Testing the prognostic accuracy of the updated pediatric sepsis biomarker risk model

Biomarker-Based Risk Stratification Tool in Pediatric Acute Respiratory Distress Syndrome: Single-Center, Longitudinal Validation in a 2014-2019 Cohort

OBJECTIVES

The Pediatric Acute Respiratory Distress Syndrome Biomarker Risk Model (PARDSEVERE) used age and three plasma biomarkers measured within 24 hours of pediatric acute respiratory distress syndrome (ARDS) onset to predict mortality in a pilot cohort of 152 patients. However, longitudinal performance of PARDSEVERE has not been evaluated, and it is unclear whether the risk model can be used to prognosticate after day 0. We, therefore, sought to determine the test characteristics of PARDSEVERE model and population over the first 7 days after ARDS onset.

DESIGN

Secondary unplanned post hoc analysis of data from a prospective observational cohort study carried out 2014-2019.

SETTING

University-affiliated PICU.

PATIENTS

Mechanically ventilated children with ARDS.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Between July 2014 and December 2019, 279 patients with ARDS had plasma collected at day 0, 266 at day 3 (11 nonsurvivors, two discharged between days 0 and 3), and 207 at day 7 (27 nonsurvivors, 45 discharged between days 3 and 7). The actual prevalence of mortality on days 0, 3, and 7, was 23% (64/279), 14% (38/266), and 13% (27/207), respectively. The PARDSEVERE risk model for mortality on days 0, 3, and 7 had area under the receiver operating characteristic curve (AUROC [95% CI]) of 0.76 (0.69-0.82), 0.68 (0.60-0.76), and 0.74 (0.65-0.83), respectively. The AUROC data translate into prevalence thresholds for the PARDSEVERE model for mortality (i.e., using the sensitivity and specificity values) of 37%, 27%, and 24% on days 0, 3, and 7, respectively. Negative predictive value (NPV) was high throughout (0.87-0.90 for all three-time points).

CONCLUSIONS

In this exploratory analysis of the PARDSEVERE model of mortality risk prediction in a population longitudinal series of data from days 0, 3, and 7 after ARDS diagnosis, the diagnostic performance is in the "acceptable" category. NPV was good. A major limitation is that actual mortality is far below the prevalence threshold for such testing. The model may, therefore, be more useful in cohorts with higher mortality rates (e.g., immunocompromised, other countries), and future enhancements to the model should be explored.

Biomarkers estimating baseline mortality risk for neonatal sepsis: nPERSEVERE: neonate-specific sepsis biomarker risk model

BACKGROUND

Prognostic biomarker research neonatal sepsis is lacking. We assessed the utility of a validated pediatric prognostic tool called PERSEVERE II that uses decision tree methodology to predict mortality at discharge in neonates who experienced sepsis.

METHODS

Prospective study in a dual-center cohort of neonates with sepsis admitted between June 2020 and December 2021. Biomarker analysis was done on serum samples obtained at the time of evaluation for the event.

RESULTS

In a cohort of 59 neonates with a mortality rate of 15.3%, PERSEVERE II was 67% sensitive and 59% specific for mortality, p 0.27. Amongst PERSEVERE II biomarkers, IL-8 showed good prognostic performance for mortality prediction with a cutoff of 300 pg/mL (sensitivity 100%, specificity 65%, negative predictive value 100%, AUC 0.87, p 0.0003). We derived a new decision tree that is neonate specific (nPERSEVERE) with improved performance compared to IL-8 (sensitivity 100%, specificity 86%, negative predictive value 100%, AUC 0.95, p < 0.0001).

CONCLUSIONS

IL-8 and nPERSEVERE demonstrated good prognostic performance in a small cohort of neonates with sepsis. Moving toward precision medicine in sepsis, our study proposes an important tool for clinical trial prognostic enrichment that needs to be validated in larger studies.

IMPACT

Prognostic and predictive biomarker research is lacking in the newborn intensive care unit. Biomarkers can be used at the time of evaluation for neonatal sepsis (blood culture acquisition) to identify neonates with high baseline mortality risk. Stratification is an important step toward precision medicine in neonatal sepsis.

Biomarker-based risk model to predict persistent multiple organ dysfunctions after congenital heart surgery: a prospective observational cohort study

BACKGROUND

Multiple organ dysfunction syndrome (MODS) is an important cause of post-operative morbidity and mortality for children undergoing cardiac surgery requiring cardiopulmonary bypass (CPB). Dysregulated inflammation is widely regarded as a key contributor to bypass-related MODS pathobiology, with considerable overlap of pathways associated with septic shock. The pediatric sepsis biomarker risk model (PERSEVERE) is comprised of seven protein biomarkers of inflammation and reliably predicts baseline risk of mortality and organ dysfunction among critically ill children with septic shock. We aimed to determine if PERSEVERE biomarkers and clinical data could be combined to derive a new model to assess the risk of persistent CPB-related MODS in the early post-operative period.

METHODS

This study included 306 patients < 18 years old admitted to a pediatric cardiac ICU after surgery requiring cardiopulmonary bypass (CPB) for congenital heart disease. Persistent MODS, defined as dysfunction of two or more organ systems on postoperative day 5, was the primary outcome. PERSEVERE biomarkers were collected 4 and 12 h after CPB. Classification and regression tree methodology were used to derive a model to assess the risk of persistent MODS.

RESULTS

The optimal model containing interleukin-8 (IL-8), chemokine ligand 3 (CCL3), and age as predictor variables had an area under the receiver operating characteristic curve (AUROC) of 0.86 (0.81-0.91) for differentiating those with or without persistent MODS and a negative predictive value of 99% (95-100). Ten-fold cross-validation of the model yielded a corrected AUROC of 0.75 (0.68-0.84).

CONCLUSIONS

We present a novel risk prediction model to assess the risk for development of multiple organ dysfunction after pediatric cardiac surgery requiring CPB. Pending prospective validation, our model may facilitate identification of a high-risk cohort to direct interventions and studies aimed at improving outcomes via mitigation of post-operative organ dysfunction.

