Objective To systematically review the risk prediction models for intradialytic hypotension in maintenance hemodialysis patients,with a view to provide references for clinical practice.Methods PubMed,Embase,Web of Science,Cochrane Library,CINAHL,CNKI,VIP,Wanfang and CBM were searched from inception to May 29,2023.2 reviewers independently screened the literature,extracted information and assessed methodological quality using the Prediction Model Risk of Bias Assessment Tool.Results A total of 20 studies and 25 models were included with the sample size of 68～9 292 cases and the incidence of outcome events of 2.1～51％.Baseline systolic blood pressure,age,ultrafiltration rate,diabetes and dialysis duration were the top 5 predictors of repeated reporting of the models.20 models reported the area under the curve of ranging from 0.649 to 0.969,and 5 models reported calibration metrics.There were 9 internal validations and 4 combined internal and external validation models.The overall applicability of the 20 studies was good,but all had a high risk of bias,mainly in data analysis.Conclusion Research on risk prediction models for intradialytic hypotension in maintenance hemodialysis patients is still in the developmental stage.Future studies should improve the research design and reporting process,and validation studies of existing models should be carried out to further evaluate the effectiveness and feasibility in clinical practice.

Objective:To analyze the epidemiological features of respiratory syncytial virus (RSV) infection in Beijing, and monitor the sequence variations in RSV glycoprotein (G) gene and clinical features of infected children.Methods:Respiratory tract specimens were collected from children with acute respiratory infection in the Children′s Hospital Affiliated to Capital Institute of Pediatrics from January 1, 2023 to December 31, 2023. RSV-positive specimens screened by multiple nucleic acid testing were subjected to PCR to amplify the full-length RSV G gene. A phylogenetic tree was constructed after gene sequencing to analyze RSV subtypes and trace G gene variants. Clinical data were retrieved from the medical record system to analyze the clinical features of children with RSV infection in Beijing.Results:A total of 5 489 respiratory specimens were collected from 3 046 male patients and 2 443 female patients. The average age of the patients was 4.36 years. A total of 589 RSV-positive specimens (10.7%, 589/5 489) were detected with 349 from male patients and 240 from female patients. The average age of children with RSV infection was (2.51±2.78) years and the median age was 0.48 years. RSV had been circulating among children in Beijing since March 2023 with two epidemic peaks in May (24.6%, 122/496) and December (18.2%, 126/693). The predominant subtype of RSV in the first half of 2023 was subtype A, but it was replaced by subtype B from November 2023. Phylogenetic analysis revealed a novel G gene of RSV subtype B (RSV-B-BA9-954bp) with a length of 954 bp, which belonged to a new cluster in the phylogenetic tree. The percentage of patients admitted to the Intensive Care Unit (ICU) was higher in children with new variant of RSV subtype B infection than in those with common RSV subtype B infection [44.1% (15/34) vs 25.2% (31/123), χ 2=4.600, P=0.032], while the counts of white blood cells and the levels of C-reactive protein were lower in the children with new variant infection ( P<0.05). Conclusions:RSV has been prevalent among children in Beijing since March 2023 with two epidemic peaks. The predominant A subtype is gradually replaced by to B subtype. A new variant of RSV B G gene (RSV-B-BA9-954bp) is detected among the children.

The rational use of medical insurance fund(MIF) plays an important role in promoting the high-quality development of public hospitals, and the supervision of MIF is in a trend of under the rule of law, normalization, professionalization and standardization, and unannounced inspection will become the norm. The authors systematically analyzed three main trends of MIF unannounced inspections, namely, gradually increasing intensity, constantly innovating methods, and increasingly serious consequences. The problems exposed in unannounced inspections were sorted out from five dimensions: form of results, severity, scope of attribution, subjective intention, and regulatory screening ideas. The enlightenment of MIF unannounced inspections to hospital management was explored from four aspects: compliance awareness, organizational system, fine management, and daily supervision. It was proposed that public hospitals should transform their roles and positions, improve the working mechanism of departmental collaboration, and achieve fine management in policy understanding, system formulation, process design, information support, data governance, regulatory implementation, personnel training, and performance matching. At the same time, internal simulated unannounced inspections in hospitals should be regarded as a routine work.

