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

Whole transcriptome analysis to explore the impaired immunological features in critically ill elderly patients with sepsis

BACKGROUND

Sepsis is a frequent complication in critically ill patients, is highly heterogeneous and is associated with high morbidity and mortality rates, especially in the elderly population. Utilizing RNA sequencing (RNA-Seq) to analyze biological pathways is widely used in clinical and molecular genetic studies, but studies in elderly patients with sepsis are still lacking. Hence, we investigated the mortality-relevant biological features and transcriptomic features in elderly patients who were admitted to the intensive care unit (ICU) for sepsis.

METHODS

We enrolled 37 elderly patients with sepsis from the ICU at Taichung Veterans General Hospital. On day-1 and day-8, clinical and laboratory data, as well as blood samples, were collected for RNA-Seq analysis. We identified the dynamic transcriptome and enriched pathways of differentially expressed genes between day-8 and day-1 through DVID enrichment analysis and Gene Set Enrichment Analysis. Then, the diversity of the T cell repertoire was analyzed with MiXCR.

RESULTS

Overall, 37 patients had sepsis, and responders and non-responders were grouped through principal component analysis. Significantly higher SOFA scores at day-7, longer ventilator days, ICU lengths of stay and hospital mortality were found in the non-responder group, than in the responder group. On day-8 in elderly ICU patients with sepsis, genes related to innate immunity and inflammation, such as ZDHCC19, ALOX15, FCER1A, HDC, PRSS33, and PCSK9, were upregulated. The differentially expressed genes (DEGs) were enriched in the regulation of transcription, adaptive immune response, immunoglobulin production, negative regulation of transcription, and immune response. Moreover, there was a higher diversity of T-cell receptors on day-8 in the responder group, than on day-1, indicating that they had better regulated recovery from sepsis compared with the non-response patients.

CONCLUSION

Sepsis mortality and incidence were both high in elderly individuals. We identified mortality-relevant biological features and transcriptomic features with functional pathway and MiXCR analyses based on RNA-Seq data; and found that the responder group had upregulated innate immunity and increased T cell diversity; compared with the non-responder group. RNA-Seq may be able to offer additional complementary information for the accurate and early prediction of treatment outcome.

Surgical Science and the Evolution of Critical Care Medicine

Surgical science has driven innovation and inquiry across adult and pediatric disciplines that provide critical care regardless of location. Surgically originated but broadly applicable knowledge has been globally shared within the pages Critical Care Medicine over the last 50 years.

LEUKOCYTE PHENOTYPING IN SEPSIS USING OMICS, FUNCTIONAL ANALYSIS, AND IN SILICO MODELING

ABSTRACT

Sepsis is a major health issue and a leading cause of death in hospitals globally. The treatment of sepsis is largely supportive, and there are no therapeutics available that target the underlying pathophysiology of the disease. The development of therapeutics for the treatment of sepsis is hindered by the heterogeneous nature of the disease. The presence of multiple, distinct immune phenotypes ranging from hyperimmune to immunosuppressed can significantly impact the host response to infection. Recently, omics, biomarkers, cell surface protein expression, and immune cell profiles have been used to classify immune status of sepsis patients. However, there has been limited studies of immune cell function during sepsis and even fewer correlating omics and biomarker alterations to functional consequences. In this review, we will discuss how the heterogeneity of sepsis and associated immune cell phenotypes result from changes in the omic makeup of cells and its correlation with leukocyte dysfunction. We will also discuss how emerging techniques such as in silico modeling and machine learning can help in phenotyping sepsis patients leading to precision medicine.

Biological Phenotyping in Sepsis and Acute Respiratory Distress Syndrome

Heterogeneity in sepsis and acute respiratory distress syndrome (ARDS) is increasingly being recognized as one of the principal barriers to finding efficacious targeted therapies. The advent of multiple high-throughput biological data ("omics"), coupled with the widespread access to increased computational power, has led to the emergence of phenotyping in critical care. Phenotyping aims to use a multitude of data to identify homogenous subgroups within an otherwise heterogenous population. Increasingly, phenotyping schemas are being applied to sepsis and ARDS to increase understanding of these clinical conditions and identify potential therapies. Here we present a selective review of the biological phenotyping schemas applied to sepsis and ARDS. Further, we outline some of the challenges involved in translating these conceptual findings to bedside clinical decision-making tools.

Distinct subsets of neutrophils crosstalk with cytokines and metabolites in patients with sepsis

Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Despite continued efforts to understand the pathophysiology of sepsis, no effective therapies are currently available. While singular components of the aberrant immune response have been investigated, comprehensive studies linking different data layers are lacking. Using an integrated systems immunology approach, we evaluated neutrophil phenotypes and concomitant changes in cytokines and metabolites in patients with sepsis. Our findings identify differentially expressed mature and immature neutrophil subsets in patients with sepsis. These subsets correlate with various proteins, metabolites, and lipids, including pentraxin-3, angiopoietin-2, and lysophosphatidylcholines, in patients with sepsis. These results enabled the construction of a statistical model based on weighted multi-omics linear regression analysis for sepsis biomarker identification. These findings could help inform early patient stratification and treatment options, and facilitate further mechanistic studies targeting the trifecta of surface marker expression, cytokines, and metabolites.

[Pathophysiology of sepsis]

Up to now, sepsis is one of the most threatening diseases and its therapy remains challenging. Sepsis is currently defined as a severely dysregulated immune response to an infection resulting in organ dysfunction. The pathophysiology is mainly driven by exogenous PAMPs ("pathogen-associated molecular patterns") and endogenous DAMPs ("damage-associated molecular patterns"), which can activate PRRs ("pattern recognition receptors") on different cell types (mainly immune cells), leading to the initiation of manifold downstream pathways and a perpetuation of patients' immune response. Sepsis is neither an exclusive pro- nor an anti-inflammatory disease: both processes take place in parallel, resulting in an individual immunologic disease state depending on the severity of each component at different time points. Septic shock is a complex disorder of the macro- and microcirculation, provoking a severe lack of oxygenation further aggravating sepsis defining organ dysfunctions. An in-depth knowledge of the heterogeneity and the time-dependency of the septic immunopathology will be essential for the design of future sepsis trials and therapy planning in patients with sepsis. The big aim is to achieve a more individualized treatment strategy in patients suffering from sepsis or septic shock.

