The 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.

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.

Biomarker Assessment of a High-Risk, Data-Driven Pediatric Sepsis Phenotype Characterized by Persistent Hypoxemia, Encephalopathy, and Shock

OBJECTIVES

Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. We sought to the determine reproducibility of the data-driven "persistent hypoxemia, encephalopathy, and shock" (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk strata.

DESIGN

We retrained and validated a random forest classifier using organ dysfunction subscores in the 2012-2018 electronic health record (EHR) dataset used to derive the PHES phenotype. We used this classifier to assign phenotype membership in a test set consisting of prospectively (2003-2023) enrolled pediatric septic shock patients. We compared profiles of the PERSEVERE family of biomarkers among those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk strata.

SETTING

Twenty-five PICUs across the United States.

PATIENTS

EHR data from 15,246 critically ill patients with sepsis-associated MODS split into derivation and validation sets and 1,270 pediatric septic shock patients in the test set of whom 615 had complete biomarker data.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

The area under the receiver operator characteristic curve of the modified classifier to predict PHES phenotype membership was 0.91 (95% CI, 0.90-0.92) in the EHR validation set. In the test set, PHES phenotype membership was associated with both increased adjusted odds of complicated course (adjusted odds ratio [aOR] 4.1; 95% CI, 3.2-5.4) and 28-day mortality (aOR of 4.8; 95% CI, 3.11-7.25) after controlling for age, severity of illness, and immunocompromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and were more likely to be stratified as high risk based on PERSEVERE biomarkers predictive of death and persistent MODS.

CONCLUSIONS

The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlapped with higher risk strata based on prospectively validated biomarker approaches.

Host gene expression signatures to identify infection type and organ dysfunction in children evaluated for sepsis: a multicentre cohort study

BACKGROUND

Sepsis is defined as dysregulated host response to infection that leads to life-threatening organ dysfunction. Biomarkers characterising the dysregulated host response in sepsis are lacking. We aimed to develop host gene expression signatures to predict organ dysfunction in children with bacterial or viral infection.

METHODS

This cohort study was done in emergency departments and intensive care units of four hospitals in Queensland, Australia, and recruited children aged 1 month to 17 years who, upon admission, underwent a diagnostic test, including blood cultures, for suspected sepsis. Whole-blood RNA sequencing of blood was performed with Illumina NovaSeq (San Diego, CA, USA). Samples with completed phenotyping, monitoring, and RNA extraction by March 31, 2020, were included in the discovery cohort; samples collected or completed thereafter and by Oct 27, 2021, constituted the Rapid Paediatric Infection Diagnosis in Sepsis (RAPIDS) internal validation cohort. An external validation cohort was assembled from RNA sequencing gene expression count data from the observational European Childhood Life-threatening Infectious Disease Study (EUCLIDS), which recruited children with severe infection in nine European countries between 2012 and 2016. Feature selection approaches were applied to derive novel gene signatures for disease class (bacterial vs viral infection) and disease severity (presence vs absence of organ dysfunction 24 h post-sampling). The primary endpoint was the presence of organ dysfunction 24 h after blood sampling in the presence of confirmed bacterial versus viral infection. Gene signature performance is reported as area under the receiver operating characteristic curves (AUCs) and 95% CI.

FINDINGS

Between Sept 25, 2017, and Oct 27, 2021, 907 patients were enrolled. Blood samples from 595 patients were included in the discovery cohort, and samples from 312 children were included in the RAPIDS validation cohort. We derived a ten-gene disease class signature that achieved an AUC of 94·1% (95% CI 90·6-97·7) in distinguishing bacterial from viral infections in the RAPIDS validation cohort. A ten-gene disease severity signature achieved an AUC of 82·2% (95% CI 76·3-88·1) in predicting organ dysfunction within 24 h of sampling in the RAPIDS validation cohort. Used in tandem, the disease class and disease severity signatures predicted organ dysfunction within 24 h of sampling with an AUC of 90·5% (95% CI 83·3-97·6) for patients with predicted bacterial infection and 94·7% (87·8-100·0) for patients with predicted viral infection. In the external EUCLIDS validation dataset (n=362), the disease class and disease severity predicted organ dysfunction at time of sampling with an AUC of 70·1% (95% CI 44·1-96·2) for patients with predicted bacterial infection and 69·6% (53·1-86·0) for patients with predicted viral infection.

INTERPRETATION

In children evaluated for sepsis, novel host transcriptomic signatures specific for bacterial and viral infection can identify dysregulated host response leading to organ dysfunction.

FUNDING

Australian Government Medical Research Future Fund Genomic Health Futures Mission, Children's Hospital Foundation Queensland, Brisbane Diamantina Health Partners, Emergency Medicine Foundation, Gold Coast Hospital Foundation, Far North Queensland Foundation, Townsville Hospital and Health Services SERTA Grant, and Australian Infectious Diseases Research Centre.

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

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

Pediatric sepsis 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.

