Improved Risk Stratification in Pediatric Septic Shock Using Both Protein and mRNA Biomarkers. PERSEVERE-XP

An update on the role of fluid overload in the prediction of outcome in acute kidney injury

Over the past two decades, our understanding of the impact of acute kidney injury, disorders of fluid balance, and their interplay have increased significantly. In recent years, the epidemiology and impact of fluid balance, including the pathologic state of fluid overload on outcomes has been studied extensively across multiple pediatric and neonatal populations. A detailed understating of fluid balance has become increasingly important as it is recognized as a target for intervention to continue to work to improve outcomes in these populations. In this review, we provide an update on the epidemiology and outcomes associated with fluid balance disorders and the development of fluid overload in children with acute kidney injury (AKI). This will include a detailed review of consensus definitions of fluid balance, fluid overload, and the methodologies to define them, impact of fluid balance on the diagnosis of AKI and the concept of fluid corrected serum creatinine. This review will also provide detailed descriptions of future directions and the changing paradigms around fluid balance and AKI in critical care nephrology, including the incorporation of the sequential utilization of risk stratification, novel biomarkers, and functional kidney tests (furosemide stress test) into research and ultimately clinical care. Finally, the review will conclude with novel methods currently under study to assess fluid balance and distribution (point of care ultrasound and bioimpedance).

Potential biomarkers used for risk estimation of pediatric sepsis-associated organ dysfunction and immune dysregulation

Pediatric sepsis is a serious issue globally and is a significant cause of illness and death among infants and children. Refractory septic shock and multiple organ dysfunction syndrome are the primary causes of mortality in children with sepsis. However, there is incomplete understanding of mechanistic insight of sepsis associated organ dysfunction. Biomarkers present during the body's response to infection-related inflammation can be used for screening, diagnosis, risk stratification/prognostication, and/or guidance in treatment decision-making. Research on biomarkers in children with sepsis can provide information about the risk of poor outcomes and sepsis-related organ dysfunction. This review focuses on clinically used biomarkers associated with immune dysregulation and organ dysfunction in pediatric sepsis, which could be useful for developing precision medicine strategies in pediatric sepsis management in the future. IMPACT: Sepsis is a complex syndrome with diverse clinical presentations, where organ dysfunction is a key factor in morbidity and mortality. Early detection of organ complications is vital in sepsis management, and potential biomarkers offer promise for precision medicine in pediatric cases. Well-designed studies are needed to identify phase-specific biomarkers and improve outcomes through more precise management.

Profiling the dysregulated immune response in sepsis: overcoming challenges to achieve the goal of precision medicine

Sepsis is characterised by a dysregulated host immune response to infection. Despite recognition of its significance, immune status monitoring is not implemented in clinical practice due in part to the current absence of direct therapeutic implications. Technological advances in immunological profiling could enhance our understanding of immune dysregulation and facilitate integration into clinical practice. In this Review, we provide an overview of the current state of immune profiling in sepsis, including its use, current challenges, and opportunities for progress. We highlight the important role of immunological biomarkers in facilitating predictive enrichment in current and future treatment scenarios. We propose that multiple immune and non-immune-related parameters, including clinical and microbiological data, be integrated into diagnostic and predictive combitypes, with the aid of machine learning and artificial intelligence techniques. These combitypes could form the basis of workable algorithms to guide clinical decisions that make precision medicine in sepsis a reality and improve patient outcomes.

Pediatric Sepsis Diagnosis, Management, and Sub-phenotypes

Sepsis and septic shock are major causes of morbidity, mortality, and health care costs for children worldwide, including >3 million deaths annually and, among survivors, risk for new or worsening functional impairments, including reduced quality of life, new respiratory, nutritional, or technological assistance, and recurrent severe infections. Advances in understanding sepsis pathophysiology highlight a need to update the definition and diagnostic criteria for pediatric sepsis and septic shock, whereas new data support an increasing role for automated screening algorithms and biomarker combinations to assist earlier recognition. Once sepsis or septic shock is suspected, attention to prompt initiation of broad-spectrum empiric antimicrobial therapy, fluid resuscitation, and vasoactive medications remain key components to initial management with several new and ongoing studies offering new insights into how to optimize this approach. Ultimately, a key goal is for screening to encompass as many children as possible at risk for sepsis and trigger early treatment without increasing unnecessary broad-spectrum antibiotics and preventable hospitalizations. Although the role for adjunctive treatment with corticosteroids and other metabolic therapies remains incompletely defined, ongoing studies will soon offer updated guidance for optimal use. Finally, we are increasingly moving toward an era in which precision therapeutics will bring novel strategies to improve outcomes, especially for the subset of children with sepsis-induced multiple organ dysfunction syndrome and sepsis subphenotypes for whom antibiotics, fluid, vasoactive medications, and supportive care remain insufficient.