Peripheral blood RNA biomarkers for cardiovascular disease from bench to bedside: a position paper from the EU-CardioRNA COST action CA17129

Despite significant advances in the diagnosis and treatment of cardiovascular diseases, recent calls have emphasized the unmet need to improve precision-based approaches in cardiovascular disease. Although some studies provide preliminary evidence of the diagnostic and prognostic potential of circulating coding and non-coding RNAs, the complex RNA biology and lack of standardization have hampered the translation of these markers into clinical practice. In this position paper of the CardioRNA COST action CA17129, we provide recommendations to standardize the RNA development process in order to catalyse efforts to investigate novel RNAs for clinical use. We list the unmet clinical needs in cardiovascular disease, such as the identification of high-risk patients with ischaemic heart disease or heart failure who require more intensive therapies. The advantages and pitfalls of the different sample types, including RNAs from plasma, extracellular vesicles, and whole blood, are discussed in the sample matrix, together with their respective analytical methods. The effect of patient demographics and highly prevalent comorbidities, such as metabolic disorders, on the expression of the candidate RNA is presented and should be reported in biomarker studies. We discuss the statistical and regulatory aspects to translate a candidate RNA from a research use only assay to an in-vitro diagnostic test for clinical use. Optimal planning of this development track is required, with input from the researcher, statistician, industry, and regulatory partners.

Immunopathophysiology of human sepsis

Sepsis is an ill-defined syndrome yet is a leading cause of morbidity and mortality worldwide. The most recent consensus defines sepsis as life-threatening organ dysfunction caused by a dysregulated host response to infection. However, this definition belies the complexity and breadth of immune mechanisms involved in sepsis, which are characterized by simultaneous hyperinflammation and immune suppression. In this review, we describe the immunopathogenesis of sepsis and highlight some recent pathophysiological findings that have expanded our understanding of sepsis. Sepsis endotypes can be used to divide sepsis patients in different groups with distinct immune profiles and outcomes. We also summarize evidence on the role of the gut microbiome in sepsis immunity. The challenge of the coming years will be to translate our increasing knowledge about the molecular mechanisms underlying sepsis into therapies that improve relevant patient outcomes.

Sepsis biomarkers and diagnostic tools with a focus on machine learning

Over the last years, there have been advances in the use of data-driven techniques to improve the definition, early recognition, subtypes characterisation, prognostication and treatment personalisation of sepsis. Some of those involve the discovery or evaluation of biomarkers or digital signatures of sepsis or sepsis sub-phenotypes. It is hoped that their identification may improve timeliness and accuracy of diagnosis, suggest physiological pathways and therapeutic targets, inform targeted recruitment into clinical trials, and optimise clinical management. Given the complexities of the sepsis response, panels of biomarkers or models combining biomarkers and clinical data are necessary, as well as specific data analysis methods, which broadly fall under the scope of machine learning. This narrative review gives a brief overview of the main machine learning techniques (mainly in the realms of supervised and unsupervised methods) and published applications that have been used to create sepsis diagnostic tools and identify biomarkers.

Sepsis as a Challenge for Personalized Medicine

Sepsis is a clinical syndrome of systemic inflammation induced by infection, now defined as life-threatening organ dysfunction caused by a dysregulated immune response to infection [...].

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.

Timing and Spectrum of Antibiotic Treatment for Suspected Sepsis and Septic Shock: Why so Controversial?

Sepsis guidelines and mandates encourage increasingly aggressive time-to-antibiotic targets for broad-spectrum antimicrobials for suspected sepsis and septic shock. This has caused considerable controversy due to weaknesses in the underlying evidence and fear that overly strict antibiotic deadlines may harm patients by perpetuating or escalating overtreatment. Indeed, a third or more of patients currently treated for sepsis and septic shock have noninfectious or nonbacterial conditions. These patients risk all the potential harms of antibiotics without their possible benefits. Updated Surviving Sepsis Campaign guidelines now emphasize the importance of tailoring antibiotics to each patient's likelihood of infection, risk for drug-resistant pathogens, and severity-of-illness.

Mortality Risk in Pediatric Sepsis Based on C-reactive Protein and Ferritin Levels

OBJECTIVES

Interest in using bedside C-reactive protein (CRP) and ferritin levels to identify patients with hyperinflammatory sepsis who might benefit from anti-inflammatory therapies has piqued with the COVID-19 pandemic experience. Our first objective was to identify patterns in CRP and ferritin trajectory among critically ill pediatric sepsis patients. We then examined the association between these different groups of patients in their inflammatory cytokine responses, systemic inflammation, and mortality risks.

DATA SOURCES

A prospective, observational cohort study.

STUDY SELECTION

Children with sepsis and organ failure in nine pediatric intensive care units in the United States.

DATA EXTRACTION

Two hundred and fifty-five children were enrolled. Five distinct clinical multi-trajectory groups were identified. Plasma CRP (mg/dL), ferritin (ng/mL), and 31 cytokine levels were measured at two timepoints during sepsis (median Day 2 and Day 5). Group-based multi-trajectory models (GBMTM) identified groups of children with distinct patterns of CRP and ferritin.

DATA SYNTHESIS

Group 1 had normal CRP and ferritin levels ( n = 8; 0% mortality); Group 2 had high CRP levels that became normal, with normal ferritin levels throughout ( n = 80; 5% mortality); Group 3 had high ferritin levels alone ( n = 16; 6% mortality); Group 4 had very high CRP levels, and high ferritin levels ( n = 121; 11% mortality); and Group 5 had very high CRP and very high ferritin levels ( n = 30; 40% mortality). Cytokine responses differed across the five groups, with ferritin levels correlated with macrophage inflammatory protein 1α levels and CRP levels reflective of many cytokines.

CONCLUSIONS

Bedside CRP and ferritin levels can be used together to distinguish groups of children with sepsis who have different systemic inflammation cytokine responses and mortality risks. These data suggest future potential value in personalized clinical trials with specific targets for anti-inflammatory therapies.

Construction of Autophagy-Related Gene Classifier for Early Diagnosis, Prognosis and Predicting Immune Microenvironment Features in Sepsis by Machine Learning Algorithms

Background

The immune system plays a fundamental role in the pathophysiology of sepsis, and autophagy and autophagy-related molecules are crucial in innate and adaptive immune responses; however, the potential roles of autophagy-related genes (ARGs) in sepsis are not comprehensively understood.

Methods

A systematic search was conducted in ArrayExpress and Gene Expression Omnibus (GEO) cohorts from July 2005 to May 2022. Machine learning approaches, including modified Lasso penalized regression, support vector machine, and artificial neural network, were applied to identify hub ARGs, thereby developing a prediction model termed ARG classifier. Diagnostic and prognostic performance of the model was comprehensively analyzed using multi-transcriptome data. Subsequently, we systematically correlated the ARG classifier/hub ARGs with immunological characteristics of multiple aspects, including immune cell infiltration, immune and molecular pathways, cytokine levels, and immune-related genes. Further, we collected clinical specimens to preliminarily investigate ARG expression levels and to assess the diagnostic performance of ARG classifier.

