Assessment of serum proprotein convertase subtilisin/kexin type 9 in pediatric sepsis syndrome.

Sepsis is a life-threatening condition that arises when the body's response to infection causes injury to its tissues and organs. Proprotein convertase subtilisin/kexin type 9 (PCSK9) is an enzyme released in response to the drop in cholesterol level occurring in sepsis. Our study aimed to evaluate the prognostic role of serum Proprotein convertase subtilisin/kexin type 9 (PCSK9) level in children with sepsis and severe sepsis. Sixty children were included in this study. They were divided into two groups: 30 children in the sepsis group and 30 in the severe sepsis group. Another 30 apparently healthy children were included as a control group. Blood samples were withdrawn from all included children for complete blood count (CBC), renal function tests (RFT), liver function tests (LFT), LDL-cholesterol (LDL-C), blood culture, and serum PCSK9. In this study, PCSK9 and LDL-C were higher in the two sepsis groups than in the control group (p < 0.05). They were also higher in the severe sepsis group than the sepsis group and in the non-survivors than in the survivors (p < 0.05). PCSK9 was positively correlated with length of hospital stay in surviving children (r = 0.67, p = 0.001) and had predicted significant hematological dysfunction (adjusted B = - 96.95, p = 0.03). In conclusion, the PCSK9 assay can be used as a biomarker for bad prognosis in children suffering from clinical sepsis.

Characteristics and Risk Factors for Pediatric Sepsis.

OBJECTIVE: Sepsis is considered a major cause of health loss in children and had high mortality and morbidity. Currently, there is no reliable model for predicting the prognosis of pediatric patients with sepsis. This study aimed to analyze the clinical characteristics of sepsis in children and assess the risk factors associated with poor prognosis in pediatric sepsis patients to identify timely interventions and improve their outcomes. METHODS: This study analyzed the clinical indicators and laboratory results of septic patients hospitalized in the Pediatric Intensive Care Unit of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, China, from January 1, 2019, to December 31, 2021. Risk factors for sepsis were identified by logistic regression analyses. RESULTS: A total of 355 children with sepsis were enrolled, with 333 children (93.8%) in the good prognosis group, and 22 children (6.2%) in the poor prognosis group. Among them, there were 255 patients (71.8%) in the sepsis group, and 100 patients (28.2%) in the severe sepsis group. The length of hospital stay in the poor prognosis group was longer than that in the good prognosis group (P<0.01). The levels of interleukin 1ß (IL-1ß) in the poor prognosis group were higher than those in the good prognosis group (P>0.05), and the platelet (PLT), albumin (ALB), and hemoglobin (Hb) levels were lower in the poor prognosis group (P<0.01). The IL-8 levels in the severe sepsis group were higher than those in the sepsis group (P<0.05). Multiple logistic regression analysis suggested that lower Hb levels, ALB levels, peak PLT counts, and higher IL-1ß levels were independent risk factors for poor prognosis in children with sepsis. CONCLUSION: Lower Hb, ALB, and PLT counts and elevated IL-1ß are independent risk factors for poor prognosis in children with sepsis.

Screening and Identification of Neutrophil Extracellular Trap-related Diagnostic Biomarkers for Pediatric Sepsis by Machine Learning.

