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

A Multicenter Network Assessment of Three Inflammation Phenotypes in Pediatric Sepsis-Induced Multiple Organ Failure

OBJECTIVES

Ongoing adult sepsis clinical trials are assessing therapies that target three inflammation phenotypes including 1) immunoparalysis associated, 2) thrombotic microangiopathy driven thrombocytopenia associated, and 3) sequential liver failure associated multiple organ failure. These three phenotypes have not been assessed in the pediatric multicenter setting. We tested the hypothesis that these phenotypes are associated with increased macrophage activation syndrome and mortality in pediatric sepsis.

DESIGN

Prospective severe sepsis cohort study comparing children with multiple organ failure and any of these phenotypes to children with multiple organ failure without these phenotypes and children with single organ failure.

SETTING

Nine PICUs in the Eunice Kennedy Shriver National Institutes of Child Health and Human Development Collaborative Pediatric Critical Care Research Network.

PATIENTS

Children with severe sepsis and indwelling arterial or central venous catheters.

INTERVENTIONS

Clinical data collection and twice weekly blood sampling until PICU day 28 or discharge.

MEASUREMENTS AND MAIN RESULTS

Of 401 severe sepsis cases enrolled, 112 (28%) developed single organ failure (0% macrophage activation syndrome 0/112; < 1% mortality 1/112), whereas 289 (72%) developed multiple organ failure (9% macrophage activation syndrome 24/289; 15% mortality 43/289). Overall mortality was higher in children with multiple organ and the phenotypes (24/101 vs 20/300; relative risk, 3.56; 95% CI, 2.06-6.17). Compared to the 188 multiple organ failure patients without these inflammation phenotypes, the 101 multiple organ failure patients with these phenotypes had both increased macrophage activation syndrome (19% vs 3%; relative risk, 7.07; 95% CI, 2.72-18.38) and mortality (24% vs 10%; relative risk, 2.35; 95% CI, 1.35-4.08).

CONCLUSIONS

These three inflammation phenotypes were associated with increased macrophage activation syndrome and mortality in pediatric sepsis-induced multiple organ failure. This study provides an impetus and essential baseline data for planning multicenter clinical trials targeting these inflammation phenotypes in children.

Murine sepsis phenotypes and differential treatment effects in a randomized trial of prompt antibiotics and fluids

BACKGROUND

Clinical and biologic phenotypes of sepsis are proposed in human studies, yet it is unknown whether prognostic or drug response phenotypes are present in animal models of sepsis. Using a biotelemetry-enhanced, murine cecal ligation and puncture (CLP) model, we determined phenotypes of polymicrobial sepsis prior to physiologic deterioration, and the association between phenotypes and outcome in a randomized trial of prompt or delayed antibiotics and fluids.

METHODS

We performed a secondary analysis of male C57BL/6J mice in two observational cohorts and two randomized, laboratory animal experimental trials. In cohort 1, mice (n = 118) underwent biotelemetry-enhanced CLP, and we applied latent class mixed models to determine optimal number of phenotypes using clinical data collected between injury and physiologic deterioration. In cohort 2 (N = 73 mice), inflammatory cytokines measured at 24 h after deterioration were explored by phenotype. In a subset of 46 mice enrolled in two trials from cohort 1, we tested the association of phenotypes with the response to immediate (0 h) vs. delayed (2 to 4 h) antibiotics or fluids initiated after physiologic deterioration.

RESULTS

Latent class mixture modeling derived a two-class model in cohort 1. Class 2 (N = 97) demonstrated a shorter time to deterioration (mean SD 7.3 (0.9) vs. 9.7 (3.2) h, p < 0.001) and lower heart rate at 7 h after injury (mean (SD) 564 (55) vs. 626 (35) beats per minute, p < 0.001). Overall mortality was similar between phenotypes (p = 0.75). In cohort 2 used for biomarker measurement, class 2 mice had greater plasma concentrations of IL6 and IL10 at 24 h after CLP (p = 0.05). In pilot randomized trials, the effects of sepsis treatment (immediate vs. delayed antibiotics) differed by phenotype (p = 0.03), with immediate treatment associated with greater survival in class 2 mice only. Similar differential treatment effect by class was observed in the trial of immediate vs. delayed fluids (p = 0.02).

CONCLUSIONS

We identified two sepsis phenotypes in a murine cecal ligation and puncture model, one of which is characterized by faster deterioration and more severe inflammation. Response to treatment in a randomized trial of immediate versus delayed antibiotics and fluids differed on the basis of phenotype.