Identification of a pediatric acute hypoxemic respiratory failure signature in peripheral blood leukocytes at 24 hours post-ICU admission with machine learning

Background

There is no generalizable transcriptomics signature of pediatric acute respiratory distress syndrome. Our goal was to identify a whole blood differential gene expression signature for pediatric acute hypoxemic respiratory failure (AHRF) using transcriptomic microarrays within twenty-four hours of diagnosis. We used publicly available human whole-blood gene expression arrays of a Berlin-defined pediatric acute respiratory distress syndrome (GSE147902) cohort and a sepsis-triggered AHRF (GSE66099) cohort within twenty-four hours of diagnosis and compared those children with a PaO2/FiO2 < 200 to those with a PaO2/FiO2 ≥ 200.

Results

We used stability selection, a bootstrapping method of 100 simulations using logistic regression as a classifier, to select differentially expressed genes associated with a PaO2/FiO2 < 200 vs. PaO2/FiO2 ≥ 200. The top-ranked genes that contributed to the AHRF signature were selected in each dataset. Genes common to both of the top 1,500 ranked gene lists were selected for pathway analysis. Pathway and network analysis was performed using the Pathway Network Analysis Visualizer (PANEV) and Reactome was used to perform an over-representation gene network analysis of the top-ranked genes common to both cohorts. Changes in metabolic pathways involved in energy balance, fundamental cellular processes such as protein translation, mitochondrial function, oxidative stress, immune signaling, and inflammation are differentially regulated early in pediatric ARDS and sepsis-induced AHRF compared to both healthy controls and to milder acute hypoxemia. Specifically, fundamental pathways related to the severity of hypoxemia emerged and included (1) ribosomal and eukaryotic initiation of factor 2 (eIF2) regulation of protein translation and (2) the nutrient, oxygen, and energy sensing pathway, mTOR, activated via PI3K/AKT signaling.

Conclusions

Cellular energetics and metabolic pathways are important mechanisms to consider to further our understanding of the heterogeneity and underlying pathobiology of moderate and severe pediatric acute respiratory distress syndrome. Our findings are hypothesis generating and support the study of metabolic pathways and cellular energetics to understand heterogeneity and underlying pathobiology of moderate and severe acute hypoxemic respiratory failure in children.

Sepsis in the Pediatric Cardiac Intensive Care Unit: An Updated Review

Sepsis remains among the most common causes of mortality in children with congenital heart disease (CHD). Extensive literature is available regarding managing sepsis in pediatric patients without CHD. Because the cardiovascular pathophysiology of children with CHD differs entirely from their typical peers, the available diagnosis and management recommendations for sepsis cannot be implemented directly in children with CHD. This review discusses the risk factors, etiopathogenesis, available diagnostic tools, resuscitation protocols, and anesthetic management of pediatric patients suffering from various congenital cardiac lesions. Further research should focus on establishing a standard guideline for managing children with CHD with sepsis and septic shock admitted to the intensive care unit.

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.

Biomarker-based risk model to predict persistent multiple organ dysfunctions after congenital heart surgery â€" A prospective observational cohort study

Background: Multiple organ dysfunction syndrome (MODS) is an important cause of post-operative morbidity and mortality for children undergoing cardiac surgery requiring cardiopulmonary bypass (CPB). Dysregulated inflammation is widely regarded as a key contributor to bypass-related MODS pathobiology, with considerable overlap of pathways associated with septic shock. The pediatric sepsis biomarker risk model (PERSEVERE) is comprised of seven protein biomarkers of inflammation, and reliably predicts baseline risk of mortality and organ dysfunction among critically ill children with septic shock. We aimed to determine if PERSEVERE biomarkers and clinical data could be combined to derive a new model to assess the risk of persistent CPB-related MODS in the early post-operative period. Methods: This study included 306 patients <18 years old admitted to a pediatric cardiac ICU after surgery requiring cardiopulmonary bypass (CPB) for congenital heart disease. Persistent MODS, defined as dysfunction of two or more organ systems on postoperative day 5, was the primary outcome. PERSEVERE biomarkers were collected 4 and 12 hours after CPB. Classification and Regression Tree methodology was used to derive a model to assess the risk of persistent MODS. Results: The optimal model containing interleukin-8 (IL-8), chemokine ligand 3 (CCL3), and age as predictor variables, had an area under the receiver operating characteristic curve (AUROC) of 0.86 (0.81-0.91) for differentiating those with or without persistent MODS, and a negative predictive value of 99% (95-100). Ten-fold cross-validation of the model yielded a corrected AUROC of 0.75. Conclusions: We present a novel risk prediction model to assess the risk for development of multiple organ dysfunction after pediatric cardiac surgery requiring CPB. Pending prospective validation, our model may facilitate identification of a high-risk cohort to direct interventions and studies aimed at improving outcomes via mitigation of post-operative organ dysfunction. Clinical Trial Registration Number: This study does not meet criteria for a clinical trial per the WHO International Clinical Trials Registry Platform as no intervention was performed.

Presepsin: Hope in the Quest for the Holy Grail

How to cite this article: Samprathi A, Samprathi M, Reddy M. Presepsin: Hope in the Quest for the Holy Grail. Indian J Crit Care Med 2022;26(6):664-666.

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.

Endothelial Dysfunction and Neutrophil Degranulation as Central Events in Sepsis Physiopathology

Sepsis is a major health problem worldwide. It is a time-dependent disease, with a high rate of morbidity and mortality. In this sense, an early diagnosis is essential to reduce these rates. The progressive increase of both the incidence and prevalence of sepsis has translated into a significant socioeconomic burden for health systems. Currently, it is the leading cause of noncoronary mortality worldwide and represents one of the most prevalent pathologies both in hospital emergency services and in intensive care units. In this article, we review the role of both endothelial dysfunction and neutrophil dysregulation in the physiopathology of this disease. The lack of a key symptom in sepsis makes it difficult to obtain a quick and accurate diagnosis of this condition. Thus, it is essential to have fast and reliable diagnostic tools. In this sense, the use of biomarkers can be a very important alternative when it comes to achieving these goals. Both new biomarkers and treatments related to endothelial dysfunction and neutrophil dysregulation deserve to be further investigated in order to open new venues for the diagnosis, treatment and prognosis of sepsis.

Prognostic models will be victims of their own success, unless…

Predictive analytics have begun to change the workflows of healthcare by giving insight into our future health. Deploying prognostic models into clinical workflows should change behavior and motivate interventions that affect outcomes. As users respond to model predictions, downstream characteristics of the data, including the distribution of the outcome, may change. The ever-changing nature of healthcare necessitates maintenance of prognostic models to ensure their longevity. The more effective a model and intervention(s) are at improving outcomes, the faster a model will appear to degrade. Improving outcomes can disrupt the association between the model's predictors and the outcome. Model refitting may not always be the most effective response to these challenges. These problems will need to be mitigated by systematically incorporating interventions into prognostic models and by maintaining robust performance surveillance of models in clinical use. Holistically modeling the outcome and intervention(s) can lead to resilience to future compromises in performance.

