Heterogeneity in sepsis: new biological evidence with clinical applications

Pediatric Sepsis - Sailing the Unchartered Waters with Omics

How to cite this article: Baalaaji M. Pediatric Sepsis - Sailing the Unchartered Waters with Omics. Indian J Crit Care Med 2024;28(9):818-819.

HETEROGENEOUS EXPANSION OF POLYMORPHONUCLEAR MYELOID-DERIVED SUPPRESSOR CELLS DISTINGUISHES HIGH-RISK SEPSIS IMMUNOPHENOTYPES IN UGANDA

ABSTRACT

Background: Understanding of immune cell phenotypes associated with inflammatory and immunosuppressive host responses in sepsis is imprecise, particularly in low- and middle-income countries, where the global sepsis burden is concentrated. In these settings, elucidation of clinically relevant immunophenotypes is necessary to determine the relevance of emerging therapeutics and refine mechanistic investigations of sepsis immunopathology. Methods: In a prospective cohort of adults hospitalized with suspected sepsis in Uganda (N = 43; median age 46 years [IQR 36-59], 24 [55.8%] living with HIV, 16 [37.2%] deceased at 60 days), we combined high-dimensional flow cytometry with unsupervised machine learning and manual gating to define peripheral immunophenotypes associated with increased risk of 60-day mortality. Results: Patients who died showed heterogeneous expansion of polymorphonuclear myeloid-derived suppressor cells, with increased and decreased abundance of CD16 - PD-L1 dim and CD16 bright PD-L1 bright subsets, respectively, significantly associated with mortality. While differences between CD16 - PD-L1 dim cell abundance and mortality risk appeared consistent throughout the course of illness, those for the CD16 bright PD-L1 bright subset were more pronounced early after illness onset. Independent of HIV co-infection, depletion of CD4 + T cells, dendritic cells, and CD56 - CD16 bright NK cells were significantly associated with mortality risk, as was expansion of immature, CD56 + CD16 - CD11c + NK cells. Abundance of T cells expressing inhibitory checkpoint proteins (PD-1, CTLA-4, LAG-3) was similar between patients who died versus those who survived. Conclusions: This is the first study to define high-risk immunophenotypes among adults with sepsis in sub-Saharan Africa, an immunologically distinct region where biologically informed treatment strategies are needed. More broadly, our findings highlight the clinical importance and complexity of myeloid derived suppressor cell expansion during sepsis and support emerging data that suggest a host-protective role for PD-L1 myeloid checkpoints in acute critical illness.

Early Diagnosis of Sepsis: The Role of Biomarkers and Rapid Microbiological Tests

Sepsis is a medical emergency resulting from a dysregulated response to an infection, causing preventable deaths and a high burden of morbidity. Protocolized and accurate interventions in sepsis are time-critical. Therefore, earlier recognition of cases allows for preventive interventions, early treatment, and improved outcomes. Clinical diagnosis of sepsis by clinical scores cannot be considered an early diagnosis, given that underlying molecular pathophysiological mechanisms have been activated in the preceding hour or days. There is a lack of a widely available tool enhancing preclinical diagnosis of sepsis. Sophisticated technologies for sepsis prediction have several limitations, including high costs. Novel technologies for fast molecular and microbiological diagnosis are focusing on bedside point-of-care combined testing to reach most settings where sepsis represents a challenge.

R, Leuvenink HGD, van der Heijden JW, Vogt L. Alkaline phosphatase treatment of acute kidney injury-an update

Through improved insights into the increasing incidence and detrimental effects of acute kidney injury (AKI), its clinical relevance has become more and more apparent. Although treatment strategies for AKI have also somewhat improved, an adequate remedy still does not exist. Finding one is complicated by a multifactorial pathophysiology and by heterogeneity in the patient population. Alkaline phosphatase (ALP) has been suggested as a therapy for sepsis-associated AKI because of its protective effects against lipopolysaccharide (LPS)-induced inflammation and kidney injury in animals. However, its effectiveness as an AKI treatment has not been demonstrated definitively. Because the anti-inflammatory properties of ALP are likely not reliant on a direct effect on LPS itself, we postulate that other pathways are much more important in explaining the renoprotective properties ascribed to ALP. The re-evaluation of which properties of the ALP enzyme are responsible for the benefit seen in the lab is an important step in determining where the true potential of ALP as a treatment strategy for AKI in the clinic lies. In this review we will discuss how ALP can prevent activation of harmful pro-inflammatory receptors, redirect cell-cell signalling and protect barrier tissues, which together form the basis for current knowledge of the role of ALP in the kidney. With this knowledge in mind and by analysing currently available clinical evidence, we propose directions for new research that can determine whether ALP as a treatment strategy for AKI has a future in the clinical field.

The Global Burden of Sepsis and Septic Shock

A dysregulated host response to infection causes organ dysfunction in sepsis and septic shock, two potentially fatal diseases. They continue to be major worldwide health burdens with high rates of morbidity and mortality despite advancements in medical care. The goal of this thorough review was to present a thorough summary of the current body of knowledge about the prevalence of sepsis and septic shock worldwide. Using widely used computerized databases, a comprehensive search of the literature was carried out, and relevant studies were chosen in accordance with predetermined inclusion and exclusion criteria. A narrative technique was used to synthesize the data that were retrieved. The review's conclusions show how widely different locations and nations differ in terms of sepsis and septic shock's incidence, prevalence, and fatality rates. Compared to high-income countries (HICs), low- and middle-income countries (LMICs) are disproportionately burdened more heavily. We talk about risk factors, comorbidities, and difficulties in clinical management and diagnosis in a range of healthcare settings. The review highlights the need for more research, enhanced awareness, and context-specific interventions in order to successfully address the global burden of sepsis and septic shock.

Social determinants of health and sepsis: a case-control study

PURPOSE

We aimed to identify whether social determinants of health (SDoH) are associated with the development of sepsis and assess the differences between individuals living within systematically disadvantaged neighbourhoods compared with those living outside these neighbourhoods.

METHODS

We conducted a single-centre case-control study including 300 randomly selected adult patients (100 patients with sepsis and 200 patients without sepsis) admitted to the emergency department of a large academic tertiary care hospital in Hamilton, ON, Canada. We collected data on demographics and a limited set of SDoH variables, including neighbourhood household income, smoking history, social support, and history of alcohol disorder. We analyzed study data using multivariate logistic regression models.

RESULTS

The study included 100 patients with sepsis with a median [interquartile range (IQR)] age of 75 [58-84] yr and 200 patients without sepsis with a median [IQR] age of 72 [60-83] yr. Factors significantly associated with sepsis included arrival by ambulance, absence of a family physician, higher Hamilton Early Warning Score, and a recorded history of dyslipidemia. Important SDoH variables, such as individual or household income and race, were not available in the medical chart. In patients with SDoH available in their medical records, no SDoH was significantly associated with sepsis. Nevertheless, compared with their proportion of the Hamilton population, the rate of sepsis cases and sepsis deaths was approximately two times higher among patients living in systematically disadvantaged neighbourhoods.

CONCLUSIONS

This study revealed the lack of available SDoH data in electronic health records. Despite no association between the SDoH variables available and sepsis, we found a higher rate of sepsis cases and sepsis deaths among individuals living in systematically disadvantaged neighbourhoods. Including SDoH in electronic health records is crucial to study their effect on the risk of sepsis and to provide equitable care.

Diagnostic and prognostic value of heparin-binding protein in sepsis: A systematic review and meta-analysis

BACKGROUND

Sepsis remains a leading cause of death worldwide. In this context, heparin-binding protein (HBP) has emerged as a possible biomarker, drawing significant attention for its diagnostic and prognostic usefulness in septic patients. Despite this advancement, the literature yields conflicting results. This study is intended to critically evaluate the diagnostic and prognostic value of HBP in critically ill septic patients.

METHODS

We searched multiple databases, including PubMed, SCOPUS, Web of Science, and EBSCO, to identify relevant studies on April 27, 2023. We included studies investigating sepsis or its severe outcomes that reported HBP levels and the required data to create 2 × 2 tables. We used R version 4.2.2 and R Studio to analyze the pooled diagnostic accuracy outcomes. The diagmeta package was utilized to calculate the optimum cutoff value.

RESULTS

In our meta-analysis, we incorporated 28 studies including 5508 patients. The analysis revealed that HBP has a sensitivity of 0.71 (95% CI: 0.60; 0.79) and a specificity of 0.68 (95% CI: 0.51; 0.81) in diagnosing sepsis, respectively. HBP demonstrated moderate prognostic accuracy for mortality at a cutoff value of 161.415 ng/mL, with a sensitivity and specificity of 72%, and for severe sepsis outcomes at a cutoff value of 58.907 ng/mL, with a sensitivity and specificity of 71%.

CONCLUSION

Our findings indicate a relatively moderate diagnostic and prognostic accuracy of HBP for sepsis. Future studies are required to verify the accuracy of HBP as a biomarker for sepsis.