Neutrophil extracellular trap (NET) is released by neutrophils to trap invading pathogens and can lead to dysregulation of immune responses and disease pathogenesis. However, systematic evaluation of NET-related genes (NETRGs) for the diagnosis of pediatric sepsis is still lacking. Three datasets were taken from the Gene Expression Omnibus (GEO) database: GSE13904, GSE26378, and GSE26440. After NETRGs and differentially expressed genes (DEGs) were identified in the GSE26378 dataset, crucial genes were identified by using LASSO regression analysis and random forest analysis on the genes that overlapped in both DEGs and NETRGs. These crucial genes were then employed to build a diagnostic model. The diagnostic model's effectiveness in identifying pediatric sepsis across the three datasets was confirmed through receiver operating characteristic curve (ROC) analysis. In addition, clinical pediatric sepsis samples were collected to measure the expression levels of important genes and evaluate the diagnostic model's performance using qRT-PCR in identifying pediatric sepsis in actual clinical samples. Next, using the CIBERSORT database, the relationship between invading immune cells and diagnostic markers was investigated in more detail. Lastly, to evaluate NET formation, we measured myeloperoxidase (MPO)-DNA complex levels using ELISA. A group of five important genes (MME, BST1, S100A12, FCAR, and ALPL) were found among the 13 DEGs associated with NET formation and used to create a diagnostic model for pediatric sepsis. Across all three cohorts, the sepsis group had consistently elevated expression levels of these five critical genes as compared to the normal group. Area under the curve (AUC) values of 1, 0.932, and 0.966 indicate that the diagnostic model performed exceptionally well in terms of diagnosis. Notably, when applied to the clinical samples, the diagnostic model also showed good diagnostic capacity with an AUC of 0.898, outperforming the effectiveness of traditional inflammatory markers such as PCT, CRP, WBC, and NEU%. Lastly, we discovered that children with high ratings for sepsis also had higher MPO-DNA complex levels. In conclusion, the creation and verification of a five-NETRGs diagnostic model for pediatric sepsis performs better than established markers of inflammation.

Serum monocyte chemotactic protein 1 and soluble mannose receptor aid predictive diagnosis of pediatric sepsis.

BACKGROUND: To investigate the value of serum monocyte chemotactic protein 1 (MCP-1) and soluble mannose receptor (sMR) for predictive diagnosis of pediatric sepsis. METHODS: This study retrospectively analyzed the data of 82 children with acute and severe signs of inflammation. According to the diagnostic criteria of sepsis, these children were divided into a sepsis group (40 cases) and a non-sepsis group (42 cases). In addition, 50 children who received health examinations during the same time period in Cangzhou Central Hospital were selected as a control group. According to the prognosis of the children in the sepsis group, they were further divided into a survival group (33 cases) and a death group (7 cases). The levels of blood indicators, inflammatory markers, liver and kidney function indicators, MCP-1 level, and sMR were collected from the children. The efficacy of using sMR and MCP-1 levels in the predictive diagnosis of sepsis was analyzed by using the area under the ROC curve (AUC). RESULTS: Serum levels of MCP-1 and sMR were (452.32±2.79) µg/ml and (97.23±.15) µg/ml, respectively, in the sepsis group, significantly higher than those in all controls (P<0.001). In the death group, the levels of white blood cells (WBC), C-reactive protein (CRP), procalcitonin (PCT), sMR, and MCP-1 were significantly higher compared to the survival group (P<0.05). The AUC for CRP in predictive diagnosis of sepsis was 0.9075; the AUC for PCT was 0.8759; the AUC for sMR was 0.9244; and the AUC for MCP-1 was 0.9406. CONCLUSIONS: Serum sMR and MCP-1 levels can help predict the diagnosis of pediatric sepsis.

Predictive Value of a Diagnostic Five-Gene Biomarker for Pediatric Sepsis.

Background: Pediatric sepsis has a very high morbidity and mortality rate. The purpose of this study was to evaluate diagnostic biomarkers and immune cell infiltration in pediatric sepsis. Methods: Three datasets (GSE13904, GSE26378, and GSE26440) were downloaded from the gene expression omnibus (GEO) database. After identifying overlapping genes in differentially expressed genes (DEGs) and modular sepsis genes selected via a weighted gene co-expression network (WGCNA) in the GSE26378 dataset, pivotal genes were further identified by using LASSO regression and random forest analysis to construct a diagnostic model. Receiver operating characteristic curve (ROC) analysis was used to validate the efficacy of the diagnostic model for pediatric sepsis. Furthermore, we used qRT-PCR to detect the expression levels of pivotal genes and validate the diagnostic model's ability to diagnose pediatric sepsis in 65 actual clinical samples. Results: Among 294 overlapping genes of DEGs and modular sepsis genes, five pivotal genes (STOM, MS4A4A, CD177, MMP8, and MCEMP1) were screened to construct a diagnostic model of pediatric sepsis. The expression of the five pivotal genes was higher in the sepsis group than in the normal group. The diagnostic model showed good diagnostic ability with AUCs of 1, 0.986, and 0.968. More importantly, the diagnostic model showed good diagnostic ability with AUCs of 0.937 in the 65 clinical samples and showed better efficacy compared to conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%). Conclusion: We developed and tested a five-gene diagnostic model that can reliably identify pediatric sepsis and also suggest prospective candidate genes for peripheral blood diagnostic testing in pediatric sepsis patients.