Transcriptomic profiles of multiple organ dysfunction syndrome phenotypes in pediatric critical influenza

Background

Influenza virus is responsible for a large global burden of disease, especially in children. Multiple Organ Dysfunction Syndrome (MODS) is a life-threatening and fatal complication of severe influenza infection.

Methods

We measured RNA expression of 469 biologically plausible candidate genes in children admitted to North American pediatric intensive care units with severe influenza virus infection with and without MODS. Whole blood samples from 191 influenza-infected children (median age 6.4 years, IQR: 2.2, 11) were collected a median of 27 hours following admission; for 45 children a second blood sample was collected approximately seven days later. Extracted RNA was hybridized to NanoString mRNA probes, counts normalized, and analyzed using linear models controlling for age and bacterial co-infections (FDR q<0.05).

Results

Comparing pediatric samples collected near admission, children with Prolonged MODS for ≥7 days (n=38; 9 deaths) had significant upregulation of nine mRNA transcripts associated with neutrophil degranulation (RETN, TCN1, OLFM4, MMP8, LCN2, BPI, LTF, S100A12, GUSB) compared to those who recovered more rapidly from MODS (n=27). These neutrophil transcripts present in early samples predicted Prolonged MODS or death when compared to patients who recovered, however in paired longitudinal samples, they were not differentially expressed over time. Instead, five genes involved in protein metabolism and/or adaptive immunity signaling pathways (RPL3, MRPL3, HLA-DMB, EEF1G, CD8A) were associated with MODS recovery within a week.

Conclusion

Thus, early increased expression of neutrophil degranulation genes indicated worse clinical outcomes in children with influenza infection, consistent with reports in adult cohorts with influenza, sepsis, and acute respiratory distress syndrome.

Longitudinal Analysis of Contrasts in Gene Expression Data

We are interested in detecting a departure from the baseline in a longitudinal analysis in the context of multiple organ dysfunction syndrome (MODS). In particular, we are given gene expression reads at two time points for a fixed number of genes and individuals. The individuals can be subdivided into two groups, denoted as groups A and B. Using the two time points, we compute a contrast of gene expression reads per individual and gene. The age of each individual is known and it is used to compute, for each gene separately, a linear regression of the gene expression contrasts on the individual's age. Looking at the intercept of the linear regression to detect a departure from the baseline, we aim to reliably single out those genes for which there is a difference in the intercept among those individuals in group A and not in group B. In this work, we develop testing methodology for this setting based on two hypothesis tests-one under the null and one under an appropriately formulated alternative. We demonstrate the validity of our approach using a dataset created by bootstrapping from a real data application in the context of multiple organ dysfunction syndrome (MODS).

Drp1/p53 interaction mediates p53 mitochondrial localization and dysfunction in septic cardiomyopathy

BACKGROUND

Previous studies have implicated p53-dependent mitochondrial dysfunction in sepsis induced end organ injury, including sepsis-induced myocardial dysfunction (SIMD). However, the mechanisms behind p53 localization to the mitochondria have not been well established. Dynamin-related protein 1 (Drp1), a mediator of mitochondrial fission, may play a role in p53 mitochondrial localization. Here we examined the role of Drp1/p53 interaction in SIMD using in vitro and murine models of sepsis.

METHODS

H9c2 cardiomyoblasts and BALB/c mice were exposed to lipopolysaccharide (LPS) to model sepsis phenotype. Pharmacologic inhibitors of Drp1 activation (ψDrp1) and of p53 mitochondrial binding (pifithrin μ, PFTμ) were utilized to assess interaction between Drp1 and p53, and the subsequent downstream impact on mitochondrial morphology and function, cardiomyocyte function, and sepsis phenotype.

