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

Neonatal bacterial sepsis

Neonatal sepsis remains one of the key challenges of neonatal medicine, and together with preterm birth, causes almost 50% of all deaths globally for children younger than 5 years. Compared with advances achieved for other serious neonatal and early childhood conditions globally, progress in reducing neonatal sepsis has been much slower, especially in low-resource settings that have the highest burden of neonatal sepsis morbidity and mortality. By contrast to sepsis in older patients, there is no universally accepted neonatal sepsis definition. This poses substantial challenges in clinical practice, research, and health-care management, and has direct practical implications, such as diagnostic inconsistency, heterogeneous data collection and surveillance, and inappropriate treatment, health-resource allocation, and education. As the clinical manifestation of neonatal sepsis is frequently non-specific and the current diagnostic standard blood culture has performance limitations, new improved diagnostic techniques are required to guide appropriate and warranted antimicrobial treatment. Although antimicrobial therapy and supportive care continue as principal components of neonatal sepsis therapy, refining basic neonatal care to prevent sepsis through education and quality improvement initiatives remains paramount.

Examining the relationship between alterations in plasma cholesterol, vascular endothelin-1 levels, and the severity of sepsis in children: An observational study

Considering the significant impact of total cholesterol (TC) and vascular endothelin-1 (ET-1) on children sepsis outcomes, this research aimed to explore the association between the levels of plasma cholesterol and vascular endothelin-1 and the severity of sepsis and evaluated its clinical implications. In this study, we examined 250 pediatric patients diagnosed with sepsis between February 2019 and April 2021, collecting data on their plasma levels of TC and ET-1. Depending on the observed outcomes, the participants were divided into 2 categories: a group with a positive prognosis (control group, n = 100) and a group with a negative prognosis (n = 50). We assessed the significance of plasma TC and ET-1 levels in forecasting the outcomes for these pediatric patients. Patients in the group with a poor prognosis experienced notably longer hospital stays and higher treatment expenses than those in the control group (P < .05). Within the first 24 hours of admission and again on days 3 and 7, the levels of ET-1 were significantly higher in the poor prognosis group, whereas plasma TC levels were notably lower in comparison to the control group (P < .05). A Spearman correlation analysis identified a significant correlation between the levels of plasma TC and ET-1 and the severity of sepsis among the children (P < .05). The diagnostic performance for the severity of sepsis in children, as measured by the area under the curve (AUC), was 0.805 for plasma TC, 0.777 for ET-1 levels, and 0.938 when both were combined. This investigation underscores a meaningful relationship between the levels of plasma TC and ET-1 in pediatric sepsis patients, suggesting these biomarkers are highly valuable in predicting patient outcomes. High levels of ET-1 and low levels of TC in these patients signify a grave condition and a poor prognosis.

Identification of Early Warning Signals of Infectious Diseases in Hospitals by Integrating Clinical Treatment and Disease Prevention

The global incidence of infectious diseases has increased in recent years, posing a significant threat to human health. Hospitals typically serve as frontline institutions for detecting infectious diseases. However, accurately identifying warning signals of infectious diseases in a timely manner, especially emerging infectious diseases, can be challenging. Consequently, there is a pressing need to integrate treatment and disease prevention data to conduct comprehensive analyses aimed at preventing and controlling infectious diseases within hospitals. This paper examines the role of medical data in the early identification of infectious diseases, explores early warning technologies for infectious disease recognition, and assesses monitoring and early warning mechanisms for infectious diseases. We propose that hospitals adopt novel multidimensional early warning technologies to mine and analyze medical data from various systems, in compliance with national strategies to integrate clinical treatment and disease prevention. Furthermore, hospitals should establish institution-specific, clinical-based early warning models for infectious diseases to actively monitor early signals and enhance preparedness for infectious disease prevention and control.