Results

A total of ten GEO and three ArrayExpress datasets were included in this study. Based on machine learning algorithms, eight key ARGs (ATG4C, BAX, BIRC5, ERBB2, FKBP1B, HIF1A, NCKAP1, and NFKB1) were integrated to establish ARG classifier. The model exhibited excellent diagnostic values (AUC > 0.85) in multiple datasets and multiple points in time and superiorly distinguished sepsis from other critical illnesses. ARG classifier showed significant correlations with clinical characteristics or endotypes and performed better in predicting mortality (AUC = 0.70) than other clinical characteristics. Additionally, the identified hub ARGs were significantly associated with immune cell infiltration (B, T, NK, dendritic, T regulatory, and myeloid-derived suppressor cells), immune and molecular pathways (inflammation-promoting pathways, HLA, cytolytic activity, apoptosis, type-II IFN response, complement and coagulation cascades), levels of several cytokines (PDGFRB, IL-10, IFNG, and TNF), which indicated that ARG classifier/hub ARGs adequately reflected the immune microenvironment during sepsis. Finally, using clinical specimens, the expression levels of key ARGs in patients with sepsis were found to differ significantly from those of control patients, and ARG classifier exhibited superior diagnostic performance, compared to procalcitonin and C-reactive protein.

Conclusion

Collectively, a diagnostic and prognostic model (ARG classifier) based on eight ARGs was developed which may assist clinicians in diagnosis of sepsis and recognizing patient at high risk to guide personalized treatment. Additionally, the ARG classifier effectively reflected the immune microenvironment diversity of sepsis and may facilitate personalized counseling for specific therapy.

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.

Clustering ICU patients with sepsis based on the patterns of their circulating biomarkers: A secondary analysis of the CAPTAIN prospective multicenter cohort study

BACKGROUND

Although sepsis is a life-threatening condition, its heterogeneous presentation likely explains the negative results of most trials on adjunctive therapy. This study in patients with sepsis aimed to identify subgroups with similar immune profiles and their clinical and outcome correlates.

METHODS

A secondary analysis used data of a prospective multicenter cohort that included patients with early assessment of sepsis. They were described using Predisposition, Insult, Response, Organ failure sepsis (PIRO) staging system. Thirty-eight circulating biomarkers (27 proteins, 11 mRNAs) were assessed at sepsis diagnosis, and their patterns were determined through principal component analysis (PCA). Hierarchical clustering was used to group the patients and k-means algorithm was applied to assess the internal validity of the clusters.

RESULTS

Two hundred and three patients were assessed, of median age 64.5 [52.0-77.0] years and SAPS2 score 55 [49-61] points. Five main patterns of biomarkers and six clusters of patients (including 42%, 21%, 17%, 9%, 5% and 5% of the patients) were evidenced. Clusters were distinguished according to the certainty of the causal infection, inflammation, use of organ support, pro- and anti-inflammatory activity, and adaptive profile markers.

CONCLUSIONS

In this cohort of patients with suspected sepsis, we individualized clusters which may be described with criteria used to stage sepsis. As these clusters are based on the patterns of circulating biomarkers, whether they might help to predict treatment responsiveness should be addressed in further studies.

TRIAL REGISTRATION

The CAPTAIN study was registered on clinicaltrials.gov on June 22, 2011, # NCT01378169.

Antithrombin as Therapeutic Intervention against Sepsis-Induced Coagulopathy and Disseminated Intravascular Coagulation: Lessons Learned from COVID-19-Associated Coagulopathy

Recent research has contributed significantly to our understanding of the pathogenesis of acute disseminated intravascular coagulation. COVID-19 can be considered as a new underlying condition of disseminated intravascular coagulation. In this narrative review, current evidence is presented regarding biomarker differences between sepsis-induced and COVID-19-associated coagulopathies, supporting the importance of acquired antithrombin deficiency in the early differential diagnosis of septic coagulopathy and its potential impact on treatment with endogenous anticoagulants. Establishing new scoring systems for septic coagulopathy in combination with endogenous anticoagulant biomarker activities may allow for the identification of those in the heterogeneous population of sepsis patients who are more likely to benefit from targeted specific treatment interventions.

Sepsis-induced immunosuppression: mechanisms, diagnosis and current treatment options

Sepsis is a common complication of combat injuries and trauma, and is defined as a life-threatening organ dysfunction caused by a dysregulated host response to infection. It is also one of the significant causes of death and increased health care costs in modern intensive care units. The use of antibiotics, fluid resuscitation, and organ support therapy have limited prognostic impact in patients with sepsis. Although its pathophysiology remains elusive, immunosuppression is now recognized as one of the major causes of septic death. Sepsis-induced immunosuppression is resulted from disruption of immune homeostasis. It is characterized by the release of anti-inflammatory cytokines, abnormal death of immune effector cells, hyperproliferation of immune suppressor cells, and expression of immune checkpoints. By targeting immunosuppression, especially with immune checkpoint inhibitors, preclinical studies have demonstrated the reversal of immunocyte dysfunctions and established host resistance. Here, we comprehensively discuss recent findings on the mechanisms, regulation and biomarkers of sepsis-induced immunosuppression and highlight their implications for developing effective strategies to treat patients with septic shock.

Genomics and Pediatric Sepsis

Sepsis is a clinical syndrome manifested by a dysregulation of the immune system triggered by an infection. The severity of illness is variable, which can include mild symptoms with no organ dysfunction to severe symptoms and multiorgan failure, eventually leading to death. Advances in bioinformatics have elucidated distinct sepsis endotypes and have allowed for a better understanding of the pathophysiologic mechanisms. As we learn more about these sepsis endotypes, more precise therapies will emerge for use as adjuncts to antibiotics. [Pediatr Ann. 2022;51(10):e387-e389.].

The End of “One Size Fits All” Sepsis Therapies: Toward an Individualized Approach

Sepsis, defined as life-threatening organ dysfunction caused by a dysregulated host response to an infection, remains a major challenge for clinicians and trialists. Despite decades of research and multiple randomized clinical trials, a specific therapeutic for sepsis is not available. The evaluation of therapeutics targeting components of host response anomalies in patients with sepsis has been complicated by the inability to identify those in this very heterogeneous population who are more likely to benefit from a specific intervention. Additionally, multiple and diverse host response aberrations often co-exist in sepsis, and knowledge of which dysregulated biological organ system or pathway drives sepsis-induced pathology in an individual patient is limited, further complicating the development of effective therapies. Here, we discuss the drawbacks of previous attempts to develop sepsis therapeutics and delineate a future wherein interventions will be based on the host response profile of a patient.