RESULTS

Both in vitro and murine models demonstrated an increase in physical Drp1/p53 interaction following LPS treatment, which was associated with increased p53 mitochondrial localization, and mitochondrial dysfunction. This Drp1/p53 interaction was inhibited by ΨDrp1, suggesting that this interaction is dependent on Drp1 activation. Treatment of H9c2 cells with either ΨDrp1 or PFTμ inhibited the LPS mediated localization of Drp1/p53 to the mitochondria, decreased oxidative stress, improved cellular respiration and ATP production. Similarly, treatment of BALB/c mice with either ΨDrp1 or PFTμ decreased LPS-mediated mitochondrial localization of p53, mitochondrial ROS in cardiac tissue, and subsequently improved cardiomyocyte contractile function and survival.

CONCLUSION

Drp1/p53 interaction and mitochondrial localization is a key prodrome to mitochondrial damage in SIMD and inhibiting this interaction may serve as a therapeutic target.

The Role of Transcriptomics in Redefining Critical Illness

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

Refining empiric subgroups of pediatric sepsis using machine-learning techniques on observational data

Sepsis contributes to 1 of every 5 deaths globally with 3 million per year occurring in children. To improve clinical outcomes in pediatric sepsis, it is critical to avoid "one-size-fits-all" approaches and to employ a precision medicine approach. To advance a precision medicine approach to pediatric sepsis treatments, this review provides a summary of two phenotyping strategies, empiric and machine-learning-based phenotyping based on multifaceted data underlying the complex pediatric sepsis pathobiology. Although empiric and machine-learning-based phenotypes help clinicians accelerate the diagnosis and treatments, neither empiric nor machine-learning-based phenotypes fully encapsulate all aspects of pediatric sepsis heterogeneity. To facilitate accurate delineations of pediatric sepsis phenotypes for precision medicine approach, methodological steps and challenges are further highlighted.

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.

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.].

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.

Endothelial Damage in Sepsis: The Importance of Systems Biology

The early diagnosis and appropriate stratification of sepsis continues to be one of the most important challenges in modern medicine. Single isolated biomarkers have not been enough to improve diagnostic and prognostic strategies and to progress toward therapeutic goals. The information generated by the human genome project has allowed a more holistic approach to the problem. The integration of genomics, transcriptomics, proteomics and metabolomics in sepsis has allowed us to progress in the knowledge of new pathways which are pathophysiologically involved in this disease. Thus, we have understood the importance of and complex interaction between the inflammatory response and the endothelium. Understanding the role of important parts of the microcirculation, such as the endothelial glycocalyx and its interaction with the inflammatory response, has provided early recognition elements for clinical practice that allow the rational use of traditional medical interventions in sepsis. This comprehensive approach, which differs from the classical mechanistic approach, uses systems biology to increase the diagnostic and prognostic spectrum of endothelial damage biomarkers in sepsis, and to provide information on new pathways involved in the pathophysiology of the disease. This, in turn, provides tools for perfecting traditional medical interventions, using them at the appropriate times according to the disease's pathophysiological context, while at the same time discovering new and improved therapeutic alternatives. We have the challenge of transferring this ideal scenario to our daily clinical practice to improve our patients' care. The purpose of this article is to provide a general description of the importance of systems biology in integrating the complex interaction between the endothelium and the inflammatory response in sepsis.

Immune System Dysfunction Criteria in Critically Ill Children: The PODIUM Consensus Conference

CONTEXT

Immune system dysfunction is poorly represented in pediatric organ dysfunction definitions.

OBJECTIVE

To evaluate evidence for criteria that define immune system dysfunction in critically ill children and associations with adverse outcomes and develop consensus criteria for the diagnosis of immune system dysfunction in critically ill children.

DATA SOURCES

We conducted electronic searches of PubMed and Embase from January 1992 to January 2020, using medical subject heading terms and text words to define immune system dysfunction and outcomes of interest.

STUDY SELECTION

Studies of critically ill children with an abnormality in leukocyte numbers or function that is currently measurable in the clinical laboratory in which researchers assessed patient-centered outcomes were included. Studies of adults or premature infants, animal studies, reviews and commentaries, case series (≤10 subjects), and studies not published in English with inability to determine eligibility criteria were excluded.

DATA EXTRACTION

Data were abstracted from eligible studies into a standard data extraction form along with risk of bias assessment by a task force member.