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.

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.

Gene Expression Profiles in Children With Suspected Sepsis

STUDY OBJECTIVE

Sepsis recognition is a clinical challenge in children. We aim to determine whether peripheral blood gene expression profiles are associated with pathogen type and sepsis severity in children with suspected sepsis.

METHODS

This was a prospective pilot observational study in a tertiary pediatric emergency department with a convenience sample of children enrolled. Participants were older than 56 days and younger than 18 years, had suspected sepsis, and had not received broad-spectrum antibiotics in the previous 4 hours. Primary outcome was source pathogen, defined as confirmed bacterial source from sterile body fluid or confirmed viral source. Secondary outcome was sepsis severity, defined as maximum therapy required for shock reversal in the first 3 hospital days. We drew peripheral blood for ribonucleic acid isolation at the sepsis protocol activation, obtained gene expression measures with the GeneChip Human Gene 2.0 ST Array, and conducted differential expression analysis.

RESULTS

We collected ribonucleic acid samples from a convenience sample of 122 children with suspected sepsis and 12 healthy controls. We compared the 66 children (54%) with confirmed bacterial or viral infection and found 558 differentially expressed genes, many related to interferon signaling or viral immunity. We did not find statistically significant gene expression differences in patients according to sepsis severity.

CONCLUSION

The study demonstrates feasibility of evaluating gene expression profiling data in children evaluated for sepsis in the pediatric emergency department setting. Our results suggest that gene expression profiling may facilitate identification of source pathogen in children with suspected sepsis, which could ultimately lead to improved tailoring of sepsis treatment and antimicrobial stewardship.

Quantification of Immune Dysregulation by Next-generation Polymerase Chain Reaction to Improve Sepsis Diagnosis in Surgical Patients

OBJECTIVES

To quantify immunological dysfunction in surgical patients with presence/absence of sepsis using a droplet digital polymerase chain reaction (ddPCR) transcriptomic analysis. The study also aims to evaluate this approach for improving identification of sepsis in these patients.

BACKGROUND

Immune dysregulation is a central event in sepsis. Quantification of the expression of immunological genes participating in the pathogenesis of sepsis could represent a new avenue to improve its diagnosis.

METHODS

Expression of 6 neutrophil protease genes (MMP8, OLFM4, LCN2/NGAL, LTF, PRTN3, MPO) and also of 5 genes involved in the immunological synapse (HLA-DRA, CD40LG, CD3E, CD28, ICOS) was quantified in blood from 101 surgical patients with sepsis, 53 uninfected surgical patients, and 16 blood donors by using ddPCR. Areas under receiver operating characteristic curves (AUROC) and multivariate regression analysis were employed to test individual genes and gene ratios to identify sepsis, in comparison with procalcitonin.

RESULTS

Sepsis-induced overexpression of neutrophil protease genes and depressed expression of immunological synapse genes. MMP8/HLA-DRA, LCN2/HLA-DRA outperformed procalcitonin in differentiating between patients with sepsis and surgical controls in the AUROC analysis: LCN2/HLA-DRA: 0.90 (0.85-0.96), MMP8/HLA-DRA: 0.89 (0.84-0.95), procalcitonin: 0.80 (0.73-0.88) (AUROC, confidence interval 95%), and also in the multivariate analysis: LCN2/HLA-DRA: 8.57 (2.25-32.62); MMP8/HLA-DRA: 8.03 (2.10-30.76), procalcitonin: 4.20 (1.15-15.43) [odds ratio (confidence interval 95%)]. Gene expression levels of HLA-DRA were an independent marker of hospital mortality.

CONCLUSIONS

Quantifying the transcriptomic ratios MMP8/HLA-DRA, LCN2/HLA-DRA by ddPCR is a promising approach to improve sepsis diagnosis in surgical patients.

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.

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.

The Pediatric Sepsis Biomarker Risk Model (PERSEVERE) Biomarkers Predict Clinical Deterioration and Mortality in Immunocompromised Children Evaluated for Infection

Pediatric sepsis and bacterial infection cause significant morbidity and mortality worldwide, with immunocompromised patients being at particularly high risk of rapid deterioration and death. This study evaluated if PERSEVERE, PERSEVERE-II, or the PERSEVERE biomarkers, can reliably estimate the risk of clinical deterioration and 28-day mortality among immunocompromised pediatric patients. This is a single-center prospective cohort study conducted from July 2016 through September 2017 incorporating 400 episodes of suspected bacterial infection from the inpatient units at Cincinnati Children’s Hospital Medical Center, a large, tertiary care children’s hospital. The primary analysis assessed clinical deterioration within 72 hours of evaluation for infection. Secondarily, we assessed 28-day mortality. Clinical deterioration was seen in 15% of subjects. Twenty-eight day mortality was 5%, but significantly higher among critically ill patients. Neither PERSEVERE nor PERSEVERE-II performed well to predict clinical deterioration or 28-day mortality, thus we derived new stratification models using the PERSEVERE biomarkers with both high sensitivity and negative predictive value. In conclusion, we evaluated previously validated biomarker risk models in a novel population of largely non-critically ill immunocompromised pediatric patients, and attempted to stratify patients based on a new outcome metric, clinical deterioration. The new highly predictive models indicate common physiologic pathways to clinical deterioration or death from bacterial infection.

Sepsis Biomarkers

Sepsis-related biomarkers have a variety of potential applications. The most well-known application is to differentiate patients with signs of systemic inflammation caused by infection, from those with systemic inflammation due to a non-infectious cause. This application is important for timely and judicious prescription of antibiotics. Apart from diagnostic applications, biomarkers can also be used to identify patients with sepsis who are at risk for poor outcome and to subgroup patients with sepsis based on biological commonalities. The latter two applications embody the concepts of prognostic and predictive enrichment, which are fundamental to precision medicine. This review will elaborate on these concepts, provide relevant examples, and discuss important considerations in the process of biomarkers discovery and development.