Biomarkers and Precision Medicine: State of the Art

Critical illness syndromes, including sepsis and the acute respiratory distress syndrome (ARDS), are identified using consensus definitions that are based on broad, clinically available criteria and include patients with heterogeneous biology. This heterogeneity is a barrier to developing and testing effective therapies for these syndromes. Biomarkers identify clinically distinct molecular phenotypes of ARDS and sepsis. These molecular phenotypes are associated with differences in mortality and predict response to several treatments in retrospective analyses of clinical trials. Biomarkers can be used for prognostic and predictive enrichment of clinical trials in critical illness to incorporate precision medicine in critical care.

Biomarkers of Infection and Sepsis

The role of biomarkers for detection of sepsis has come a long way. Molecular biomarkers are taking front stage at present, but machine learning and other computational measures using bigdata sets are promising. Clinical research in sepsis is hampered by lack of specificity of the diagnosis; sepsis is a syndrome with no uniformly agreed definition. This lack of diagnostic precision means there is no gold standard for this diagnosis. The final conclusion is expert opinion, which is not bad but not perfect. Perhaps machine learning will displace expert opinion as the final and most accurate definition for sepsis.

Precision Medicine in Critical Illness: Sepsis and Acute Respiratory Distress Syndrome

Sepsis and the acute respiratory distress syndrome (ARDS) each cause substantial morbidity and mortality. In contrast to other lung diseases, the entire course of disease in these syndromes is measured in days to weeks rather than months to years, which raises unique challenges in achieving precision medicine. We review advances in sepsis and ARDS resulting from omics studies, including those involving genome-wide association, gene expression, targeted proteomics, and metabolomics approaches. We focus on promising evidence of biological subtypes in both sepsis and ARDS that consistently display high risk for death. In sepsis, a gene expression signature with dysregulated adaptive immune signaling has evidence for a differential response to systemic steroid therapy, whereas in ARDS, a hyperinflammatory pattern identified in plasma using targeted proteomics responded more favorably to randomized interventions including high positive end-expiratory pressure, volume conservative fluid therapy, and simvastatin therapy. These early examples suggest heterogeneous biology that may be challenging to detect by clinical factors alone and speak to the promise of a precision approach that targets the right treatment at the right time to the right patient.

Prognostic and predictive enrichment in sepsis

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

Putting the data before the algorithm in big data addressing personalized healthcare

Technologies leveraging big data, including predictive algorithms and machine learning, are playing an increasingly important role in the delivery of healthcare. However, evidence indicates that such algorithms have the potential to worsen disparities currently intrinsic to the contemporary healthcare system, including racial biases. Blame for these deficiencies has often been placed on the algorithm-but the underlying training data bears greater responsibility for these errors, as biased outputs are inexorably produced by biased inputs. The utility, equity, and generalizability of predictive models depend on population-representative training data with robust feature sets. So while the conventional paradigm of big data is deductive in nature-clinical decision support-a future model harnesses the potential of big data for inductive reasoning. This may be conceptualized as clinical decision questioning, intended to liberate the human predictive process from preconceived lenses in data solicitation and/or interpretation. Efficacy, representativeness and generalizability are all heightened in this schema. Thus, the possible risks of biased big data arising from the inputs themselves must be acknowledged and addressed. Awareness of data deficiencies, structures for data inclusiveness, strategies for data sanitation, and mechanisms for data correction can help realize the potential of big data for a personalized medicine era. Applied deliberately, these considerations could help mitigate risks of perpetuation of health inequity amidst widespread adoption of novel applications of big data.

Biomarkers of Acute Lung Injury The Individualized Approach: for Phenotyping, Risk Stratification and Treatment Surveillance

Do we need biomarkers of lung damage and infection: For what purpose and how should they be used properly? Biomarkers of lung damage can be used for diagnosis, risk stratification/prediction, treatment surveillance and adjustment of targeted therapy. Additionally, novel "omics" methods may offer a completely different and effective way of improving the understanding of pathogenesis of lung damage and a way to develop new candidate lung damage biomarkers. In the current review, we give an overview within the field of acute lung damage of (i) disease mechanism biomarkers, (ii) of "ready to use" evidence-based biomarker-guided lung infection management, (iii) of novel strategies of inflammatory phenotyping and how this can be used to tailor corticosteroid treatment, (iv) a future perspective of where "omics" technologies and mindsets may become increasingly important in developing new strategies for treatment and for understanding the development of acute lung damage.

Diagnosing and Managing Sepsis by Probing the Host Response to Infection: Advances, Opportunities, and Challenges

Sepsis is a major source of mortality and morbidity globally. Accurately diagnosing sepsis remains challenging due to the heterogeneous nature of the disease, and delays in diagnosis and intervention contribute to high mortality rates. Measuring the host response to infection enables more rapid diagnosis of sepsis than is possible through direct detection of the causative pathogen, and recent advances in host response diagnostics and prognostics hold promise for improving outcomes. The current review discusses recent advances in the technologies used to probe the host response to infection, particularly those based on transcriptomics. These are discussed in the context of contemporary approaches to diagnosing and prognosing sepsis, and recommendations are made for successful development and validation of host response technologies.