The landscape of immune dysregulation in pediatric sepsis at a single-cell resolution.

Recognizing immune dysregulation as a hallmark of sepsis pathophysiology, leukocytes have attracted major attention of investigation. While adult and pediatric sepsis are clinically distinct, their immunological delineation remains limited. Single cell technologies facilitated the characterization of immune signatures. We tackled to delineate immunological profiles of pediatric sepsis at a single-cell level by analyzing blood samples from six septic children, at both acute and recovery phases, and four healthy children. 16 single-cell transcriptomic datasets were analyzed and compared to adult sepsis dataset. We showed a unique shift in neutrophil subpopulations and functions between acute and recovery phases, along with the regulatory role of resistin. Neutrophil signatures were comparable between adult and pediatric sepsis. Innate-like CD4 T cells were predominantly and uniquely observed in acute phase of pediatric sepsis. Our study serves as a rich source of information about the phenotypic diversity and trajectory of circulating immune cells during pediatric sepsis.

Balanced crystalloids versus isotonic saline in pediatric sepsis: a comprehensive systematic review and meta-analysis.

Purpose: We conducted a comprehensive meta-analysis to compare the effects of balanced crystalloids (BC) and isotonic saline (IS) in pediatric sepsis. Methods: A systematic search was performed for studies comparing BC and IS in pediatric sepsis. Outcomes included mortality, acute kidney injury (AKI), need for renal replacement therapy (RRT), hospital length of stay (LOS), and pediatric intensive care unit (PICU) LOS. A random-effect models was used to calculated pooled odds ratios (OR) and mean differences (MD) with 95% confidence intervals (CIs). Results: The analysis included six studies with 8753 children. BC demonstrated significant reductions in overall mortality (OR 0.84, 95% CI 0.71 to 0.98, P = 0.03, I2 = 0%) and AKI (OR 0.74, 95% CI 0.57 to 0.96, P = 0.03, I2 = 37%) compared to IS. RRT need was similar between the BC and IS groups (OR 0.79, 95% CI 0.60 to 1.02, P = 0.07, I2 = 0%). Hospital and PICU LOS did not differ significantly. However, subgroup analysis of randomized controlled trials revealed significantly shorter hospital LOS in the BC group (mean difference -0.66 days, 95% CI -1.10 to -0.23, P = 0.003, I2 = 0%). Conclusion: Our meta-analysis demonstrates that using BC in pediatric sepsis is associated with reduced mortality, AKI, and hyperchloremia rates compared to IS, while maintaining similar hospital and PICU LOS. Large-scale randomized controlled trials are needed to validate these findings.

Deciphering the molecular classification of pediatric sepsis: integrating WGCNA and machine learning-based classification with immune signatures for the development of an advanced diagnostic model.

Introduction: Pediatric sepsis (PS) is a life-threatening infection associated with high mortality rates, necessitating a deeper understanding of its underlying pathological mechanisms. Recently discovered programmed cell death induced by copper has been implicated in various medical conditions, but its potential involvement in PS remains largely unexplored. Methods: We first analyzed the expression patterns of cuproptosis-related genes (CRGs) and assessed the immune landscape of PS using the GSE66099 dataset. Subsequently, PS samples were isolated from the same dataset, and consensus clustering was performed based on differentially expressed CRGs. We applied weighted gene co-expression network analysis to identify hub genes associated with PS and cuproptosis. Results: We observed aberrant expression of 27 CRGs and a specific immune landscape in PS samples. Our findings revealed that patients in the GSE66099 dataset could be categorized into two cuproptosis clusters, each characterized by unique immune landscapes and varying functional classifications or enriched pathways. Among the machine learning approaches, Extreme Gradient Boosting demonstrated optimal performance as a diagnostic model for PS. Discussion: Our study provides valuable insights into the molecular mechanisms underlying PS, highlighting the involvement of cuproptosis-related genes and immune cell infiltration.