Use of Immune Profiling Panel to assess the immune response of septic patients for prediction of worsening as a composite endpoint

Sepsis induces intense, dynamic and heterogeneous host response modulations. Despite improvement of patient management, the risk of mortality and healthcare-associated infections remains high. Treatments to counterbalance immune response are under evaluation, but effective biomarkers are still lacking to perform patient stratification. The design of the present study was defined to alleviate the limitations of existing literature: we selected patients who survived the initial hyperinflammatory response and are still hospitalized at day 5-7 after ICU admission. Using the Immune Profiling Panel (IPP), a fully automated RT-qPCR multiplex prototype, we optimized a machine learning model combining the IPP gene expression levels for the identification of patients at high risk of worsening, a composite endpoint defined as death or secondary infection, within one week after sampling. This was done on 332 sepsis patients selected from two retrospective studies. The IPP model identified a high-risk group comprising 30% of patients, with a significant increased proportion of worsening events at day 28 compared to the low-risk group (49% vs. 28%, respectively). These preliminary results underline the potential clinical application of IPP for sepsis patient stratification in a personalized medicine perspective, that will be confirmed in a larger prospective multicenter study.

DIFFERENTIAL SIGNALING EFFECTS OF ESCHERICHIA COLI AND STAPHYLOCOCCUS AUREUS IN HUMAN WHOLE BLOOD INDICATE DISTINCT REGULATION OF THE NRF2 PATHWAY

ABSTRACT

Escherichia coli and Staphylococcus aureus are two of the most common bacterial species responsible for sepsis. While it is observed that they have disparate clinical phenotypes, the signaling differences elicited by each bacteria that drive this variance remain unclear. Therefore, we used human whole blood exposed to heat-killed E. coli or S. aureus and measured the transcriptomic signatures. Relative to unstimulated control blood, heat-killed bacteria exposure led to significant dysregulation (upregulated and downregulated) of >5,000 genes for each experimental condition, with a slight increase in gene alterations by S. aureus. While there was significant overlap regarding proinflammatory pathways, Gene Ontology overrepresentation analysis of the most altered genes suggested biological processes like macrophage differentiation and ubiquinone biosynthesis were more unique to heat-killed S. aureus, compared with heat-killed E. coli exposure. Using Ingenuity Pathway Analysis, it was demonstrated that nuclear factor erythroid 2-related factor 2 signaling, a main transcription factor in antioxidant responses, was predominately upregulated in S. aureus exposed blood relative to E. coli. Furthermore, the use of pharmacologics that preferentially targeted the nuclear factor erythroid 2-related factor 2 pathway led to differential cytokine profiles depending on the type of bacterial exposure. These findings reveal significant inflammatory dysregulation between E. coli and S. aureus and provide insight into the targeting of unique pathways to curb bacteria-specific responses.

Distinct functional neutrophil phenotypes in sepsis patients correlate with disease severity

Purpose

Sepsis is a clinical syndrome defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis is a highly heterogeneous syndrome with distinct phenotypes that impact immune function and response to infection. To develop targeted therapeutics, immunophenotyping is needed to identify distinct functional phenotypes of immune cells. In this study, we utilized our Organ-on-Chip assay to categorize sepsis patients into distinct phenotypes using patient data, neutrophil functional analysis, and proteomics.

Methods

Following informed consent, neutrophils and plasma were isolated from sepsis patients in the Temple University Hospital ICU (n=45) and healthy control donors (n=7). Human lung microvascular endothelial cells (HLMVEC) were cultured in the Organ-on-Chip and treated with buffer or cytomix ((TNF/IL-1β/IFNγ). Neutrophil adhesion and migration across HLMVEC in the Organ-on-Chip were used to categorize functional neutrophil phenotypes. Quantitative label-free global proteomics was performed on neutrophils to identify differentially expressed proteins. Plasma levels of sepsis biomarkers and neutrophil extracellular traps (NETs) were determined by ELISA.

Results

We identified three functional phenotypes in critically ill ICU sepsis patients based on ex vivo neutrophil adhesion and migration patterns. The phenotypes were classified as: Hyperimmune characterized by enhanced neutrophil adhesion and migration, Hypoimmune that was unresponsive to stimulation, and Hybrid with increased adhesion but blunted migration. These functional phenotypes were associated with distinct proteomic signatures and differentiated sepsis patients by important clinical parameters related to disease severity. The Hyperimmune group demonstrated higher oxygen requirements, increased mechanical ventilation, and longer ICU length of stay compared to the Hypoimmune and Hybrid groups. Patients with the Hyperimmune neutrophil phenotype had significantly increased circulating neutrophils and elevated plasma levels NETs.

Conclusion

Neutrophils and NETs play a critical role in vascular barrier dysfunction in sepsis and elevated NETs may be a key biomarker identifying the Hyperimmune group. Our results establish significant associations between specific neutrophil functional phenotypes and disease severity and identify important functional parameters in sepsis pathophysiology that may provide a new approach to classify sepsis patients for specific therapeutic interventions.

Circulating N-lactoyl-amino acids and N-formyl-methionine reflect mitochondrial dysfunction and predict mortality in septic shock

INTRODUCTION

Sepsis is a highly morbid condition characterized by multi-organ dysfunction resulting from dysregulated inflammation in response to acute infection. Mitochondrial dysfunction may contribute to sepsis pathogenesis, but quantifying mitochondrial dysfunction remains challenging.

OBJECTIVE

To assess the extent to which circulating markers of mitochondrial dysfunction are increased in septic shock, and their relationship to severity and mortality.

METHODS

We performed both full-scan and targeted (known markers of genetic mitochondrial disease) metabolomics on plasma to determine markers of mitochondrial dysfunction which distinguish subjects with septic shock (n = 42) from cardiogenic shock without infection (n = 19), bacteremia without sepsis (n = 18), and ambulatory controls (n = 19) - the latter three being conditions in which mitochondrial function, proxied by peripheral oxygen consumption, is presumed intact.

RESULTS

Nine metabolites were significantly increased in septic shock compared to all three comparator groups. This list includes N-formyl-L-methionine (f-Met), a marker of dysregulated mitochondrial protein translation, and N-lactoyl-phenylalanine (lac-Phe), representative of the N-lactoyl-amino acids (lac-AAs), which are elevated in plasma of patients with monogenic mitochondrial disease. Compared to lactate, the clinical biomarker used to define septic shock, there was greater separation between survivors and non-survivors of septic shock for both f-Met and the lac-AAs measured within 24 h of ICU admission. Additionally, tryptophan was the one metabolite significantly decreased in septic shock compared to all other groups, while its breakdown product kynurenate was one of the 9 significantly increased.

CONCLUSION

Future studies which validate the measurement of lac-AAs and f-Met in conjunction with lactate could define a sepsis subtype characterized by mitochondrial dysfunction.

Thromboelastometry and prediction of in-hospital mortality in neonates with sepsis

INTRODUCTION

This study aimed at evaluating the role of rotational thromboelastometry (ROTEM) assays in the prediction of in-hospital mortality of neonates with sepsis.

METHODS

Over a 6-year period, 129 neonates with confirmed sepsis, hospitalized in our neonatal intensive care unit (NICU) were included in the study. Demographics, clinical, and laboratory data were recorded at the sepsis onset and ROTEM assays were performed. Modified neonatal multiple organ dysfunction (NEOMOD) and neonatal sequential organ failure assessment (nSOFA) were calculated simultaneously. Mortality during in-hospital stay was the main outcome measure.

RESULTS

In-hospital mortality was associated with patient intense hypocoagulability expressed by lower ROTEM MCF in the INTEM assay. The INTEM MCF demonstrated the best prognostic performance for NICU mortality in septic neonates among the other ROTEM parameters but without statistical significance (area under the curve [AUC] = 0.731; 95% confidence interval [CI]: 0.593-0.869).

CONCLUSION

Our results indicate that ROTEM INTEM MCF parameter has good predictive capacity for in-hospital mortality of septic neonates, similar to that of modified NEOMOD score, nSOFA score, and platelet count, highlighting the integral role of coagulation in sepsis pathophysiology. Hence, ROTEM could serve as a valuable monitoring tool to identify neonates at risk.

Predictors of Treatment Failure and Mortality among Patients with Septic Shock Treated with Meropenem in the Intensive Care Unit

Background

The aim of the study was to determine the predictors of meropenem treatment failure and mortality in the Intensive Care Unit (ICU).

Methods

This was a retrospective study, involving sepsis and septic shock patients who were admitted to the ICU and received intravenous meropenem. Treatment failure is defined as evidence of non-resolved fever, non-reduced total white cell (TWC), non-reduced C-reactive protein (CRP), subsequent culture negative and death in ICU.