Early Biomarker Signatures in Surgical Sepsis

INTRODUCTION

Sepsis has complex, time-sensitive pathophysiology and important phenotypic subgroups. The objective of this study was to use machine learning analyses of blood and urine biomarker profiles to elucidate the pathophysiologic signatures of subgroups of surgical sepsis patients.

METHODS

This prospective cohort study included 243 surgical sepsis patients admitted to a quaternary care center between January 2015 and June 2017. We applied hierarchical clustering to clinical variables and 42 blood and urine biomarkers to identify phenotypic subgroups in a development cohort. Clinical characteristics and short-term and long-term outcomes were compared between clusters. A naïve Bayes classifier predicted cluster labels in a validation cohort.

RESULTS

The development cohort contained one cluster characterized by early organ dysfunction (cluster I, n = 18) and one cluster characterized by recovery (cluster II, n = 139). Cluster I was associated with higher Acute Physiologic Assessment and Chronic Health Evaluation II (30 versus 16, P < 0.001) and SOFA scores (13 versus 5, P < 0.001), greater prevalence of chronic cardiovascular and renal disease (P < 0.001) and septic shock (78% versus 17%, P < 0.001). Cluster I had higher mortality within 14 d of sepsis onset (11% versus 1.5%, P = 0.001) and within 1 y (44% versus 20%, P = 0.032), and higher incidence of chronic critical illness (61% versus 30%, P = 0.001). The Bayes classifier achieved 95% accuracy and identified two clusters that were similar to development cohort clusters.

CONCLUSIONS

Machine learning analyses of clinical and biomarker variables identified an early organ dysfunction sepsis phenotype characterized by inflammation, renal dysfunction, endotheliopathy, and immunosuppression, as well as poor short-term and long-term clinical outcomes.

Identification of biomarkers and the mechanisms of multiple trauma complicated with sepsis using metabolomics

Sepsis after trauma increases the risk of mortality rate for patients in intensive care unit (ICUs). Currently, it is difficult to predict outcomes in individual patients with sepsis due to the complexity of causative pathogens and the lack of specific treatment. This study aimed to identify metabolomic biomarkers in patients with multiple trauma and those with multiple trauma accompanied with sepsis. Therefore, the metabolic profiles of healthy persons designated as normal controls (NC), multiple trauma patients (MT), and multiple trauma complicated with sepsis (MTS) (30 cases in each group) were analyzed with ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS)-based untargeted plasma metabolomics using collected plasma samples. The differential metabolites were enriched in amino acid metabolism, lipid metabolism, glycometabolism and nucleotide metabolism. Then, nine potential biomarkers, namely, acrylic acid, 5-amino-3-oxohexanoate, 3b-hydroxy-5-cholenoic acid, cytidine, succinic acid semialdehyde, PE [P-18:1(9Z)/16:1(9Z)], sphinganine, uracil, and uridine, were found to be correlated with clinical variables and validated using receiver operating characteristic (ROC) curves. Finally, the three potential biomarkers succinic acid semialdehyde, uracil and uridine were validated and can be applied in the clinical diagnosis of multiple traumas complicated with sepsis.

In-Hospital Mortality of Sepsis Differs Depending on the Origin of Infection: An Investigation of Predisposing Factors

Sepsis is a heterogeneous syndrome characterized by a variety of clinical features. Analysis of large clinical datasets may serve to define groups of sepsis with different risks of adverse outcomes. Clinical experience supports the concept that prognosis, treatment, severity, and time course of sepsis vary depending on the source of infection. We analyzed a large publicly available database to test this hypothesis. In addition, we developed prognostic models for the three main types of sepsis: pulmonary, urinary, and abdominal sepsis. We used logistic regression using routinely available clinical data for mortality prediction in each of these groups. The data was extracted from the eICU collaborative research database, a multi-center intensive care unit with over 200,000 admissions. Sepsis cohorts were defined using admission diagnosis codes. We used univariate and multivariate analyses to establish factors relevant for outcome prediction in all three cohorts of sepsis (pulmonary, urinary and abdominal). For logistic regression, input variables were automatically selected using a sequential forward search algorithm over 10 dataset instances. Receiver operator characteristics were generated for each model and compared with established prognostication tools (APACHE IV and SOFA). A total of 3,958 sepsis admissions were included in the analysis. Sepsis in-hospital mortality differed depending on the cause of infection: abdominal 18.93%, pulmonary 19.27%, and renal 12.81%. Higher average heart rate was associated with increased mortality risk. Increased average Mean Arterial Pressure (MAP) showed a reduced mortality risk across all sepsis groups. Results from the LR models found significant factors that were relevant for specific sepsis groups. Our models outperformed APACHE IV and SOFA scores with AUC between 0.63 and 0.74. Predictive power decreased over time, with the best results achieved for data extracted for the first 24 h of admission. Mortality varied significantly between the three sepsis groups. We also demonstrate that factors of importance show considerable heterogeneity depending on the source of infection. The factors influencing in-hospital mortality vary depending on the source of sepsis which may explain why most sepsis trials have failed to identify an effective treatment. The source of infection should be considered when considering mortality risk. Planning of sepsis treatment trials may benefit from risk stratification based on the source of infection.

Comparison of Rapid Cytokine Immunoassays for Functional Immune Phenotyping

Background

Cell-based functional immune-assays may allow for risk stratification of patients with complex, heterogeneous immune disorders such as sepsis. Given the heterogeneity of patient responses and the uncertain immune pathogenesis of sepsis, these assays must first be defined and calibrated in the healthy population.

Objective

Our objective was to compare the internal consistency and practicality of two immune assays that may provide data on surrogate markers of the innate and adaptive immune response. We hypothesized that a rapid turnaround, microfluidic-based immune assay (ELLA) would be comparable to a dual-color, enzyme-linked immunospot (ELISpot) assay in identifying tumor necrosis factor (TNF) and interferon (IFN)γ production following ex vivo whole blood stimulation.

Design

This was a prospective, observational cohort analysis. Whole blood samples from ten healthy, immune-competent volunteers were stimulated for either 4 hours or 18 hours with lipopolysaccharide, anti-CD3/anti-CD28 antibodies, or phorbol 12-myristate 13-acetate with ionomycin to interrogate innate and adaptive immune responses, respectively.

Measurements and Main Results

ELLA analysis produced more precise measurement of TNF and IFNγ concentrations as compared with ELISpot, as well as a four- to five-log₁₀ dynamic range for TNF and IFNγ concentrations, as compared with a two-log₁₀ dynamic range with ELISpot. Unsupervised clustering accurately predicted the ex vivo immune stimulant used for 90% of samples analyzed via ELLA, as compared with 72% of samples analyzed via ELISpot.