An Enrichment Strategy For Sepsis Clinical Trials

INTRODUCTION

Selecting participants for research based on their risk is an enrichment strategy with potential for enhancing clinical trials in sepsis. Adult Septic Shock Information and Stratification (ASSIST) is a tool for estimating mortality risk that incorporates a panel of biomarkers, age, lactate, and chronic health status. We assessed the utility of ASSIST as an enrichment strategy in a clinical trial testing the efficacy of a polyclonal antitumor necrosis factor-α fragment antibody (AZD9773) in adults with severe sepsis or septic shock. We hypothesized that the effects of AZD9773 are dependent on baseline mortality risk, as estimated by ASSIST.

METHODS

This was a post hoc analysis of a recently completed trial of 300 subjects, randomized to placebo, low-dose AZD9773, or high-dose AZD9773. The study's primary endpoint was number of ventilator-free days. There were 286 subjects with available plasma samples from study entry. We measured the ASSIST biomarkers and assigned each subject to low, intermediate, and high-risk strata based on their ASSIST mortality probability. To mirror the study's original statistical plan, we estimated the least squares mean ventilator-free days for each study arm within risk strata.

RESULTS

The effect of study arm within ASSIST-based risk strata was significant (P = 0.017). Within the low-risk group, there was an increase in ventilator-free days for both drug arms. Within the intermediate-risk group, there was an increase in ventilator-free days among those in the low-dose arm, but a decrease in the high-dose arm. Among high-risk patients, there was a decrease of ventilator-free days in both drug arms. Analogous associations were observed when modeling 28-day mortality.

CONCLUSIONS

In this study, a beneficial effect of AZD9773 might have been observed if the trial selected low to intermediate-risk patients. ASSIST has the potential to serve as an enrichment tool for sepsis clinical trials.

Adaptation of a Biomarker-Based Sepsis Mortality Risk Stratification Tool for Pediatric Acute Respiratory Distress Syndrome

OBJECTIVES

The original Pediatric Sepsis Biomarker Risk Model and revised (Pediatric Sepsis Biomarker Risk Model-II) biomarker-based risk prediction models have demonstrated utility for estimating baseline 28-day mortality risk in pediatric sepsis. Given the paucity of prediction tools in pediatric acute respiratory distress syndrome, and given the overlapping pathophysiology between sepsis and acute respiratory distress syndrome, we tested the utility of Pediatric Sepsis Biomarker Risk Model and Pediatric Sepsis Biomarker Risk Model-II for mortality prediction in a cohort of pediatric acute respiratory distress syndrome, with an a priori plan to revise the model if these existing models performed poorly.

DESIGN

Prospective observational cohort study.

SETTING

University affiliated PICU.

PATIENTS

Mechanically ventilated children with acute respiratory distress syndrome.

INTERVENTIONS

Blood collection within 24 hours of acute respiratory distress syndrome onset and biomarker measurements.

MEASUREMENTS AND MAIN RESULTS

In 152 children with acute respiratory distress syndrome, Pediatric Sepsis Biomarker Risk Model performed poorly and Pediatric Sepsis Biomarker Risk Model-II performed modestly (areas under receiver operating characteristic curve of 0.61 and 0.76, respectively). Therefore, we randomly selected 80% of the cohort (n = 122) to rederive a risk prediction model for pediatric acute respiratory distress syndrome. We used classification and regression tree methodology, considering the Pediatric Sepsis Biomarker Risk Model biomarkers in addition to variables relevant to acute respiratory distress syndrome. The final model was comprised of three biomarkers and age, and more accurately estimated baseline mortality risk (area under receiver operating characteristic curve 0.85, p < 0.001 and p = 0.053 compared with Pediatric Sepsis Biomarker Risk Model and Pediatric Sepsis Biomarker Risk Model-II, respectively). The model was tested in the remaining 20% of subjects (n = 30) and demonstrated similar test characteristics.

CONCLUSIONS

A validated, biomarker-based risk stratification tool designed for pediatric sepsis was adapted for use in pediatric acute respiratory distress syndrome. The newly derived Pediatric Acute Respiratory Distress Syndrome Biomarker Risk Model demonstrates good test characteristics internally and requires external validation in a larger cohort. Tools such as Pediatric Acute Respiratory Distress Syndrome Biomarker Risk Model have the potential to provide improved risk stratification and prognostic enrichment for future trials in pediatric acute respiratory distress syndrome.

Curcumin suppresses inflammatory cytokines and heat shock protein 70 release and improves metabolic parameters during experimental sepsis

CONTEXT

Curcumin has been reported to have anti-inflammatory, antioxidant and hypoglycaemic properties, besides reducing mortality in sepsis.

OBJECTIVE

This study evaluates the biological activities of a curcumin dispersion formulated by spray-drying in experimental sepsis.

MATERIALS AND METHODS

Male Wistar rats were subjected to sepsis by caecal ligation and puncture (CLP), controls were sham operated. The animals were treated with curcumin dispersion (100 mg/kg, p.o.) or water for 7 days prior to CLP and at 2 h after surgery. One group was used to analyze curcumin absorption through HPLC; another had the survival rate assessed during 48 h; and from a third group, blood was collected by decapitation to analyze metabolic and inflammatory parameters.

RESULTS

The plasma curcumin levels reached 2.5 ng/mL at 4 h, dropped significantly (p < 0.001) at 6 h (1.2 ng/mL), and were undetectable at 24 h in both groups. Curcumin temporarily increased the survival rate of the septic rats by 20%. Moreover, it attenuated glycaemia (p < 0.05) and volemia (p < 0.05) alterations typically observed during sepsis, and decreased the levels of the proinflammatory cytokines IL-1β and IL-6 in plasma (p < 0.001) and peritoneal lavage fluid (p < 0.05) of septic rats. Serum HSP70 levels were decreased (p < 0.01) at 24 h after CLP.

DISCUSSION AND CONCLUSION

Our results show that the curcumin dispersion dose employed was not detrimental to the septic rats. In fact, it temporarily increased their survival rate, improved important metabolic parameters, reduced proinflammatory cytokines and HSP70 production.

Procalcitonin and Pancreatic Stone Protein Function as Biomarkers in Early Diagnosis of Pediatric Acute Osteomyelitis

Background

High plasma levels of procalcitonin (PCT) are typically seen in children with severe bacterial infection, particularly in cases of septic shock or bacteremia. Similarly, pancreatic stone protein (PSP) is associated with inflammation, infection, and other disease-related stimuli. However, the prognostic value of PSP in critically ill pediatric patients is unknown. This study investigated the early diagnostic value of PCT and PSP in pediatric acute osteomyelitis.

