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.

Given the critical role of immune cells and their responses in sepsis pathogenesis, this study aimed to evaluate the prognostic significance of various immune cell ratios in septic children through the collection and analysis of clinical data. Clinical data were collected from septic children admitted to the pediatric intensive care unit (PICU) of Shenzhen Children’s Hospital between January 2019 and September 2021. The peripheral blood immune cell ratios included the neutrophil to lymphocyte ratio (NLR), the derived neutrophil to lymphocyte ratio (dNLR), the neutrophil to lymphocyte and platelet ratio (NLPR), the monocyte to lymphocyte ratio (MLR), and the platelet to lymphocyte ratio (PLR). To investigate the associations between these immune cell ratios and mortality, we utilized the locally weighted scatterplot smoothing (LOWESS) method, receiver operating characteristic (ROC) analysis, and Kaplan‒Meier (K‒M) analysis. A total of 230 septic children were enrolled in the study. When comparing the immune cell ratios between the deceased and surviving groups, all ratios except for the PLR were elevated in the deceased group. Using the LOWESS method, we observed that the MLR, NLR, dNLR, and NLPR exhibited an approximately linear association with in-hospital mortality. Among the various immune cell ratios, the NLPR exhibited the highest AUC of 0.748, which was statistically comparable to that of the Pediatric Critical Illness Score (PCIS) (0.748 vs. 0.738, P = 0.852). The NLR (0.652), MLR (0.638), and dNLR (0.615) followed in terms of AUC values. K‒M analysis revealed that children with elevated MLR, NLR, dNLR, and NLPR exhibited increased 30-day mortality. The predictive capacity of the NLPR is comparable to that of the PCIS, suggesting that the NLPR has potential as a robust prognostic indicator for septic children.

Background: To summarize the clinical features of severe influenza in children and the high-risk factors for influenza-related deaths and to raise awareness among pediatricians.Methods: A retrospective study of clinical manifestations, laboratory tests, and diagnosis and treatment of 243 children with severe influenza admitted to Shenzhen Children's Hospital from January 2009 to December 2022 was conducted. Univariate logistic regression analysis and Boruta analysis were also performed to identify potentially critical clinical characteristics associated with death, and clinically significant were used in further multivariate logistic regression analysis. Subject receiver operating characteristic (ROC) curves were applied to assess the efficacy of death-related independent risk factors to predict death from severe influenza.Results: There were 169 male and 74 female patients with severe influenza, with a median age of 3 years and 2 months and 77.4% of patients under six. There were 46 cases (18.9%) in the death group. The most common pathogen was Influenza A virus (IAV) (81.5%). The most common complication in the death group was influenza-associated acute necrotizing encephalopathy (ANE [52.2%]). Severe influenza in children decreased significantly during the COVID-19 pandemic, with a median age of 5 years, a high predominance of neurological symptoms such as ANE (P = 0.001), and the most common pathogen being H3N2 (P < 0.001). D-dimer, acute respiratory distress syndrome (ARDS), and acute necrotizing encephalopathy (ANE) were significant independent risk factors for severe influenza-associated death. Furthermore, the ROC curves showed that the combined diagnosis of independent risk factors had significant early diagnostic value for severe influenza-related deaths.Conclusion: Neurological disorders such as ANE are more significant in children with severe influenza after the COVID-19 pandemic. Influenza virus infection can cause serious multisystem complications such as ARDS and ANE, and D-dimer has predictive value for early diagnosis and determination of the prognosis of children with severe influenza.

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.

Purpose This study aims to investigate the spectrum of viruses leading to severe viral pneumonia (SVP) and the associated risk factors for mortality among pediatric patients in the pediatric intensive care unit (PICU). Methods Taking the outbreak and end of the COVID-19 pandemic as a aboundary, The pre-pandemic period of COVID-19 spans from 01/2017 to 12/2019, the pandemic period from 01/2020 to 12/2021, and the post-pandemic period from 01/2022 to 12/2023. Patients were subsequently stratified into survivor and non-survivor groups based on clinical outcomes. Results A total of 1007 patients (median age 1.42 years, range 0.58–4.00; male: female ratio 1.7:1) diagnosed with SVP. Cases were stratified into pre-pandemic (n = 419, 41.6%), pandemic (n = 272, 27.0%), and post-pandemic (n = 316, 31.4%) periods. Viral predominance varied across phases: Pre-pandemic: Influenza A (IVA, 37.0% [155/419]), respiratory syncytial virus (RSV, 29.8%), adenovirus (19.8%), and influenza B (15.5%). Pandemic phase: Human rhinovirus (HRV, 40.1% [109/272]), RSV (33.1%), parainfluenza viruses (11.4%), and bocavirus (HBoV, 10.7%). Post-pandemic: HRV (24.4% [77/316]), RSV (22.8%), HBoV (14.2%), and IVA (13.6%). Comparative analysis revealed significant intergroup differences in the proportion of patients aged < 3 years, primary immunodeficiency disorders (PIDs), and sepsis between pure viral infection deaths and coinfection-associated fatalities among SVP cases. Logistic regression identified eight independent mortality predictors: acute leukemia, other malignant tumors, PIDs, moderate-to-severe underweight, rhabdomyolysis, acute respiratory distress syndrome (ARDS), infection-related encephalopathy, and multiorgan dysfunction syndrome (MODS). The prediction model demonstrated robust discriminative capacity for SVP mortality: sensitivity 73.8%, specificity 90.2%, and AUC 0.888 (95%CI 0.838–0.938) via ROC curve analysis. Conclusions The COVID-19 pandemic has altered the landscape of respiratory viruses causing SVP in children. The presence of underlying health conditions, particularly acute leukemia, other malignancies, and immunodeficiency, significantly increases the risk of death in children with viral pneumonia. The risk prediction model offers a reliable tool for clinical practice to predict mortality in these patients.