Advances and Challenges in Sepsis Management: Modern Tools and Future Directions

Sepsis, a critical condition marked by systemic inflammation, profoundly impacts both innate and adaptive immunity, often resulting in lymphopenia. This immune alteration can spare regulatory T cells (Tregs) but significantly affects other lymphocyte subsets, leading to diminished effector functions, altered cytokine profiles, and metabolic changes. The complexity of sepsis stems not only from its pathophysiology but also from the heterogeneity of patient responses, posing significant challenges in developing universally effective therapies. This review emphasizes the importance of phenotyping in sepsis to enhance patient-specific diagnostic and therapeutic strategies. Phenotyping immune cells, which categorizes patients based on clinical and immunological characteristics, is pivotal for tailoring treatment approaches. Flow cytometry emerges as a crucial tool in this endeavor, offering rapid, low cost and detailed analysis of immune cell populations and their functional states. Indeed, this technology facilitates the understanding of immune dysfunctions in sepsis and contributes to the identification of novel biomarkers. Our review underscores the potential of integrating flow cytometry with omics data, machine learning and clinical observations to refine sepsis management, highlighting the shift towards personalized medicine in critical care. This approach could lead to more precise interventions, improving outcomes in this heterogeneously affected patient population.

Fluid Balance and Its Association With Mortality and Health-Related Quality of Life: A Nonprespecified Secondary Analysis of the Life After Pediatric Sepsis Evaluation

OBJECTIVES

To evaluate the association between fluid balance (FB) and health-related quality of life (HRQL) among children at 1 month following community-acquired septic shock.

DESIGN

Nonprespecified secondary analysis of the Life After Pediatric Sepsis Evaluation. FB was defined as 100 × [(cumulative PICU fluid input - cumulative PICU fluid output)/PICU admission weight]. Three subgroups were identified: low FB (< 5%), medium FB (5%-15%), and high FB (> 15%) based on cumulative FB on days 0-3 of ICU stay. HRQL was measured at ICU admission and 1 month after using Pediatric Quality of Life Inventory 4.0 Generic Core or Infant Scales or the Stein-Jessop Functional Status Scale. The primary outcome was a composite of mortality or greater than 25% decline in HRQL 1 month after admission compared with baseline.

SETTING

Twelve academic PICUs in the United States.

PATIENTS

Critically ill children between 1 month and 18 years, with community-acquired septic shock who survived to at least day 4.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Two hundred ninety-three patients were included of whom 66 (23%) had low FB, 127 (43%) had medium FB, and 100 (34%) had high FB. There was no difference in Pediatric Risk of Mortality Score 3 (median 11 [6, 17]), age (median 5 [1, 12]), or gender (47% female) between FB groups. After adjusting for potential confounders and comparing with medium FB, higher odds of mortality or greater than 25% HRQL decline were seen in both the low FB (odds ratio [OR] 2.79 [1.20, 6.57]) and the high FB (OR 2.16 [1.06, 4.47]), p = 0.027. Compared with medium FB, low FB (OR 4.3 [1.62, 11.84]) and high FB (OR 3.29 [1.42, 8.00]) had higher odds of greater than 25% HRQL decline.

CONCLUSIONS

Over half of the children who survived septic shock had low or high FB, which was associated with a significant decline in HRQL scores. Prospective studies are needed to determine if optimization of FB can improve HRQL outcomes.

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.

External validation and biomarker assessment of a high-risk, data-driven pediatric sepsis phenotype characterized by persistent hypoxemia, encephalopathy, and shock

Objective

Identification of children with sepsis-associated multiple organ dysfunction syndrome (MODS) at risk for poor outcomes remains a challenge. Data-driven phenotyping approaches that leverage electronic health record (EHR) data hold promise given the widespread availability of EHRs. We sought to externally validate the data-driven 'persistent hypoxemia, encephalopathy, and shock' (PHES) phenotype and determine its association with inflammatory and endothelial biomarkers, as well as biomarker-based pediatric risk-strata.

Design

We trained and validated a random forest classifier using organ dysfunction subscores in the EHR dataset used to derive the PHES phenotype. We used the classifier to assign phenotype membership in a test set consisting of prospectively enrolled pediatric septic shock patients. We compared biomarker profiles of those with and without the PHES phenotype and determined the association with established biomarker-based mortality and MODS risk-strata.

Setting

25 pediatric intensive care units (PICU) across the U.S.

Patients

EHR data from 15,246 critically ill patients sepsis-associated MODS and 1,270 pediatric septic shock patients in the test cohort of whom 615 had biomarker data.

Interventions

None.

Measurements and Main Results

The area under the receiver operator characteristic curve (AUROC) of the new classifier to predict PHES phenotype membership was 0.91(95%CI, 0.90-0.92) in the EHR validation set. In the test set, patients with the PHES phenotype were independently associated with both increased odds of complicated course (adjusted odds ratio [aOR] of 4.1, 95%CI: 3.2-5.4) and 28-day mortality (aOR of 4.8, 95%CI: 3.11-7.25) after controlling for age, severity of illness, and immuno-compromised status. Patients belonging to the PHES phenotype were characterized by greater degree of systemic inflammation and endothelial activation, and overlapped with high risk-strata based on PERSEVERE biomarkers predictive of death and persistent MODS.

Conclusions

The PHES trajectory-based phenotype is reproducible, independently associated with poor clinical outcomes, and overlap with higher risk-strata based on validated biomarker approaches.