The critical role of neutrophil-endothelial cell interactions in sepsis: new synergistic approaches employing organ-on-chip, omics, immune cell phenotyping and in silico modeling to identify new therapeutics

Sepsis is a global health concern accounting for more than 1 in 5 deaths worldwide. Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. Sepsis can develop from bacterial (gram negative or gram positive), fungal or viral (such as COVID) infections. However, therapeutics developed in animal models and traditional in vitro sepsis models have had little success in clinical trials, as these models have failed to fully replicate the underlying pathophysiology and heterogeneity of the disease. The current understanding is that the host response to sepsis is highly diverse among patients, and this heterogeneity impacts immune function and response to infection. Phenotyping immune function and classifying sepsis patients into specific endotypes is needed to develop a personalized treatment approach. Neutrophil-endothelium interactions play a critical role in sepsis progression, and increased neutrophil influx and endothelial barrier disruption have important roles in the early course of organ damage. Understanding the mechanism of neutrophil-endothelium interactions and how immune function impacts this interaction can help us better manage the disease and lead to the discovery of new diagnostic and prognosis tools for effective treatments. In this review, we will discuss the latest research exploring how in silico modeling of a synergistic combination of new organ-on-chip models incorporating human cells/tissue, omics analysis and clinical data from sepsis patients will allow us to identify relevant signaling pathways and characterize specific immune phenotypes in patients. Emerging technologies such as machine learning can then be leveraged to identify druggable therapeutic targets and relate them to immune phenotypes and underlying infectious agents. This synergistic approach can lead to the development of new therapeutics and the identification of FDA approved drugs that can be repurposed for the treatment of sepsis.

World Health Organization Danger Signs to predict bacterial sepsis in young infants: A pragmatic cohort study

Bacterial sepsis is generally a major concern in ill infants. To help triaging decisions by front-line health workers in these situations, the World Health Organization (WHO) has developed danger signs (DS). The objective of this study was to evaluate the extent to which nine DS predict bacterial sepsis in young infants presenting with suspected sepsis in a low-income country setting. The study pragmatically evaluated nine DS in infants younger than 3 months with suspected sepsis in a regional hospital in Lilongwe, Malawi, between June 2018 and April 2020. Main outcomes were positive blood or cerebrospinal fluid (CSF) cultures for neonatal pathogens, and mortality. Among 401 infants (gestational age [mean ± SD]: 37.1±3.3 weeks, birth weight 2865±785 grams), 41 had positive blood or CSF cultures for a neonatal pathogen. In-hospital mortality occurred in 9.7% of infants overall (N = 39/401), of which 61.5% (24/39) occurred within 48 hours of admission. Mortality was higher in infants with bacterial sepsis compared to other infants (22.0% [9/41] versus 8.3% [30/360]; p = 0.005). All DS were associated with mortality except for temperature instability and tachypnea, whereas none of the DS were significantly associated with bacterial sepsis, except for "unable to feed" (OR 2.25; 95%CI: 1.17-4.44; p = 0.017). The number of DS predicted mortality (OR: 1.75; 95%CI: 1.43-2.17; p<0.001; AUC: 0.756), but was marginally associated with positive cultures with a neonatal pathogen (OR 1.22; 95%CI: 1.00-1.49; p = 0.046; AUC: 0.743). The association between number of DS and mortality remained significant after adjusting for admission weight, the only statistically significant co-variable (OR 1.75 [95% CI: 1.39-2.23]; p<0.001). Considering all positive cultures including potential bacterial contaminants resulted a non-significant association between number of DS and sepsis (OR 1.09 [95% CI: 0.93-1.28]; p = 0.273). In conclusion, this study shows that DS were strongly associated with death, but were marginally associated with culture-positive pathogen sepsis in a regional hospital setting. These data imply that the incidence of bacterial sepsis and attributable mortality in infants in LMIC settings may be inaccurately estimated based on clinical signs alone.