Pathophysiological role of respiratory dysbiosis in hospital-acquired pneumonia

Hospital-acquired pneumonia is a major cause of morbidity and mortality. The incidence of hospital-acquired pneumonia remains high globally and treatment can often be ineffective. Here, we review the available data and unanswered questions surrounding hospital-acquired pneumonia, discuss alterations of the respiratory microbiome and of the mucosal immunity in patients admitted to hospital, and explore potential approaches to stratify patients for tailored treatments. The lungs have been considered a sterile organ for decades because microbiological culture techniques had shown negative results. Culture-independent techniques have shown that healthy lungs harbour a diverse and dynamic ecosystem of bacteria, changing our comprehension of respiratory physiopathology. Understanding dysbiosis of the respiratory microbiome and altered mucosal immunity in patients with critical illness holds great promise to develop targeted host-directed immunotherapy to reduce ineffective treatment, to improve patient outcomes, and to tackle the global threat of resistant bacteria that cause these infections.

Insights into rheumatic diseases from next-generation sequencing

Rheumatic diseases have complex aetiologies that are not fully understood, which makes the study of pathogenic mechanisms in these diseases a challenge for researchers. Next-generation sequencing (NGS) and related omics technologies, such as transcriptomics, epigenomics and genomics, provide an unprecedented genome-wide view of gene expression, environmentally responsive epigenetic changes and genetic variation. The integrated application of NGS technologies to samples from carefully phenotyped clinical cohorts of patients has the potential to solve remaining mysteries in the pathogenesis of several rheumatic diseases, to identify new therapeutic targets and to underpin a precision medicine approach to the diagnosis and treatment of rheumatic diseases. This Review provides an overview of the NGS technologies available, showcases important advances in rheumatic disease research already powered by these technologies and highlights NGS approaches that hold particular promise for generating new insights and advancing the field.

Precision medicine in pediatric sepsis

PURPOSE OF REVIEW

Pediatric sepsis is a heterogeneous state associated with significant morbidity and mortality, but treatment strategies are limited. Clinical trials of immunomodulators in sepsis have shown no benefit, despite having a strong biological rationale. There is considerable interest in application of a precision medicine approach to pediatric sepsis to identify patients who are more likely to benefit from targeted therapeutic interventions.

RECENT FINDINGS

Precision medicine requires a clear understanding of the molecular basis of disease. 'Omics data' and bioinformatics tools have enabled identification of endotypes of pediatric septic shock, with corresponding biological pathways. Further, using a multibiomarker-based approach, patients at highest risk of poor outcomes can be identified at disease onset. Enrichment strategies, both predictive and prognostic, may be used to optimize patient selection in clinical trials and identify a subpopulation in whom therapy of interest may be trialed. A bedside-to-bench-to-bedside model may offer clinicians pragmatic tools to aid in decision-making.

SUMMARY

Precision medicine approaches may be used to subclassify, risk-stratify, and select pediatric patients with sepsis who may benefit from new therapies. Application of precision medicine will require robust basic and translational research, rigorous clinical trials, and infrastructure to collect and analyze big data.

Derivation, Validation, and Potential Treatment Implications of Novel Clinical Phenotypes for Sepsis

IMPORTANCE

Sepsis is a heterogeneous syndrome. Identification of distinct clinical phenotypes may allow more precise therapy and improve care.

OBJECTIVE

To derive sepsis phenotypes from clinical data, determine their reproducibility and correlation with host-response biomarkers and clinical outcomes, and assess the potential causal relationship with results from randomized clinical trials (RCTs).

DESIGN, SETTINGS, AND PARTICIPANTS

Retrospective analysis of data sets using statistical, machine learning, and simulation tools. Phenotypes were derived among 20 189 total patients (16 552 unique patients) who met Sepsis-3 criteria within 6 hours of hospital presentation at 12 Pennsylvania hospitals (2010-2012) using consensus k means clustering applied to 29 variables. Reproducibility and correlation with biological parameters and clinical outcomes were assessed in a second database (2013-2014; n = 43 086 total patients and n = 31 160 unique patients), in a prospective cohort study of sepsis due to pneumonia (n = 583), and in 3 sepsis RCTs (n = 4737).

EXPOSURES

All clinical and laboratory variables in the electronic health record.

MAIN OUTCOMES AND MEASURES

Derived phenotype (α, β, γ, and δ) frequency, host-response biomarkers, 28-day and 365-day mortality, and RCT simulation outputs.