Relative Adrenal Insufficiency Is a Risk Factor for Pediatric Sepsis: A Proof-of-Concept Study.

Glucocorticoid (GC) therapy had been strongly recommended for pediatric sepsis (grade 1A). However, the recommendation was changed to grade 2C in 2020 due to weak evidence. About 32.8% of patients with pediatric septic develop relative adrenal insufficiency (RAI). But whether GC therapy should be determined by RAI status is controversial. This study utilized 21-day-old SF1CreSRBIfl/fl mice as the first pediatric RAI mouse model to assess the pathogenesis of RAI and evaluate GC therapy. RAI mice exhibited a substantially higher mortality rate in cecal ligation and puncture and cecal slurry-induced sepsis. These mice featured persistent inflammatory responses and were effectively rescued by GC therapy. RNA sequencing analysis revealed persistent inflammatory responses in RAI mice, caused by transcriptional dysregulation of AP-1 and NF-κB, and cytokine-induced secondary inflammatory response. Our findings support a precision medicine approach to guide GC therapy for pediatric patients based on the status of RAI.

The use of probabilistic graphical models in pediatric sepsis: a feasibility and scoping review.

Background: Recent research has demonstrated that machine learning (ML) has the potential to improve several aspects of medical application for critical illness, including sepsis. This scoping review aims to evaluate the feasibility of probabilistic graphical model (PGM) methods in pediatric sepsis application and describe the use of pediatric sepsis definition in these studies. Methods: Literature searches were conducted in PubMed, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL+), and Web of Sciences from 2000-2023. Keywords included "pediatric", "neonates", "infants", "machine learning", "probabilistic graphical model", and "sepsis". Results: A total of 3,244 studies were screened, and 72 were included in this scoping review. Sepsis was defined using positive microbiology cultures in 19 studies (26.4%), followed by the 2005's international pediatric sepsis consensus definition in 11 studies (15.3%), and Sepsis-3 definition in seven studies (9.7%). Other sepsis definitions included: bacterial infection, the international classification of diseases, clinicians' assessment, and antibiotic administration time. Among the most common ML approaches used were logistic regression (n=27), random forest (n=24), and Neural Network (n=18). PGMs were used in 13 studies (18.1%), including Bayesian classifiers (n=10), and the Markov Model (n=3). When applied on the same dataset, PGMs show a relatively inferior performance to other ML models in most cases. Other aspects of explainability and transparency were not examined in these studies. Conclusions: Current studies suggest that the performance of probabilistic graphic models is relatively inferior to other ML methods. However, its explainability and transparency advantages make it a potentially viable method for several pediatric sepsis studies and applications.

Identification of the Shared Gene Signatures of HCK, NOG, RNF125 and Biological Mechanism in Pediatric Acute Lymphoblastic Leukaemia and Pediatric Sepsis.

The shared mechanisms between pediatric acute lymphoblastic leukaemia (ALL) and pediatric sepsis are currently unclear. This study was aimed to explore the shared key genes of pediatric ALL and pediatric sepsis. The datasets involved were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control samples in GSE13904 and GSE79533 were intersected. The least absolute shrinkage and selection operator (LASSO) and the boruta analyses were performed in GSE13904 and GSE79533 separately based on shared DEGs, and shared key genes were obtained by taking the intersection of sepsis-related key genes and ALL-related key genes. Three shared key genes (HCK, NOG, RNF125) were obtained, that have a good diagnostic value for both sepsis and ALL. The correlation between shared key genes and differentially expressed immune cells was higher in GSE13904 and conversely, the correlation of which was lower in GSE79533. Suggesting that the sharing key genes had a different impact on the immune environment in pediatric ALL and pediatric sepsis. We make the case that this study provides a new perspective to study the relationship between pediatric ALL and pediatric sepsis.

