Children's Pain Identification Based on Skin Potential Signal.

Pain management is a crucial concern in medicine, particularly in the case of children who may struggle to effectively communicate their pain. Despite the longstanding reliance on various assessment scales by medical professionals, these tools have shown limitations and subjectivity. In this paper, we present a pain assessment scheme based on skin potential signals, aiming to convert subjective pain into objective indicators for pain identification using machine learning methods. We have designed and implemented a portable non-invasive measurement device to measure skin potential signals and conducted experiments involving 623 subjects. From the experimental data, we selected 358 valid records, which were then divided into 218 silent samples and 262 pain samples. A total of 38 features were extracted from each sample, with seven features displaying superior performance in pain identification. Employing three classification algorithms, we found that the random forest algorithm achieved the highest accuracy, reaching 70.63%. While this identification rate shows promise for clinical applications, it is important to note that our results differ from state-of-the-art research, which achieved a recognition rate of 81.5%. This discrepancy arises from the fact that our pain stimuli were induced by clinical operations, making it challenging to precisely control the stimulus intensity when compared to electrical or thermal stimuli. Despite this limitation, our pain assessment scheme demonstrates significant potential in providing objective pain identification in clinical settings. Further research and refinement of the proposed approach may lead to even more accurate and reliable pain management techniques in the future.

Association between Overweight/Obesity and the Safety and Efficacy of COVID-19 Vaccination: A Systematic Review.

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.

Risk factors for central venous catheter-associated deep venous thrombosis in pediatric critical care settings identified by fusion model.

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.

Risk factors of postoperative low cardiac output syndrome in children with congenital heart disease: A systematic review and meta-analysis.

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.

Prediction of central venous catheter-associated deep venous thrombosis in pediatric critical care settings.

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.

[Application of three-in-one intelligent screening in outpatient department of children's hospital during COVID-19 epidemic].

OBJECTIVE: To evaluate the application of three-in-one intelligent screening in outpatient pre-inspection in children's hospital. METHODS: We randomly enrolled 100 children pre-screened by traditional method in the outpatient department of Children's Hospital of Zhejiang University from February 6th to 16th, 2020, and another 100 children by the intelligent three-in-one mode from February 17th to 27th, 2020. The traditional triage was conducted by nurses based on face-to-face, one-by-one interview of the epidemiological history and consultation department, and the temperature was measured before manual triage. The intelligent three-in-one model combined online rapid pre-inspection and triage, on-site manual confirmation, as well as synchronized online health education system. For on-line registered patients, the system automatically sent the COVID-19 epidemiological pre-screening triage questionnaire one hour before the appointment, requiring parents to complete and submit online before arriving at the hospital. The on-site registered patients were controlled at 100 m away from the hospital entrance. The nurses guided the parents to scan the QR code and fill in the COVID-19 epidemiological pre-examination triage questionnaire. At the entrance of the hospital, the nurse checked the guidance sheet and took the temperature again. The children with red guidance sheet were checked again and confirmed by pre-examination nurses, and accompanied to the isolation clinic through COVID-19 patients-only entrance. The children with yellow guidance sheet were guided to fever clinic. The children with green guidance sheet could go with their parents to the designated area, and then went to the corresponding consultation area. Health education was carried out throughout the treatment, and the system automatically posted the corresponding outpatient instructions and education courses. Parents would read the courses on their mobile phones and counsel online. The time of pre-examination and the coincidence rate of triage were compared between the two groups. RESULTS: The three-in-one intelligent pre-inspection mode took an average of (25.6±8.0) s for each child, which was significantly shorter than the traditional pre-inspection mode (74.8±36.4) s (t=13.182, P<0.01). The triage coincidence rate of the intelligent pre-inspection model was 98%, which was similar to that of traditional model (97%, χ2=0.251, P>0.05). CONCLUSIONS: The three-in-one intelligent pre-inspection model can effectively shorten the patient pre-check time, with similar triage coincidence rate to traditional model.