RESULTS

The derivation cohort included 20 189 patients with sepsis (mean age, 64 [SD, 17] years; 10 022 [50%] male; mean maximum 24-hour Sequential Organ Failure Assessment [SOFA] score, 3.9 [SD, 2.4]). The validation cohort included 43 086 patients (mean age, 67 [SD, 17] years; 21 993 [51%] male; mean maximum 24-hour SOFA score, 3.6 [SD, 2.0]). Of the 4 derived phenotypes, the α phenotype was the most common (n = 6625; 33%) and included patients with the lowest administration of a vasopressor; in the β phenotype (n = 5512; 27%), patients were older and had more chronic illness and renal dysfunction; in the γ phenotype (n = 5385; 27%), patients had more inflammation and pulmonary dysfunction; and in the δ phenotype (n = 2667; 13%), patients had more liver dysfunction and septic shock. Phenotype distributions were similar in the validation cohort. There were consistent differences in biomarker patterns by phenotype. In the derivation cohort, cumulative 28-day mortality was 287 deaths of 5691 unique patients (5%) for the α phenotype; 561 of 4420 (13%) for the β phenotype; 1031 of 4318 (24%) for the γ phenotype; and 897 of 2223 (40%) for the δ phenotype. Across all cohorts and trials, 28-day and 365-day mortality were highest among the δ phenotype vs the other 3 phenotypes (P < .001). In simulation models, the proportion of RCTs reporting benefit, harm, or no effect changed considerably (eg, varying the phenotype frequencies within an RCT of early goal-directed therapy changed the results from >33% chance of benefit to >60% chance of harm).

CONCLUSIONS AND RELEVANCE

In this retrospective analysis of data sets from patients with sepsis, 4 clinical phenotypes were identified that correlated with host-response patterns and clinical outcomes, and simulations suggested these phenotypes may help in understanding heterogeneity of treatment effects. Further research is needed to determine the utility of these phenotypes in clinical care and for informing trial design and interpretation.

AKIRIN1: A Potential New Reference Gene in Human Natural Killer Cells and Granulocytes in Sepsis

Timely and reliable distinction of sepsis from non-infectious systemic inflammatory response syndrome (SIRS) supports adequate antimicrobial therapy and saves lives but is clinically challenging. Blood transcriptional profiling promises to deliver insights into the pathomechanisms of SIRS and sepsis and to accelerate the discovery of urgently sought sepsis biomarkers. However, suitable reference genes for normalizing gene expression in these disease conditions are lacking. In addition, variability in blood leukocyte subtype composition complicates gene profile interpretation. Here, we aimed to identify potential reference genes in natural killer (NK) cells and granulocytes from patients with SIRS and sepsis on intensive care unit (ICU) admission. Discovery by a two-step probabilistic selection from microarray data followed by validation through branched DNA assays in independent patients revealed several candidate reference genes in NK cells including AKIRIN1, PPP6R3, TAX1BP1, and ADRBK1. Initially, no candidate genes could be validated in patient granulocytes. However, we determined highly similar AKIRIN1 expression also in SIRS and sepsis granulocytes and no change by in vitro LPS challenge in granulocytes from healthy donors. Inspection of external neutrophil transcriptome datasets further support unchanged AKIRIN1 expression in human systemic inflammation. As a potential new reference gene in NK cells and granulocytes in infectious and inflammatory diseases, AKIRIN1 may improve our pathomechanistic understanding of SIRS and sepsis and help identifying new sepsis biomarkers.

Composed endotypes to guide antibiotic discontinuation in sepsis

Overuse of empiric antibiotic therapy in the ICU is responsible for promoting the dissemination of multidrug-resistant (MDR) bacteria. Shortened antibiotic treatment duration could contribute to palliating the emergence of MDR. Uncertainty about patient evolution is a major concern for deciding to stop antibiotics. Biomarkers could represent a complementary tool to identify those patients for whom antibiotic treatment could be safely discontinued. The biomarker most extensively studied to guide antibiotic withdrawal is procalcitonin (PCT), but its real impact on decreasing the duration of antibiotic treatment is a matter of controversy. Combining biomarkers to rule out complicated outcomes in sepsis patients could represent a better option. Some candidate biomarkers, including mid-regional proadrenomedullin, the percentage of human leukocyte antigen DR (HLA-DR)-positive monocytes, means of fluorescence intensities of HLA-DR on monocytes, interleukin-7 receptor expression levels, immunoglobulin M levels in the serum or the absence of increased proteolysis, have already demonstrated the potential to exclude the risk of progression to septic shock, nosocomial infections, and mortality when tested along the sepsis course. Other promising biomarkers to rule out complicated outcomes are neutrophil protease activity, the adaptive/coagulopathic signatures identified by whole transcriptome analysis by Sweeney et al., and the SRS1 signature identified by Davenport et al. In conclusion, there are a number of promising biomarkers involved in proteolytic, vascular, immunological, and coagulation alterations that could be useful to build composed endotypes to predict uncomplicated outcomes in sepsis. These endotypes could help to identify patients deserving the discontinuation of antibiotics.