Lysosome-Related Diagnostic Biomarkers for Pediatric Sepsis Integrated by Machine Learning.

Background: There is currently no biomarker that can reliably identify sepsis, despite recent scientific advancements. We systematically evaluated the value of lysosomal genes for the diagnosis of pediatric sepsis. Methods: Three datasets (GSE13904, GSE26378, and GSE26440) were obtained from the gene expression omnibus (GEO) database. LASSO regression analysis and random forest analysis were employed for screening pivotal genes to construct a diagnostic model between the differentially expressed genes (DEGs) and lysosomal genes. The efficacy of the diagnostic model for pediatric sepsis identification in the three datasets was validated through receiver operating characteristic curve (ROC) analysis. Furthermore, a total of 30 normal samples and 35 pediatric sepsis samples were gathered to detect the expression levels of crucial genes and assess the diagnostic model's efficacy in diagnosing pediatric sepsis in real clinical samples through real-time quantitative PCR (qRT-PCR). Results: Among the 83 differentially expressed genes (DEGs) related to lysosomes, four key genes (STOM, VNN1, SORT1, and RETN) were identified to develop a diagnostic model for pediatric sepsis. The expression levels of these four key genes were consistently higher in the sepsis group compared to the normal group across all three cohorts. The diagnostic model exhibited excellent diagnostic performance, as evidenced by area under the curve (AUC) values of 1, 0.971, and 0.989. Notably, the diagnostic model also demonstrated strong diagnostic ability with an AUC of 0.917 when applied to the 65 clinical samples, surpassing the efficacy of conventional inflammatory indicators such as procalcitonin (PCT), white blood cell (WBC) count, C-reactive protein (CRP), and neutrophil percentage (NEU%). Conclusion: A four-gene diagnostic model of lysosomal function was devised and validated, aiming to accurately detect pediatric sepsis cases and propose potential target genes for lysosomal intervention in affected children.

Children are small adults (when properly normalized): Transferrable/generalizable sepsis prediction.

Background: Though governed by the same underlying biology, the differential physiology of children causes the temporal evolution from health to a septic/diseased state to follow trajectories that are distinct from adult cases. As pediatric sepsis data sets are less readily available than for adult sepsis, we aim to leverage this shared underlying biology by normalizing pediatric physiological data such that it would be directly comparable to adult data, and then develop machine-learning (ML) based classifiers to predict the onset of sepsis in the pediatric population. We then externally validated the classifiers in an independent adult dataset. Methods: Vital signs and laboratory observables were obtained from the Pediatric Intensive Care (PIC) database. These data elements were normalized for age and placed on a continuous scale, termed the Continuous Age-Normalized SOFA (CAN-SOFA) score. The XGBoost algorithm was used to classify pediatric patients that are septic. We tested the trained model using adult data from the MIMIC-IV database. Results: On the pediatric population, the sepsis classifier has an accuracy of 0.84 and an F1-Score of 0.867. On the adult population, the sepsis classifier has an accuracy of 0.80 and an F1-score of 0.88; when tested on the adult population, the model showed similar performance degradation ("data drift") as in the pediatric population. Conclusions: In this work, we demonstrate that, using a straightforward age-normalization method, EHR's can be generalizable compared (at least in the context of sepsis) between the pediatric and adult populations.

Accuracy of Pancreatic Stone Protein for diagnosis of sepsis in children admitted to pediatric intensive care or high-dependency care: a pilot study.