Results

An Acute Physiology and Chronic Health Evaluation II (APACHE II) and duration of antibiotic treatment less than 5 days were associated with treatment failure with adjusted OR = 1.24 (95% CI: 1.15, 1.33; P < 0.001), OR = 65.43 (95% CI: 21.70, 197.23; P < 0.001). A higher risk of mortality was observed with higher APACHE and Sequential Organ Failure Assessment (SOFA) scores, initiating antibiotics > 72 h of sepsis, duration of antibiotic treatment less than 5 days and meropenem with renal adjustment dose with an adjusted OR = 1.21 (95% CI: 1.12, 1.30; P < 0.001), adjusted OR = 1.23 (95% CI: 1.08, 1.41; P < 0.001), adjusted OR = 6.38 (95% CI: 1.67, 24.50; P = 0.007), adjusted OR = 0.03 (95% CI: 0.01, 0.14; P < 0.001), adjusted OR = 0.30 (95% CI: 0.14, 0.64; P = 0.002).

Conclusion

A total of 50 (14.12%) patients had a treatment failure with meropenem with 120 (48.02%) ICU mortality. The predictors of meropenem failure are higher APACHE score and shorter duration of meropenem treatment. The high APACHE, high SOFA score, initiating antibiotics more than 72 h of sepsis, shorter duration of treatment and meropenem with renal adjustment dose were predictors of mortality.

Interferon-Gamma-Release assay and absolute CD8 lymphocyte count for acquired immunosuppression monitoring in critically ill patients

Guided biomarker-personalized immunotherapy is advancing rapidly as a means to rejuvenate immune function in injured patients who are the most immunosuppressed. A recent study introduced a fully automated interferon-γ release assay (IGRA) for monitoring the functionality of T lymphocytes in patients with septic shock. While a significant decrease in IFN-γ release capacity was observed, a significant correlation with CD8 lymphocyte absolute count was also reported, raising the question of whether ex-vivo IFN-γ production would be only a surrogate marker for lymphocyte count or if these two parameters conveyed distinct and complementary information. In a large cohort of more than 353 critically ill patients following various injuries (sepsis, trauma, major surgery), the primary objective of the present study was to simultaneously evaluate the association between ex vivo IFN-γ release and CD8 cell count with regard to adverse outcome. Our findings provide a clear-cut result, as they distinctly demonstrate that IGRA offers higher-quality information than CD8 count in terms of an independent association with the occurrence of an adverse outcome. These results strengthen the case for incorporating IGRA into the array of biomarkers of interest for defining endotypes in sepsis. This holds especially true given that fully automated tests are now readily available and could be used in routine clinical practice.

Identification of clinically relevant patient endotypes in traumatic brain injury using latent class analysis

Traumatic brain injury (TBI) is a complex condition where heterogeneity impedes the advancement of care. Understanding the diverse presentations of TBI is crucial for personalized medicine. Our study aimed to identify clinically relevant patient endotypes in TBI using latent class analysis based on comorbidity data. We used the Medical Information Mart for Intensive Care III database, which includes 2,629 adult TBI patients. We identified five stable endotypes characterized by specific comorbidity profiles: Heart Failure and Arrhythmia, Healthy, Renal Failure with Hypertension, Alcohol Abuse, and Hypertension. Each endotype had distinct clinical characteristics and outcomes: The Heart Failure and Arrhythmia endotype had lower survival rates than the Renal Failure with Hypertension despite featuring fewer comorbidities overall. Patients in the Hypertension endotype had higher rates of neurosurgical intervention but shorter stays in contrast to the Alcohol Abuse endotype which had lower rates of neurosurgical intervention but significantly longer hospital stays. Both endotypes had high overall survival rates comparable to the Healthy endotype. Logistic regression models showed that endotypes improved the predictability of survival compared to individual comorbidities alone. This study validates clinical endotypes as an approach to addressing heterogeneity in TBI and demonstrates the potential of this methodology in other complex conditions.

The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and in silico modeling to identify new therapeutics

Sepsis is a global health concern accounting for more than 1 in 5 deaths worldwide. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can develop from bacterial (gram negative or gram positive), fungal or viral (such as COVID) infections. However, therapeutics developed in animal models and traditional in vitro sepsis models have had little success in clinical trials, as these models have failed to fully replicate the underlying pathophysiology and heterogeneity of the disease. The current understanding is that the host response to sepsis is highly diverse among patients, and this heterogeneity impacts immune function and response to infection. Phenotyping immune function and classifying sepsis patients into specific endotypes is needed to develop a personalized treatment approach. Neutrophil-endothelium interactions play a critical role in sepsis progression, and increased neutrophil influx and endothelial barrier disruption have important roles in the early course of organ damage. Understanding the mechanism of neutrophil-endothelium interactions and how immune function impacts this interaction can help us better manage the disease and lead to the discovery of new diagnostic and prognosis tools for effective treatments. In this review, we will discuss the latest research exploring how in silico modeling of a synergistic combination of new organ-on-chip models incorporating human cells/tissue, omics analysis and clinical data from sepsis patients will allow us to identify relevant signaling pathways and characterize specific immune phenotypes in patients. Emerging technologies such as machine learning can then be leveraged to identify druggable therapeutic targets and relate them to immune phenotypes and underlying infectious agents. This synergistic approach can lead to the development of new therapeutics and the identification of FDA approved drugs that can be repurposed for the treatment of sepsis.

Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future

Sepsis remains a major challenge internationally for healthcare systems. Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management. In sepsis, mortality increases with every hour left untreated. Artificial intelligence (AI) is transforming worldwide healthcare delivery at present. This review has outlined how AI can augment strategies to address this global disease burden. AI and machine learning (ML) algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods. Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs. This gives providers additional time to plan and execute treatment plans, thereby avoiding increasing complications associated with delayed diagnosis of sepsis. The potential for cost savings with AI implementation is also discussed, including improving workflow efficiencies, reducing administrative costs, and improving healthcare outcomes. Despite these advantages, clinicians have been slow to adopt AI into clinical practice. Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use. Furthermore, the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies. Finally, we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology.

Highly secreted tryptophanyl tRNA synthetase 1 as a potential theranostic target for hypercytokinemic severe sepsis

Despite intensive clinical and scientific efforts, the mortality rate of sepsis remains high due to the lack of precise biomarkers for patient stratification and therapeutic guidance. Secreted human tryptophanyl-tRNA synthetase 1 (WARS1), an endogenous ligand for Toll-like receptor (TLR) 2 and TLR4 against infection, activates the genes that signify the hyperinflammatory sepsis phenotype. High plasma WARS1 levels stratified the early death of critically ill patients with sepsis, along with elevated levels of cytokines, chemokines, and lactate, as well as increased numbers of absolute neutrophils and monocytes, and higher Sequential Organ Failure Assessment (SOFA) scores. These symptoms were recapitulated in severely ill septic mice with hypercytokinemia. Further, injection of WARS1 into mildly septic mice worsened morbidity and mortality. We created an anti-human WARS1-neutralizing antibody that suppresses proinflammatory cytokine expression in marmosets with endotoxemia. Administration of this antibody into severe septic mice attenuated cytokine storm, organ failure, and early mortality. With antibiotics, the antibody almost completely prevented fatalities. These data imply that blood-circulating WARS1-guided anti-WARS1 therapy may provide a novel theranostic strategy for life-threatening systemic hyperinflammatory sepsis.

A comprehensive analysis of immune features and construction of an immune gene diagnostic model for sepsis

Sepsis is a life-threatening syndrome resulting from immune system dysfunction that is caused by infection. It is of great importance to analyze the immune characteristics of sepsis, identify the key immune system related genes, and construct diagnostic models for sepsis. In this study, the sepsis transcriptome and expression profiling data were merged into an integrated dataset containing 277 sepsis samples and 117 non-sepsis control samples. Single-sample gene set enrichment analysis (ssGSEA) was used to assess the immune cell infiltration. Two sepsis immune subtypes were identified based on the 22 differential immune cells between the sepsis and the healthy control groups. Weighted gene co-expression network analysis (WCGNA) was used to identify the key module genes. Then, 36 differentially expressed immune-related genes were identified, based on which a robust diagnostic model was constructed with 11 diagnostic genes. The expression of 11 diagnostic genes was finally assessed in the training and validation datasets respectively. In this study, we provide comprehensive insight into the immune features of sepsis and establish a robust diagnostic model for sepsis. These findings may provide new strategies for the early diagnosis of sepsis in the future.

A gene network database for the identification of key genes for diagnosis, prognosis, and treatment in sepsis

Sepsis and sepsis-related diseases cause a high rate of mortality worldwide. The molecular and cellular mechanisms of sepsis are still unclear. We aim to identify key genes in sepsis and reveal potential disease mechanisms. Six sepsis-related blood transcriptome datasets were collected and analyzed by weighted gene co-expression network analysis (WGCNA). Functional annotation was performed in the gProfiler tool. DSigDB was used for drug signature enrichment analysis. The proportion of immune cells was estimated by the CIBERSORT tool. The relationships between modules, immune cells, and survival were identified by correlation analysis and survival analysis. A total of 37 stable co-expressed gene modules were identified. These modules were associated with the critical biology process in sepsis. Four modules can independently separate patients with long and short survival. Three modules can recurrently separate sepsis and normal patients with high accuracy. Some modules can separate bacterial pneumonia, influenza pneumonia, mixed bacterial and influenza A pneumonia, and non-infective systemic inflammatory response syndrome (SIRS). Drug signature analysis identified drugs associated with sepsis, such as testosterone, phytoestrogens, ibuprofen, urea, dichlorvos, potassium persulfate, and vitamin B12. Finally, a gene co-expression network database was constructed ( https://liqs.shinyapps.io/sepsis/ ). The recurrent modules in sepsis may facilitate disease diagnosis, prognosis, and treatment.