Transcriptomic Signatures in Sepsis and a Differential Response to Steroids. From the VANISH Randomized Trial

RATIONALE

There remains uncertainty about the role of corticosteroids in sepsis with clear beneficial effects on shock duration, but conflicting survival effects. Two transcriptomic sepsis response signatures (SRSs) have been identified. SRS1 is relatively immunosuppressed, whereas SRS2 is relatively immunocompetent.

OBJECTIVES

We aimed to categorize patients based on SRS endotypes to determine if these profiles influenced response to either norepinephrine or vasopressin, or to corticosteroids in septic shock.

METHODS

A post hoc analysis was performed of a double-blind, randomized clinical trial in septic shock (VANISH [Vasopressin vs. Norepinephrine as Initial Therapy in Septic Shock]). Patients were included within 6 hours of onset of shock and were randomized to receive norepinephrine or vasopressin followed by hydrocortisone or placebo. Genome-wide gene expression profiling was performed and SRS endotype was determined by a previously established model using seven discriminant genes.

MEASUREMENTS AND MAIN RESULTS

Samples were available from 176 patients: 83 SRS1 and 93 SRS2. There was no significant interaction between SRS group and vasopressor assignment (P = 0.50). However, there was an interaction between assignment to hydrocortisone or placebo, and SRS endotype (P = 0.02). Hydrocortisone use was associated with increased mortality in those with an SRS2 phenotype (odds ratio = 7.9; 95% confidence interval = 1.6-39.9).

CONCLUSIONS

Transcriptomic profile at onset of septic shock was associated with response to corticosteroids. Those with the immunocompetent SRS2 endotype had significantly higher mortality when given corticosteroids compared with placebo. Clinical trial registered with www.clinicaltrials.gov (ISRCTN 20769191).

Heterogeneity in sepsis: new biological evidence with clinical applications

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2019. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2019. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901.

Point of care technologies for sepsis diagnosis and treatment

Sepsis is a rapidly progressing, life threatening immune response triggered by infection that affects millions worldwide each year. Current clinical diagnosis relies on broad physiological parameters and time consuming lab-based cell culture. If proper treatment is not provided, cases of sepsis can drastically increase in severity over the course of a few hours. Development of new point of care tools for sepsis has the potential to improve diagnostic speed and accuracy, leading to prompt administration of appropriate therapeutics, thereby reducing healthcare costs and improving patient outcomes. In this review we examine developing and commercially available technologies to assess the feasibility of rapid, accurate sepsis diagnosis, with emphasis on point of care.

Profile of the SeptiCyte™ LAB gene expression assay to diagnose infection in critically ill patients

Sepsis is a severe and frequently occurring clinical syndrome, caused by the inflammatory response to infections. Recent studies on the human transcriptome during sepsis have yielded several gene-expression assays that might assist physicians during clinical assessment of patients suspected of sepsis. SeptiCyte™ LAB (Immunexpress, Seattle, WA) is the first gene expression assay that was cleared by the FDA in the United States to distinguish infectious from non-infectious causes of systemic inflammation in critically ill patients. The test consists of the simultaneous amplification of four RNA transcripts (CEACAM4, LAMP1, PLAC8, and PLA2G7) in whole blood using a quantitative real-time PCR reaction. This review provides an overview of the challenges in the diagnosis of sepsis, the development of gene expression signatures, and a detailed description of available clinical performance studies evaluating SeptiCyte™ LAB.

Biomarkers for Acute Respiratory Distress syndrome and prospects for personalised medicine

Acute lung injury (ALI) affects over 10% of patients hospitalised in critical care, with acute respiratory distress syndrome (ARDS) being the most severe form of ALI and having a mortality rate in the region of 40%. There has been slow but incremental progress in identification of biomarkers that contribute to the pathophysiology of ARDS, have utility in diagnosis and monitoring, and that are potential therapeutic targets (Calfee CS, Delucchi K, Parsons PE, Thompson BT, Ware LB, Matthay MA, Thompson T, Ware LB, Matthay MA, Lancet Respir Med 2014, 2:611–-620). However, a major issue is that ARDS is such a heterogeneous, multi-factorial, end-stage condition that the strategies for “lumping and splitting” are critical (Prescott HC, Calfee CS, Thompson BT, Angus DC, Liu VX, Am J Respir Crit Care Med 2016, 194:147–-155). Nevertheless, sequencing of the human genome, the availability of improved methods for analysis of transcription to mRNA (gene expression), and development of sensitive immunoassays has allowed the application of network biology to ARDS, with these biomarkers offering potential for personalised or precision medicine (Sweeney TE, Khatri P, Toward precision medicine Crit Care Med; 2017 45:934-939).