Material/Methods

A total of 187 patients with suspected acute osteomyelitis and 80 healthy control children were enrolled. The serum expression of PTC and PSP was measured. Pearson correlation analysis was conducted to correlate PTC with PSP. ROC analysis was used to test the value of PTC and PSP in early diagnosis of pediatric acute osteomyelitis.

Results

Acute osteomyelitis was diagnosed in 49.2% of the patients (n=92) based on the layered bone puncture. The serum levels of PTC and PSP in pediatric acute osteomyelitis were higher than in the non-acute osteomyelitis group (P<0.01). Serum PTC concentrations showed a significantly positive correlation with PSP levels (P<0.001). ROC analysis showed that the AUC values of PTC and PSP were 0.767 (95% CI, 0.700–0.826), and 0.796 (95% CI, 0.731–0.855), respectively. The AUC value of PTC & PSP was 0.903 (95% CI: 0.851–0.941), which was markedly increased compared with PTC or PSP (P<0.01).

Conclusions

Serum levels of PCT and PSP are promising biomarkers for early diagnosis of pediatric acute osteomyelitis.

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

RATIONALE

We previously derived and validated the Pediatric Sepsis Biomarker Risk Model (PERSEVERE) to estimate baseline mortality risk in children with septic shock. The PERSEVERE biomarkers are serum proteins selected from among the proteins directly related to 80 mortality risk assessment genes. The initial approach to selecting the PERSEVERE biomarkers left 68 genes unconsidered.

OBJECTIVES

To determine if the 68 previously unconsidered genes can improve upon the performance of PERSEVERE and to provide biological information regarding the pathophysiology of septic shock.

METHODS

We reduced the number of variables by determining the biological linkage of the 68 previously unconsidered genes. The genes identified through variable reduction were combined with the PERSEVERE-based mortality probability to derive a risk stratification model for 28-day mortality using classification and regression tree methodology (n = 307). The derived tree, PERSEVERE-XP, was then tested in a separate cohort (n = 77).

MEASUREMENTS AND MAIN RESULTS

Variable reduction revealed a network consisting of 18 mortality risk assessment genes related to tumor protein 53 (TP53). In the derivation cohort, PERSEVERE-XP had an area under the receiver operating characteristic curve (AUC) of 0.90 (95% confidence interval, 0.85-0.95) for differentiating between survivors and nonsurvivors. In the test cohort, the AUC was 0.96 (95% confidence interval, 0.91-1.0). The AUC of PERSEVERE-XP was superior to that of PERSEVERE.

CONCLUSIONS

PERSEVERE-XP combines protein and mRNA biomarkers to provide mortality risk stratification with possible clinical utility. PERSEVERE-XP significantly improves on PERSEVERE and suggests a role for TP53-related cellular division, repair, and metabolism in the pathophysiology of septic shock.

Use of biomarkers in pediatric sepsis: literature review

Despite advances in recent years, sepsis is still a leading cause of hospitalization and mortality in infants and children. The presence of biomarkers during the response to an infectious insult makes it possible to use such biomarkers in screening, diagnosis, prognosis (risk stratification), monitoring of therapeutic response, and rational use of antibiotics (for example, the determination of adequate treatment length). Studies of biomarkers in sepsis in children are still relatively scarce. This review addresses the use of biomarkers in sepsis in pediatric patients with emphasis on C-reactive protein, procalcitonin, interleukins 6, 8, and 18, human neutrophil gelatinase, and proadrenomedullin. Assessment of these biomarkers may be useful in the management of pediatric sepsis.

Biotunable Nanoplasmonic Filter on Few-Layer MoS2 for Rapid and Highly Sensitive Cytokine Optoelectronic Immunosensing

Monitoring of the time-varying immune status of a diseased host often requires rapid and sensitive detection of cytokines. Metallic nanoparticle-based localized surface plasmon resonance (LSPR) biosensors hold promise to meet this clinical need by permitting label-free detection of target biomolecules. These biosensors, however, continue to suffer from relatively low sensitivity as compared to conventional immunoassay methods that involve labeling processes. Their response speeds also need to be further improved to enable rapid cytokine quantification for critical care in a timely manner. In this paper, we report an immunobiosensing device integrating a biotunable nanoplasmonic optical filter and a highly sensitive few-layer molybdenum disulfide (MoS₂) photoconductive component, which can serve as a generic device platform to meet the need of rapid cytokine detection with high sensitivity. The nanoplasmonic filter consists of anticytokine antibody-conjugated gold nanoparticles on a SiO₂ thin layer that is placed 170 μm above a few-layer MoS₂ photoconductive flake device. The principle of the biosensor operation is based on tuning the delivery of incident light to the few-layer MoS₂ photoconductive flake thorough the nanoplasmonic filter by means of biomolecular surface binding-induced LSPR shifts. The tuning is dependent on cytokine concentration on the nanoplasmonic filter and optoelectronically detected by the few-layer MoS₂ device. Using the developed optoelectronic biosensor, we have demonstrated label-free detection of IL-1β, a pro-inflammatory cytokine, with a detection limit as low as 250 fg/mL (14 fM), a large dynamic range of 10⁶, and a short assay time of 10 min. The presented biosensing approach could be further developed and generalized for point-of-care diagnosis, wearable bio/chemical sensing, and environmental monitoring.

Pediatric Sepsis Biomarker Risk Model-II: Redefining the Pediatric Sepsis Biomarker Risk Model With Septic Shock Phenotype

OBJECTIVE

The Pediatric Sepsis Biomarker Risk Model (PERSEVERE), a pediatric sepsis risk model, uses biomarkers to estimate baseline mortality risk for pediatric septic shock. It is unknown how PERSEVERE performs within distinct septic shock phenotypes. We tested PERSEVERE in children with septic shock and thrombocytopenia-associated multiple organ failure (TAMOF), and in those without new onset thrombocytopenia but with multiple organ failure (MOF).

DESIGN

PERSEVERE-based mortality risk was generated for each study subject (n = 660). A priori, we determined that if PERSEVERE did not perform well in both the TAMOF and the MOF cohorts, we would revise PERSEVERE to incorporate admission platelet counts.

SETTING

Multiple PICUs in the United States.

INTERVENTIONS

Standard care.

