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

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.

Pediatric sepsis inflammatory blood biomarkers that correlate with clinical variables and severity of illness scores

BACKGROUND

Sepsis is a dysregulated systemic inflammatory response triggered by infection, resulting in organ dysfunction. A major challenge in clinical pediatrics is to identify sepsis early and then quickly intervene to reduce morbidity and mortality. As blood biomarkers hold promise as early sepsis diagnostic tools, we aimed to measure a large number of blood inflammatory biomarkers from pediatric sepsis patients to determine their predictive ability, as well as their correlations with clinical variables and illness severity scores.

METHODS

Pediatric patients that met sepsis criteria were enrolled, and clinical data and blood samples were collected. Fifty-eight inflammatory plasma biomarker concentrations were determined using immunoassays. The data were analyzed with both conventional statistics and machine learning.

RESULTS

Twenty sepsis patients were enrolled (median age 13 years), with infectious pathogens identified in 75%. Vasopressors were administered to 85% of patients, while 55% received invasive ventilation and 20% were ventilated non-invasively. A total of 24 inflammatory biomarkers were significantly different between sepsis patients and age/sex-matched healthy controls. Nine biomarkers (IL-6, IL-8, MCP-1, M-CSF, IL-1RA, hyaluronan, HSP70, MMP3, and MMP10) yielded AUC parameters > 0.9 (95% CIs: 0.837-1.000; p < 0.001). Boruta feature reduction yielded 6 critical biomarkers with their relative importance: IL-8 (12.2%), MCP-1 (11.6%), HSP70 (11.6%), hyaluronan (11.5%), M-CSF (11.5%), and IL-6 (11.5%); combinations of 2 biomarkers yielded AUC values of 1.00 (95% CI: 1.00-1.00; p < 0.001). Specific biomarkers strongly correlated with illness severity scoring, as well as other clinical variables. IL-3 specifically distinguished bacterial versus viral infection (p < 0.005).

CONCLUSIONS

Specific inflammatory biomarkers were identified as markers of pediatric sepsis and strongly correlated to both clinical variables and sepsis severity.

Role of diagnostic tests for sepsis in children: a review

Paediatric sepsis has a significant global impact and highly heterogeneous clinical presentation. The clinical pathway encompasses recognition, escalation and de-escalation. In each aspect, diagnostics have a fundamental influence over outcomes in children. Biomarkers can aid in creating a larger low-risk group of children from those in the clinical grey area who would otherwise receive antibiotics 'just in case'. Current biomarkers include C reactive protein and procalcitonin, which are limited in their clinical use to guide appropriate and rapid treatment. Biomarker discovery has focused on single biomarkers, which, so far, have not outperformed current biomarkers, as they fail to recognise the complexity of sepsis. The identification of multiple host biomarkers that may form a panel in a clinical test has the potential to recognise the complexity of sepsis and provide improved diagnostic performance. In this review, we discuss novel biomarkers and novel ways of using existing biomarkers in the assessment and management of sepsis along with the significant challenges in biomarker discovery at present. Validation of biomarkers is made less meaningful due to methodological heterogeneity, including variations in sepsis diagnosis, biomarker cut-off values and patient populations. Therefore, the utilisation of platform studies is necessary to improve the efficiency of biomarkers in clinical practice.

Prediction of attributable mortality in pediatric patients with cancer admitted to the intensive care unit for suspected infection: A comprehensive evaluation of risk scores

OBJECTIVE

To evaluate the performance of existing sepsis scores for prediction of adverse outcomes in children with cancer admitted to the ICU with suspected sepsis.

DESIGN

Retrospective chart review using data available at 1, 6, 12, and 24 h after ICU admission to calculate the Pediatric Risk of Mortality 3 (PRISM-3), Pediatric Sequential Organ Failure Assessment (pSOFA), Paediatric Logistic Organ Dysfunction 2 (PELOD-2), and Quick Pediatric Sequential Organ Failure Assessment (qSOFA) scores. Area under the receiver operator characteristic curve (AUROC) was used to evaluate performance for prediction of attributable mortality. Sensitivity analyses included recalculation of scores using worst preceding values for each variable, excluding hematologic parameters, and prediction of alternative outcomes.

SETTING

St. Jude Children's Research Hospital, a pediatric comprehensive cancer center in the USA.

PATIENTS

Pediatric patients (<25 years of age) receiving conventional therapy for cancer admitted to the ICU with suspected sepsis between 2013 and 2019.