Biomarker panels have potential applications in molecular phenotyping for identifying patients at risk of developing ARDS, diagnosis of ARDS, risk stratification and monitoring. Two subphenotypes of ARDS have been identified on the basis of blood biomarkers: hypo-inflammatory and hyper-inflammatory. The hyper-inflammatory subphenotype is associated with shock, metabolic acidosis and worst clinical outcomes. Biomarkers of particular interest have included interleukins (IL-6 and IL-8), interferon gamma (IFN-γ), surfactant proteins (SPD and SPB), von Willebrand factor antigen, angiopoietin 1/2 and plasminogen activator inhibitor-1 (PAI-1). In terms of gene expression (mRNA) in blood there have been found to be increases in neutrophil-related genes in sepsis-induced and influenza-induced ARDS, but whole blood expression does not give a robust diagnostic test for ARDS.

Despite improvements in management of ARDS on the critical care unit, this complex disease continues to be a major life-threatening event. Clinical trials of β₂-agonists, statins, surfactants and keratinocyte growth factor (KGF) have been disappointing. In addition, monoclonal antibodies (anti-TNF) and TNFR fusion protein have also been unconvincing. However, there have been major advances in methods of mechanical ventilation, a neuromuscular blocker (cisatracurium besilate) has shown some benefit, and stem cell therapy is being developed. In the future, by understanding the role of biomarkers in the pathophysiology of ARDS and lung injury, it is hoped that this will provide rational therapeutic targets and ultimately improve clinical care (Seymour CW, Gomez H, Chang CH, Clermont G, Kellum JA, Kennedy J, Yende S, Angus DC, Crit Care 2017, 21:257).

Identification of subclasses of sepsis that showed different clinical outcomes and responses to amount of fluid resuscitation: a latent profile analysis

BACKGROUND AND OBJECTIVE

Sepsis is a heterogeneous disease and identification of its subclasses may facilitate and optimize clinical management. This study aimed to identify subclasses of sepsis and its responses to different amounts of fluid resuscitation.

METHODS

This was a retrospective study conducted in an intensive care unit at a large tertiary care hospital. The patients fulfilling the diagnostic criteria of sepsis from June 1, 2001 to October 31, 2012 were included. Clinical and laboratory variables were used to perform the latent profile analysis (LPA). A multivariable logistic regression model was used to explore the independent association of fluid input and mortality outcome.

RESULTS

In total, 14,993 patients were included in the study. The LPA identified four subclasses of sepsis: profile 1 was characterized by the lowest mortality rate and having the largest proportion and was considered the baseline type; profile 2 was characterized by respiratory dysfunction; profile 3 was characterized by multiple organ dysfunction (kidney, coagulation, liver, and shock), and profile 4 was characterized by neurological dysfunction. Profile 3 showed the highest mortality rate (45.4%), followed by profile 4 (27.4%), 2 (18.2%), and 1 (16.9%). Overall, the amount of fluid needed for resuscitation was the largest on day 1 (median 5115 mL, interquartile range (IQR) 2662 to 8800 mL) and decreased rapidly on day 2 (median 2140 mL, IQR 900 to 3872 mL). Higher cumulative fluid input in the first 48 h was associated with reduced risk of hospital mortality for profile 3 (odds ratio (OR) 0.89, 95% CI 0.83 to 0.95 for each 1000 mL increase in fluid input) and with increased risk of death for profile 4 (OR 1.20, 95% CI 1.11 to 1.30).

CONCLUSION

The study identified four subphenotypes of sepsis, which showed different mortality outcomes and responses to fluid resuscitation. Prospective trials are needed to validate our findings.

Red-flag sepsis and SOFA identifies different patient population at risk of sepsis-related deaths on the general ward

Controversy exists regarding the best diagnostic and screening tool for sepsis outside the intensive care unit (ICU). Sequential organ failure assessment (SOFA) score has been shown to be superior to systemic inflammatory response syndrome (SIRS) criteria, however, the performance of "Red Flag sepsis criteria" has not been tested formally.The aim of the study was to investigate the ability of Red Flag sepsis criteria to identify the patients at high risk of sepsis-related death in comparison to SOFA based sepsis criteria. We also investigated the comparison of Red Flag sepsis to quick SOFA (qSOFA), SIRS, and national early warning score (NEWS) scores and factors influencing patient mortality.Patients were recruited into a 24-hour point-prevalence study on the general wards and emergency departments across all Welsh acute hospitals. Inclusion criteria were: clinical suspicion of infection and NEWS 3 or above in-line with established escalation criteria in Wales. Data on Red Flag sepsis and SOFA criteria was collected together with qSOFA and SIRS scores and 90-day mortality.459 patients were recruited over a 24-hour period. 246 were positive for Red Flag sepsis, mortality 33.7% (83/246); 241 for SOFA based sepsis criteria, mortality 39.4% (95/241); 54 for qSOFA, mortality 57.4% (31/54), and 268 for SIRS, mortality 33.6% (90/268). 55 patients were not picked up by any criteria. We found that older age was associated with death with OR (95% CI) of 1.03 (1.02-1.04); higher frailty score 1.24 (1.11-1.40); DNA-CPR order 1.74 (1.14-2.65); ceiling of care 1.55 (1.02-2.33); and SOFA score of 2 and above 1.69 (1.16-2.47).The different clinical tools captured different subsets of the at-risk population, with similar sensitivity. SOFA score 2 or above was independently associated with increased risk of death at 90 days. The sequalae of infection-related organ dysfunction cannot be reliably captured based on routine clinical and physiological parameters alone.