Biomarker-Based Risk Stratification in Pediatric Sepsis From a Low-Middle Income Country

OBJECTIVES

Most biomarker studies of sepsis originate from high-income countries, whereas mortality risk is higher in low- and middle-income countries. The second version of the Pediatric Sepsis Biomarker Risk Model (PERSEVERE-II) has been validated in multiple North American PICUs for prognosis. Given differences in epidemiology, we assessed the performance of PERSEVERE-II in septic children from Pakistan, a low-middle income country. Due to uncertainty regarding how well PERSEVERE-II would perform, we also assessed the utility of other select biomarkers reflecting endotheliopathy, coagulopathy, and lung injury.

DESIGN

Prospective cohort study.

SETTING

PICU in Aga Khan University Hospital in Karachi, Pakistan.

PATIENTS

Children (< 18 yr old) meeting pediatric modifications of adult Sepsis-3 criteria between November 2020 and February 2022 were eligible.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Plasma was collected within 24 hours of admission and biomarkers quantified. The area under the receiver operating characteristic curve for PERSEVERE-II to discriminate 28-day mortality was determined. Additional biomarkers were compared between survivors and nonsurvivors and between subjects with and without acute respiratory distress syndrome. In 86 subjects (20 nonsurvivors, 23%), PERSEVERE-II discriminated mortality (area under the receiver operating characteristic curve, 0.83; 95% CI, 0.72-0.94) and stratified the cohort into low-, medium-, and high-risk of mortality. Biomarkers reflecting endotheliopathy (angiopoietin 2, intracellular adhesion molecule 1) increased across worsening risk strata. Angiopoietin 2, soluble thrombomodulin, and plasminogen activator inhibitor 1 were higher in nonsurvivors, and soluble receptor for advanced glycation end-products and surfactant protein D were higher in children meeting acute respiratory distress syndrome criteria.

CONCLUSIONS

PERSEVERE-II performs well in septic children from Aga Khan University Hospital, representing the first validation of PERSEVERE-II in a low-middle income country. Patients possessed a biomarker profile comparable to that of sepsis from high-income countries, suggesting that biomarker-based enrichment strategies may be effective in this setting.

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.

Pediatric Critical Care in the Twenty-first Century and Beyond

Pediatric critical care addresses prevention, diagnosis, and treatment of organ dysfunction in the setting of increasingly complex patients, therapies, and environments. Soon burgeoning data science will enable all aspects of intensive care: driving facilitated diagnostics, empowering a learning health-care environment, promoting continuous advancement of care, and informing the continuum of critical care outside the intensive care unit preceding and following critical illness/injury. Although novel technology will progressively objectify personalized critical care, humanism, practiced at the bedside, defines the essence of pediatric critical care now and in the future.

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.

The Association of Corticosteroids and Pediatric Sepsis Biomarker Risk Model (PERSEVERE)-II Biomarker Risk Stratification With Mortality in Pediatric Septic Shock

OBJECTIVES

Mortality risk stratification may identify a subset of children who benefit from or are harmed by corticosteroid administration. The Pediatric Sepsis Biomarker Risk Model (PERSEVERE)-II score is a biomarker-based mortality risk stratification tool for pediatric sepsis. Our objective was to assess the association of corticosteroid administration with 28-day mortality within different levels of baseline mortality risk (PERSEVERE-II) in a cohort of children with septic shock.

DESIGN

We performed a secondary analysis using prospectively collected data (January 2015 to December 2018).

SETTING

PICUs in 13 tertiary care, academic centers in the United States.

PATIENTS

Children with septic shock.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

We assessed the association of corticosteroid administration within PERSEVERE-II risk score categories and 28-day mortality, ICU-free days, and maximum failed organs in children with septic shock. We analyzed a total of 461 patients (215 with corticosteroids exposure, 246 without corticosteroid exposure) with an average age of 7.1 years (interquartile range, 2.2-13.6 yr). In the subgroup of patients with a high PERSEVERE-II score, corticosteroid administration was associated with an increased adjusted risk of 28-day mortality (odds ratio [OR] 4.10 [95% CI 1.70-9.86]; p = 0.002), but not in the low risk group (OR 0.20 [95% CI 0.02-1.73]; p = 0.15). A significant interaction between PERSEVERE-II score and corticosteroids was seen for both secondary outcomes complicated course ( p = 0.01) and maximum failed organs ( p < 0.001). Corticosteroid exposure was associated with fewer ICU-free days ( p < 0.0001).

CONCLUSIONS

In our multicenter observational study, corticosteroid administration was associated with increased mortality in a subgroup of children with a high PERSEVERE-II risk score.

Advances in Shock Management and Fluid Resuscitation in Children

Shock in children is associated with significant mortality and morbidity, particularly in resource-limited settings. The principles of management include early recognition, fluid resuscitation, appropriate inotropes, antibiotic therapy in sepsis, supportive therapy for organ dysfunction, and regular hemodynamic monitoring. During the past decade, each step has undergone several changes and evolved as evidence that has been translated into recommendations and practice. There is a paradigm shift from protocolized-based care to personalized management, from liberal strategies to restrictive strategies in terms of fluids, blood transfusion, ventilation, and antibiotics, and from clinical monitoring to multimodal monitoring using bedside technologies. However, uncertainties are still prevailing in terms of the volume of fluids, use of steroids, and use of extracorporeal and newer therapies while managing shock. These changes have been summarized along with evidence in this article with the aim of adopting an evidence-based approach while managing children with shock.