Conclusions

We describe, for the first time, a rapid and precise assay for functional interrogation of the innate and adaptive arms of the immune system in healthy volunteers. The advantages of the ELLA microfluidic platform may represent a step forward in generating a point-of-care test with clinical utility, for identifying deranged immune phenotypes in septic patients.

Sepsis subphenotyping based on organ dysfunction trajectory

BACKGROUND

Sepsis is a heterogeneous syndrome, and the identification of clinical subphenotypes is essential. Although organ dysfunction is a defining element of sepsis, subphenotypes of differential trajectory are not well studied. We sought to identify distinct Sequential Organ Failure Assessment (SOFA) score trajectory-based subphenotypes in sepsis.

METHODS

We created 72-h SOFA score trajectories in patients with sepsis from four diverse intensive care unit (ICU) cohorts. We then used dynamic time warping (DTW) to compute heterogeneous SOFA trajectory similarities and hierarchical agglomerative clustering (HAC) to identify trajectory-based subphenotypes. Patient characteristics were compared between subphenotypes and a random forest model was developed to predict subphenotype membership at 6 and 24 h after being admitted to the ICU. The model was tested on three validation cohorts. Sensitivity analyses were performed with alternative clustering methodologies.

RESULTS

A total of 4678, 3665, 12,282, and 4804 unique sepsis patients were included in development and three validation cohorts, respectively. Four subphenotypes were identified in the development cohort: Rapidly Worsening (n = 612, 13.1%), Delayed Worsening (n = 960, 20.5%), Rapidly Improving (n = 1932, 41.3%), and Delayed Improving (n = 1174, 25.1%). Baseline characteristics, including the pattern of organ dysfunction, varied between subphenotypes. Rapidly Worsening was defined by a higher comorbidity burden, acidosis, and visceral organ dysfunction. Rapidly Improving was defined by vasopressor use without acidosis. Outcomes differed across the subphenotypes, Rapidly Worsening had the highest in-hospital mortality (28.3%, P-value < 0.001), despite a lower SOFA (mean: 4.5) at ICU admission compared to Rapidly Improving (mortality:5.5%, mean SOFA: 5.5). An overall prediction accuracy of 0.78 (95% CI, [0.77, 0.8]) was obtained at 6 h after ICU admission, which increased to 0.87 (95% CI, [0.86, 0.88]) at 24 h. Similar subphenotypes were replicated in three validation cohorts. The majority of patients with sepsis have an improving phenotype with a lower mortality risk; however, they make up over 20% of all deaths due to their larger numbers.

CONCLUSIONS

Four novel, clinically-defined, trajectory-based sepsis subphenotypes were identified and validated. Identifying trajectory-based subphenotypes has immediate implications for the powering and predictive enrichment of clinical trials. Understanding the pathophysiology of these differential trajectories may reveal unanticipated therapeutic targets and identify more precise populations and endpoints for clinical trials.

Mortality Prediction in Sepsis With an Immune-Related Transcriptomics Signature: A Multi-Cohort Analysis

Background

Novel biomarkers are needed to progress toward individualized patient care in sepsis. The immune profiling panel (IPP) prototype has been designed as a fully-automated multiplex tool measuring expression levels of 26 genes in sepsis patients to explore immune functions, determine sepsis endotypes and guide personalized clinical management. The performance of the IPP gene set to predict 30-day mortality has not been extensively characterized in heterogeneous cohorts of sepsis patients.

Methods

Publicly available microarray data of sepsis patients with widely variable demographics, clinical characteristics and ethnical background were co-normalized, and the performance of the IPP gene set to predict 30-day mortality was assessed using a combination of machine learning algorithms.

Results

We collected data from 1,801 arrays sampled on sepsis patients and 598 sampled on controls in 17 studies. When gene expression was assayed at day 1 following admission (1,437 arrays sampled on sepsis patients, of whom 1,161 were alive and 276 (19.2%) were dead at day 30), the IPP gene set showed good performance to predict 30-day mortality, with an area under the receiving operating characteristics curve (AUROC) of 0.710 (CI 0.652-0.768). Importantly, there was no statistically significant improvement in predictive performance when training the same models with all genes common to the 17 microarray studies (n = 7,122 genes), with an AUROC = 0.755 (CI 0.697-0.813, p = 0.286). In patients with gene expression data sampled at day 3 following admission or later, the IPP gene set had higher performance, with an AUROC = 0.804 (CI 0.643-0.964), while the total gene pool had an AUROC = 0.787 (CI 0.610-0.965, p = 0.811).

Conclusion

Using pooled publicly-available gene expression data from multiple cohorts, we showed that the IPP gene set, an immune-related transcriptomics signature conveys relevant information to predict 30-day mortality when sampled at day 1 following admission. Our data also suggests that higher predictive performance could be obtained when assaying gene expression at later time points during the course of sepsis. Prospective studies are needed to confirm these findings using the IPP gene set on its dedicated measurement platform.

Redefining critical illness

Research and practice in critical care medicine have long been defined by syndromes, which, despite being clinically recognizable entities, are, in fact, loose amalgams of heterogeneous states that may respond differently to therapy. Mounting translational evidence-supported by research on respiratory failure due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection-suggests that the current syndrome-based framework of critical illness should be reconsidered. Here we discuss recent findings from basic science and clinical research in critical care and explore how these might inform a new conceptual model of critical illness. De-emphasizing syndromes, we focus on the underlying biological changes that underpin critical illness states and that may be amenable to treatment. We hypothesize that such an approach will accelerate critical care research, leading to a richer understanding of the pathobiology of critical illness and of the key determinants of patient outcomes. This, in turn, will support the design of more effective clinical trials and inform a more precise and more effective practice at the bedside.

Machine learning derivation of four computable 24-h pediatric sepsis phenotypes to facilitate enrollment in early personalized anti-inflammatory clinical trials

BACKGROUND

Thrombotic microangiopathy-induced thrombocytopenia-associated multiple organ failure and hyperinflammatory macrophage activation syndrome are important causes of late pediatric sepsis mortality that are often missed or have delayed diagnosis. The National Institutes of General Medical Science sepsis research working group recommendations call for application of new research approaches in extant clinical data sets to improve efficiency of early trials of new sepsis therapies. Our objective is to apply machine learning approaches to derive computable 24-h sepsis phenotypes to facilitate personalized enrollment in early anti-inflammatory trials targeting these conditions.

METHODS

We applied consensus, k-means clustering analysis to our extant PHENOtyping sepsis-induced Multiple organ failure Study (PHENOMS) dataset of 404 children. 24-hour computable phenotypes are derived using 25 available bedside variables including C-reactive protein and ferritin.

