Background: An increase in the incidence of central venous catheter (CVC)-related thrombosis (CRT) has been reported in pediatric intensive care patients over the past decade. Risk factors for the development of CRT are not well understood, especially in children. The study objective was to identify potential clinical risk factors associated with CRT with novel fusion machine learning models. Methods: Patients aged 0–18 who were admitted to intensive care units from December 2015 to December 2018 and underwent at least one CVC placement were included. Two fusion model approaches (stacking and blending) were used to build a better performance model based on three widely used machine learning models (logistic regression, random forest and gradient boosting decision tree). High-impact risk factors were identified based on their contribution in both fusion artificial intelligence models. Results: A total of 478 factors of 3871 patients and 3927 lines were used to build fusion models, one of which achieved quite satisfactory performance (AUC = 0.82, recall = 0.85, accuracy = 0.65) in 5-fold cross validation. A total of 11 risk factors were identified based on their independent contributions to the two fusion models. Some risk factors, such as D-dimer, thrombin time, blood acid-base balance-related factors, dehydrating agents, lymphocytes and basophils were identified or confirmed to play an important role in CRT in children. Conclusions: The fusion model, which achieves better performance in CRT prediction, can better understand the risk factors for CRT and provide potential biomarkers and measures for thromboprophylaxis in pediatric intensive care settings.

Objective: The objective of this study was to appraise the interrelation between overweight/obesity and the safety and efficacy of COVID-19 vaccination by synthesizing the currently available evidence. Methods: A systematic review of published studies on the safety and efficacy of the COVID-19 vaccine in people who were overweight or obese was conducted. Databases including Embase, Medline Epub (Ovid), PsychInfo (Ovid), Web of Science, PubMed, CINAHL, and Google Scholar were searched to identify relevant studies. The databases of the Centers for Disease Control (CDC) and World Health Organization (WHO) were also searched for relevant unpublished and gray literature. Results: Fifteen studies were included in the review. All the included studies used observational study designs; there were ten cohort studies and five cross-sectional studies. The sample size of these studies ranged from 21 to 9,171,524. Thirteen studies reported using BNT162b2 (Pfizer-BioNTech, USA), four reported using ChAdOx-nCov19 (AstraZeneca, U.K), two were reported using CoronaVac (Sinovac, China), and two were reported using mRNA1273 (Moderna, USA). The efficacy and safety of COVID-19 vaccines have been extensively studied in individuals with overweight/obesity. Most studies have shown that the humoral response decreases with increasing BMI. The available evidence does not conclusively indicate that these vaccines are generally safe in this population. Conclusion: While the efficacy of the COVID-19 vaccine may be less than ideal in people who are overweight or obese, it does not mean that obese people should not be vaccinated, as the vaccine can still provide some protection. There is a lack of evidence for conclusions to be drawn about the safety of the vaccine in the population. This study calls on health professionals, policymakers, caregivers, and all other stakeholders to focus on monitoring the possible adverse effects of injections in overweight/obese people.

Background: Low cardiac output syndrome (LCOS) is the most common complication after cardiac surgery, which is associated with the extension of postoperative hospital stay and postoperative death in children with congenital heart disease (CHD). Although there are some studies on the risk factors of LCOS in children with CHD, an unified conclusion is lack at present.Purposes: To synthesize the risk factors of LCOS after CHD in children, and to provide evidence-based insights into the early identification and early intervention of LCOS.Methods: The databases of the China National Knowledge Infrastructure (CNKI), Wanfang Database, China Science and Technology Journal Database (VIP), PubMed, Cochrane Library, Embase and Web of Science were searched for relevant articles that were published between the establishing time of each database and January 2022. Based on retrospective records or cohort studies, the influencing factors of postoperative low cardiac output in children with congenital heart disease were included in Meta analysis.This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The risk of bias was evaluated according to the Newcastle-Ottawa Scale (NOS). RevMan 5.4 software was used to conduct the meta-analysis.Results: A total of 1,886 records were screened, of which 18 were included in the final review. In total, 37 risk factors were identified in the systematic review. Meta- analysis showed that age, type of CHD, cardiac reoperation, biventricular shunt before operation, CPB duration, ACC duration, postoperative residual shunt, cTn-1 level 2 h after CPB > 14 ng/ml and postoperative 24 h MR-ProADM level > 1.5 nmol/l were independent risk factors of LCOS. Additionally, the level of blood oxygen saturation before the operation was found to have no statistically significant relationship with LOCS.Conclusion: The risk factors of postoperative LCOS in children with CHD are related to disease condition, intraoperative time and postoperative related indexes, so early prevention should be aimed at high-risk children.Systematic Review Registration: https://www.crd.york.ac.uk/prospero/, identifier: CRD42022323043.