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.

Understanding clinical and biological heterogeneity to advance precision medicine in paediatric acute respiratory distress syndrome

Paediatric acute respiratory distress syndrome (PARDS) is a heterogeneous clinical syndrome that is associated with high rates of mortality and long-term morbidity. Factors that distinguish PARDS from adult acute respiratory distress syndrome (ARDS) include changes in developmental stage and lung maturation with age, precipitating factors, and comorbidities. No specific treatment is available for PARDS and management is largely supportive, but methods to identify patients who would benefit from specific ventilation strategies or ancillary treatments, such as prone positioning, are needed. Understanding of the clinical and biological heterogeneity of PARDS, and of differences in clinical features and clinical course, pathobiology, response to treatment, and outcomes between PARDS and adult ARDS, will be key to the development of novel preventive and therapeutic strategies and a precision medicine approach to care. Studies in which clinical, biomarker, and transcriptomic data, as well as informatics, are used to unpack the biological and phenotypic heterogeneity of PARDS, and implementation of methods to better identify patients with PARDS, including methods to rapidly identify subphenotypes and endotypes at the point of care, will drive progress on the path to precision medicine.

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.

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 P_aO₂/F_iO₂ < 200 to those with a P_aO₂/F_iO₂ ≥ 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 P_aO₂/F_iO₂ < 200 vs. P_aO₂/F_iO₂ ≥ 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.

Integrated PERSEVERE and endothelial biomarker risk model predicts death and persistent MODS in pediatric septic shock: a secondary analysis of a prospective observational study

Background

Multiple organ dysfunction syndrome (MODS) is a critical driver of sepsis morbidity and mortality in children. Early identification of those at risk of death and persistent organ dysfunctions is necessary to enrich patients for future trials of sepsis therapeutics. Here, we sought to integrate endothelial and PERSEVERE biomarkers to estimate the composite risk of death or organ dysfunctions on day 7 of septic shock.

Methods

We measured endothelial dysfunction markers from day 1 serum among those with existing PERSEVERE data. TreeNet® classification model was derived incorporating 22 clinical and biological variables to estimate risk. Based on relative variable importance, a simplified 6-biomarker model was developed thereafter.

Results

Among 502 patients, 49 patients died before day 7 and 124 patients had persistence of MODS on day 7 of septic shock. Area under the receiver operator characteristic curve (AUROC) for the newly derived PERSEVEREnce model to predict death or day 7 MODS was 0.93 (0.91–0.95) with a summary AUROC of 0.80 (0.76–0.84) upon tenfold cross-validation. The simplified model, based on IL-8, HSP70, ICAM-1, Angpt2/Tie2, Angpt2/Angpt1, and Thrombomodulin, performed similarly. Interaction between variables—ICAM-1 with IL-8 and Thrombomodulin with Angpt2/Angpt1—contributed to the models’ predictive capabilities. Model performance varied when estimating risk of individual organ dysfunctions with AUROCS ranging from 0.91 to 0.97 and 0.68 to 0.89 in training and test sets, respectively.

Conclusions

The newly derived PERSEVEREnce biomarker model reliably estimates risk of death or persistent organ dysfunctions on day 7 of septic shock. If validated, this tool can be used for prognostic enrichment in future pediatric trials of sepsis therapeutics.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-022-04070-5.

The Value of Perinatal Factors, Blood Biomarkers and Microbiological Colonization Screening in Predicting Neonatal Sepsis

Background: Neonatal sepsis is one of the most important causes of elevated morbidity and mortality rates in neonatal intensive care units worldwide. While the clinical manifestations of neonatal sepsis tend to be nonspecific, its rapid development and life-threatening potential call for reliable markers for early detection. Methods: We conducted a retrospective single-center study including all neonates suspected of having developed neonatal sepsis from 2013 to 2016. Perinatal and clinical characteristics as well as microbiological and laboratory findings were evaluated. Neonatal sepsis was defined as either culture-proven sepsis (positive blood culture) or clinical sepsis (at least one symptom and elevated C-reactive protein (CRP) concentrations within 72 h with negative blood culture). We further differentiated between early-onset (EOS) and late-onset (LOS) sepsis. Results: Microbiological colonization screening by throat and rectal swabs frequently did not detect the organism that subsequently caused the sepsis. Depending on the age of the newborn with sepsis (EOS or LOS), associations between different anamnestic and clinical factors (prenatal or postnatal ones) were found. In particular, the central−peripheral temperature difference showed a strong association with LOS. Laboratory results useful for the early detection of neonatal sepsis included interleukin-6 (IL-6) and CRP concentrations. Conclusions: Elevated IL-6 >100 ng/L was a strong marker for neonatal sepsis. When choosing the antibiotics for treatment, data from microbiological colonization screening should be considered but not solely relied on. Some indicators of infection also depended on postnatal age.