BACKGROUND: Pancreatic Stone Protein (PSP) is one of the most promising diagnostic and prognostic markers. The aim of the study was to assess the accuracy of PSP, compared to C-Reactive Protein (CRP), and Procalcitonin (PCT) for sepsis diagnosis in pediatric patients. Furthermore, we explored the correlation of PSP levels with sepsis severity and organ failure measured with PELOD-2 score. METHODS: Forty pediatric patients were enrolled following admission to pediatric intensive care, high dependency care or pediatric ward. PSP blood levels were measured in Emergency Department (nanofluidic point-of-care immunoassay; abioSCOPE, Abionic SA, Switzerland) on day 1, 2, 3, 5 and 7 from the onset of the clinical signs and symptoms of sepsis or SIRS. Inclusion criteria were: 1) patient age (1 month to 18 years old), 2) signs and symptoms of SIRS, irrespective of association with organ dysfunction. Exclusion criteria were: 1) hemato-oncological diseases and/or immunodeficiencies, 2) pancreatic diseases. RESULTS: Septic patients showed higher PSP levels than those with non-infectious systemic inflammation. The optimal cut-off in diagnosis of sepsis for PSP at day 1 was 167 ng/ml resulted in a sensitivity of 59% (95% IC 36%-79%) and a specificity of 83% (95% IC 58%-96%) with an AUC of 0.636 for PSP in comparison to AUC of 0.722 for PCT and 0.503 for C-RP. ROC analysis for outcome (survival versus no survival) has showed AUC 0.814 for PSP; AUC 0.814 for PCT; AUC of 0.657 for C-RP. CONCLUSIONS: PSP could distinguish sepsis from non-infectious systemic inflammation; however, our results need to be confirmed in larger pediatric population.

Microdialysis as a safe and feasible method to study target-site piperacillin-tazobactam disposition in septic piglets and children.

OBJECTIVES: Knowledge on the tissue penetration of piperacillin-tazobactam in children with sepsis is lacking. In this study, the feasibility and performance of microdialysis experiments were explored in septic piglets and children as part of a translational research project. METHODS: Multiple-day microdialysis investigations were performed in muscle tissue of 22 piglets (of which 11 were septic) and 6 children with sepsis. An in vitro experiment preceded the (pre)clinical trials to derive optimal experimental settings and calibration technique. Linear mixed-effects models quantified the impact of sepsis on relative recovery (RR) and intercatheter, interindividual, interoccasion, and residual variability. RESULTS: In vivo microdialysis was well tolerated in piglets and children, with no significant adverse events reported. Using identical experimental settings, lower RR values were recorded in healthy and septic piglets (range: piperacillin, 17.2-29.1% and tazobactam, 23.5-29.1%) compared with the in vitro experiment (piperacillin, 43.3% and tazobactam, 55.3%), and there were unacceptably low values in children with sepsis (<10%). As a result, methodological changes were made in the pediatric trial. Realistic tissue concentration-time curves were derived in piglets and children. In piglets, sepsis reduced the RR. The greatest contributors to RR variability were residual (>40%) and interoccasion (>30%) variability. The internal standard method was the preferred calibration technique in both piglets and children. CONCLUSIONS: Microdialysis is a safe and applicable method for the measurement of tissue drug concentrations in piglets and children. This study demonstrated the impact of experimental settings, sepsis, and target population on individual RR.

Cost-effectiveness analysis of Smart Triage, a data-driven pediatric sepsis triage platform in Eastern Uganda.