Unsupervised clustering identifies sub-phenotypes and reveals novel outcome predictors in patients with dialysis-requiring sepsis-associated acute kidney injury

INTRODUCTION

Heterogeneity exists in sepsis-associated acute kidney injury (SA-AKI). This study aimed to perform unsupervised consensus clustering in critically ill patients with dialysis-requiring SA-AKI.

PATIENTS AND METHODS

This prospective observational cohort study included all septic patients, defined by the Sepsis-3 criteria, with dialysis-requiring SA-AKI in surgical intensive care units in Taiwan between 2009 and 2018. We employed unsupervised consensus clustering based on 23 clinical variables upon initializing renal replacement therapy. Multivariate-adjusted Cox regression models and Fine-Gray sub-distribution hazard models were built to test associations between cluster memberships with mortality and being free of dialysis at 90 days after hospital discharge, respectively.

RESULTS

Consensus clustering among 999 enrolled patients identified three sub-phenotypes characterized with distinct clinical manifestations upon renal replacement therapy initiation (n = 352, 396 and 251 in cluster 1, 2 and 3, respectively). They were followed for a median of 48 (interquartile range 9.5-128.5) days. Phenotypic cluster 1, featured by younger age, lower Charlson Comorbidity Index, higher baseline estimated glomerular filtration rate but with higher severity of acute illness was associated with an increased risk of death (adjusted hazard ratio of 3.05 [95% CI, 2.35-3.97]) and less probability to become free of dialysis (adjusted sub-distribution hazard ratio of 0.55 [95% CI, 0.38-0.8]) than cluster 3. By examining distinct features of the sub-phenotypes, we discovered that pre-dialysis hyperlactatemia ≥3.3 mmol/L was an independent outcome predictor. A clinical model developed to determine high-risk sub-phenotype 1 in this cohort (C-static 0.99) can identify a sub-phenotype with high in-hospital mortality risk (adjusted hazard ratio of 1.48 [95% CI, 1.25-1.74]) in another independent multi-centre SA-AKI cohort.

CONCLUSIONS

Our data-driven approach suggests sub-phenotypes with clinical relevance in dialysis-requiring SA-AKI and serves an outcome predictor. This strategy represents further development toward precision medicine in the definition of high-risk sub-phenotype in patients with SA-AKI.Key messagesUnsupervised consensus clustering can identify sub-phenotypes of patients with SA-AKI and provide a risk prediction.Examining the features of patient heterogeneity contributes to the discovery of serum lactate levels ≥ 3.3 mmol/L upon initializing RRT as an independent outcome predictor.This data-driven approach can be useful for prognostication and lead to a better understanding of therapeutic strategies in heterogeneous clinical syndromes.

Kidney function as a key driver of the pharmacokinetic response to high-dose L-carnitine in septic shock

STUDY OBJECTIVE

Levocarnitine (L-carnitine) has shown promise as a metabolic-therapeutic for septic shock, where mortality approaches 40%. However, high-dose (≥ 6 grams) intravenous supplementation results in a broad range of serum concentrations. We sought to describe the population pharmacokinetics (PK) of high-dose L-carnitine, test various estimates of kidney function, and assess the correlation of PK parameters with pre-treatment metabolites in describing drug response for patients with septic shock.

DESIGN

Population PK analysis was done with baseline normalized concentrations using nonlinear mixed effect models in the modeling platform Monolix. Various estimates of kidney function, patient demographics, dose received, and organ dysfunction were tested as population covariates.

DATA SOURCE

We leveraged serum samples and metabolomics data from a phase II trial of L-carnitine in vasopressor-dependent septic shock. Serum was collected at baseline (T0); end-of-infusion (T12); and 24, 48, and 72 h after treatment initiation.

PATIENTS AND INTERVENTION

Patients were adaptively randomized to receive intravenous L-carnitine (6 grams, 12 grams, or 18 grams) or placebo.

MEASUREMENTS AND MAIN RESULTS

The final dataset included 542 serum samples from 130 patients randomized to L-carnitine. A two-compartment model with linear elimination and a fixed volume of distribution (17.1 liters) best described the data and served as a base structural model. Kidney function estimates as a covariate on the elimination rate constant (k) reliably improved model fit. Estimated glomerular filtration rate (eGFR), based on the 2021 Chronic Kidney Disease Epidemiology collaboration (CKD-EPI) equation with creatinine and cystatin C, outperformed creatinine clearance (Cockcroft-Gault) and older CKD-EPI equations that use an adjustment for self-identified race.

CONCLUSIONS

High-dose L-carnitine supplementation is well-described by a two-compartment population PK model in patients with septic shock. Kidney function estimates that leverage cystatin C provided superior model fit. Future investigations into high-dose L-carnitine supplementation should consider baseline metabolic status and dose adjustments based on renal function over a fixed or weight-based dosing paradigm.

Endotoxic Septic Shock: Diagnosis and Treatment

Endotoxin, also referred to as lipopolysaccharide (LPS), is a potent stimulator of the inflammatory cascade which may progress to sepsis and septic shock. The term endotoxic septic shock has been used for patients who have a clinical phenotype that is characterized by high endotoxin activity in addition to a high burden of organ failure; especially a pattern of organ failure including hepatic dysfunction, acute kidney injury, and various forms of endothelial dysfunction. Endotoxic septic shock has been a target for drug therapy for decades with no success. A likely barrier to their success was the inability to quantify endotoxin in the bloodstream. The Endotoxin Activity Assay (EAA) is positioned to change this landscape. In addition, medical devices using adsorptive technology in an extra-corporeal circulation has been shown to remove large quantities of endotoxin from the bloodstream. Focusing on the use of EAA to determine high concentrations of endotoxin will allow patients with endotoxic septic shock to be identified quickly and these patients may benefit most from removal of endotoxin using extracorporeal methods.

Antibiotic failure: Beyond antimicrobial resistance

Despite significant progress in antibiotic discovery, millions of lives are lost annually to infections. Surprisingly, the failure of antimicrobial treatments to effectively eliminate pathogens frequently cannot be attributed to genetically-encoded antibiotic resistance. This review aims to shed light on the fundamental mechanisms contributing to clinical scenarios where antimicrobial therapies are ineffective (i.e., antibiotic failure), emphasizing critical factors impacting this under-recognized issue. Explored aspects include biofilm formation and sepsis, as well as the underlying microbiome. Therapeutic strategies beyond antibiotics, are examined to address the dimensions and resolution of antibiotic failure, actively contributing to this persistent but escalating crisis. We discuss the clinical relevance of antibiotic failure beyond resistance, limited availability of therapies, potential of new antibiotics to be ineffective, and the urgent need for novel anti-infectives or host-directed therapies directly addressing antibiotic failure. Particularly noteworthy is multidrug adaptive resistance in biofilms that represent 65 % of infections, due to the lack of approved therapies. Sepsis, responsible for 19.7 % of all deaths (as well as severe COVID-19 deaths), is a further manifestation of this issue, since antibiotics are the primary frontline therapy, and yet 23 % of patients succumb to this condition.

The role of endotoxin in septic shock

Septic shock can be caused by a variety of mechanisms including direct effects of bacterial toxins such as endotoxin. Annually, approximately 5-7 million patients worldwide develop sepsis with very high endotoxin activity in the blood and more than half die. The term endotoxic septic shock has been used for these patients but it is important to emphasize that endotoxin may be a factor in all forms of septic shock including non-bacterial etiologies like COVID-19 since translocation of bacterial products is a common feature of septic shock. A pattern of organ failure including hepatic dysfunction, acute kidney injury and various forms of endothelial dysfunction ranging from disseminated intravascular coagulation to thrombotic microangiopathy characterize endotoxic septic shock. However, while characteristic, the clinical phenotype is not unique to patients with high endotoxin, and the diagnosis relies on the measurement of endotoxin activity in addition to clinical assessment. Therapies for endotoxic septic shock are limited with immune modulating therapies under investigation and extracorporeal blood purification still controversial in many parts of the world.

Time to embrace sepsis pathways and antibiotic prescribing decision support in the emergency department: Observations from a retrospective single site clinical audit

OBJECTIVE

To compare clinician documentation of sepsis for infective presentations in the ED against a formal sepsis pathway in the ED and to assess appropriateness of the initial parenteral antibiotic prescription for adult patients in ED.