Choline supplementation attenuates experimental sepsis-associated acute kidney injury

Acute kidney injury (AKI) is common in critically ill patients, and sepsis is its leading cause. Sepsis-associated AKI (SA-AKI) causes greater morbidity and mortality than other AKI etiologies, yet the underlying mechanisms are incompletely understood. Metabolomic technologies can characterize cellular energy derangements, but few discovery analyses have evaluated the metabolomic profile of SA-AKI. To identify metabolic derangements amenable to therapeutic intervention, we assessed plasma and urine metabolites in septic mice and critically ill children and compared them by AKI status. Metabolites related to choline and central carbon metabolism were differentially abundant in SA-AKI in both mice and humans. Gene expression of enzymes related to choline metabolism was altered in the kidneys and liver of mice with SA-AKI. Treatment with intraperitoneal choline improved renal function in septic mice. Because pediatric patients with sepsis displayed similar metabolomic profiles to septic mice, choline supplementation may attenuate pediatric septic AKI.NEW & NOTEWORTHY Altered choline metabolism plays a role in both human and murine sepsis-associated acute kidney injury (SA-AKI), and choline administration in experimental SA-AKI improved renal function. These findings indicate that 1) mouse models can help interrogate clinically relevant mechanisms and 2) choline supplementation may ameliorate human SA-AKI. Future research will investigate clinically the impact of choline supplementation on human renal function in sepsis and, using model systems, how choline mediates its effects.

Evolution of multiple omics approaches to define pathophysiology of pediatric acute respiratory distress syndrome

Pediatric acute respiratory distress syndrome (PARDS), though both common and deadly in critically ill children, lacks targeted therapies. The development of effective pharmacotherapies has been limited, in part, by lack of clarity about the pathobiology of pediatric ARDS. Epithelial lung injury, vascular endothelial activation, and systemic immune activation are putative drivers of this complex disease process. Prior studies have used either hypothesis-driven (e.g., candidate genes and proteins, in vitro investigations) or unbiased (e.g., genome-wide association, transcriptomic, metabolomic) approaches to predict clinical outcomes and to define subphenotypes. Advances in multiple omics technologies, including genomics, transcriptomics, proteomics, and metabolomics, have permitted more comprehensive investigation of PARDS pathobiology. However, omics studies have been limited in children compared to adults, and analyses across multiple tissue types are lacking. Here, we synthesized existing literature on the molecular mechanism of PARDS, summarized our interrogation of publicly available genomic databases to determine the association of candidate genes with PARDS phenotypes across multiple tissues and cell types, and integrated recent studies that used single-cell RNA sequencing (scRNA-seq). We conclude that novel profiling methods such as scRNA-seq, which permits more comprehensive, unbiased evaluation of pathophysiological mechanisms across tissue and cell types, should be employed to investigate the molecular mechanisms of PRDS toward the goal of identifying targeted therapies.

Research in Pediatric Intensive Care

Many important clinical questions remain unanswered in the practice of pediatric intensive care due to the lack of high-quality evidence. Although challenges exist in conducting research in pediatric intensive care units, identification of research priorities, interdisciplinary collaborations, innovative trial designs, and the use of common datasets and outcome measures helps to bring new knowledge to our field. The topic of "Research in PICUs" is extremely broad; therefore, this review focuses on a few common themes receiving increased attention in the literature, including research agendas, core outcome sets, precision medicine, and novel clinical trial strategies.

A Three-Protein Panel to Support the Diagnosis of Sepsis in Children

Sepsis is a syndrome without a standard validated diagnostic test. Early recognition is crucial. Serum proteome analysis in children with sepsis may identify new biomarkers. This study aimed to find suitable blood biomarkers for an early diagnosis of sepsis. An analytical observational case-control study was carried out in a single center. Children admitted to a Pediatric Intensive Care Unit with clinical diagnosed sepsis were eligible for study. A proteomic analysis conducted by mass spectrometry was performed. Forty patients with sepsis and 24 healthy donors were recruited. Proteomics results revealed 44 proteins differentially expressed between patients and healthy controls. Six proteins were selected to be validated: lactoferrin, serum amyloid-A1 (SAA-1), complement factor B, leucine-rich alpha-2 glycoprotein (LRG1), soluble interleukin-2 alpha chain receptor (sCD25) and soluble haptoglobin−hemoglobin receptor. Our results showed that sCD25, SAA-1, and LRG1 had high levels of specificity and sensitivity, as well as an excellent area under the ROC curve (>0.9). Our study provides a serum proteomic analysis that identifies new diagnostic biomarkers in sepsis. SAA-1, sCD25 and LRG1 were able to separate septic from healthy donor, so they could be used together with other clinical and analytical features to improve sepsis diagnosis in children.

Pediatric sepsis phenotypes for enhanced therapeutics: An application of clustering to electronic health records

OBJECTIVE

The heterogeneity of pediatric sepsis patients suggests the potential benefits of clustering analytics to derive phenotypes with distinct host response patterns that may help guide personalized therapeutics. We evaluate the relative performance of latent class analysis (LCA) and K-means, 2 commonly used clustering methods toward the derivation of clinically useful pediatric sepsis phenotypes.