RESULTS

Of 207 included episodes of suspected sepsis, attributable mortality was 16 (7.7%) and all evaluated sepsis scores performed poorly (maximal AUROC of 0.73 for qSOFA at 1 and 24 h). Sensitivity analyses did not identify an alternative approach that significantly improved prediction.

CONCLUSIONS

Currently available sepsis scores perform poorly for prediction of attributable mortality in children with cancer who present to ICU with suspected sepsis. More research is needed to identify reliable predictors of adverse outcomes in this population.

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.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

Serum lactate is associated with increased illness severity in immunocompromised pediatric hematology oncology patients presenting to the emergency department with fever

Introduction

Determining which febrile pediatric hematology/oncology (PHO) patients will decompensate from severe infection is a significant challenge. Serum lactate is a well-established marker of illness severity in general adult and pediatric populations, however its utility in PHO patients is unclear given that chemotherapy, organ dysfunction, and cancer itself can alter lactate metabolism. In this retrospective analysis, we studied the association of initial serum lactate in febrile immunosuppressed PHO patients with illness severity, defined by the incidence of clinical deterioration events (CDE) and invasive bacterial infection (IBI) within 48 hours.

Methods

Receiver operating characteristic (ROC) curves were reported using initial lactate within two hours of arrival as the sole predictor for CDE and IBI within 48 hours. Using a generalized estimating equations (GEE) approach, the association of lactate with CDE and IBI within 48 hours was tested in univariate and multivariable analyses including covariates based on Quasi-likelihood under Independence Model Criterion (QIC). Additionally, the association of lactate with secondary outcomes (i.e., hospital length of stay (LOS), intensive care unit (PICU) admission, PICU LOS, non-invasive infection) was assessed.

Results

Among 897 encounters, 48 encounters had ≥1 CDE (5%), and 96 had ≥1 IBI (11%) within 48 hours. Elevated lactate was associated with increased CDE in univariate (OR 1.77, 95%CI: 1.48-2.12, p<0.001) and multivariable (OR 1.82, 95%CI: 1.43-2.32, p<0.001) analyses, longer hospitalization (OR 1.15, 95%CI: 1.07-1.24, p<0.001), increased PICU admission (OR 1.68, 95%CI: 1.41-2.0, p<0.001), and longer PICU LOS (OR 1.21, 95%CI: 1.04-1.4, p=0.01). Elevated lactate was associated with increased IBI in univariate (OR 1.40, 95%CI: 1.16-1.69, p<0.001) and multivariable (OR 1.49, 95%CI: 1.23-1.79, p<0.001) analyses. Lactate level was not significantly associated with increased odds of non-invasive infection (p=0.09). The QIC of the model was superior with lactate included for both CDE (305 vs. 325) and IBI (563 vs. 579).

Conclusions

These data demonstrated an independent association of elevated initial lactate level and increased illness severity in febrile PHO patients, suggesting that serum lactate could be incorporated into future risk stratification strategies for this population.

Introduction to Personalized Medicine in Pediatrics

Exciting new developments in biomedical and computational sciences provide an extraordinary and unparalleled opportunity to compile, connect, and analyze multiple types of "big data," driving the development of personalized medicine. These insights must begin in early life (ie, pregnancy, neonatal, and infancy) and focus on early prevention, diagnosis, and intervention-areas of medicine where pediatricians are poised to lead the way to a personalized medicine future. The rapid growth of genomics (including pharmacogenomics), transcriptomics, and related "omics" has revolutionized the diagnosis of rare monogenic disorders. It is now clarifying the pathogenesis of complex conditions ranging from autism spectrum disorder to asthma. Collaborations between clinicians and basic scientists integrating multiomics approaches in evaluating children with severe illness are transforming the fields of perinatal, neonatal, and pediatric critical care medicine. Improvements in rapid diagnostic and prognostic information suggest that pediatric personalized medicine is under way and has an exciting future. [Pediatr Ann. 2022;51(10):e381-e386.].

Monocyte Distribution Width in Children With Systemic Inflammatory Response: Retrospective Cohort Examining Association With Early Sepsis

OBJECTIVES

To investigate the association between increased monocyte distribution width (MDW) and pediatric sepsis in the emergency department (ED).

DESIGN

Retrospective cohort study.

SETTING

A single academic hospital study.

PATIENTS

Patients from birth to the age of 18 years who presented at the ED of an academic hospital with systemic inflammatory response syndrome (SIRS) were consecutively enrolled. Sepsis was diagnosed using the International Pediatric Surviving Sepsis Campaign criteria.

INTERVENTIONS

Antibiotic treatment was administrated once infection was suspected.