RESULTS

We identified the following criteria for immune system dysfunction: (1) peripheral absolute neutrophil count <500 cells/μL, (2) peripheral absolute lymphocyte count <1000 cells/μL, (3) reduction in CD4+ lymphocyte count or percentage of total lymphocytes below age-specific thresholds, (4) monocyte HLA-DR expression <30%, or (5) reduction in ex vivo whole blood lipopolysaccharide-induced TNFα production capacity below manufacturer-provided thresholds.

LIMITATIONS

Many measures of immune system function are currently limited to the research environment.

CONCLUSIONS

We present consensus criteria for the diagnosis of immune system dysfunction in critically ill children.

Pediatric Sepsis Research: Where Are We and Where Are We Going?

Sepsis continues to be one of the leading causes of admission to the Pediatric Intensive Care Unit, representing a great challenge for researchers and healthcare staff. This mini review aims to assess research on pediatric sepsis over the years. Of the 2,698 articles retrieved from the Scopus database, the 100 most cited were selected (50 published since 2000 and 50 published since 2016). The most cited studies, published in the 21st century, are highlighted, with their main findings and perspectives.

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.

Utility of item 9 of the patient health questionnaire in the prospective identification of adolescents at risk of suicide attempt

INTRODUCTION

Previous studies report that item 9 of the Patient Health Questionnaire (PHQ9) is useful for stratifying risk of suicide attempt in adults. This study re-produced the utility of item 9 of PHQ9 in assessing risk of suicide attempt in adolescents.

MATERIALS AND METHODS

Individuals aged 13 to 17 years in 4 health systems with a diagnosis of depression and history of treatment were included. We estimated time to first observed fatal or non-fatal suicide attempt in the 2 years following completion of a PHQ9, stratified by response to item 9.

RESULTS

There were 51,807 PHQ9 questionnaires for 20,363 youth and 861 instances of suicide attempt. Cumulative probability of suicide attempt ranged from approximately 3.3% (95% CI, 3.0 to 3.5%) for those responding "not at all" on item 9 to 10.8% (95% CI, 9.2 to 12.4%) for those responding "nearly every day". These probabilities are more than 3 times higher than previously reported in adults.

CONCLUSION

PHQ item 9 is useful for stratifying risk of suicide attempt in the 2 years following completion of the questionnaire. Monitoring PHQ item 9 over time for patients in treatment for depression can be useful for population health management of adolescents with depression.

Proprotein Convertase Subtilisin/Kexin Type 9 Loss-of-Function Is Detrimental to the Juvenile Host With Septic Shock

OBJECTIVES

Proprotein convertase subtilisin/kexin type 9 is a central regulator of lipid metabolism and has been implicated in regulating the host response to sepsis. Proprotein convertase subtilisin/kexin type 9 loss-of-function is associated with improved sepsis outcomes in the adult host through increased hepatic bacterial clearance. Thus, there is interest in leveraging proprotein convertase subtilisin/kexin type 9 inhibitors as a therapeutic strategy in adults with sepsis. We sought to validate this association in children with septic shock and in a juvenile murine model of sepsis.

DESIGN

Prospectively enrolled cohort of children with septic shock; experimental mice.

SETTING

Seventeen participating institutions; research laboratory.

PATIENTS AND SUBJECTS

Five-hundred twenty-two children with septic shock; juvenile (14 d old) and adult (10-14 wk) mice with constitutive proprotein convertase subtilisin/kexin type 9 null and wildtype control mice (C57BL/6).

INTERVENTIONS

Proprotein convertase subtilisin/kexin type 9 single-nucleotide polymorphisms, serum proprotein convertase subtilisin/kexin type 9, and lipid profiles in patients. Cecal slurry murine model of sepsis; survival studies in juvenile and adult mice, assessment of lipoprotein fractions, bacterial burden, and inflammation in juvenile mice.

MEASUREMENTS AND MAIN RESULTS

PCSK9 loss-of-function genetic variants were independently associated with increased odds of complicated course and mortality in children with septic shock. PCSK9, low-density lipoprotein, and high-density lipoprotein concentrations were lower among patients with complicated course relative to those without. PCSK9 concentrations negatively correlated with proinflammatory cytokine interleukin-8. Proprotein convertase subtilisin/kexin type 9 loss-of-function decreased survival in juvenile mice, but increased survival in adult mice with sepsis. PCSK9 loss-of-function resulted in low lipoproteins and decreased hepatic bacterial burden in juvenile mice.