MEASUREMENTS AND MAIN RESULTS

PERSEVERE performed well in the TAMOF cohort (areas under the receiver operating characteristic curves [AUC], 0.84 [95% CI, 0.77-0.90]), but less well in the MOF cohort (AUC, 0.71 [0.61-0.80]). PERSEVERE was revised using 424 subjects previously reported in the derivation phase. PERSEVERE-II had an AUC of 0.89 (0.85-0.93) and performed equally well across TAMOF and MOF cohorts. PERSEVERE-II performed well when tested in 236 newly enrolled subjects. Sample size calculations for a clinical trial testing the efficacy of plasma exchange for children with septic shock and TAMOF indicated PERSEVERE-II-based stratification could substantially reduce the number of patients necessary, when compared with no stratification.

CONCLUSIONS

Testing PERSEVERE in the context of septic shock phenotypes prompted a revision incorporating platelet count. PERSEVERE-II performs well upon testing, independent of TAMOF or MOF status. PERSEVERE-II could potentially serve as a prognostic enrichment tool.

[Predictive value of miRNA-29a and miRNA-10a-5p for 28-day mortality in patients with sepsis-induced acute kidney injury]

OBJECTIVE

To investigate the value of miR-29a and miR-10a-5p in predicting 28-day mortality in patients with sepsis-induced acute kidney injury.

METHODS

Seventy-four patients with sepsis-induced acute kidney injury (AKI) and 41 patients with sepsis but without AKI (control) were examined for serum levels of miR-29a and miR-10a-5p using RT-PCR. The patients were followed up for 28 days to record their survival. Pearson correlation analysis was used to test the correlations of miR-29a and miR-10a-5p with serum creatinine (Scr), cystatin C (Cys-C), and KIM-1 in patients with AKI. Multivariate logistic regression analysis was used to analyze the correlations of miR-29a, miR-10a-5p, Scr, Cys-C, KIM-1 and other risk factors with the 28-day mortality in patients with sepsis. The predictive value of these indicators for evaluating the prognosis of patients with sepsis was analyzed using ROC curve, and miR-29a combined with miR-10a-5p was assessed for their value in predicting the prognosis of the patients.

RESULT

During the follow-up for 28 days, 21 of the 74 (35.53%) AKI patients died. Compared with the survivors, the patients died within 28 days showed significantly increased serum levels of Scr , Cys-C, KIM-1, miR-29a, and miR-10a-5p (P<0.05). Pearson correlation analysis showed that miR-29a and miR-10a-5p were positively correlated with serum Scr, Cys-C, and KIM-1 levels; multivariate regression analysis identified miR-29a and miR-10a-5p as the independent risk factors for mortality in the septic patients. The ROC curve analysis showed that the area under the curve (AUC) of miR-29a and miR-10a-5p was 0.82 (95%CI: 0.71-0.89) and 0.75 (95%CI: 0.64-0.85), and that of Scr, Cys-C and KIM-1 was 0.72 (95%CI: 0.66-0.86) , 0.71 (95% CI: 0.63-0.84) and 0.81 (95% CI: 0.72-0.81), respectively. The AUC of miR-29a combined with miR-10a-5p was significantly greater than that of miR-29a, miR-10a-5p, Scr, Cys-C and KIM-1 alone (P<0.05).

CONCLUSION

miR-29a and miR-10a-5p have good predictive value in assessing the 28-day mortality of patients with sepsis.

[Predictive value of miRNA-29a and miRNA-10a-5p for 28-day mortality in patients with sepsis-induced acute kidney injury]

OBJECTIVE

To investigate the value of miR-29a and miR-10a-5p in predicting 28-day mortality in patients with sepsis-induced acute kidney injury.

METHODS

Seventy-four patients with sepsis-induced acute kidney injury (AKI) and 41 patients with sepsis but without AKI (control) were examined for serum levels of miR-29a and miR-10a-5p using RT-PCR. The patients were followed up for 28 days to record their survival. Pearson correlation analysis was used to test the correlations of miR-29a and miR-10a-5p with serum creatinine (Scr), cystatin C (Cys-C), and KIM-1 in patients with AKI. Multivariate logistic regression analysis was used to analyze the correlations of miR-29a, miR-10a-5p, Scr, Cys-C, KIM-1 and other risk factors with the 28-day mortality in patients with sepsis. The predictive value of these indicators for evaluating the prognosis of patients with sepsis was analyzed using ROC curve, and miR-29a combined with miR-10a-5p was assessed for their value in predicting the prognosis of the patients.

RESULT

During the follow-up for 28 days, 21 of the 74 (35.53%) AKI patients died. Compared with the survivors, the patients died within 28 days showed significantly increased serum levels of Scr , Cys-C, KIM-1, miR-29a, and miR-10a-5p (P<0.05). Pearson correlation analysis showed that miR-29a and miR-10a-5p were positively correlated with serum Scr, Cys-C, and KIM-1 levels; multivariate regression analysis identified miR-29a and miR-10a-5p as the independent risk factors for mortality in the septic patients. The ROC curve analysis showed that the area under the curve (AUC) of miR-29a and miR-10a-5p was 0.82 (95%CI: 0.71-0.89) and 0.75 (95%CI: 0.64-0.85), and that of Scr, Cys-C and KIM-1 was 0.72 (95%CI: 0.66-0.86) , 0.71 (95% CI: 0.63-0.84) and 0.81 (95% CI: 0.72-0.81), respectively. The AUC of miR-29a combined with miR-10a-5p was significantly greater than that of miR-29a, miR-10a-5p, Scr, Cys-C and KIM-1 alone (P<0.05).

CONCLUSION

miR-29a and miR-10a-5p have good predictive value in assessing the 28-day mortality of patients with sepsis.

AC Electroosmosis-Enhanced Nanoplasmofluidic Detection of Ultralow-Concentration Cytokine

Label-free, nanoparticle-based plasmonic optical biosensing, combined with device miniaturization and microarray integration, has emerged as a promising approach for rapid, multiplexed biomolecular analysis. However, limited sensitivity prevents the wide use of such integrated label-free nanoplasmonic biosensors in clinical and life science applications where low-abundance biomolecule detection is needed. Here, we present a nanoplasmofluidic device integrated with microelectrodes for rapid, label-free analysis of a low-abundance cell signaling protein, detected by AC electroosmosis-enhanced localized surface plasmon resonance (ACE-LSPR) biofunctional nanoparticle imaging. The ACE-LSPR device is constructed using both bottom-up and top-down sensor fabrication methods, allowing the seamless integration of antibody-conjugated gold nanorod (AuNR) biosensor arrays with microelectrodes on the same microfluidic platform. Applying an AC voltage to microelectrodes while scanning the scattering light intensity variation of the AuNR biosensors results in significantly enhanced biosensing performance. The AC electroosmosis (ACEO) based enhancement of the biosensor performance enables rapid (5-15 min) quantification of IL-1β, a pro-inflammatory cytokine biomarker, with a sensitivity down to 158.5 fg/mL (9.1 fM) for spiked samples in PBS and 1 pg/mL (58 fM) for diluted human serum. Together with the optimized detection sensitivity and speed, our study presents the first critical step toward the application of nanoplasmonic biosensing technology to immune status monitoring guided by low-abundance cytokine measurement.