MEASUREMENTS AND MAIN RESULTS

Routine complete blood cell count, neutrophil-to-lymphocyte ratio (NLR), and MDW, a new inflammatory biomarker, were evaluated in the ED. Logistic regression models were used to explore associations with early pediatric sepsis. We included 201 patients with sepsis and 1,050 without sepsis. In the multivariable model, MDW greater than 23 U (odds ratio [OR], 4.97; 95% CI, 3.42-7.22; p < 0.0001), NLR greater than 6 (OR, 2.06; 95% CI, 1.43-2.94; p = 0.0001), WBC greater than 11,000 cells/µL (OR, 6.52; 95% CI, 4.45-9.53; p < 0.0001), and the SIRS score (OR, 3.42; 95% CI, 2.57-4.55; p < 0.0001) were associated with pediatric sepsis. In subgroup analysis, MDW greater than 23 U remained significantly associated with sepsis for children 6-12 years old (OR, 6.76; 95% CI, 2.60-17.57; p = 0.0001) and 13-18 years (OR, 17.49; 95% CI, 7.69-39.76; p = 0.0001) with an area under the receiver operating curve of 0.8-0.9.

CONCLUSIONS

MDW greater than 23 U at presentation is associated with the early diagnosis of sepsis in children greater than or equal to 6 years old. This parameter should be considered as a stratification variable in studies of pediatric sepsis.

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.

Candidate Biomarkers for the Detection of Serious Infections in Children: A Prospective Clinical Study

Serious bacterial infections (SBI) in children are associated with considerable morbidity and mortality, and their early identification remains challenging. The role of laboratory tests in this setting is still debated, and new biomarkers are needed. This prospective, observational, single-center study aims to evaluate the diagnostic role of blood biomarkers in detecting SBI in children presenting with signs of systemic inflammatory response syndrome (SIRS). A panel of biomarkers was performed, including C-reactive protein (CRP), procalcitonin (PCT), white blood cell count (WBC), absolute neutrophil count (ANC), interleukin (IL)-6, IL-8, IL-10, human terminal complement complex (C5b-9), Plasmalemma-Vesicle-associated protein 1 (PV-1), Intercellular Adhesion Molecule-1 (ICAM-1), and Phospholipase A2 (PLA2). Among 103 patients (median age 2.9 years, 60% males), 39 had a diagnosis of SBI (38%). Significant predictors of SBI were CRP (p = 0.001) and ICAM-1 (p = 0.043). WBC (p = 0.035), ANC (p = 0.012) and ANC/WBC ratio (p = 0.015) were also significantly associated with SBI in children without pre-existing neutropenia. ROC curves, however, revealed suboptimal performance for all variables. Nevertheless, a model that combined CRP and ANC/WBC ratio had more in-depth diagnostic accuracy than either of the two variables. Overall, this study confirms the limited usefulness of blood biomarkers for the early diagnosis of SBI. WBC, ANC, ANC/WBC ratio, CRP, and ICAM-1 showed the best, albeit moderate, diagnostic accuracy.

Global health systems' data science approach for precision diagnosis of sepsis in early life

Neonates and children in low-income and middle-income countries (LMICs) contribute to the highest number of sepsis-associated deaths globally. Interventions to prevent sepsis mortality are hampered by a lack of comprehensive epidemiological data and pathophysiological understanding of biological pathways. In this review, we discuss the challenges faced by LMICs in diagnosing sepsis in these age groups. We highlight a role for multi-omics and health care data to improve diagnostic accuracy of clinical algorithms, arguing that health-care systems urgently need precision medicine to avoid the pitfalls of missed diagnoses, misdiagnoses, and overdiagnoses, and associated antimicrobial resistance. We discuss ethical, regulatory, and systemic barriers related to the collection and use of big data in LMICs. Technologies such as cloud computing, artificial intelligence, and medical tricorders might help, but they require collaboration with local communities. Co-partnering (joint equal development of technology between producer and end-users) could facilitate integration of these technologies as part of future care-delivery systems, offering a chance to transform the global management and prevention of sepsis for neonates and children.