CONCLUSIONS

In contrast to the adult host, proprotein convertase subtilisin/kexin type 9 loss-of-function is detrimental to the juvenile host with septic shock. PCSK9 loss-of-function, in the context of low lipoproteins, may result in reduced hepatic bacterial clearance in the juvenile host with septic shock. Our data indicate that children should be excluded in sepsis clinical trials involving proprotein convertase subtilisin/kexin type 9 inhibitors.

Sepsis: deriving biological meaning and clinical applications from high-dimensional data

The pathophysiology of sepsis is multi-facetted and highly complex. As sepsis is a leading cause of global mortality that still lacks targeted therapies, increased understanding of its pathogenesis is vital for improving clinical care and outcomes. An increasing number of investigations seeks to unravel the complexity of sepsis through high-dimensional data analysis, enabled by advances in -omics technologies. Here, we summarize progress in the following major -omics fields: genomics, epigenomics, transcriptomics, proteomics, lipidomics, and microbiomics. We describe what these fields can teach us about sepsis, and highlight current trends and future challenges. Finally, we focus on multi-omics integration, and discuss the challenges in deriving biological meaning and clinical applications from these types of data.

Balancing precision versus cohort transcriptomic analysis of acute and recovery phase of viral bronchiolitis

Viral infections affecting the lower respiratory tract place enormous burdens on hospitals. As neither vaccines nor antiviral agents exist for many viruses, understanding risk factors and outcomes in each patient using minimally invasive analysis, such as blood, can lead to improved health care delivery. A cohort of PAXgene RNA sequencing of infants admitted with moderate or severe acute bronchiolitis and respiratory syncytial virus were compared with case-control statistical analysis and cohort-based outlier mapping for precision transcriptomics. Patients with severe bronchiolitis had signatures connected to the immune system, interferon signaling, and cytokine signaling, with marked sex differences in XIST, RPS4Y1, KDM5D, and LINC00278 for severity. Several patients had unique secondary infections, cytokine activation, immune responses, biological pathways, and immune cell activation, highlighting the need for defining patient-level transcriptomic signatures. Balancing relative contributions of cohort-based biomarker discoveries with patient's biological responses is needed to understand the totality of mechanisms of adverse outcomes in viral bronchiolitis.

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

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

Biomarkers for Estimating Risk of Hospital Mortality and Long-Term Quality-of-Life Morbidity After Surviving Pediatric Septic Shock: A Secondary Analysis of the Life After Pediatric Sepsis Evaluation Investigation

OBJECTIVES

The Life After Pediatric Sepsis Evaluation investigation recently reported that one-third of children who survive sepsis experience significant health-related quality-of-life impairment compared with baseline at 1 year after hospitalization. Pediatric Sepsis Biomarker Risk Model is a multibiomarker tool for estimating baseline risk of mortality among children with septic shock. We determined if the Pediatric Sepsis Biomarker Risk Model biomarkers have predictive capacity for estimating the risk of hospital mortality and long-term health-related quality-of-life morbidity among children with community-acquired septic shock.

DESIGN

Secondary analysis.

SETTING

Twelve academic PICUs.

PATIENTS

A subset of Life After Pediatric Sepsis Evaluation subjects (n = 173) with available blood samples.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Three predefined outcomes from the Life After Pediatric Sepsis Evaluation investigation were evaluated: all-cause hospital mortality (n = 173), and the composite outcome of mortality or persistent, serious deterioration of health-related quality of life (> 25% below baseline) among surviving children at 1 month (n = 125) or 3 months (n = 117). Pediatric Sepsis Biomarker Risk Model had an area under the receiver operating characteristic curve of 0.73 (95% CI, 0.59-0.87; p = 0.002) for estimating the risk of hospital mortality and was independently associated with increased odds of hospital mortality. In multivariable analyses, Pediatric Sepsis Biomarker Risk Model was not independently associated with increased odds of the composite outcome of mortality or deterioration of persistent, serious deterioration health-related quality of life greater than 25% below baseline. A new decision tree using the Pediatric Sepsis Biomarker Risk Model biomarkers had an area under the receiver operating characteristic curve of 0.87 (95% CI, 0.80-0.95) for estimating the risk of persistent, serious deterioration health-related quality of life at 3 months among children who survived septic shock.