METHODS

Data were extracted from anonymized medical records of 6446 pediatric patients that presented to 1 of 6 emergency departments (EDs) between 2013 and 2018 and were thereafter admitted. Using International Classification of Diseases (ICD)-9 and ICD-10 discharge codes, 151 patients were identified with a sepsis continuum diagnosis that included septicemia, sepsis, severe sepsis, and septic shock. Using feature sets used in related clustering studies, LCA and K-means algorithms were used to derive 4 distinct phenotypic pediatric sepsis segmentations. Each segmentation was evaluated for phenotypic homogeneity, separation, and clinical use.

RESULTS

Using the 2 feature sets, LCA clustering resulted in 2 similar segmentations of 4 clinically distinct phenotypes, while K-means clustering resulted in segmentations of 3 and 4 phenotypes. All 4 segmentations identified at least 1 high severity phenotype, but LCA-identified phenotypes reflected superior stratification, high entropy approaching 1 (eg, 0.994) indicating excellent separation between estimated phenotypes, and differential treatment/treatment response, and outcomes that were non-randomly distributed across phenotypes (P < 0.001).

CONCLUSION

Compared to K-means, which is commonly used in clustering studies, LCA appears to be a more robust, clinically useful statistical tool in analyzing a heterogeneous pediatric sepsis cohort toward informing targeted therapies. Additional prospective studies are needed to validate clinical utility of predictive models that target derived pediatric sepsis phenotypes in emergency department settings.

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

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

Mechanisms and modulation of sepsis-induced immune dysfunction in children

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

Pediatric Sepsis Biomarker Risk Model Biomarkers and Estimation of Myocardial Dysfunction in Pediatric Septic Shock

OBJECTIVES

Sepsis-associated myocardial dysfunction is common in pediatric septic shock and negatively impacts outcomes. Early estimation of sepsis-associated myocardial dysfunction risk has the potential to inform clinical care and improve clinical trial design. The Pediatric Sepsis Biomarker Risk Model II is validated as a biomarker-based enrichment algorithm to discriminate children with septic shock with high baseline mortality probability. The objectives were to determine if Pediatric Sepsis Biomarker Risk Model biomarkers are associated with risk for sepsis-associated myocardial dysfunction in pediatric septic shock and to develop a biomarker-based model to reliably estimate sepsis-associated myocardial dysfunction risk.

DESIGN

Secondary analysis of prospective cohort study.

SETTING

Single-center, quaternary-care PICU.

PATIENTS

Children less than 18 years old admitted to the PICU from 2003 to 2018 who had Pediatric Sepsis Biomarker Risk Model biomarkers measured for determination of Pediatric Sepsis Biomarker Risk Model II mortality probability and an echocardiogram performed within 48 hours of septic shock identification.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Pediatric Sepsis Biomarker Risk Model II mortality probability was calculated from serum biomarker concentrations and admission platelet count. Echocardiograms were reread by a single cardiologist blinded to Pediatric Sepsis Biomarker Risk Model II data, and sepsis-associated myocardial dysfunction was defined as left ventricular ejection fraction less than 45% for primary analyses. Multivariable logistic regression analyzed the association of Pediatric Sepsis Biomarker Risk Model II mortality probability with sepsis-associated myocardial dysfunction. Classification and regression tree methodology was employed to derive a Pediatric Sepsis Biomarker Risk Model biomarker-based model for sepsis-associated myocardial dysfunction. Thirty-two of 181 children with septic shock demonstrated sepsis-associated myocardial dysfunction. Pediatric Sepsis Biomarker Risk Model II mortality probability was independently associated with sepsis-associated myocardial dysfunction (odds ratio, 1.45; 95% CI, 1.17-1.81; p = 0.001). Modeling with Pediatric Sepsis Biomarker Risk Model biomarkers estimated sepsis-associated myocardial dysfunction risk with an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.85-0.95). Upon 10-fold cross-validation, the derived model had a summary area under the receiver operating characteristic curve of 0.74. Model characteristics were similar when sepsis-associated myocardial dysfunction was defined by both low left ventricular ejection fraction and abnormal global longitudinal strain.

CONCLUSIONS

A newly derived Pediatric Sepsis Biomarker Risk Model biomarker-based model reliably estimates risk of sepsis-associated myocardial dysfunction in pediatric septic shock, but independent prospective validation is needed.

Multiple Organ Dysfunction Interactions in Critically Ill Children

INTRODUCTION

Multiple organ dysfunction (MOD) is a common pathway to morbidity and death in critically ill children. Defining organ dysfunction is challenging, as we lack a complete understanding of the complex pathobiology. Current pediatric organ dysfunction criteria assign the same diagnostic value-the same "weight"- to each organ system. While each organ dysfunction in isolation contributes to the outcome, there are likely complex interactions between multiple failing organs that are not simply additive.

OBJECTIVE

Determine whether certain combinations of organ system dysfunctions have a significant interaction associated with higher risk of morbidity or mortality in critically ill children.