BACKGROUND: Sepsis, characterized by organ dysfunction due to presumed or proven infection, has a case-fatality over 20% in severe cases in low-and-middle income countries. Early diagnosis and treatment have proven benefits, prompting our implementation of Smart Triage at Jinja Regional Referral Hospital in Uganda, a program that expedites treatment through a data-driven triage platform. We conducted a cost-effectiveness analysis of Smart Triage to explore its impact on patients and inform multicenter scale up. METHODS: The parent clinical trial for Smart Triage was pre-post in design, using the proportion of children receiving sepsis treatment within one hour as the primary outcome, a measure linked to mortality benefit in existing literature. We used a decision-analytic model with Monte Carlo simulation to calculate the cost per year-of-life-lost (YLL) averted of Smart Triage from societal, government, and patient perspectives. Healthcare utilization and lost work for seven days post-discharge were translated into costs and productivity losses via secondary linkage data. RESULTS: In 2021 United States dollars, Smart Triage requires an annuitized program cost of only $0.05 per child, but results in $15.32 saved per YLL averted. At a willingness-to-pay threshold of only $3 per YLL averted, well below published cost-effectiveness threshold estimates for Uganda, Smart Triage approaches 100% probability of cost-effectiveness over the baseline manual triage system. This cost-effectiveness was observed from societal, government, and patient perspectives. The cost-effectiveness observed was driven by a reduction in admission that, while explainable by an improved triage mechanism, may also be partially attributable to changes in healthcare utilization influenced by the coronavirus pandemic. However, Smart Triage remains cost-effective in sensitivity analyses introducing a penalty factor of up to 50% in the reduction in admission. CONCLUSION: Smart Triage's ability to both save costs and avert YLLs indicates that patients benefit both economically and clinically, while its high probability of cost-effectiveness strongly supports multicenter scale up. Areas for further research include the incorporation of years lived with disability when sepsis disability weights in low-resource settings become available and analyzing budget impact during multicenter scale up. TRIAL REGISTRATION: NCT04304235 (registered on 11/03/2020, clinicaltrials.gov).

Prognostic factors of sepsis in children with acute leukemia admitted to the pediatric intensive care unit.

OBJECTIVE: To analyze the prognostic factors of sepsis in children with acute leukemia admitted to the pediatric intensive care unit (PICU) and to compare the efficacy of different scoring systems for predicting the outcome of children. METHODS: Patients with an acute leukemia diagnosis admitted to a tertiary care university hospital PICU due to sepsis during chemotherapy between May 2015 and August 2022 were retrospectively analyzed through an electronic medical record system. RESULTS: During this period, 693 children with acute leukemia initially diagnosed were admitted to the center, and 155 (22.3%) of them were transferred to PICU due to deterioration of the disease during treatment. Total 109 (70.3%) patients were transferred to PICU due to sepsis. Here, 17 patients was excluded (prior treatment from another hospital; referring from other hospitals; discontinued treatment; incomplete medical record). Of the 92 patients studied, the mortality rate was 35.9%. Multivariate analysis revealed that remission status, lactate level, invasive mechanical ventilation (IMV), and inotropic support within 48 hours after PICU transfer were independent risk factors for PICU mortality. The pediatric sequential organ failure assessment (PSOFA) score had the greatest predictive validity for hospital mortality (area under the receiver operating characteristic curve [AUROC]: 0.83, 95% confidence intervals [CI]: 0.74-0.92), followed by the pediatric early warning score (PEWS) (0.82, 0.73-0.91) and pediatric critical illness score (PCIS) (0.79, 0.69-0.88). CONCLUSION: The mortality rate among children with acute leukemia complicated with sepsis is high after being transferred to the PICU. Various scoring systems can be used to monitor the clinical status of patients, identify sepsis early, detect critical illness, and determine the optimal time for transfer to the PICU for supportive treatment, thereby improving the prognosis of these patients.

Effective diagnosis of sepsis in critically ill children using probabilistic graphical model.