METHODS

A retrospective, clinical audit of adult patients who received at least one parenteral antibiotic in ED over a 10-week period in 2018. Documented initial clinical impression was compared with an approved sepsis pathway. Antibiotic appropriateness was assessed using National Antimicrobial Prescribing Survey definitions. Assessment was carried out by an infectious diseases pharmacist, with input from an infectious diseases physician.

RESULTS

Two hundred and nineteen infective presentations were included in the analysis. There was a discordance between the initial documented clinical impression compared with the classification when a sepsis pathway was applied. An initial documented clinical impression of sepsis and septic shock was present in 38 (60.3%) of the presentations compared to 63 presentations when a formal sepsis pathway was applied as a screening tool. There was a significant difference in the proportion of patients in each diagnostic group (infection, sepsis and septic shock) according to documented clinical impression versus sepsis pathway classification (P = 0.0002). There were 386 prescriptions for antibiotics as part of the initial management. Antibiotic appropriateness for the initial prescription was assessed as 63.7% appropriate, 27.2% inappropriate and 9.1% not assessable.

CONCLUSION

Our observations demonstrate that use of a formal sepsis pathway may improve the screening and early diagnosis of sepsis and septic shock and that there is a need for antibiotic prescribing guidance in the ED.

Sepsis heterogeneity

BACKGROUND

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection, with extremely high mortality. Notably, sepsis is a heterogeneous syndrome characterized by a vast, multidimensional array of clinical and biologic features, which has hindered advances in the therapeutic field beyond the current standards.

DATA SOURCES

We used PubMed to search the subject-related medical literature by searching for the following single and/or combination keywords: sepsis, heterogeneity, personalized treatment, host response, infection, epidemiology, mortality, incidence, age, children, sex, comorbidities, gene susceptibility, infection sites, bacteria, fungi, virus, host response, organ dysfunction and management.

RESULTS

We found that host factors (age, biological sex, comorbidities, and genetics), infection etiology, host response dysregulation and multiple organ dysfunctions can all result in different disease manifestations, progression, and response to treatment, which make it difficult to effectively treat and manage sepsis patients.

CONCLUSIONS

Herein, we have summarized contributing factors to sepsis heterogeneity, including host factors, infection etiology, host response dysregulation, and multiple organ dysfunctions, from the key elements of pathogenesis of sepsis. An in-depth understanding of the factors that contribute to the heterogeneity of sepsis will help clinicians understand the complexity of sepsis and enable researchers to conduct more personalized clinical studies for homogenous patients.

Bacteremia in Patients with Sepsis in the ICU: Does It Make a Difference?

Sepsis (and septic shock) is on of the most common causes of death worldwide. Bacteremia often, but not necessarily, occurs in septic patients, but the impact of true bacteremia on a patient's clinical characteristics and outcome remains unclear. The main aim of this study was to compare the characteristics and outcome of a well-defined cohort of 258 septic patients with and without bacteremia treated in the intensive care unit (ICU) of a tertiary center hospital in Prague, Czech Republic. As expected, more frequently, bacteremia was present in patients without previous antibiotic treatment. A higher proportion of bacteremia was observed in patients with infective endocarditis as well as catheter-related and soft tissue infections in contrast to respiratory sepsis. Multivariant analysis showed increased severity of clinical status and higher Charlson comorbidity index (CCI) as variables with significant influence on mortality. Bacteremia appears to be associated with higher mortality rates and length of ICU stay in comparison with nonbacteremic counterparts, but this difference did not reach statistical significance. The presence of bacteremia, apart from previous antibiotic treatment, may be related to the site of infection.

Cluster analysis driven by unsupervised latent feature learning of medications to identify novel pharmacophenotypes of critically ill patients

Unsupervised clustering of intensive care unit (ICU) medications may identify unique medication clusters (i.e., pharmacophenotypes) in critically ill adults. We performed an unsupervised analysis with Restricted Boltzmann Machine of 991 medications profiles of patients managed in the ICU to explore pharmacophenotypes that correlated with ICU complications (e.g., mechanical ventilation) and patient-centered outcomes (e.g., length of stay, mortality). Six unique pharmacophenotypes were observed, with unique medication profiles and clinically relevant differences in ICU complications and patient-centered outcomes. While pharmacophenotypes 2 and 4 had no statistically significant difference in ICU length of stay, duration of mechanical ventilation, or duration of vasopressor use, their mortality differed significantly (9.0% vs. 21.9%, p < 0.0001). Pharmacophenotype 4 had a mortality rate of 21.9%, compared with the rest of the pharmacophenotypes ranging from 2.5 to 9%. Phenotyping approaches have shown promise in classifying the heterogenous syndromes of critical illness to predict treatment response and guide clinical decision support systems but have never included comprehensive medication information. This first-ever machine learning approach revealed differences among empirically-derived subgroups of ICU patients that are not typically revealed by traditional classifiers. Identification of pharmacophenotypes may enable enhanced decision making to optimize treatment decisions.

Optimizing Pharmacist Team-Integration for ICU Patient Management: Rationale, Study Design, and Methods for a Multicentered Exploration of Pharmacist-to-Patient Ratio

BACKGROUND

The workload of healthcare professionals including physicians and nurses in the ICU has an established relationship to patient outcomes, including mortality, length of stay, and other quality indicators; however, the relationship of critical care pharmacist workload to outcomes has not been rigorously evaluated and determined. The objective of our study is to characterize the relationship of critical care pharmacist workload in the ICU as it relates to patient-centered outcomes of critically ill patients.

METHODS

Optimizing Pharmacist Team-Integration for ICU patient Management is a multicenter, observational cohort study with a target enrollment of 20,000 critically ill patients. Participating critical care pharmacists will enroll patients managed in the ICU. Data collection will consist of two observational phases: prospective and retrospective. During the prospective phase, critical care pharmacists will record daily workload data (e.g., census, number of rounding teams). During the retrospective phase, patient demographics, severity of illness, medication regimen complexity, and outcomes will be recorded. The primary outcome is mortality. Multiple methods will be used to explore the primary outcome including multilevel multiple logistic regression with stepwise variable selection to exclude nonsignificant covariates from the final model, supervised and unsupervised machine learning techniques, and Bayesian analysis.

RESULTS

Our protocol describes the processes and methods for an observational study in the ICU.

CONCLUSIONS

This study seeks to determine the relationship between pharmacist workload, as measured by pharmacist-to-patient ratio and the pharmacist clinical burden index, and patient-centered outcomes, including mortality and length of stay.

Clinical Phenotypes of Sepsis in a Cohort of Hospitalized Patients According to Infection Site

OBJECTIVES

Clinical sepsis phenotypes may be defined by a wide range of characteristics such as site of infection, organ dysfunction patterns, laboratory values, and demographics. There is a paucity of literature regarding the impact of site of infection on the timing and pattern of clinical sepsis markers. This study hypothesizes that important phenotypic variation in clinical markers and outcomes of sepsis exists when stratified by infection site.

DESIGN

Retrospective cohort study.

SETTING

Five hospitals within the Wake Forest Health System from June 2019 to December 2019.

PATIENTS

Six thousand seven hundred fifty-three hospitalized adults with a discharge International Classification of Diseases, 10th Revision code for acute infection who met systemic inflammatory response syndrome (SIRS), quick Sepsis-related Organ Failure Assessment (qSOFA), or Sequential Organ Failure Assessment (SOFA) criteria during the index hospitalization.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The primary outcome of interest was a composite of 30-day mortality or shock. Infection site was determined by a two-reviewer process. Significant demographic, vital sign, and laboratory result differences were seen across all infection sites. For the composite outcome of shock or 30-day mortality, unknown or unspecified infections had the highest proportion (21.34%) and CNS infections had the lowest proportion (8.11%). Respiratory, vascular, and unknown or unspecified infection sites showed a significantly increased adjusted and unadjusted odds of the composite outcome as compared with the other infection sites except CNS. Hospital time prior to SIRS positivity was shortest in unknown or unspecified infections at a median of 0.88 hours (interquartile range [IQR], 0.22-5.05 hr), and hospital time prior to qSOFA and SOFA positivity was shortest in respiratory infections at a median of 54.83 hours (IQR, 9.55-104.67 hr) and 1.88 hours (IQR, 0.47-17.40 hr), respectively.

CONCLUSIONS

Phenotypic variation in illness severity and mortality exists when stratified by infection site. There is a significantly higher adjusted and unadjusted odds of the composite outcome of 30-day mortality or shock in respiratory, vascular, and unknown or unspecified infections as compared with other sites.

Defining critical illness using immunological endotypes in patients with and without sepsis: a cohort study

BACKGROUND

Sepsis is a heterogenous syndrome with limited therapeutic options. Identifying immunological endotypes through gene expression patterns in septic patients may lead to targeted interventions. We investigated whether patients admitted to a surgical intensive care unit (ICU) with sepsis and with high risk of mortality express similar endotypes to non-septic, but still critically ill patients using two multiplex transcriptomic metrics obtained both on admission to a surgical ICU and at set intervals.