Background: An increase in the incidence of central venous catheter (CVC)-associated deep venous thrombosis (CADVT) has been reported in pediatric patients over the past decade. At the same time, current screening guidelines for venous thromboembolism risk have low sensitivity for CADVT in hospitalized children. This study utilized a multimodal deep learning model to predict CADVT before it occurs. Methods: Children who were admitted to intensive care units (ICUs) between December 2015 and December 2018 and with CVC placement at least 3 days were included. The variables analyzed included demographic characteristics, clinical conditions, laboratory test results, vital signs and medications. A multimodal deep learning (MMDL) model that can handle temporal data using long short-term memory (LSTM) and gated recurrent units (GRUs) was proposed for this prediction task. Four benchmark machine learning models, logistic regression (LR), random forest (RF), gradient boosting decision tree (GBDT) and a published cutting edge MMDL, were used to compare and evaluate the models with a fivefold cross-validation approach. Accuracy, recall, area under the ROC curve (AUC), and average precision (AP) were used to evaluate the discrimination of each model at three time points (24 h, 48 h and 72 h) before CADVT occurred. Brier score and Spiegelhalter’s z test were used measure the calibration of these prediction models. Results: A total of 1830 patients were included in this study, and approximately 15% developed CADVT. In the CADVT prediction task, the model proposed in this paper significantly outperforms both traditional machine learning models and existing multimodal deep learning models at all 3 time points. It achieved 77% accuracy and 90% recall at 24 h before CADVT was discovered. It can be used to accurately predict the occurrence of CADVT 72 h in advance with an accuracy of greater than 75%, a recall of more than 87%, and an AUC value of 0.82. Conclusion: In this study, a machine learning method was successfully established to predict CADVT in advance. These findings demonstrate that artificial intelligence (AI) could provide measures for thromboprophylaxis in a pediatric intensive care setting.

Background: With rising concerns over physical inactivity in children and adolescents, gamified digital exercise interventions have emerged as promising strategies to enhance engagement and adherence to physical activity (PA). However, their overall effectiveness and the key behavior change techniques (BCTs) that drive success remain unclear. Methods: This systematic review will assess the effects of gamified and digital exercise interventions on PA and health outcomes in children and adolescents aged 6–18 years. We will include randomized controlled trials and non-randomized studies of interventions focusing on interventions using digital technologies such as exergames, mobile applications, virtual reality, augmented reality, and wearables, with at least one gamification element. A comprehensive search will be conducted in PubMed, Web of Science, Scopus, Embase, CINAHL, PsycINFO, Cochrane Library, IEEE Xplore, CNKI, and Wanfang Data, covering studies from January 2014 to August 2025. BCTs will be categorized using the BCT Taxonomy v1 (BCTTv1), and data will be synthesized through meta-analysis or narrative synthesis depending on study homogeneity. The Promise Ratio will be used to identify the most effective BCTs. Conclusion: This review will provide valuable insights into the effectiveness of gamified and digital exercise interventions, helping to identify key BCTs that contribute to successful outcomes. The findings will guide the design and optimization of future exercise interventions among children and adolescents, with the goal of improving their overall health and well-being. Prospero registration number: CRD420250651337.