Cohort profile of the Biomarkers of Acute Serious Illness in Children (BASIC) study: a prospective multicentre cohort study in critically ill children

PURPOSE

Despite significant progress, challenges remain in the management of critically ill children, including early identification of infection and organ failure and robust early risk stratification to predict poor outcome. The Biomarkers of Acute Serious Illness in Children study aims to identify genetic and biological pathways underlying the development of critical illness in infections and organ failure and those leading to poor outcome (death or severe disability) in children requiring emergency intensive care.

PARTICIPANTS

We recruited a prospective cohort of critically ill children undergoing emergency transport to four paediatric intensive care units (PICUs) in Southeast England between April 2014 and December 2016.

FINDINGS TO DATE

During the study period, 1017 patients were recruited by the regional PICU transport team, and blood and urine samples were obtained at/around first contact with the patient by the transport team. Consent for participation in the study was deferred until after PICU admission and 674 parents/carers were consented. Further samples (blood, urine, stool and throat swabs) were collected after consent. Samples were processed and stored for genomic, transcriptomic, proteomic and metabolomic analyses. Demographic, clinical and laboratory data at first contact, during PICU stay and at discharge, were collected, as were detailed data regarding infectious or non-infectious aetiology. In addition, 115 families have completed 12-month validated follow-up questionnaires to assess quality of life and child behaviour.The first phase of sample analyses (transcriptomic profiling) is currently in progress.

FUTURE PLANS

Stored samples will be analysed using genomic, proteomic and metabolic profiling. Advanced bioinformatics techniques will be used to identify biomarkers for early diagnosis of infection, identification of organ failure and risk stratification to predict poor outcome (death/severe disability).

TRIAL REGISTRATION NUMBER

NCT03238040.

Latent class analysis of ARDS subphenotypes: a secondary analysis of the statins for acutely injured lungs from sepsis (SAILS) study

PURPOSE

Using latent class analysis (LCA), we have consistently identified two distinct subphenotypes in four randomized controlled trial cohorts of ARDS. One subphenotype has hyper-inflammatory characteristics and is associated with worse clinical outcomes. Further, within three negative clinical trials, we observed differential treatment response by subphenotype to randomly assigned interventions. The main purpose of this study was to identify ARDS subphenotypes in a contemporary NHLBI Network trial of infection-associated ARDS (SAILS) using LCA and to test for differential treatment response to rosuvastatin therapy in the subphenotypes.

METHODS

LCA models were constructed using a combination of biomarker and clinical data at baseline in the SAILS study (n = 745). LCA modeling was then repeated using an expanded set of clinical class-defining variables. Subphenotypes were tested for differential treatment response to rosuvastatin.

RESULTS

The two-class LCA model best fit the population. Forty percent of the patients were classified as the "hyper-inflammatory" subphenotype. Including additional clinical variables in the LCA models did not identify new classes. Mortality at day 60 and day 90 was higher in the hyper-inflammatory subphenotype. No differences in outcome were observed between hyper-inflammatory patients randomized to rosuvastatin therapy versus placebo.

CONCLUSIONS

LCA using a two-subphenotype model best described the SAILS population. The subphenotypes have features consistent with those previously reported in four other cohorts. Addition of new class-defining variables in the LCA model did not yield additional subphenotypes. No treatment effect was observed with rosuvastatin. These findings further validate the presence of two subphenotypes and demonstrate their utility for patient stratification in ARDS.

Sepsis: Personalized Medicine Utilizing 'Omic' Technologies-A Paradigm Shift?

Sepsis has over the years proven a considerable challenge to physicians and researchers. Numerous pharmacological and non-pharmacological interventions have been tested in trials, but have unfortunately failed to improve the general prognosis. This has led to the speculation that the sepsis population may be too heterogeneous to be targeted with the traditional one treatment suits all’ approach. Recent advances in genetic and biochemical analyses now allow genotyping and biochemical characterisation of large groups of patients via the ‘omics’ technologies. These new opportunities could lead to a paradigm shift in the approach to sepsis towards personalised treatments with interventions targeted towards specific pathophysiological mechanisms activated in the patient. In this article, we review the potentials and pitfalls of using new advanced technologies to deepen our understanding of the clinical syndrome of sepsis.