Prognostic Value of High-Sensitivity C-Reactive Protein, Procalcitonin and Pancreatic Stone Protein in Pediatric Sepsis

Background

To investigate the prognostic value of procalcitonin (PCT), high-sensitivity C-reactive protein (hs-CRP), and pancreatic stone protein (PSP) in children with sepsis.

Material/Methods

A total of 214 patients with sepsis during hospitalization were enrolled. Serum levels of PCT, hs-CRP, and PSP were measured on day 1 of hospitalization and the survival rates of children were recorded after a follow-up of 28 days. Pearson’s correlation analysis was conducted to test the association of PCT, hs-CRP, and PSP with pediatric critical illness score (PCIS). Logistic regression models were used to analyze the risk factors contributing to patients’ death. The AUC was used to determine the value of PCT, hs-CRP, and PSP in the prognosis of patients with sepsis.

Results

The expression of PCT, hs-CRP, and PSP in the dying patients was higher than in the surviving patients (p<0.001). Pearson’s correlation analysis showed that serum PCT, hs-CRP, and PSP levels were negatively correlated with PCIS (p<0.001). Multivariate logistic regression revealed that PCT, hs-CRP, and PSP were independent risk factors for the prognosis of patients with sepsis (p<0.001). ROC analysis showed the AUC values of PCT, hs-CRP, and PSP were 0.83 (95% CI, 0.77–0.88), 0.76 (95% CI, 0.70–0.82), and 0.73 (95% CI, 0.67–0.79), respectively. The combined AUC value of PCT, hs-CRP, and PSP, was 0.92 (95% CI, 0.87–0.95), which was significantly increased compared with PCT, hs-CRP, or PSP (p<0.001).

Conclusions

The combination of serum PCT, hs-CRP, and PSP represents a promising biomarker of risk, and is a useful clinical tool for risk stratification of children with sepsis.

Monitoring Severity of Multiple Organ Dysfunction Syndrome: New Technologies

OBJECTIVE

To describe new technologies (biomarkers and tests) used to assess and monitor the severity and progression of multiple organ dysfunction syndrome in children as discussed as part of the Eunice Kennedy Shriver National Institute of Child Health and Human Development MODS Workshop (March 26-27, 2015).

DATA SOURCES

Literature review, research data, and expert opinion.

STUDY SELECTION

Not applicable.

DATA EXTRACTION

Moderated by an experienced expert from the field, investigators developing and assessing new technologies to improve the care and understanding of critical illness presented their research and the relevant literature.

DATA SYNTHESIS

Summary of presentations and discussion supported and supplemented by relevant literature.

CONCLUSIONS

There are many innovative tools and techniques with the potential application for the assessment and monitoring of severity of multiple organ dysfunction syndrome. If the reliability and added value of these candidate technologies can be established, they hold promise to enhance the understanding, monitoring, and perhaps, treatment of multiple organ dysfunction syndrome in children.

Sepsis in Pediatric Cardiac Intensive Care

OBJECTIVES

In this review, we will discuss risk factors for developing sepsis; the role of biomarkers in establishing an early diagnosis, in monitoring therapeutic efficacy, in stratification, and for the identification of sepsis endotypes; and the pathophysiology and management of severe sepsis and septic shock, with an emphasis on the impact of sepsis on cardiovascular function.

DATA SOURCE

MEDLINE and PubMed.

CONCLUSIONS

There is a lot of excitement in the field of sepsis research today. Scientific advances in the diagnosis and clinical staging of sepsis, as well as a personalized approach to the treatment of sepsis, offer tremendous promise for the future. However, at the same time, it is also evident that sepsis mortality has not improved enough, even with progress in our understanding of the molecular pathophysiology of sepsis.

Development and Validation of a Disease Severity Scoring Model for Pediatric Sepsis

BACKGROUND

Multiple severity scoring systems have been devised and evaluated in adult sepsis, but a simplified scoring model for pediatric sepsis has not yet been developed. This study aimed to develop and validate a new scoring model to stratify the severity of pediatric sepsis, thus assisting the treatment of sepsis in children.

METHODS

Data from 634 consecutive patients who presented with sepsis at Children's hospital of Hunan province in China in 2011-2013 were analyzed, with 476 patients placed in training group and 158 patients in validation group. Stepwise discriminant analysis was used to develop the accurate discriminate model. A simplified scoring model was generated using weightings defined by the discriminate coefficients. The discriminant ability of the model was tested by receiver operating characteristic curves (ROC).

RESULTS

The discriminant analysis showed that prothrombin time, D-dimer, total bilirubin, serum total protein, uric acid, PaO2/FiO2 ratio, myoglobin were associated with severity of sepsis. These seven variables were assigned with values of 4, 3, 3, 4, 3, 3, 3 respectively based on the standardized discriminant coefficients. Patients with higher scores had higher risk of severe sepsis. The areas under ROC (AROC) were 0.836 for accurate discriminate model, and 0.825 for simplified scoring model in validation group.

CONCLUSIONS

The proposed disease severity scoring model for pediatric sepsis showed adequate discriminatory capacity and sufficient accuracy, which has important clinical significance in evaluating the severity of pediatric sepsis and predicting its progress.

Inflammation and innate immune function in critical illness

PURPOSE OF REVIEW

The purpose of review is to highlight the inflammatory response in critical illness and the importance of immune monitoring and modulation in the diagnosis and treatment of critical illness-induced innate immune suppression.

RECENT FINDINGS

The pro and anti-inflammatory responses are known to be concurrently activated in many patients requiring intensive care, with innate immune suppression emerging as an important, and potentially reversible, complication of critical illness.

