Objective. The effect of serum magnesium on the prognosis of children with sepsis in the pediatric intensive care unit (PICU) is unclear. This study was designed to assess the risk of inpatient mortality for children with sepsis in the PICU based on serum magnesium levels at admission. Methods. We collected patients’ clinical information from the Pediatric Intensive Care database and then performed locally weighted scatterplot smoothing (LOWESS) analysis, Kaplan–Meier analysis, and multivariate logistic regression to determine the relationship between admission serum magnesium and inpatient mortality in children with sepsis. Results. A total of 974 critically ill children with sepsis were included, with 246 patients in the hypomagnesemia group, 666 in the normal group, and 62 in the hypermagnesemia group. The chi-square test suggested that the hypermagnesemia group had higher in-hospital mortality than the normal group (14.5% vs. 2.4%, ). Kaplan–Meier curves revealed that the 30-day overall survival rate was lower in the hypermagnesaemia group than in the normal group ( ). The multivariate logistic regression model revealed that hypermagnesaemia was a risk factor related to inpatient mortality (odds ratio 4.22, 95% CI 1.55-11.50), while hypomagnesaemia was not a significant factor for inpatient mortality (odds ratio 0.78, 95% CI 0.26-2.32). Conclusion. Hypermagnesaemia, but not hypomagnesaemia, is a predictor of inpatient mortality in critically ill children with sepsis.

Objectives: In the early stage of sepsis, identifying high-risk paediatric patients with a poor prognosis and providing timely and adequate treatment are critical. This study aimed to evaluate the effect of average body temperature within 24 hours of admission on the short-term prognosis of paediatric patients with sepsis. Design: A retrospective cohort study. Setting: A single-centre, tertiary care hospital in China, containing patient data from 2010 to 2018. Participants: 1144 patients with sepsis were included. Intervention: None. Primary and secondary outcome measures: The main outcome measure was in-hospital mortality, which was defined as death from any cause during hospitalisation. The secondary outcome was the length of hospital stay. Results: The LOWESS method showed a roughly ‘U’-shaped relationship between body temperature on the first day and in-hospital mortality. Multivariate logistic regression showed that severe hypothermia (OR 14.72, 95% CI 4.84 to 44.75), mild hypothermia (OR 3.71, 95% CI 1.26 to 10.90), mild hyperthermia (OR 3.41, 95% CI 1.17 to 9.90) and severe hyperthermia (OR 5.15, 95% CI 1.84 to 14.43) were independent risk factors for in-hospital mortality. Compared with other variables, the Wald χ ² value of temperature on the first day minus the degree of freedom was the highest. Conclusions: Whether hypothermic or hyperthermic, the more abnormal the temperature on the first day is, the higher the risk of in-hospital death in children with sepsis.

Objective: The pathogenesis of sepsis is still unclear due to its complexity, especially in children. This study aimed to analyse the immune microenvironment and regulatory networks related to sepsis in children at the molecular level and to identify key immune-related genes to provide a new basis for the early diagnosis of sepsis. Methods: The GSE145227 and GSE26440 datasets were downloaded from the Gene Expression Omnibus. The analyses included differentially expressed genes (DEGs), functional enrichment, immune cell infiltration, the competing endogenous RNA (ceRNA) interaction network, weighted gene coexpression network analysis (WGCNA), protein–protein interaction (PPI) network, key gene screening, correlation of sepsis molecular subtypes/immune infiltration with key gene expression, the diagnostic capabilities of key genes, and networks describing the interaction of key genes with transcription factors and small-molecule compounds. Finally, real-time quantitative PCR (RT–qPCR) was performed to verify the expression of key genes. Results: A total of 236 immune-related DEGs, most of which were enriched in immune-related biological functions, were found. Further analysis of immune cell infiltration showed that M0 macrophages and neutrophils infiltrated more in the sepsis group, while fewer activated memory CD4⁺ T cells, resting memory CD4⁺ T cells, and CD8⁺ T cells did. The interaction network of ceRNA was successfully constructed. Six key genes (FYN, FBL, ATM, WDR75, FOXO1 and ITK) were identified by WGCNA and PPI analysis. We found strong associations between key genes and constructed septic molecular subtypes or immune cell infiltration. Receiver operating characteristic analysis showed that the area under the curve values of the key genes for diagnosis were all greater than 0.84. Subsequently, we successfully constructed an interaction network of key genes and transcription factors/small-molecule compounds. Finally, the key genes in the samples were verified by RT–qPCR. Conclusion: Our results offer new insights into the pathogenesis of sepsis in children and provide new potential diagnostic biomarkers for the disease.

Objective: Identifying high-risk children with poor prognoses during the early stages of sepsis and providing timely and appropriate interventions are imperative. The objective of this study was to develop a prognostic prediction model for pediatric sepsis utilizing the neutrophil to lymphocyte and platelet ratio (NLPR). Methods: A multivariable logistic regression analysis was conducted to investigate the association between the NLPR and in-hospital mortality among septic children upon admission. To minimize the potential confounding factors that could introduce bias, a propensity score matching analysis was employed. Subsequently, a nomogram prediction model was developed to assess the risk of in-hospital mortality in septic children, incorporating the NLPR as a key factor. The performance of this prediction model was then evaluated. Results: A total of 230 septic children were enrolled in the study. Multivariate logistic regression analysis revealed that the NLPR was an independent risk factor for in-hospital mortality, with an odds ratio of 8.31 (95% CI 3.69– 18.68). The finding remained consistent after propensity score matching analysis. A nomogram prediction model was developed that incorporates the NLPR, arterial blood lactate level, and Pediatric Critical Illness Score (PCIS). Among the various models, this nomogram exhibited the highest area under the curve (AUC) value of 0.831. The calibration curve demonstrated good agreement between the predicted and observed outcomes. Decision curve analysis indicated that the prediction model outperformed the PCIS. Internal validation of the model yielded an AUC value of 0.824 and a kappa value of 0.420, indicating its reliability and accuracy. Conclusion: The NLPR serves as an independent risk factor for in-hospital mortality among septic children. The nomogram prognostic prediction model could effectively guide clinicians in accurately predicting the prognosis of septic children, thus enabling timely and effective treatment interventions.