METHODS

We analyzed transcriptomic data from 522 patients in two single-site, prospective, observational cohorts admitted to surgical ICUs over a 5-year period ending in July 2020. Using an FDA-cleared analytical platform (nCounter FLEX^®, NanoString, Inc.), we assessed a previously validated 29-messenger RNA transcriptomic classifier for likelihood of 30-day mortality (IMX-SEV-3) and a 33-messenger RNA transcriptomic endotype classifier. Clinical outcomes included all-cause mortality, development of chronic critical illness, and secondary infections. Univariate and multivariate analyses were performed to assess for true effect and confounding.

RESULTS

Sepsis was associated with a significantly higher predicted and actual hospital mortality. At enrollment, the predominant endotype for both septic and non-septic patients was adaptive, though with significantly different distributions. Inflammopathic and coagulopathic septic patients, as well as inflammopathic non-septic patients, showed significantly higher frequencies of secondary infections compared to those with adaptive endotypes (p < 0.01). Endotypes changed during ICU hospitalization in 57.5% of patients. Patients who remained adaptive had overall better prognosis, while those who remained inflammopathic or coagulopathic had worse overall outcomes. For severity metrics, patients admitted with sepsis and a high predicted likelihood of mortality showed an inflammopathic (49.6%) endotype and had higher rates of cumulative adverse outcomes (67.4%). Patients at low mortality risk, whether septic or non-septic, almost uniformly presented with an adaptive endotype (100% and 93.4%, respectively).

CONCLUSION

Critically ill surgical patients express different and evolving immunological endotypes depending upon both their sepsis status and severity of their clinical course. Future studies will elucidate whether endotyping critically ill, septic patients can identify individuals for targeted therapeutic interventions to improve patient management and outcomes.

The Gut Microbiota Contributes to Systemic Responses and Liver Injury in Gut-Derived Sepsis

The gut microbiota, as a major source of opportunistic pathogens, poses a great threat to systemic infection, whereas the role of the gut microbiota in sepsis is underestimated. Here, we aimed to explore the effects of different gut microbiota patterns (namely, enterotypes) in cecal ligation and puncture (CLP)-induced murine sepsis. To achieve this purpose, we built four kinds of enterotypes by exposing mice to different types of antibiotics (azithromycin, amoxicillin, metronidazole, and levofloxacin). The results showed that antibiotic exposure induced different enterotypes, which, in turn, led to varying levels of systemic inflammation in septic mice, with amoxicillin-associated enterotypes exhibiting the most severe inflammation, followed by metronidazole, azithromycin, and levofloxacin. Specifically, the amoxicillin-associated enterotype was characterized by an abundance of intestinal opportunistic pathogens, including Enterobacteriaceae, Sutterellaceae, and Morganellaceae. This enterotype played a significant role in promoting the pathogenic potential of the gut microbiota, ultimately contributing to the development of severe systemic inflammation. Furthermore, the amoxicillin-associated enterotype exaggerated the sepsis-related liver injury, as evidenced by higher levels of alanine aminotransferase, aspartate transaminase, and hepatic malondialdehyde. The results of the RNA sequencing and the fecal suspension intraperitoneal injection sepsis model indicated that the amoxicillin-associated enterotype provoked acute hepatic immune responses and led to more significant metabolic compensation in the event of sepsis. Collectively, we concluded that the gut microbiota was one crucial factor for heterogeneity in sepsis, where the modulated gut microbiota likely prevented or reduced the serious consequences of sepsis, at least in gut-derived sepsis.

Pharmacophenotype identification of intensive care unit medications using unsupervised cluster analysis of the ICURx common data model

BACKGROUND

Identifying patterns within ICU medication regimens may help artificial intelligence algorithms to better predict patient outcomes; however, machine learning methods incorporating medications require further development, including standardized terminology. The Common Data Model for Intensive Care Unit (ICU) Medications (CDM-ICURx) may provide important infrastructure to clinicians and researchers to support artificial intelligence analysis of medication-related outcomes and healthcare costs. Using an unsupervised cluster analysis approach in combination with this common data model, the objective of this evaluation was to identify novel patterns of medication clusters (termed 'pharmacophenotypes') correlated with ICU adverse events (e.g., fluid overload) and patient-centered outcomes (e.g., mortality).

METHODS

This was a retrospective, observational cohort study of 991 critically ill adults. To identify pharmacophenotypes, unsupervised machine learning analysis with automated feature learning using restricted Boltzmann machine and hierarchical clustering was performed on the medication administration records of each patient during the first 24 h of their ICU stay. Hierarchical agglomerative clustering was applied to identify unique patient clusters. Distributions of medications across pharmacophenotypes were described, and differences among patient clusters were compared using signed rank tests and Fisher's exact tests, as appropriate.

RESULTS

A total of 30,550 medication orders for the 991 patients were analyzed; five unique patient clusters and six unique pharmacophenotypes were identified. For patient outcomes, compared to patients in Clusters 1 and 3, patients in Cluster 5 had a significantly shorter duration of mechanical ventilation and ICU length of stay (p < 0.05); for medications, Cluster 5 had a higher distribution of Pharmacophenotype 1 and a smaller distribution of Pharmacophenotype 2, compared to Clusters 1 and 3. For outcomes, patients in Cluster 2, despite having the highest severity of illness and greatest medication regimen complexity, had the lowest overall mortality; for medications, Cluster 2 also had a comparably higher distribution of Pharmacophenotype 6.

CONCLUSION

The results of this evaluation suggest that patterns among patient clusters and medication regimens may be observed using empiric methods of unsupervised machine learning in combination with a common data model. These results have potential because while phenotyping approaches have been used to classify heterogenous syndromes in critical illness to better define treatment response, the entire medication administration record has not been incorporated in those analyses. Applying knowledge of these patterns at the bedside requires further algorithm development and clinical application but may have the future potential to be leveraged in guiding medication-related decision making to improve treatment outcomes.

The Gut Microbiome Modulates Body Temperature Both in Sepsis and Health

Rationale: Among patients with sepsis, variation in temperature trajectories predicts clinical outcomes. In healthy individuals, normal body temperature is variable and has decreased consistently since the 1860s. The biologic underpinnings of this temperature variation in disease and health are unknown. Objectives: To establish and interrogate the role of the gut microbiome in calibrating body temperature. Methods: We performed a series of translational analyses and experiments to determine whether and how variation in gut microbiota explains variation in body temperature in sepsis and in health. We studied patient temperature trajectories using electronic medical record data. We characterized gut microbiota in hospitalized patients using 16S ribosomal RNA gene sequencing. We modeled sepsis using intraperitoneal LPS in mice and modulated the microbiome using antibiotics, germ-free, and gnotobiotic animals. Measurements and Main Results: Consistent with prior work, we identified four temperature trajectories in patients hospitalized with sepsis that predicted clinical outcomes. In a separate cohort of 116 hospitalized patients, we found that the composition of patients' gut microbiota at admission predicted their temperature trajectories. Compared with conventional mice, germ-free mice had reduced temperature loss during experimental sepsis. Among conventional mice, heterogeneity of temperature response in sepsis was strongly explained by variation in gut microbiota. Healthy germ-free and antibiotic-treated mice both had lower basal body temperatures compared with control animals. The Lachnospiraceae family was consistently associated with temperature trajectories in hospitalized patients, experimental sepsis, and antibiotic-treated mice. Conclusions: The gut microbiome is a key modulator of body temperature variation in both health and critical illness and is thus a major, understudied target for modulating physiologic heterogeneity in sepsis.

Sustained Perturbation of Metabolism and Metabolic Subphenotypes Are Associated With Mortality and Protein Markers of the Host Response

Perturbed host metabolism is increasingly recognized as a pillar of sepsis pathogenesis, yet the dynamic alterations in metabolism and its relationship to other components of the host response remain incompletely understood. We sought to identify the early host-metabolic response in patients with septic shock and to explore biophysiological phenotyping and differences in clinical outcomes among metabolic subgroups.

DESIGN

We measured serum metabolites and proteins reflective of the host-immune and endothelial response in patients with septic shock.

SETTING

We considered patients from the placebo arm of a completed phase II, randomized controlled trial conducted at 16 U.S. medical centers. Serum was collected at baseline (within 24 hr of the identification of septic shock), 24-hour, and 48-hour postenrollment. Linear mixed models were built to assess the early trajectory of protein analytes and metabolites stratified by 28-day mortality status. Unsupervised clustering of baseline metabolomics data was conducted to identify subgroups of patients.

PATIENTS

Patients with vasopressor-dependent septic shock and moderate organ dysfunction that were enrolled in the placebo arm of a clinical trial.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Fifty-one metabolites and 10 protein analytes were measured longitudinally in 72 patients with septic shock. In the 30 patients (41.7%) who died prior to 28 days, systemic concentrations of acylcarnitines and interleukin (IL)-8 were elevated at baseline and persisted at T24 and T48 throughout early resuscitation. Concentrations of pyruvate, IL-6, tumor necrosis factor-α, and angiopoietin-2 decreased at a slower rate in patients who died. Two groups emerged from clustering of baseline metabolites. Group 1 was characterized by higher levels of acylcarnitines, greater organ dysfunction at baseline and postresuscitation (p < 0.05), and greater mortality over 1 year (p < 0.001).

