 0.98]. We then validated these 11 genes in 15 independent cohorts comparing (i) time-matched infected versus noninfected trauma patients (4 cohorts), (ii) ICU/trauma patients with infections over the clinical time course (3 cohorts), and (iii) healthy subjects versus sepsis patients (8 cohorts). In the discovery Glue Grant cohort, SIRS plus the 11-gene set improved prediction of infection (compared to SIRS alone) with a continuous net reclassification index of 0.90. Overall, multicohort analysis of time-matched cohorts yielded 11 genes that robustly distinguish sterile inflammation from infectious inflammation.

Early changes of the kinetics of monocyte trem-1 reflect final outcome in human sepsis

Background

TREM-1 (triggering receptor expressed on myeloid cells), a receptor expressed on neutrophils and monocytes, is upregulated in sepsis and seems to tune the inflammatory response. We explored the expression of TREM-1 at the gene level and on cell membranes of monocytes and association with clinical outcome.

Methods

Peripheral venous blood was sampled from 75 septic patients (39 patients with sepsis, 25 with severe sepsis and 11 with septic shock) on sepsis days 1, 3 and 7. TREM-1 on monocytes was measured by flow cytometry; gene expression of TREM-1 in circulating mononuclear cells was assessed by real-time PCR. sTREM-1 was measured in serum by an enzyme immunoassay.

Results

Although surface TREM-1, sTREM-1 and TREM-1 gene expression did not differ between sepsis, severe sepsis and septic shock on day 1, survivors had greater expression of surface TREM-1 on days 3 and 7 compared to non-survivors. sTREM-1 on non-survivors decreased on day 3 compared to baseline. Patients with increase of monocyte gene expression of TREM-1 from day 1 to day 3 had prolonged survival compared to patients with decrease of gene expression of TREM-1 from day 1 to day 3 (p: 0.031).

Conclusions

Early decrease of gene expression of TREM-1 in monocytes is associated with poor outcome. A reciprocal decrease of the pro-inflammatory surface receptor TREM-1 linked with sepsis-induced immunosuppression may be part of the explanation.

Electronic supplementary material

The online version of this article (doi:10.1186/s12865-014-0063-y) contains supplementary material, which is available to authorized users.

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