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.

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.

BackgroundLow 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.PurposesTo 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.MethodsThe 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.ResultsA 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.ConclusionThe 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 Registrationhttps://www.crd.york.ac.uk/prospero/, identifier: CRD42022323043.

Background: The global spread of coronavirus disease 2019 (COVID-19) has reached pandemic proportions. Attempts to control its spread have included a range of early screening and triage measures developed in several nations and areas. Objectives: This study aimed to determine how to prioritize pediatric fever patients to limit the time they had to wait for a consultation and, therefore, the potential of worsening and crises under the burden of COVID-19. Methods: The triage and emergency care process of children in the Fever Clinic of Children’s Hospital of Zhejiang University School of Medicine, Zhejiang, China, within 2019 - 2020 included flow charts, guidance signs, publicity materials, noon and night articulation, and emergency calls. To enhance the management of pre-consultation and triage, the incidence of adverse event injuries was tallied, and satisfaction surveys were conducted. The prevalence of infectious diseases was characterized by demographic and seasonal factors, and the chi-square test was employed to test for differences between groups. Results: From January 2019 to December 2020, four peak periods were observed in each year, namely February, July, September, and December in 2019 and March, June, September, and December in 2020. The peak of common respiratory virus infection was seasonal; however, a significant increase (χ2 = 52.17; P < 0.001) in the case of patients who needed emergency care was observed secondary to fever. The patients within the age range of 1 - 3 years were more in need of emergency care than any other age group (54.70%; 99/181). The most common disease requiring emergency care was febrile convulsions (55.2%). No infectious diseases were missed or underreported during the study period, and no medical personnel was infected. Conclusions: An effective pre-consultation assessment and triage management system and streamlined workflow are of great importance in safeguarding acute patients while preventing infectious diseases.

Background: This study aimed to systematically evaluate the effectiveness of a clinical nurse specialists training program in Zhejiang Province, China, from participants’ perspectives. Methods: This cross-sectional study was conducted with 209 PICU nurse specialists who participated in the training program from 2016 to 2021. All participants completed an online questionnaire two years after graduation. We collected their demographic characteristics and their development status after the training (e.g., continuous improvement of core competence, research skills, and promotion). Chi-squared test was applied to assess the differences in effectiveness across subgroups. Results: In total, 209 (87.8%) out of 238 nurses responded to the survey among whom 73.7% launched new projects in their hospital after training and 75% published research articles in peer-reviewed journals. Also, 32.4% received promotions, and 67% participated in ICU-related continuing education programs. Based on multivariate regression analysis, the execution of new projects was closely related to the nurse’s position and the level of their working hospitals. The job title and position were associated with publication, research performance, promotion, and continuing education. Conclusions: The nurse specialists of the PICU carried out a set of new projects implanting new skills that they had learned from the training program. Their core competence was improved, including theoretical knowledge and operation skills, teaching capacities, and scientific research abilities. Many trainees published papers, applied for research grants, got promotions, and had further opportunities for continuing education.