CONCLUSIONS

Among patients with septic shock, nonsurvivors exhibited a more profound and persistent dysregulation in protein analytes attributable to neutrophil activation and disruption of mitochondrial-related metabolism than survivors.

Circulating protein and lipid markers of early sepsis diagnosis and prognosis: a scoping review

PURPOSE OF REVIEW

Sepsis is the extreme response to infection associated with high mortality, yet reliable biomarkers for its identification and stratification are lacking.

RECENT FINDINGS

Our scoping review of studies published from January 2017 to September 2022 that investigated circulating protein and lipid markers to inform non-COVID-19 sepsis diagnosis and prognosis identified interleukin (IL)-6, IL-8, heparin-binding protein (HBP), and angiopoietin-2 as having the most evidence. Biomarkers can be grouped according to sepsis pathobiology to inform biological data interpretation and four such physiologic processes include: immune regulation, endothelial injury and coagulopathy, cellular injury, and organ injury. Relative to proteins, the pleiotropic effects of lipid species' render their categorization more difficult. Circulating lipids are relatively less well studied in sepsis, however, low high-density lipoprotein (HDL) is associated with poor outcome.

SUMMARY

There is a lack of robust, large, and multicenter studies to support the routine use of circulating proteins and lipids for sepsis diagnosis or prognosis. Future studies will benefit from standardizing cohort design as well as analytical and reporting strategies. Incorporating biomarker dynamic changes and clinical data in statistical modeling may improve specificity for sepsis diagnosis and prognosis. To guide future clinical decisions at the bedside, point-of-care circulating biomarker quantification is needed.

The Role of Transcriptomics in Redefining Critical Illness

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2023. Other selected articles can be found online at  https://www.biomedcentral.com/collections/annualupdate2023 . Further information about the Annual Update in Intensive Care and Emergency Medicine is available from  https://link.springer.com/bookseries/8901 .

Utility of monocyte HLA-DR and rationale for therapeutic GM-CSF in sepsis immunoparalysis

Sepsis, a heterogeneous clinical syndrome, features a systemic inflammatory response to tissue injury or infection, followed by a state of reduced immune responsiveness. Measurable alterations occur in both the innate and adaptive immune systems. Immunoparalysis, an immunosuppressed state, associates with worsened outcomes, including multiple organ dysfunction syndrome, secondary infections, and increased mortality. Multiple immune markers to identify sepsis immunoparalysis have been proposed, and some might offer clinical utility. Sepsis immunoparalysis is characterized by reduced lymphocyte numbers and downregulation of class II human leukocyte antigens (HLA) on innate immune monocytes. Class II HLA proteins present peptide antigens for recognition by and activation of antigen-specific T lymphocytes. One monocyte class II protein, mHLA-DR, can be measured by flow cytometry. Downregulated mHLA-DR indicates reduced monocyte responsiveness, as measured by ex-vivo cytokine production in response to endotoxin stimulation. Our literature survey reveals low mHLA-DR expression on peripheral blood monocytes correlates with increased risks for infection and death. For mHLA-DR, 15,000 antibodies/cell appears clinically acceptable as the lower limit of immunocompetence. Values less than 15,000 antibodies/cell are correlated with sepsis severity; and values at or less than 8000 antibodies/cell are identified as severe immunoparalysis. Several experimental immunotherapies have been evaluated for reversal of sepsis immunoparalysis. In particular, sargramostim, a recombinant human granulocyte-macrophage colony-stimulating factor (rhu GM-CSF), has demonstrated clinical benefit by reducing hospitalization duration and lowering secondary infection risk. Lowered infection risk correlates with increased mHLA-DR expression on peripheral blood monocytes in these patients. Although mHLA-DR has shown promising utility for identifying sepsis immunoparalysis, absence of a standardized, analytically validated method has thus far prevented widespread adoption. A clinically useful approach for patient inclusion and identification of clinically correlated output parameters could address the persistent high unmet medical need for effective targeted therapies in sepsis.

Biomaterial mediated simultaneous delivery of spermine and alpha ketoglutarate modulate metabolism and innate immune cell phenotype in sepsis mouse models

Although different metabolic pathways have been associated with distinct macrophage phenotypes, the field of utilizing metabolites to modulate macrophage phenotype is in a nascent stage. In this report, we developed microparticles based on polymerization of alpha-ketoglutarate (a Krebs cycle metabolite), with or without encapsulation of spermine (a polyamine metabolite), to modulate cell phenotype that are critical for resolution of inflammation. Poly (alpha-ketoglutarate) microparticles encapsulated and released spermine (spermine (encap)paKG MPs) in vitro, which was accelerated in an acidic environment. When delivered to bone marrow-derived-macrophages, spermine (encap)paKG MPs induced a complex phenotypic profile outside of the typical M1/M2 paradigm, with distinct effects in the presence or absence of the pro-inflammatory stimulus lipopolysaccharide. Of particular interest was the increase in expression of CD163, which has been linked to anti-inflammatory responses in sepsis. Therefore, we systemically administered spermine (encap)paKG MPs to two different murine models of sepsis using acute or chronic injection of LPS. Macrophages and neutrophils in the liver and spleen of animals treated with spermine (encap)paKG MPs increased expression of CD163, concomitant with normalizing of glycolysis and oxidative phosphorylation, in both models. Overall, these results show that spermine (encap)paKG MPs modulate macrophage phenotype in vitro and in vivo, with potential applications in inflammation-associated diseases.

Systemic Metabolomic Profiles in Adult Patients with Bacterial Sepsis: Characterization of Patient Heterogeneity at the Time of Diagnosis

Sepsis is a dysregulated host response to infection that causes potentially life-threatening organ dysfunction. We investigated the serum metabolomic profile at hospital admission for patients with bacterial sepsis. The study included 60 patients; 35 patients fulfilled the most recent 2016 Sepsis-3 criteria whereas the remaining 25 patients only fulfilled the previous Sepsis-2 criteria and could therefore be classified as having systemic inflammatory response syndrome (SIRS). A total of 1011 identified metabolites were detected in our serum samples. Ninety-seven metabolites differed significantly when comparing Sepsis-3 and Sepsis-2/SIRS patients; 40 of these metabolites constituted a heterogeneous group of amino acid metabolites/peptides. When comparing patients with and without bacteremia, we identified 51 metabolites that differed significantly, including 16 lipid metabolites and 11 amino acid metabolites. Furthermore, 42 metabolites showed a highly significant association with the maximal total Sequential Organ Failure Assessment (SOFA )score during the course of the disease (i.e., Pearson's correlation test, p-value < 0.005, and correlation factor > 0.6); these top-ranked metabolites included 23 amino acid metabolites and a subset of pregnenolone/progestin metabolites. Unsupervised hierarchical clustering analyses based on all 42 top-ranked SOFA correlated metabolites or the subset of 23 top-ranked amino acid metabolites showed that most Sepsis-3 patients differed from Sepsis-2/SIRS patients in their systemic metabolic profile at the time of hospital admission. However, a minority of Sepsis-3 patients showed similarities with the Sepsis-2/SIRS metabolic profile even though several of them showed a high total SOFA score. To conclude, Sepsis-3 patients are heterogeneous with regard to their metabolic profile at the time of hospitalization.

Predicting severity in COVID-19 disease using sepsis blood gene expression signatures

Severely-afflicted COVID-19 patients can exhibit disease manifestations representative of sepsis, including acute respiratory distress syndrome and multiple organ failure. We hypothesized that diagnostic tools used in managing all-cause sepsis, such as clinical criteria, biomarkers, and gene expression signatures, should extend to COVID-19 patients. Here we analyzed the whole blood transcriptome of 124 early (1-5 days post-hospital admission) and late (6-20 days post-admission) sampled patients with confirmed COVID-19 infections from hospitals in Quebec, Canada. Mechanisms associated with COVID-19 severity were identified between severity groups (ranging from mild disease to the requirement for mechanical ventilation and mortality), and established sepsis signatures were assessed for dysregulation. Specifically, gene expression signatures representing pathophysiological events, namely cellular reprogramming, organ dysfunction, and mortality, were significantly enriched and predictive of severity and lethality in COVID-19 patients. Mechanistic endotypes reflective of distinct sepsis aetiologies and therapeutic opportunities were also identified in subsets of patients, enabling prediction of potentially-effective repurposed drugs. The expression of sepsis gene expression signatures in severely-afflicted COVID-19 patients indicates that these patients should be classified as having severe sepsis. Accordingly, in severe COVID-19 patients, these signatures should be strongly considered for the mechanistic characterization, diagnosis, and guidance of treatment using repurposed drugs.