GPX4 is a key ferroptosis biomarker and correlated with immune cell populations and immune checkpoints in childhood sepsis

Sepsis is the uncontrolled reaction of the body to infection-induced inflammation, which results in life-threatening multiple-organ dysfunction (MODS). Although the research on sepsis has advanced significantly in recent years, its pathophysiology remains entirely unknown. Ferroptosis is a new-fashioned type of programmed cell death that may have an impact on sepsis development. However, the precise mechanism still needs to be explored. In this paper, Four pediatric sepsis datasets [training datasets (GSE26378 and GSE26440) and validation datasets (GSE11755 and GSE11281)] were chosen through the GEO (Gene Expression Omnibus) database, and 63 differentially expressions of ferroptosis-relation-genes (DE-FRGs) were eventually discovered using bioinformatics investigation. Functional annotation was performed using GO and KEGG pathway enrichment analysis. Then, four Core-FRGs (FTH1, GPX4, ACSL1, and ACSL6) were extracted after the construction of the protein-protein interaction (PPI) network and the research of the MCODE module. Consequently, Hub-FRG (GPX4) was found using the validation datasets, and correlation exploration of immunity populations (neutrophils, r =  - 0.52; CD8 T-cells, r = 0.43) and immunity checkpoints (CD274, r =  - 0.42) was implemented. The usefulness of GPX4 as a marker in sepsis was assessed in a mouse model of sepsis. The findings demonstrate that GPX4 is a crucial biomarker and a new latent immunotherapy target for the prediction and therapy of pediatric sepsis.

Machine learning applications on neonatal sepsis treatment: a scoping review

INTRODUCTION

Neonatal sepsis is a major cause of health loss and mortality worldwide. Without proper treatment, neonatal sepsis can quickly develop into multisystem organ failure. However, the signs of neonatal sepsis are non-specific, and treatment is labour-intensive and expensive. Moreover, antimicrobial resistance is a significant threat globally, and it has been reported that over 70% of neonatal bloodstream infections are resistant to first-line antibiotic treatment. Machine learning is a potential tool to aid clinicians in diagnosing infections and in determining the most appropriate empiric antibiotic treatment, as has been demonstrated for adult populations. This review aimed to present the application of machine learning on neonatal sepsis treatment.

METHODS

PubMed, Embase, and Scopus were searched for studies published in English focusing on neonatal sepsis, antibiotics, and machine learning.

RESULTS

There were 18 studies included in this scoping review. Three studies focused on using machine learning in antibiotic treatment for bloodstream infections, one focused on predicting in-hospital mortality associated with neonatal sepsis, and the remaining studies focused on developing machine learning prediction models to diagnose possible sepsis cases. Gestational age, C-reactive protein levels, and white blood cell count were important predictors to diagnose neonatal sepsis. Age, weight, and days from hospital admission to blood sample taken were important to predict antibiotic-resistant infections. The best-performing machine learning models were random forest and neural networks.

CONCLUSION

Despite the threat antimicrobial resistance poses, there was a lack of studies focusing on the use of machine learning for aiding empirical antibiotic treatment for neonatal sepsis.

Fast Track Diagnostic Tools for Clinical Management of Sepsis: Paradigm Shift from Conventional to Advanced Methods

Sepsis is one of the deadliest disorders in the new century due to specific limitations in early and differential diagnosis. Moreover, antimicrobial resistance (AMR) is becoming the dominant threat to human health globally. The only way to encounter the spread and emergence of AMR is through the active detection and identification of the pathogen along with the quantification of resistance. For better management of such disease, there is an essential requirement to approach many suitable diagnostic techniques for the proper administration of antibiotics and elimination of these infectious diseases. The current method employed for the diagnosis of sepsis relies on the conventional culture of blood suspected infection. However, this method is more time consuming and generates results that are false negative in the case of antibiotic pretreated samples as well as slow-growing microbes. In comparison to the conventional method, modern methods are capable of analyzing blood samples, obtaining accurate results from the suspicious patient of sepsis, and giving all the necessary information to identify the pathogens as well as AMR in a short period. The present review is intended to highlight the culture shift from conventional to modern and advanced technologies including their limitations for the proper and prompt diagnosing of bloodstream infections and AMR detection.