RESULTS

Four computable phenotypes (PedSep-A, B, C, and D) are derived. Compared to all other phenotypes, PedSep-A patients (n = 135; 2% mortality) were younger and previously healthy, with the lowest C-reactive protein and ferritin levels, the highest lymphocyte and platelet counts, highest heart rate, and lowest creatinine (p < 0.05); PedSep-B patients (n = 102; 12% mortality) were most likely to be intubated and had the lowest Glasgow Coma Scale Score (p < 0.05); PedSep-C patients (n = 110; mortality 10%) had the highest temperature and Glasgow Coma Scale Score, least pulmonary failure, and lowest lymphocyte counts (p < 0.05); and PedSep-D patients (n = 56, 34% mortality) had the highest creatinine and number of organ failures, including renal, hepatic, and hematologic organ failure, with the lowest platelet counts (p < 0.05). PedSep-D had the highest likelihood of developing thrombocytopenia-associated multiple organ failure (Adj OR 47.51 95% CI [18.83-136.83], p < 0.0001) and macrophage activation syndrome (Adj OR 38.63 95% CI [13.26-137.75], p < 0.0001).

CONCLUSIONS

Four computable phenotypes are derived, with PedSep-D being optimal for enrollment in early personalized anti-inflammatory trials targeting thrombocytopenia-associated multiple organ failure and macrophage activation syndrome in pediatric sepsis. A computer tool for identification of individual patient membership ( www.pedsepsis.pitt.edu ) is provided. Reproducibility will be assessed at completion of two ongoing pediatric sepsis studies.

Methods for Phenotyping Adult Patients in Sepsis and Septic Shock: A Scoping Review

Despite its heterogeneous phenotypes, sepsis or life-threatening dysfunction in response to infection is often treated empirically. Identifying patient subgroups with unique pathophysiology and treatment response is critical to the advancement of sepsis care. However, phenotyping methods and results are as heterogeneous as the disease itself. This scoping review evaluates the prognostic capabilities and treatment implications of adult sepsis and septic shock phenotyping methods.

DATA SOURCES

Medline and Embase.

STUDY SELECTION

We included clinical studies that described sepsis or septic shock and used any clustering method to identify sepsis phenotypes. We excluded conference abstracts, literature reviews, comments, letters to the editor, and in vitro studies. We assessed study quality using a validated risk of bias tool for observational cohort and cross-sectional studies.

DATA EXTRACTION

We extracted population, methodology, validation, and phenotyping characteristics from 17 studies.

DATA SYNTHESIS

Sepsis phenotyping methods most frequently grouped patients based on the degree of inflammatory response and coagulopathy using clinical, nongenomic variables. Five articles clustered patients based on genomic or transcriptomic data. Seven articles generated patient subgroups with differential response to sepsis treatments. Cluster clinical characteristics and their associations with mortality and treatment response were heterogeneous across studies, and validity was evaluated in nine of 17 articles, hindering pooled analysis of results and derivation of universal truths regarding sepsis phenotypes, their prognostic capabilities, and their associations with treatment response.

CONCLUSIONS

Sepsis phenotyping methods can identify high-risk patients and those with high probability of responding well to targeted treatments. Research quality was fair, but achieving generalizability and clinical impact of sepsis phenotyping will require external validation and direct comparison with alternative approaches.

Endotypes and the Path to Precision in Moderate and Severe Traumatic Brain Injury

Heterogeneity is recognized as a major barrier in efforts to improve the care and outcomes of patients with traumatic brain injury (TBI). Even within the narrower stratum of moderate and severe TBI, current management approaches do not capture the complexity of this condition characterized by manifold clinical, anatomical, and pathophysiologic features. One approach to heterogeneity may be to resolve undifferentiated TBI populations into endotypes, subclasses that are distinguished by shared biological characteristics. The endotype paradigm has been explored in a range of medical domains, including psychiatry, oncology, immunology, and pulmonology. In intensive care, endotypes are being investigated for syndromes such as sepsis and acute respiratory distress syndrome. This review provides an overview of the endotype paradigm as well as some of its methods and use cases. A conceptual framework is proposed for endotype research in moderate and severe TBI, together with a scientific road map for endotype discovery and validation in this population.

Gene Expression Scoring of Immune Activity Levels for Precision Use of Hydrocortisone in Vasodilatory Shock

PURPOSE

Among patients with vasodilatory shock, gene expression scores may identify different immune states. We aimed to test whether such scores are robust in identifying patients' immune state and predicting response to hydrocortisone treatment in vasodilatory shock.

MATERIALS AND METHODS

We selected genes to generate continuous scores to define previously established subclasses of sepsis. We used these scores to identify a patient's immune state. We evaluated the potential for these states to assess the differential effect of hydrocortisone in two randomized clinical trials of hydrocortisone versus placebo in vasodilatory shock.

RESULTS

We initially identified genes associated with immune-adaptive, immune-innate, immune-coagulant functions. From these genes, 15 were most relevant to generate expression scores related to each of the functions. These scores were used to identify patients as immune-adaptive prevalent (IA-P) and immune-innate prevalent (IN-P). In IA-P patients, hydrocortisone therapy increased 28-day mortality in both trials (43.3% vs 14.7%, P = 0.028) and (57.1% vs 0.0%, P = 0.99). In IN-P patients, this effect was numerically reversed.

CONCLUSIONS

Gene expression scores identified the immune state of vasodilatory shock patients, one of which (IA-P) identified those who may be harmed by hydrocortisone. Gene expression scores may help advance the field of personalized medicine.

Implications of Using Host Response-Based Molecular Diagnostics on the Management of Bacterial and Viral Infections: A Review

Host-based diagnostics are a rapidly evolving field that may serve as an alternative to traditional pathogen-based diagnostics for infectious diseases. Understanding the exact mechanisms underlying a host-immune response and deriving specific host-response signatures, biomarkers and gene transcripts will potentially achieve improved diagnostics that will ultimately translate to better patient outcomes. Several studies have focused on novel techniques and assays focused on immunodiagnostics. In this review, we will highlight recent publications on the current use of host-based diagnostics alone or in combination with traditional microbiological assays and their potential future implications on the diagnosis and prognostic accuracy for the patient with infectious complications. Finally, we will address the cost-effectiveness implications from a healthcare and public health perspective.

Managing sepsis in the era of precision medicine: challenges and opportunities

INTRODUCTION

Precision medicine is a medical model in which decisions, practices, interventions and therapies are tailored to the individual patient based on their predicted response or risk of disease. Sepsis is a life-threatening condition characterized by immune system dysregulation whose pathophysiology remains incompletely understood. There is much hope that precision medicine can lead to better outcomes in patients with sepsis.