Host Gene Expression to Predict Sepsis Progression

OBJECTIVES

Sepsis causes significant mortality. However, most patients who die of sepsis do not present with severe infection, hampering efforts to deliver early, aggressive therapy. It is also known that the host gene expression response to infection precedes clinical illness. This study seeks to develop transcriptomic models to predict progression to sepsis or shock within 72 hours of hospitalization and to validate previously identified transcriptomic signatures in the prediction of 28-day mortality.

DESIGN

Retrospective differential gene expression analysis and predictive modeling using RNA sequencing data.

PATIENTS

Two hundred seventy-seven patients enrolled at four large academic medical centers; all with clinically adjudicated infection were considered for inclusion in this study.

MEASUREMENTS AND MAIN RESULTS

Sepsis progression was defined as an increase in Sepsis 3 category within 72 hours. Transcriptomic data were generated using RNAseq of whole blood. Least absolute shrinkage and selection operator modeling was used to identify predictive signatures for various measures of disease progression. Four previously identified gene signatures were tested for their ability to predict 28-day mortality. There were no significant differentially expressed genes in 136 subjects with worsened Sepsis 3 category compared with 141 nonprogressor controls. There were 1,178 differentially expressed genes identified when sepsis progression was defined as ICU admission or 28-day mortality. A model based on these genes predicted progression with an area under the curve of 0.71. Validation of previously identified gene signatures to predict sepsis mortality revealed area under the receiver operating characteristic values of 0.70-0.75 and no significant difference between signatures.

CONCLUSIONS

Host gene expression was unable to predict sepsis progression when defined by an increase in Sepsis-3 category, suggesting this definition is not a useful framework for transcriptomic prediction methods. However, there was a differential response when progression was defined as ICU admission or death. Validation of previously described signatures predicted 28-day mortality with insufficient accuracy to offer meaningful clinical utility.

Immune Modulation in Sepsis, ARDS, and Covid-19 - The Road Traveled and the Road Ahead

Immune Modulation in Sepsis, ARDS, and Covid-19Leligdowicz et al. consider the history and future of immunomodulating therapies in sepsis and ARDS, including ARDS due to Covid-19, and remark on the larger challenge of clinical research on therapies for syndromes with profound clinical and biologic heterogeneity.

Application of oXiris-continuous hemofiltration adsorption in patients with sepsis and septic shock: A single-centre experience in China

oXiris is a new, high-adsorption membrane filter in continuous hemofiltration adsorption to reduce the inflammatory response in sepsis. The investigators retrospectively reviewed patients with sepsis/septic shock who underwent at least one oXiris-treatment from November 2020 to March 2022. The demographic data, baseline levels before treatment, clinical datas, prognosis, and the occurrence of adverse events during treatment were recorded. 90 patients were enrolled in this study. The hemodynamic indices, sequential organ failure assessment score, lactate, inflammatory biomarkers levels were significantly improved at 12 h and 24 h after treatment. Procalcitonin and interleukin-6 reduction post-treatment of oXiris were most pronounced in infection from skin and soft tissue, urinary and abdominal cavity. Logistic regression analysis showed that pre-treatment sequential organ failure assessment score (p = 0.034), percentage decrease in sequential organ failure assessment score (p = 0.004), and age (p = 0.011) were independent risk factors for intensive care unit mortality. In conclusion, oXiris-continuous hemofiltration adsorption may improve hemodynamic indicators, reduce the use of vasoactive drugs, reduce lactate level and infection indicators. Of note, oXiris improve organ function in sepsis, which may result to higher survival rate.

Iron status and the risk of sepsis and severe COVID-19: a two-sample Mendelian randomization study

Observational studies have indicated an association between iron status and risk of sepsis and COVID-19. We estimated the effect of genetically-predicted iron biomarkers on risk of sepsis and risk of being hospitalized with COVID-19, performing a two-sample Mendelian randomization study. For risk of sepsis, one standard deviation increase in genetically-predicted serum iron was associated with odds ratio (OR) of 1.14 (95% confidence interval [CI] 1.01-1.29, P = 0.031). The findings were supported in the analyses for transferrin saturation and total iron binding capacity, while the estimate for ferritin was inconclusive. We found a tendency of higher risk of hospitalization with COVID-19 for serum iron; OR 1.29 (CI 0.97-1.72, P = 0.08), whereas sex-stratified analyses showed OR 1.63 (CI 0.94-2.86, P = 0.09) for women and OR 1.21 (CI 0.92-1.62, P = 0.17) for men. Sensitivity analyses supported the main findings and did not suggest bias due to pleiotropy. Our findings suggest a causal effect of genetically-predicted higher iron status and risk of hospitalization due to sepsis and indications of an increased risk of being hospitalized with COVID-19. These findings warrant further studies to assess iron status in relation to severe infections, including the potential of improved management.

Comparative Effectiveness of Combined IgM-Enriched Immunoglobulin and Extracorporeal Blood Purification Plus Standard Care Versus Standard Care for Sepsis and Septic Shock after Cardiac Surgery

Background

The combination of surgery, bacterial spread-out, and artificial cardiopulmonary bypass surfaces results in a release of key inflammatory mediators leading to an overshooting systemic hyper-inflammatory condition frequently associated with compromised hemodynamics and organ dysfunction. A promising approach could be extracorporeal blood purification therapies in combination with IgM enriched immunoglobulin. This approach might perform a balanced control of both hyper and hypo-inflammatory phases as an immune-modulating intervention.

Methods

We performed a retrospective observational study of patients with proven infection after cardiac surgery between January 2020 and December 2021. Patients were divided into two groups: (1) the first group (Control Group) followed a standard care approach as recommended by the Surviving Sepsis Campaign Guidelines; The second group (Active Group) underwent extracorporeal blood purification therapy (EBPT) in combination with intravenous administration of IgM enriched immunoglobulin 5 mL/kg die for at least three consecutive days, in conjunction with the standard approach (SSC Guidelines). In addition, ventriculo-arterial (V/A) coupling, Interleukin 6 (IL-6), Endotoxin Activity Assay (EAA), Procalcitonin, White Blood Cells (WBC) counts, Sequential Organ Failure Assessment (SOFA) Score and Inotropic Score were assessed in both two groups at different time points.

Results

Fifty-four patients were recruited; 25 were in the Control Group, while 29 participants were in the Active Group. SOFA score significantly improved from baseline [12 (9-16)] until at T 3 [8 (3-13)] in the active group; it was associated with a median EAA reduction from 1.03 (0.39-1.20) at T 0 to 0.41 (0.2-0.9) at T 3 in the active group compared with control group 0.70 (0.50-1.00) at T 0 to 0.70 (0.50-1.00) at T 3 (p < 0.001). V/A coupling tended to be lower in patients of the active arm ranging from 1.9 (1.2-2.7) at T 0 to 0.8 (0.8-2.2) at T 3 than in those of the control arm ranging from 2.1 (1.4-2.2) at T0 to 1.75 (1.45-2.1) at T 3 (p = 0.099). The hemodynamic improvement over time was associated with evident but no significant decrease in inotropic score in the active group compared with the control group. Changes in EAA value from T 0 to T 4 were directly and significantly related (r = 0.39, p = 0.006) to those of V/A coupling.

Conclusions

EBPT, in combination with IgM enriched immunoglobulin, was associated with a mitigated postoperative response of key cytokines with a significant decrease in IL-6, Procalcitonin, and EAA and was associated with improvement of clinical and metabolic parameters.

Mechanistic Understanding of Lung Inflammation: Recent Advances and Emerging Techniques

Acute respiratory distress syndrome (ARDS) is a life-threatening lung injury characterized by an acute inflammatory response in the lung parenchyma. Hence, it is considered as the most appropriate clinical syndrome to study pathogenic mechanisms of lung inflammation. ARDS is associated with increased morbidity and mortality in the intensive care unit (ICU), while no effective pharmacological treatment exists. It is very important therefore to fully characterize the underlying pathobiology and the related mechanisms, in order to develop novel therapeutic approaches. In vivo and in vitro models are important pre-clinical tools in biological and medical research in the mechanistic and pathological understanding of the majority of diseases. In this review, we will present data from selected experimental models of lung injury/acute lung inflammation, which have been based on clinical disorders that can lead to the development of ARDS and related inflammatory lung processes in humans, including ventilation-induced lung injury (VILI), sepsis, ischemia/reperfusion, smoke, acid aspiration, radiation, transfusion-related acute lung injury (TRALI), influenza, Streptococcus (S.) pneumoniae and coronaviruses infection. Data from the corresponding clinical conditions will also be presented. The mechanisms related to lung inflammation that will be covered are oxidative stress, neutrophil extracellular traps, mitogen-activated protein kinase (MAPK) pathways, surfactant, and water and ion channels. Finally, we will present a brief overview of emerging techniques in the field of omics research that have been applied to ARDS research, encompassing genomics, transcriptomics, proteomics, and metabolomics, which may recognize factors to help stratify ICU patients at risk, predict their prognosis, and possibly, serve as more specific therapeutic targets.