Utilizing big data from electronic health records in pediatric clinical care

Big data has the capacity to transform both pediatric healthcare delivery and research, but its potential has yet to be fully realized. Curation of large multi-institutional datasets of high-quality data has allowed for significant advances in the timeliness of quality improvement efforts. Improved access to large datasets and computational power have also paved the way for the development of high-performing, data-driven decision support tools and precision medicine approaches. However, implementation of these approaches and tools into pediatric practice has been hindered by challenges in our ability to adequately capture the heterogeneity of the pediatric population as well as the nuanced complexities of pediatric diseases such as sepsis. Moreover, there are large gaps in knowledge and definitive evidence demonstrating the utility, usability, and effectiveness of these types of tools in pediatric practice, which presents significant challenges to provider willingness to leverage these solutions. The next wave of transformation for pediatric healthcare delivery and research through big data and sophisticated analytics will require focusing efforts on strategies to overcome cultural barriers to adoption and acceptance. IMPACT: Big data from EHRs can be used to drive improvement in pediatric clinical care. Clinical decision support, artificial intelligence, machine learning, and precision medicine can transform pediatric care using big data from the EHR. This article provides a review of barriers and enablers for the effective use of data analytics in pediatric clinical care using pediatric sepsis as a use case. The impact of this review is that it will inform influencers of pediatric care about the importance of current trends in data analytics and its use in improving outcomes of care through EHR-based strategies.

Improving child health through Big Data and data science

Child health is defined by a complex, dynamic network of genetic, cultural, nutritional, infectious, and environmental determinants at distinct, developmentally determined epochs from preconception to adolescence. This network shapes the future of children, susceptibilities to adult diseases, and individual child health outcomes. Evolution selects characteristics during fetal life, infancy, childhood, and adolescence that adapt to predictable and unpredictable exposures/stresses by creating alternative developmental phenotype trajectories. While child health has improved in the United States and globally over the past 30 years, continued improvement requires access to data that fully represent the complexity of these interactions and to new analytic methods. Big Data and innovative data science methods provide tools to integrate multiple data dimensions for description of best clinical, predictive, and preventive practices, for reducing racial disparities in child health outcomes, for inclusion of patient and family input in medical assessments, and for defining individual disease risk, mechanisms, and therapies. However, leveraging these resources will require new strategies that intentionally address institutional, ethical, regulatory, cultural, technical, and systemic barriers as well as developing partnerships with children and families from diverse backgrounds that acknowledge historical sources of mistrust. We highlight existing pediatric Big Data initiatives and identify areas of future research. IMPACT: Big Data and data science can improve child health. This review highlights the importance for child health of child-specific and life course-based Big Data and data science strategies. This review provides recommendations for future pediatric-specific Big Data and data science research.

Challenging molecular dogmas in human sepsis using mathematical reasoning

Sepsis is defined as a dysregulated host-response to infection, across all ages and pathogens. What defines a dysregulated state remains intensively researched but incompletely understood. Here, we dissect the meaning of this definition and its importance for the diagnosis and management of sepsis. We deliberate on pathophysiological features and dogmas that range from cytokine storms and immune paralysis to dormancy and altered homeostasis setpoints. Mathematical reasoning, used to test for plausibility, reveals three interlinked cardinal rules governing host-response trajectories in sepsis. Rule one highlights that the amplitude of the immune response while important is not sufficient and is strictly dependent on rule two, specifying bioenergetic capacity and are together dynamically driven by rule three, delineating stability and alterations in setpoints. We consider these rules and associated pathophysiological parameters for guiding data-science and artificial intelligence mining of multi-omics and big-data for improving the precision of diagnostic and therapeutic approaches to sepsis.

Funding

PG funded by the European Regional Development Fund and Welsh Government (Ser Cymru programme – Project Sepsis).