CONCLUSIONS

Pediatric Sepsis Biomarker Risk Model had modest performance for estimating hospital mortality in an external cohort of children with community-acquired septic shock. The Pediatric Sepsis Biomarker Risk Model biomarkers appear to have utility for estimating the risk of persistent, serious deterioration of health-related quality of life up to 3 months after surviving septic shock. These findings suggest an opportunity to develop a clinical tool for early assignment of risk for long-term health-related quality-of-life morbidity among children who survive septic shock.

Peripheral blood transcriptomic sub-phenotypes of pediatric acute respiratory distress syndrome

Background

Acute respiratory distress syndrome (ARDS) is heterogeneous and may be amenable to sub-phenotyping to improve enrichment for trials. We aimed to identify subtypes of pediatric ARDS based on whole blood transcriptomics.

Methods

This was a prospective observational study of children with ARDS at the Children’s Hospital of Philadelphia (CHOP) between January 2018 and June 2019. We collected blood within 24 h of ARDS onset, generated expression profiles, and performed k-means clustering to identify sub-phenotypes. We tested the association between sub-phenotypes and PICU mortality and ventilator-free days at 28 days using multivariable logistic and competing risk regression, respectively.

Results

We enrolled 106 subjects, of whom 96 had usable samples. We identified three sub-phenotypes, dubbed CHOP ARDS Transcriptomic Subtypes (CATS) 1, 2, and 3. CATS-1 subjects (n = 31) demonstrated persistent hypoxemia, had ten subjects (32%) with immunocompromising conditions, and 32% mortality. CATS-2 subjects (n = 29) had more immunocompromising diagnoses (48%), rapidly resolving hypoxemia, and 24% mortality. CATS-3 subjects (n = 36) had the fewest comorbidities and also had rapidly resolving hypoxemia and 8% mortality. The CATS-3 subtype was associated with lower mortality (OR 0.18, 95% CI 0.04–0.86) and higher probability of extubation (subdistribution HR 2.39, 95% CI 1.32–4.32), relative to CATS-1 after adjustment for confounders.

Conclusions

We identified three sub-phenotypes of pediatric ARDS using whole blood transcriptomics. The sub-phenotypes had divergent clinical characteristics and prognoses. Further studies should validate these findings and investigate mechanisms underlying differences between sub-phenotypes.

Big Data Skills Sustainable Development in Healthcare and Pharmaceuticals

Big Data technology is one of the most promising organizational processes within the Healthcare and Pharmaceutical industry and crucial for any company that wants to preserve the competitive advantage in the market, where most of the organizational structures are already struggling with the right skills and knowledge to fully support existing business needs for storing and processing and even analyzing information. This paper aims to examine the extent to which new Big Data technology and data-related processes are developing different professionals skills and competencies within the Healthcare and Pharmaceutical industries, and creating sustainable development in addressing critical organizational challenges in recruiting, retaining, and discover professional skills that can fully support the advances and exponential growth of Big Data technology benefits. This research paper also highlights the significant aspects of Big Data in professional technical and process oriented skills development, and the influence it has on organizational business processes including how various internal functions will need to adapt to new circumstances with renewed competency and skills development programs for departments that are strongly connected to the business and analytical needs. We conducted a focus group with twenty-five industry based professionals' ranges from analysts to executive directors to better assess the necessary knowledge to answer the proposed research questions: (1) which professional skills can big data influence in employee development and (2) how can organizations adapt their employee skills to big data. Regarding the key research limitations/implications most of the article and research was built on the foundation of the literature review and the performed focus group. The conceptual recommendations and observations presented provide solid empirical evidence but should be subjected to more comprehensive, large-scale empirical testing and validation. It's recommended for future research a more extensive sample of companies, organizations, and interviewees. Studying a broader set of similar research questions in more homogeneous organizations could provide deeper insights into the process, governance, and stakeholder dimensions of Big Data within specific contexts. Therefore this study contributes to explore in-depth and systematically to what extent Big Data technology and processes are currently influencing the healthcare and pharmaceuticals industries where to the best of the authors' knowledge, it is the first focus group dealing with the presented research questions.