METHODS

We conducted a retrospective observational cohort study of critically ill children at two large academic medical centers from 2010 and 2018. Patients were included in the study if they had at least two organ dysfunctions by day 3 of PICU admission based on the Pediatric Organ Dysfunction Information Update Mandate (PODIUM) criteria. Mortality was described as absolute number of deaths and mortality rate. Combinations of two pediatric organ dysfunctions were analyzed with interaction terms as independent variables and mortality or persistent MOD as the dependent variable in logistic regression models.

RESULTS

Overall, 7,897 patients met inclusion criteria and 446 patients (5.6%) died. The organ dysfunction interactions that were significantly associated with the highest absolute number of deaths were cardiovascular + endocrinologic, cardiovascular + neurologic, and cardiovascular + respiratory. Additionally, the interactions associated with the highest mortality rates were liver + cardiovascular, respiratory + hematologic, and respiratory + renal. Among patients with persistent MOD, the most common organ dysfunctions with significant interaction terms were neurologic + respiratory, hematologic + immunologic, and endocrinologic + respiratory. Further analysis using classification and regression trees (CART) demonstrated that the absence of respiratory and liver dysfunction was associated with the lowest likelihood of mortality.

IMPLICATIONS AND FUTURE DIRECTIONS

Certain combinations of organ dysfunctions are associated with a higher risk of persistent MOD or death. Notably, the three most common organ dysfunction interactions were associated with 75% of the mortality in our cohort. Critically ill children with MOD presenting with these combinations of organ dysfunctions warrant further study.

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.

The immunology of sepsis

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

Machine Learning and Precision Medicine in Emergency Medicine: The Basics

As machine learning (ML) and precision medicine become more readily available and used in practice, emergency physicians must understand the potential advantages and limitations of the technology. This narrative review focuses on the key components of machine learning, artificial intelligence, and precision medicine in emergency medicine (EM). Based on the content expertise, we identified articles from EM literature. The authors provided a narrative summary of each piece of literature. Next, the authors provided an introduction of the concepts of ML, artificial intelligence as an extension of ML, and precision medicine. This was followed by concrete examples of their applications in practice and research. Subsequently, we shared our thoughts on how to consume the existing research in these subjects and conduct high-quality research for academic emergency medicine. We foresee that the EM community will continue to adapt machine learning, artificial intelligence, and precision medicine in research and practice. We described several key components using our expertise.

Recalibration of the Renal Angina Index for Pediatric Septic Shock

Introduction

Sepsis-associated acute kidney injury (AKI) is a common diagnosis in children that is associated with poor outcomes. The lack of therapeutic options once present makes early identification of at-risk patients essential. The renal angina index (RAI) has been previously validated to predict severe AKI in heterogeneous populations of critically ill children. The performance of this score specifically in children with septic shock is unknown.

Methods

A secondary analysis of a multicenter, prospective, observational study of 379 children with septic shock to determine the ability of the RAI to predict severe AKI at day 3, and to assess for the potential need for recalibration of the RAI in this unique subset of patients.

Results

At the original cutoff of ≥8, the RAI predicted day 3 severe AKI with an area under the receiving operating characteristic (AUROC) curve 0.90 (95% confidence interval [CI]: 0.86 to 93), 95% sensitivity, and 54% specificity. A Youden's index identified a higher optimal cutoff of ≥20 (sensitivity 83%, specificity 80%), and day 1 platelet count <150 × 10³/μl was an independent predictor of severe AKI (adjusted odds ratio: 3.2; 95% CI: 1.7 to 6.3; P < 0.001). Recalibration of the RAI to include platelet count and this new threshold restored the sensitivity of the original ≥8 threshold (95%), while improving its specificity (69%).

Conclusions

The RAI appears to be a sensitive and reliable tool for prediction of severe AKI in children with septic shock, although the use of a recalibrated sepsis-specific RAI using a higher cutoff and platelet count may be beneficial.

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

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

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

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

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.

A Precision Medicine Approach to Biomarker Utilization in Pediatric Sepsis-Associated Acute Kidney Injury

Sepsis is a leading cause of morbidity and mortality in critically ill children, and acute kidney injury (AKI) is a frequent complication that confers an increased risk for poor outcomes. Despite the documented consequences of sepsis-associated AKI (SA-AKI), no effective disease-modifying therapies have been identified to date. As such, the only treatment options for these patients remain prevention and supportive care, both of which rely on the ability to promptly and accurately identify at risk and affected individuals. To achieve these goals, a variety of biomarkers have been investigated to help augment our currently limited predictive and diagnostic strategies for SA-AKI, however, these have had variable success in pediatric sepsis. In this mini-review, we will briefly outline the current use of biomarkers for SA-AKI, and propose a new framework for biomarker discovery and utilization that considers the individual patient's sepsis inflammatory response. Now recognized to be a key driver in the complex pathophysiology of SA-AKI, understanding the dysregulated host immune response to sepsis is a growing area of research that can and should be leveraged to improve the prediction and diagnosis of SA-AKI, while also potentially identifying novel therapeutic targets. Reframing SA-AKI in this manner - as a direct consequence of the individual patient's sepsis inflammatory response - will facilitate a precision medicine approach to its management, something that is required to move the care of this consequential disorder forward.