AREAS COVERED

In this review from a comprehensive literature search in PubMed for English-language studies conducted in adults, we highlight recent advances in the diagnosis and treatment of sepsis of bacterial origin in adults using precision medicine approaches including rapid diagnostic tests, predictive biomarkers, genomic methods, rapid antimicrobial susceptibility testing, and monitoring cell mediated immunity. Challenges and directions for future research are also discussed.

EXPERT OPINION

Current diagnostic testing in sepsis relies primarily on conventional cultures (e.g. blood cultures), which are time-consuming and may delay critical therapeutic decisions. Nonculture-based techniques including nucleic acid amplification technologies (NAAT), other molecular methods (biomarkers), and genomic sequencing offer promise to overcome some of the inherent limitations seen with culture-based techniques.

Molecular Patterns in Acute Pancreatitis Reflect Generalizable Endotypes of the Host Response to Systemic Injury in Humans

OBJECTIVE

Acute Pancreatitis (AP) is sudden onset pancreas inflammation that causes systemic injury with a wide and markedly heterogeneous range of clinical consequences. Here, we hypothesized that this observed clinical diversity corresponds to diversity in molecular subtypes that can be identified in clinical and multiomics data.

SUMMARY BACKGROUND DATA

Observational cohort study. n = 57 for the discovery cohort (clinical, transcriptomics, proteomics, and metabolomics data) and n = 312 for the validation cohort (clinical and metabolomics data).

METHODS

We integrated coincident transcriptomics, proteomics, and metabolomics data at serial time points between admission to hospital and up to 48 hours after recruitment from a cohort of patients presenting with acute pancreatitis. We systematically evaluated 4 different metrics for patient similarity using unbiased mathematical, biological, and clinical measures of internal and external validity.We next compared the AP molecular endotypes with previous descriptions of endotypes in a critically ill population with acute respiratory distress syndrome (ARDS).

RESULTS

Our results identify 4 distinct and stable AP molecular endotypes. We validated our findings in a second independent cohort of patients with AP.We observed that 2 endotypes in AP recapitulate disease endotypes previously reported in ARDS.

CONCLUSIONS

Our results show that molecular endotypes exist in AP and reflect biological patterns that are also present in ARDS, suggesting that generalizable patterns exist in diverse presentations of critical illness.

Omics of endothelial cell dysfunction in sepsis

During sepsis, defined as life-threatening organ dysfunction due to dysregulated host response to infection, systemic inflammation activates endothelial cells and initiates a multifaceted cascade of pro-inflammatory signaling events, resulting in increased permeability and excessive recruitment of leukocytes. Vascular endothelial cells share many common properties but have organ-specific phenotypes with unique structure and function. Thus, therapies directed against endothelial cell phenotypes are needed to address organ-specific endothelial cell dysfunction. Omics allow for the study of expressed genes, proteins and/or metabolites in biological systems and provide insight on temporal and spatial evolution of signals during normal and diseased conditions. Proteomics quantifies protein expression, identifies protein-protein interactions and can reveal mechanistic changes in endothelial cells that would not be possible to study via reductionist methods alone. In this review, we provide an overview of how sepsis pathophysiology impacts omics with a focus on proteomic analysis of mouse endothelial cells during sepsis/inflammation and its relationship with the more clinically relevant omics of human endothelial cells. We discuss how omics has been used to define septic endotype signatures in different populations with a focus on proteomic analysis in organ-specific microvascular endothelial cells during sepsis or septic-like inflammation. We believe that studies defining septic endotypes based on proteomic expression in endothelial cell phenotypes are urgently needed to complement omic profiling of whole blood and better define sepsis subphenotypes. Lastly, we provide a discussion of how in silico modeling can be used to leverage the large volume of omics data to map response pathways in sepsis.

Precision medicine in sepsis and septic shock: From omics to clinical tools

Sepsis is a heterogeneous disease with variable clinical course and several clinical phenotypes. As it is associated with an increased risk of death, patients with this condition are candidates for receipt of a very well-structured and protocolized treatment. All patients should receive the fundamental pillars of sepsis management, which are infection control, initial resuscitation, and multiorgan support. However, specific subgroups of patients may benefit from a personalized approach with interventions targeted towards specific pathophysiological mechanisms. Herein, we will review the framework for identifying subpopulations of patients with sepsis, septic shock, and multiorgan dysfunction who may benefit from specific therapies. Some of these approaches are still in the early stages of research, while others are already in routine use in clinical practice, but together will help in the effective generation and safe implementation of precision medicine in sepsis.

Mechanisms and modulation of sepsis-induced immune dysfunction in children

Immunologic responses during sepsis vary significantly among patients and evolve over the course of illness. Sepsis has a direct impact on the immune system due to adverse alteration of the production, maturation, function, and apoptosis of immune cells. Dysregulation in both the innate and adaptive immune responses during sepsis leads to a range of phenotypes consisting of both hyperinflammation and immunosuppression that can result in immunoparalysis. In this review, we discuss components of immune dysregulation in sepsis, biomarkers and functional immune assays to aid in immunophenotyping patients, and evolving immunomodulatory therapies. Important research gaps for the future include: (1) Defining how age, host factors including prior exposures, and genetics impact the trajectory of sepsis in children, (2) Developing tools for rapid assessment of immune function in sepsis, and (3) Assessing how evolving pediatric sepsis endotypes respond differently to immunomodulation. Although multiple promising immunomodulatory agents exist or are in development, access to rapid immunophenotyping will be needed to identify which children are most likely to benefit from which therapy. Advancements in the ability to perform multidimensional endotyping will be key to developing a personalized approach to children with sepsis. IMPACT: Immunologic responses during sepsis vary significantly among patients and evolve over the course of illness. The resulting spectrum of immunoparalysis that can occur due to sepsis can increase morbidity and mortality in children and adults. This narrative review summarizes the current literature surrounding biomarkers and functional immunologic assays for immune dysregulation in sepsis, with a focus on immunomodulatory therapies that have been evaluated in sepsis. A precision approach toward diagnostic endotyping and therapeutics, including gene expression, will allow for optimal clinical trials to evaluate the efficacy of individualized and targeted treatments for pediatric sepsis.

Pediatric sepsis biomarkers for prognostic and predictive enrichment

Sepsis is a major public health problem in children throughout the world. Given that the treatment guidelines emphasize early recognition, there is interest in developing biomarkers of sepsis, and most attention is focused on diagnostic biomarkers. While there is a need for ongoing discovery and development of diagnostic biomarkers for sepsis, this review will focus on less well-known applications of sepsis biomarkers. Among patients with sepsis, the biomarkers can give information regarding the risk of poor outcome from sepsis, risk of sepsis-related organ dysfunction, and subgroups of patients with sepsis who share underlying biological features potentially amenable to targeted therapeutics. These types of biomarkers, beyond the traditional concept of diagnosis, address the important concepts of prognostic and predictive enrichment, which are key components of bringing the promise of precision medicine to the bedside of children with sepsis.