Updates on pediatric sepsis

Sepsis, defined as an infection with dysregulated host response leading to life-threatening organ dysfunction, continues to carry a high potential for morbidity and mortality in children. The recognition of sepsis in children in the emergency department (ED) can be challenging, related to the high prevalence of common febrile infections, poor specificity of discriminating features, and the capacity of children to compensate until advanced stages of shock. Sepsis outcomes are strongly dependent on the timeliness of recognition and treatment, which has led to the successful implementation of quality improvement programs, increasing the reliability of sepsis treatment in many US institutions. We review clinical, laboratory, and technical modalities that can be incorporated into ED practice to facilitate the recognition, treatment, and reassessment of children with suspected sepsis. The 2020 updated pediatric sepsis guidelines are reviewed and framed in the context of ED interventions, including guidelines for antibiotic administration, fluid resuscitation, and the use of vasoactive agents. Despite a large body of literature on pediatric sepsis epidemiology in recent years, the evidence base for treatment and management components remains limited, implying an urgent need for large trials in this field. In conclusion, although the burden and impact of pediatric sepsis remains substantial, progress in our understanding of the disease and its management have led to revised guidelines and the available data emphasizes the importance of local quality improvement programs.

Prospective clinical testing and experimental validation of the Pediatric Sepsis Biomarker Risk Model

Sepsis remains a major public health problem with no major therapeutic advances over the last several decades. The clinical and biological heterogeneity of sepsis have limited success of potential new therapies. Accordingly, there is considerable interest in developing a precision medicine approach to inform more rational development, testing, and targeting of new therapies. We previously developed the Pediatric Sepsis Biomarker Risk Model (PERSEVERE) to estimate mortality risk and proposed its use as a prognostic enrichment tool in sepsis clinical trials; prognostic enrichment selects patients based on mortality risk independent of treatment. Here, we show that PERSEVERE has excellent performance in a diverse cohort of children with septic shock with potential for use as a predictive enrichment strategy; predictive enrichment selects patients based on likely response to treatment. We demonstrate that the PERSEVERE biomarkers are reliably associated with mortality in mice challenged with experimental sepsis, thus providing an opportunity to test precision medicine strategies in the preclinical setting. Using this model, we tested two clinically feasible therapeutic strategies, guided by the PERSEVERE-based enrichment, and found that mice identified as high risk for mortality had a greater bacterial burden and could be rescued by higher doses of antibiotics. The association between higher pathogen burden and higher mortality risk was corroborated among critically ill children with septic shock. This bedside to bench to bedside approach provides proof of principle for PERSEVERE-guided application of precision medicine in sepsis.

Machine learning based refined differential gene expression analysis of pediatric sepsis

Background

Differential expression (DE) analysis of transcriptomic data enables genome-wide analysis of gene expression changes associated with biological conditions of interest. Such analysis often provides a wide list of genes that are differentially expressed between two or more groups. In general, identified differentially expressed genes (DEGs) can be subject to further downstream analysis for obtaining more biological insights such as determining enriched functional pathways or gene ontologies. Furthermore, DEGs are treated as candidate biomarkers and a small set of DEGs might be identified as biomarkers using either biological knowledge or data-driven approaches.

Methods

In this work, we present a novel approach for identifying biomarkers from a list of DEGs by re-ranking them according to the Minimum Redundancy Maximum Relevance (MRMR) criteria using repeated cross-validation feature selection procedure.

Results

Using gene expression profiles for 199 children with sepsis and septic shock, we identify 108 DEGs and propose a 10-gene signature for reliably predicting pediatric sepsis mortality with an estimated Area Under ROC Curve (AUC) score of 0.89.

Conclusions

Machine learning based refinement of DE analysis is a promising tool for prioritizing DEGs and discovering biomarkers from gene expression profiles. Moreover, our reported 10-gene signature for pediatric sepsis mortality may facilitate the development of reliable diagnosis and prognosis biomarkers for sepsis.

Biomarkers in pediatric sepsis: a review of recent literature

Sepsis remains the leading cause of death in infants and children worldwide. Prompt diagnosis and monitoring of infection is pivotal to guide therapy and optimize outcomes. No single biomarker has so far been identified to accurately diagnose sepsis, monitor response and predict severity. We aimed to assess existing evidence of available sepsis biomarkers, and their utility in pediatric population. C-reactive protein and procalcitonin remain the most extensively evaluated and used biomarkers. However, biomarkers related to endothelial damage, vasodilation, oxidative stress, cytokines/chemokines and cell bioproducts have also been identified, often with regard to the site of infection and etiologic pathogen; still, with controversial utility. A multi-biomarker model driven by genomic tools could establish a personalized approach in future disease management.