SUMMARY

The initial inflammatory response to critical illness is typically driven by innate immune cells, including neutrophils, monocytes, and macrophages. The proinflammatory mediators made by these cells are responsible for many of the pathophysiologic features of critical illness. Concurrent with this, however, is a compensatory anti-inflammatory response, including the elaboration of anti-inflammatory mediators and impairment of innate immune cell function. This includes reduction of monocyte human leukocyte antigen-DR expression and impairment of the ability of innate immune cells to produce tumor necrosis factor alpha when stimulated ex vivo. In its most severe form this is referred to as immunoparalysis, and is associated with markedly increased risks for secondary infection and death in the ICU. Prospective testing can detect this phenomenon, and immunostimulatory strategies, including the use of granulocyte macrophage-colony stimulating factor, have the potential to restore innate immune function in this setting.

Innovation in Pediatric Cardiac Intensive Care: An Exponential Convergence Toward Transformation of Care

The word innovation is derived from the Latin noun innovatus, meaning renewal or change. Although companies such as Google and Apple are nearly synonymous with innovation, virtually all sectors in our current lives are imbued with yearn for innovation. This has led to organizational focus on innovative strategies as well as recruitment of chief innovation officers and teams in a myriad of organizations. At times, however, the word innovation seems like an overused cliché, as there are now more than 5,000 books in print with the word "innovation" in the title. More recently, innovation has garnered significant attention in health care. The future of health care is expected to innovate on a large scale in order to deliver sustained value for an overall transformative care. To date, there are no published reports on the state of the art in innovation in pediatric health care and in particular, pediatric cardiac intensive care. This report will address the issue of innovation in pediatric medicine with relevance to cardiac intensive care and delineate possible future directions and strategies in pediatric cardiac intensive care.

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

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

Could Biomarkers Direct Therapy for the Septic Patient?

Sepsis is a serious medical condition caused by a severe systemic inflammatory response to a bacterial, fungal, or viral infection that most commonly affects neonates and the elderly. Advances in understanding the pathophysiology of sepsis have resulted in guidelines for care that have helped reduce the risk of dying from sepsis for both children and older adults. Still, over the past three decades, a large number of clinical trials have been undertaken to evaluate pharmacological agents for sepsis. Unfortunately, all of these trials have failed, with the use of some agents even shown to be harmful. One key issue in these trials was the heterogeneity of the patient population that participated. What has emerged is the need to target therapeutic interventions to the specific patient's underlying pathophysiological processes, rather than looking for a universal therapy that would be effective in a "typical" septic patient, who does not exist. This review supports the concept that identification of the right biomarkers that can direct therapy and provide timely feedback on its effectiveness will enable critical care physicians to decrease mortality of patients with sepsis and improve the quality of life of survivors.

Label-free cytokine micro- and nano-biosensing towards personalized medicine of systemic inflammatory disorders

Systemic inflammatory disorders resulting from infection, trauma, surgery, and severe disease conditions pose serious threats to human health leading to organ dysfunction, organ failure, and mortality. The highly complex and dynamic nature of the immune system experiencing acute inflammation makes immunomodulatory therapy blocking pro-inflammatory cytokines very challenging. Successful therapy requires the ability to determine appropriate anti-cytokine drugs to be delivered at a right dose in a timely manner. Label-free micro- and nano-biosensors hold the potential to overcome the current challenges, enabling cytokine-targeted treatments to be tailored according to the immune status of an individual host with their unique cytokine biomarker detection capabilities. This review studies the recent progress in label-free cytokine biosensors, summarizes their performances and potential merits, and discusses future directions for their advancements to meet challenges towards personalized anti-cytokine drug delivery.

Prospective Testing and Redesign of a Temporal Biomarker Based Risk Model for Patients With Septic Shock: Implications for Septic Shock Biology

The temporal version of the pediatric sepsis biomarker risk model (tPERSEVERE) estimates the risk of a complicated course in children with septic shock based on biomarker changes from days 1 to 3 of septic shock. We validated tPERSEVERE performance in a prospective cohort, with an a priori plan to redesign tPERSEVERE if it did not perform well. Biomarkers were measured in the validation cohort (n = 168) and study subjects were classified according to tPERSEVERE. To redesign tPERSEVERE, the validation cohort and the original derivation cohort (n = 299) were combined and randomly allocated to training (n = 374) and test (n = 93) sets. tPERSEVERE was redesigned using the training set and CART methodology. tPERSEVERE performed poorly in the validation cohort, with an area under the curve (AUC) of 0.67 (95% CI: 0.58–0.75). Failure analysis revealed potential confounders related to clinical characteristics. The redesigned tPERSEVERE model had an AUC of 0.83 (0.79–0.87) and a sensitivity of 93% (68–97) for estimating the risk of a complicated course. Similar performance was seen in the test set. The classification tree segregated patients into two broad endotypes of septic shock characterized by either excessive inflammation or immune suppression.

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We prospectively tested the performance of the temporal version of the pediatric sepsis biomarker risk model (tPERSEVERE).

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tPERSEVERE performed poorly in the test cohort, prompting a redesign.

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The redesigned tPERSEVERE model performed well upon testing.

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The redesigned tPERSEVERE provides information regarding septic shock endotypes.

Septic shock is characterized by individual heterogeneity and it is not known who is at greatest risk of poor outcome and would thus benefit from more aggressive treatment. We designed a biomarker-based model to estimate the risk of poor outcome in children with septic shock. The model measures biomarker concentrations over the early period of disease evolution, and estimates how the biomarker changes reflect changing risk for poor outcome. The model has potential to serve as a monitor to evaluate the effectiveness of therapy in children with septic shock and may provide information regarding the biological mechanisms of septic shock.

Bacteremia in children: epidemiology, clinical diagnosis and antibiotic treatment

The diagnosis of bacteremia in children is important and it can be clinically challenging to recognize the signs and symptoms. The reported rates of bacteremia are higher in young children but with the increasing vaccine coverage, there has been a decrease in bacteremia due to the three vaccine preventable bacteria (Streptococcus pneumoniae, Haemophilus influenzae group b and Neisseria meningitidis). Notably, there have been increases in healthcare-associated bacteremias with a rise in Staphylococcus aureus and Gram negative bacteremias. This review provides a brief overview of the clinical diagnosis of bacteremia in children, focusing on the epidemiology, clinical characteristics, risk factors, antibiotic treatment, outcomes and preventative measures to reduce the incidence of bacteremia and improve morbidity and mortality.