Rapid DNA visual detection of polymicrobial bloodstream infection using filter paper

Bloodstream infection (BSI) is a major complication in patients with cancers due to therapy-induced neutropenia and underlying conditions, which increases hospitalization time and mortality rate. Targeted and timely antimicrobial management is crucial to save the patients’ lives and reduce the social and economic burdens. Blood culture is a routine clinical diagnostic method of BSI with a long turnaround time, and generally identifies monomicrobial BSI. Thus, polymicrobial BSI often goes undetected although it occurs more frequently in these patients and results in more severe outcomes compared to monomicrobial BSI. In this work, we apply glutaric anhydride, N-hydroxysuccinimide and N,N′-dicyclohexylcarbodiimide to fabricate a functional surface on cellulose filter paper. Targeting three pathogens (Escherichia coli, Saccharomyces cerevisiae, and human cytomegalovirus) commonly occurring in BSI in neutropenic patients, we demonstrate rapid and accurate triplex pathogen DNA detection using the functionalized paper. All three pathogen DNA was identified in 1–5 min with a detection limit of 0.1–0.5 ng/µL. The developed test tool has the potential to provide rapid polymicrobial BSI diagnosis in support of timely, accurate antimicrobial treatment, and could be integrated into an automatic sample-to-result portable equipment.

Biomarkers for Diagnosing Febrile Illness in Immunocompromised Children: A Systematic Review of the Literature

OBJECTIVE

This study aims to assess the performance of biomarkers used for the prediction of bacterial, viral, and fungal infection in immunocompromised children upon presentation with fever.

METHODS

We performed a literature search using PubMed and MEDLINE and In-Process & Other Non-indexed Citations databases. Cohort and case-control studies assessing biomarkers for the prediction of bacterial, viral, or fungal infection in immunocompromised children vs. conventional microbiological investigations were eligible. Studies including adult patients were eligible if pediatric data were separately assessable. Data on definitions used for infections, fever, and neutropenia and predictive values were collected. Risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies-2 tool.

RESULTS

Fifty-two studies involving 13,939 febrile episodes in 7,059 children were included. In total, 92.2% were in cancer patients (n = 48), and 15.7% also included hematopoietic stem cell transplantation patients (n = 8). Forty-three biomarkers were investigated, of which 6 (CRP, PCT, IL-8, IL-6, IL-10, and TNFα) were significantly associated with bacterial infection at admission, studied in multiple studies, and provided predictive data. Literature on the prediction of viral and fungal infection was too limited. Eight studies compared C-reactive protein (CRP) and procalcitonin (PCT), with PCT demonstrating superiority in 5. IL-6, IL-8, and IL-10 were compared with CRP in six, four, and one study, respectively, with mixed results on diagnostic superiority. No clear superior biomarker comparing PCT vs. IL-6, IL-8, or IL-10 was identified.

DISCUSSION

There is great heterogeneity in the biomarkers studied and cutoff values and definitions used, thus complicating the analysis. Literature for immunocompromised children with non-malignant disease and for non-bacterial infection is sparse. Literature on novel diagnostics was not available. We illustrated the challenges of diagnosing fever adequately in this study population and the need for improved biomarkers and clinical decision-making tools.

Predicting sepsis severity at first clinical presentation: The role of endotypes and mechanistic signatures

BACKGROUND

Inter-individual variability during sepsis limits appropriate triage of patients. Identifying, at first clinical presentation, gene expression signatures that predict subsequent severity will allow clinicians to identify the most at-risk groups of patients and enable appropriate antibiotic use.

METHODS

Blood RNA-Seq and clinical data were collected from 348 patients in four emergency rooms (ER) and one intensive-care-unit (ICU), and 44 healthy controls. Gene expression profiles were analyzed using machine learning and data mining to identify clinically relevant gene signatures reflecting disease severity, organ dysfunction, mortality, and specific endotypes/mechanisms.

FINDINGS

Gene expression signatures were obtained that predicted severity/organ dysfunction and mortality in both ER and ICU patients with accuracy/AUC of 77-80%. Network analysis revealed these signatures formed a coherent biological program, with specific but overlapping mechanisms/pathways. Given the heterogeneity of sepsis, we asked if patients could be assorted into discrete groups with distinct mechanisms (endotypes) and varying severity. Patients with early sepsis could be stratified into five distinct and novel mechanistic endotypes, named Neutrophilic-Suppressive/NPS, Inflammatory/INF, Innate-Host-Defense/IHD, Interferon/IFN, and Adaptive/ADA, each based on ∼200 unique gene expression differences, and distinct pathways/mechanisms (e.g., IL6/STAT3 in NPS). Endotypes had varying overall severity with two severe (NPS/INF) and one relatively benign (ADA) groupings, consistent with reanalysis of previous endotype studies. A 40 gene-classification tool (accuracy=96%) and several gene-pairs (accuracy=89-97%) accurately predicted endotype status in both ER and ICU validation cohorts.

INTERPRETATION

The severity and endotype signatures indicate that distinct immune signatures precede the onset of severe sepsis and lethality, providing a method to triage early sepsis patients.

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.