Biomarker Panels in Critical Care

Biomarker panels have the potential to advance the field of critical care medicine by stratifying patients according to prognosis and/or underlying pathophysiology. This article discusses the discovery and validation of biomarker panels, along with their translation to the clinical setting. The current literature on the use of biomarker panels in sepsis, acute respiratory distress syndrome, and acute kidney injury is reviewed.

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.

Differential gene expression analysis reveals novel genes and pathways in pediatric septic shock patients

Septic shock is a devastating health condition caused by uncontrolled sepsis. Advancements in high-throughput sequencing techniques have increased the number of potential genetic biomarkers under review. Multiple genetic markers and functional pathways play a part in development and progression of pediatric septic shock. We identified 53 differentially expressed pediatric septic shock biomarkers using gene expression data sampled from 181 patients admitted to the pediatric intensive care unit within the first 24 hours of their admission. The gene expression signatures showed discriminatory power between pediatric septic shock survivors and nonsurvivor types. Using functional enrichment analysis of differentially expressed genes, we validated the known genes and pathways in septic shock and identified the unexplored septic shock-related genes and functional groups. Differential gene expression analysis revealed the genes involved in the immune response, chemokine-mediated signaling, neutrophil chemotaxis, and chemokine activity and distinguished the septic shock survivor from non-survivor. The identification of the septic shock gene biomarkers may facilitate in septic shock diagnosis, treatment, and prognosis.

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.

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.

Recognition, Diagnostics, and Management of Pediatric Severe Sepsis and Septic Shock in the Emergency Department

Several new studies have emerged in recent years that have attempted to aid emergency department providers in recognizing and treating pediatric patients with severe sepsis and septic shock. National guidelines and supporting literature are unanimous in recommendations that early recognition and timely therapeutics are necessary for improved survival and decreased morbidity. The literature is less concrete in defining how emerging advances in the field can aid in time-sensitive care of these patients. This article summarizes the recent literature as it pertains to the initial presentation of severe sepsis and septic shock in the pediatric patient within the emergency department.

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.

A clinical prediction model to identify patients at high risk of hemodynamic instability in the pediatric intensive care unit

Background

Early recognition and timely intervention are critical steps for the successful management of shock. The objective of this study was to develop a model to predict requirement for hemodynamic intervention in the pediatric intensive care unit (PICU); thus, clinicians can direct their care to patients likely to benefit from interventions to prevent further deterioration.

Methods

The model proposed in this study was trained on a retrospective cohort of all patients admitted to a tertiary PICU at a single center in the United States, and validated on another retrospective cohort of all patients admitted to the PICU at a single center in the United Kingdom. The PICU clinical information system database (Intellivue Clinical Information Portfolio, Philips, UK) was interrogated to collect physiological and laboratory data. The model was trained using a variant of AdaBoost, which learned a set of low-dimensional classifiers, each of which was age adjusted.

Results

A total of 7052 patients admitted to the US PICU was used for training the model, and a total of 970 patients admitted to the UK PICU was used for validation. On the training/validation datasets, the model showed better prediction of hemodynamic intervention (area under the receiver operating characteristic (AUROC) = 0.81/0.81) than systolic blood pressure-based (AUCROC = 0.58/0.67) or shock index-based (AUCROC = 0.63/0.65) models. Both of these models were age adjusted using the same classifier.

Conclusions

The proposed model reliably predicted the need for hemodynamic intervention in PICU patients and provides better classification performance when compared to systolic blood pressure-based or shock index-based models alone. This model could readily be built into a clinical information system to identify patients at risk of hemodynamic instability.

Electronic supplementary material

The online version of this article (doi:10.1186/s13054-017-1874-z) contains supplementary material, which is available to authorized users.

Biomarkers in Sepsis

A biomarker is a characteristic by which a (patho)physiologic process can be identified. Biomarkers can be of diagnostic value (to discriminate infection from noninfectious conditions or to determine the causative pathogen), of prognostic value (assigning risk profiles and predict outcome), and in the future may be of theranostic value (aid in selection and monitoring of therapy). Systems biology provides a promising tool for the discovery of novel biomarkers. Biomarkers can be the key to personalized targeted treatment in the future clinical management of sepsis.