Patterns of Death in Patients with Sepsis and the Use of Hydrocortisone, Ascorbic Acid, and Thiamine to Prevent These Deaths

Background: In general, patients with sepsis die from the host response to the infecting pathogen rather than from the infecting pathogen itself. Four patterns of death have been identified in sepsis, namely vasoplegic shock, single-organ respiratory failure (acute respiratory distress syndrome [ARDS]), multi-system organ failure (MSOF), and persistent MSOF with ongoing inflammation and immunosuppression with recurrent infections (persistent inflammation-immunosuppression and catabolism syndrome [PICS]). To improve the outcome of sepsis adjunctive therapies that modulate the immune system have been tested; these therapies that have targeted specific molecules or pathways have universally failed. Conclusion: We propose that the combination of hydrocortisone, intravenous ascorbic acid, and thiamine (HAT therapy), which synergistically targets multiple pathways, restores the dysregulated immune system and organ injury, and reduces the risk of death and organ failure following sepsis.

Human pulmonary endothelial cell permeability after exposure to LPS-stimulated leukocyte supernatants derived from patients with early sepsis

Systemic immune activation is the hallmark of sepsis, which can result in endothelial injury and the acute respiratory distress syndrome (ARDS). The aim of this study was to investigate heterogeneity in sepsis-mediated endothelial permeability using primary human pulmonary microvascular endothelial cells (HPMECs) and the electric cell-substrate impedance sensing (ECIS) platform. After plasma removal, cellular component of whole blood from 35 intensive care unit (ICU) patients with early sepsis was diluted with media and stimulated with either lipopolysaccharide (LPS) or control media. Resulting supernatants were cocultured with HPMECs seeded on ECIS plates, and resistance was continually measured. A decrease in resistance signified increased permeability. After incubation, HPMECs were detached and cell adhesion proteins were quantified using flow cytometry and immunohistochemistry, and gene expression was analyzed with quantitative PCR. Significant heterogeneity in endothelial permeability after exposure to supernatants of LPS-stimulated leukocytes was identified. ICU patients with sepsis stratified into one of the following three groups: minimal (9/35, 26%), intermediate (18/35, 51%), and maximal (8/35, 23%) permeability. Maximal permeability was associated with increased intercellular adhesion molecule-1 protein and mRNA expression and decreased vascular endothelial-cadherin mRNA expression. These findings indicate that substantial heterogeneity in pulmonary endothelial permeability is induced by supernatants of LPS-stimulated leukocytes derived from patients with early sepsis and provide insights into some of the mechanisms that induce lung vascular injury. In addition, this in vitro model of lung endothelial permeability from LPS-stimulated leukocytes may be a useful method for testing therapeutic agents that could mitigate endothelial injury in early sepsis.

The role of glucocorticoids as adjunctive treatment for sepsis in the modern era

Glucocorticoids have been used as adjunctive therapy in patients with sepsis and septic shock for more than four decades. The rationale for the use of glucocorticoids is that this class of drugs downregulates the proinflammatory response and limits the anti-inflammatory response while preserving innate immunity. Between 1976 and 2017, 22 randomised placebo-controlled trials have been published evaluating the benefit of glucocorticoids in patients with community-acquired pneumonia, sepsis, and septic shock. These studies produced conflicting results. In 2018, two large randomised controlled trials (RCTs) were published evaluating the role of hydrocortisone in patients with septic shock. The Activated Protein C and Corticosteroids for Human Septic Shock (APROCCHSS) trial reported a reduction in 90-day mortality whereas the Adjunctive Corticosteroid Treatment in Critically Ill Patients with Septic Shock (ADRENAL) trial reported no mortality benefit. This Viewpoint critically appraises these two RCTs and evaluates the use of glucocorticoids in the treatment of sepsis and septic shock in the modern era.

The host response as a tool for infectious disease diagnosis and management

INTRODUCTION

A century of advances in infectious disease diagnosis and treatment changed the face of medicine. However, challenges continue to develop including multi-drug resistance, globalization that increases pandemic risks, and high mortality from severe infections. These challenges can be mitigated through improved diagnostics, and over the past decade, there has been a particular focus on the host response. Since this article was originally published in 2015, there have been significant developments in the field of host response diagnostics, warranting this updated review. Areas Covered: This review begins by discussing developments in single biomarkers and pauci-analyte biomarker panels. It then delves into 'omics, an area where there has been truly exciting progress. Specifically, progress has been made in sepsis diagnosis and prognosis; differentiating viral, bacterial, and fungal pathogen classes; pre-symptomatic diagnosis; and understanding disease-specific diagnostic challenges in tuberculosis, Lyme disease, and Ebola. Expert Commentary: As 'omics have become faster, more precise, and less expensive, the door has been opened for academic, industry, and government efforts to develop host-based infectious disease classifiers. While there are still obstacles to overcome, the chasm separating these scientific advances from the patient's bedside is shrinking.