Background: Probabilistic graphical model, a rich graphical framework in modelling associations between variables in complex domains, can be utilized to aid clinical diagnosis. However, its application in pediatric sepsis remains limited. This study aims to explore the utility of probabilistic graphical models in pediatric sepsis in the pediatric intensive care unit. Methods: We conducted a retrospective study on children using the first 24-hour clinical data of the intensive care unit admission from the Pediatric Intensive Care Dataset, 2010-2019. A probabilistic graphical model method, Tree Augmented Naive Bayes, was used to build diagnosis models using combinations of four categories: vital signs, clinical symptoms, laboratory, and microbiological tests. Variables were reviewed and selected by clinicians. Sepsis cases were identified with the discharged diagnosis of sepsis or suspected infection with the systemic inflammatory response syndrome. Performance was measured by the average sensitivity, specificity, accuracy, and area under the curve of ten-fold cross-validations. Results: We extracted 3,014 admissions [median age of 1.13 (interquartile range: 0.15-4.30) years old]. There were 134 (4.4%) and 2,880 (95.6%) sepsis and non-sepsis patients, respectively. All diagnosis models had high accuracy (0.92-0.96), specificity (0.95-0.99), and area under the curve (0.77-0.87). Sensitivity varied with different combinations of variables. The model that combined all four categories yielded the best performance [accuracy: 0.93 (95% confidence interval (CI): 0.916-0.936); sensitivity: 0.46 (95% CI: 0.376-0.550), specificity: 0.95 (95% CI: 0.940-0.956), area under the curve: 0.87 (95% CI: 0.826-0.906)]. Microbiological tests had low sensitivity (<0.10) with high incidence of negative results (67.2%). Conclusions: We demonstrated that the probabilistic graphical model is a feasible diagnostic tool for pediatric sepsis. Future studies using different datasets should be conducted to assess its utility to aid clinicians in the diagnosis of sepsis.

Pediatric Sepsis: a Summary of Current Definitions and Management Recommendations.

Purpose of Review: Pediatric sepsis remains an important cause of morbidity and mortality in children. This review will summarize the main aspects of the definition, the current evidence base for interventions discuss some controversial themes and point towards possible areas of improvement. Recent Findings: Controversy remains regarding the accurate definition, resuscitation fluid volume and type, choice of vasoactive/inotropic agents, and antibiotic depending upon specific infection risks. Many adjunctive therapies have been suggested with theoretical benefits, although definitive recommendations are not yet supported by data. We describe best practice recommendations based on international guidelines, a review of primary literature, and a discussion of ongoing clinical trials and the nuances of therapeutic choices. Summary: Early diagnosis and timely intervention with antibiotics, fluid resuscitation, and vasoactive medications are the most important interventions in sepsis. The implementation of protocols, resource-adjusted sepsis bundles, and advanced technologies will have an impact on reducing sepsis mortality.

Implementing Pediatric Surviving Sepsis Campaign Guidelines: Improving Compliance With Lactate Measurement in the PICU.

The 2020 pediatric Surviving Sepsis Campaign (pSSC) recommends measuring lactate during the first hour of resuscitation for severe sepsis/shock. We aimed to improve compliance with this recommendation for patients who develop severe sepsis/shock while admitted to the PICU. DESIGN: Structured, quality improvement initiative. SETTING: Single-center, 26-bed, quaternary-care PICU. PATIENTS: All patients with PICU-onset severe sepsis/shock from December 2018 to December 2021. INTERVENTIONS: Creation of a multidisciplinary local sepsis improvement team, education program targeting frontline providers (nurse practitioners, resident physicians), and peer-to-peer nursing education program with feedback to key stakeholders. MEASUREMENTS AND MAIN RESULTS: The primary outcome measure was compliance with obtaining a lactate measurement within 60 minutes of the onset of severe sepsis/shock originating in our PICU using a local Improving Pediatric Sepsis Outcomes database and definitions. The process measure was time to first lactate measurement. Secondary outcomes included number of IV antibiotic days, number of vasoactive days, number of ICU days, and number of ventilator days. A total of 166 unique PICU-onset severe sepsis/shock events and 156 unique patients were included. One year after implementation of our first interventions with subsequent Plan-Do-Study-Act cycles, overall compliance increased from 38% to 47% (24% improvement) and time to first lactate decreased from 175 to 94 minutes (46% improvement). Using a statistical process control I chart, the preshift mean for time to first lactate measurement was noted to be 179 minutes and the postshift mean was noted to be 81 minutes demonstrating a 55% improvement. CONCLUSIONS: This multidisciplinary approach led to improvement in time to first lactate measurement, an important step toward attaining our target of lactate measurement within 60 minutes of septic shock identification. Improving compliance is necessary for understanding implications of the 2020 pSSC guidelines on sepsis morbidity and mortality.