Myocardial Dysfunction Is Independently Associated With Mortality in Pediatric Septic Shock

Supplemental Digital Content is available in the text.

Objectives:

Circulatory dysfunction has been associated with mortality in children with septic shock. However, the mortality risk attributable to myocardial dysfunction per se has not been established, and the association between myocardial dysfunction and mortality is confounded by illness severity. The objective was to determine if sepsis-associated myocardial dysfunction defined by low left ventricular ejection fraction or global longitudinal strain is associated with mortality in pediatric septic shock after adjusting for baseline mortality probability.

Design:

Retrospective, observational study.

Setting:

Single-center, quaternary-care PICU.

Patients:

Children less than 18 years old admitted to the PICU from 2003 to 2018 who had an echocardiogram performed within 48 hours of septic shock identification and Pediatric Sepsis Biomarker Risk Model II data available.

Interventions:

None.

Measurements and Main Results:

All echocardiograms were reread by a cardiologist blinded to patient data for left ventricular ejection fraction and global longitudinal strain. Low left ventricular ejection fraction was defined as less than 45%, and low global longitudinal strain was defined as greater than z score of –2 for body surface area. Multivariable logistic regression separately analyzed the associations of low left ventricular ejection fraction and low global longitudinal strain with mortality, adjusting for Pediatric Sepsis Biomarker Risk Model II mortality risk. A post hoc logistic regression analyzed the association of left ventricular ejection fraction as a continuous variable with mortality, where linearity was maintained for left ventricular ejection fraction less than 65%. Eighteen percent of 181 children had low left ventricular ejection fraction. After adjusting for baseline mortality risk, low left ventricular ejection fraction remained independently associated with mortality (odds ratio, 4.4 [1.0–19.8]; p = 0.0497). Likewise, left ventricular ejection fraction was associated with mortality (odds ratio, 0.96 [0.93–0.99]; p = 0.037) on multivariable analysis for left ventricular ejection fraction less than 65%. Thirty-six percent of 169 children had low global longitudinal strain, and low global longitudinal strain was also independently associated with mortality (odds ratio, 4.6 [1.2–18.0]; p = 0.027).

Conclusions:

Sepsis-associated myocardial dysfunction, whether defined by low left ventricular ejection fraction or low global longitudinal strain, is an independent risk factor for mortality in pediatric septic shock after accounting for the confounding effects of septic shock severity.

Updates on pediatric sepsis

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

Clinical and laboratory predictors of 30-day mortality in severe acute malnourished children with severe pneumonia

OBJECTIVE

To determine the predictors of mortality within 30 days of hospital admission in a diarrhoeal disease hospital in Bangladesh.

METHODS

Cohort study of hospitalised children aged 0-59 months with severe acute malnutrition (SAM) and severe pneumonia in Dhaka Hospital, icddr,b, Bangladesh from April 2015 to March 2017. Those discharged were followed up, and survival status at 30 days from admission was determined. Children who died were compared with the survivors in terms of clinical and laboratory biomarkers. Multivariable logistic regression analysis was used for calculating adjusted odds ratio for death within 30 days of hospital admission.

RESULTS

We enrolled 191 children. Mortality within 30 days of admission was 6% (14/191). After adjusting for potential confounders (hypoxia, CRP and haematocrit) in logistic regression analysis, independent factors associated with death were female sex (aOR = 5.80, 95% CI: 1.34-25.19), LAZ <-4 (aOR = 6.51, 95% CI: 1.49-28.44) and Polymorphonuclear Leucocytes (PMNL) (>6.0 × 10⁹ /L) (aOR = 1.06, 95% CI: 1.01-1.11). Using sex, Z-score for length for age (LAZ), and PMNL percentage, we used random forest and linear regression models to achieve a cross-validated AUC of 0.83 (95% CI: 0.82, 0.84) for prediction of 30-day mortality.

CONCLUSIONS

The results of our data suggest that female sex, severe malnutrition (<-4 LAZ) and higher PMNL percentage were prone to be associated with 30-day mortality in children with severe pneumonia. Association of these factors may be used in clinical decision support for prompt identification and appropriate management for prevention of mortality in this population.

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

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

Subphenotypes in critical care: translation into clinical practice

Despite progress in the supportive care available for critically ill patients, few advances have been made in the search for effective disease-modifying therapeutic options. The fact that many trials in critical care medicine have not identified a treatment benefit is probably due, in part, to the underlying heterogeneity of critical care syndromes. Numerous approaches have been proposed to divide populations of critically ill patients into more meaningful subgroups (subphenotypes), some of which might be more useful than others. Subclassification systems driven by clinical features and biomarkers have been proposed for acute respiratory distress syndrome, sepsis, acute kidney injury, and pancreatitis. Identifying the systems that are most useful and biologically meaningful could lead to a better understanding of the pathophysiology of critical care syndromes and the discovery of new treatment targets, and allow recruitment in future therapeutic trials to focus on predicted responders. This Review discusses proposed subphenotypes of critical illness syndromes and highlights the issues that will need to be addressed to translate subphenotypes into clinical practice.

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.