Effect of Recombinant Human Soluble Thrombomodulin on Coagulation-Related Variables in Patients With Sepsis-Induced Disseminated Intravascular Coagulation: A Retrospective Observational Study

To evaluate associations among coagulation-related variables, resolution of disseminated intravascular coagulation (DIC) and mortality, we retrospectively investigated 123 patients with sepsis-induced DIC treated with recombinant human soluble thrombomodulin (rTM). Changes in coagulation-related variables before and after treatment with rTM were examined. Further, associations between coagulation-related variables and DIC resolution were evaluated. The platelet count, prothrombin international normalized ratio (PT-INR), and fibrin/fibrinogen degradation products (FDP) significantly (p < .001) improved after rTM administration in survivors (n = 98), but not in nonsurvivors (n = 25). However, the DIC score significantly (p < .001) reduced in survivors and in nonsurvivors. Among coagulation-related variables examined before rTM, only PT-INR was significantly (p = .0395) lower in survivors than in nonsurvivors, and PT-INR before rTM was significantly (p = .0029) lower in patients attaining than not attaining DIC resolution (n = 87 and 36, respectively). The 28-day mortality was significantly lower in patients attaining than not attaining DIC resolution (11.5% vs 41.7%, p = .0001). In conclusion, the initiation of rTM administration before marked PT-INR elevation may be important to induce DIC resolution and thus to decrease mortality in patients with sepsis-induced DIC. Conversely, the treatment with rTM in patients with marked PT-INR elevation may be not so effective in achieving such goals.

Identification of Endotypes of Hospitalized COVID-19 Patients

Background: Characterization of coronavirus disease 2019 (COVID-19) endotypes may help explain variable clinical presentations and response to treatments. While risk factors for COVID-19 have been described, COVID-19 endotypes have not been elucidated.

Objectives: We sought to identify and describe COVID-19 endotypes of hospitalized patients.

Methods: Consensus clustering (using the ensemble method) of patient age and laboratory values during admission identified endotypes. We analyzed data from 528 patients with COVID-19 who were admitted to telemetry capable beds at Columbia University Irving Medical Center and discharged between March 12 to July 15, 2020.

Results: Four unique endotypes were identified and described by laboratory values, demographics, outcomes, and treatments. Endotypes 1 and 2 were comprised of low numbers of intubated patients (1 and 6%) and exhibited low mortality (1 and 6%), whereas endotypes 3 and 4 included high numbers of intubated patients (72 and 85%) with elevated mortality (21 and 43%). Endotypes 2 and 4 had the most comorbidities. Endotype 1 patients had low levels of inflammatory markers (ferritin, IL-6, CRP, LDH), low infectious markers (WBC, procalcitonin), and low degree of coagulopathy (PTT, PT), while endotype 4 had higher levels of those markers.

Conclusions: Four unique endotypes of hospitalized patients with COVID-19 were identified, which segregated patients based on inflammatory markers, infectious markers, evidence of end-organ dysfunction, comorbidities, and outcomes. High comorbidities did not associate with poor outcome endotypes. Further work is needed to validate these endotypes in other cohorts and to study endotype differences to treatment responses.

The immunology of sepsis

Sepsis is defined as a life-threatening organ dysfunction caused by a dysregulated host response to an infection. This recently implemented definition does not capture the heterogeneity or the underlying pathophysiology of the syndrome, which is characterized by concurrent unbalanced hyperinflammation and immune suppression. Here, we review current knowledge of aberrant immune responses during sepsis and recent initiatives to stratify patients with sepsis into subgroups that are more alike from a clinical and/or pathobiological perspective, which could be key for identification of patients who are more likely to benefit from specific immune interventions.

Revising Host Phenotypes of Sepsis Using Microbiology

Background: There is wide heterogeneity in sepsis in causative pathogens, host response, organ dysfunction, and outcomes. Clinical and biologic phenotypes of sepsis are proposed, but the role of pathogen data on sepsis classification is unknown. Methods: We conducted a secondary analysis of the Recombinant Human Activated Protein C (rhAPC) Worldwide Evaluation in Severe Sepsis (PROWESS) Study. We used latent class analysis (LCA) to identify sepsis phenotypes using, (i) only clinical variables ("host model") and, (ii) combining clinical with microbiology variables (e.g., site of infection, culture-derived pathogen type, and anti-microbial resistance characteristics, "host-pathogen model"). We describe clinical characteristics, serum biomarkers, and outcomes of host and host-pathogen models. We tested the treatment effects of rhAPC by phenotype using Kaplan-Meier curves. Results: Among 1,690 subjects with severe sepsis, latent class modeling derived a 4-class host model and a 4-class host-pathogen model. In the host model, alpha type (N = 327, 19%) was younger and had less shock; beta type (N=518, 31%) was older with more comorbidities; gamma type (N = 532, 32%) had more pulmonary dysfunction; delta type (N = 313, 19%) had more liver, renal and hematologic dysfunction and shock. After the addition of microbiologic variables, 772 (46%) patients changed phenotype membership, and the median probability of phenotype membership increased from 0.95 to 0.97 (P < 0.01). When microbiology data were added, the contribution of individual variables to phenotypes showed greater change for beta and gamma types. In beta type, the proportion of abdominal infections (from 20 to 40%) increased, while gamma type patients had an increased rate of lung infections (from 50 to 78%) with worsening pulmonary function. Markers of coagulation such as d-dimer and plasminogen activator inhibitor (PAI)-1 were greater in the beta type and lower in the gamma type. The 28 day mortality was significantly different for individual phenotypes in host and host-pathogen models (both P < 0.01). The treatment effect of rhAPC obviously changed in gamma type when microbiology data were added (P-values of log rank test changed from 0.047 to 0.780). Conclusions: Sepsis host phenotype assignment was significantly modified when microbiology data were added to clinical variables, increasing cluster cohesiveness and homogeneity.

A Transcriptomic Severity Metric That Predicts Clinical Outcomes in Critically Ill Surgical Sepsis Patients

Supplemental Digital Content is available in the text.

Clinically deployable methods for the rapid and accurate prediction of sepsis severity that could elicit a meaningful change in clinical practice are currently lacking. We evaluated a whole-blood, multiplex host-messenger RNA expression metric, Inflammatix-Severity-2, for identifying septic, hospitalized patients’ likelihood of 30-day mortality, development of chronic critical illness, discharge disposition, and/or secondary infections.