To analyze the factors affecting the cost of hospitalization for patients and provide insights using the intrauterine lesion surgery group (DRG code NE19) as an example.

BACKGROUND:Sepsis is one of the main causes of mortality in intensive care units(ICUs).Early prediction is critical for reducing injury.As approximately 36%of sepsis occur within 24 h after emergency department(ED)admission in Medical Information Mart for Intensive Care(MIMIC-IV),a prediction system for the ED triage stage would be helpful.Previous methods such as the quick Sequential Organ Failure Assessment(qSOFA)are more suitable for screening than for prediction in the ED,and we aimed to find a light-weight,convenient prediction method through machine learning. METHODS:We accessed the MIMIC-IV for sepsis patient data in the EDs.Our dataset comprised demographic information,vital signs,and synthetic features.Extreme Gradient Boosting(XGBoost)was used to predict the risk of developing sepsis within 24 h after ED admission.Additionally,SHapley Additive exPlanations(SHAP)was employed to provide a comprehensive interpretation of the model's results.Ten percent of the patients were randomly selected as the testing set,while the remaining patients were used for training with 10-fold cross-validation. RESULTS:For 10-fold cross-validation on 14,957 samples,we reached an accuracy of 84.1%±0.3%and an area under the receiver operating characteristic(ROC)curve of 0.92±0.02.The model achieved similar performance on the testing set of 1,662 patients.SHAP values showed that the five most important features were acuity,arrival transportation,age,shock index,and respiratory rate. CONCLUSION:Machine learning models such as XGBoost may be used for sepsis prediction using only a small amount of data conveniently collected in the ED triage stage.This may help reduce workload in the ED and warn medical workers against the risk of sepsis in advance.

Objective:To design an electronic nose that can detect and identify urinary volatile organic compounds (VOCs) as marker gases for bladder cancer.Methods:Isopropyl alcohol, ethylbenzene, acetic acid, and ammonia were selected as target gases, and 8 metal oxide gas sensors were used to construct sensor arrays for testing and collecting experimental data, and different characteristics were normalized. Recursive feature elimination (RFE) was used to select the best feature subset, and principal component analysis (PCA) and linear discriminant analysis (LDA) were further introduced to reduce the data dimension and facilitate visual analysis. In addition, three machine learning algorithms, including support vector machine (SVM), random forest (RF), and K-nearest neighbor (KNN), were combined to train and verify the model.Results:When the feature number was 12, the accuracy of the model classification had the best performance. The feature subset consisted of 5 differences, 5 sensitivities, and 2 integrals, and the data was reduced to 12 dimensions. Only PCA couldn’t distinguish the four gases. The LDA classification performance was significantly better than that of PCA, except that isopropyl alcohol and acetic acid had a small overlap area. LDA could distinguish ethylbenzene and ammonia from isopropyl alcohol and acetic acid; the sample points were gathered, which means the clustering performance was also better. The prediction accuracy of SVM, RF, and KNN was 0.85, 0.56, and 0.79, respectively. After model verification, the classification accuracy of PCA+SVM, LDA+RF, and LDA+KNN was 0.97, 0.94, and 0.97, respectively.Conclusions:An electronic nose was designed to detect and identify urinary VOCs marker gases for bladder cancer.

Objective:To observe the effects of early energy intake and early enteral nutrition on prognosis of patients during extracorporeal membrane oxygenation (ECMO).Methods:Patients who received ECMO treatment in the emergency intensive care unit (EICU) of the Jiangsu Provincial Hospital (First Affiliated Hospital of Nanjing Medical University) from January 2021 to June 2022 were selected as subjects to summarize the early energy intake of ECMO patients. Logistic regression analysis and restricted cubic spline (RCS) analysis were used to determine the relationship between early energy intake and prognosis of ECMO patients. According to the results of RCS analysis, the patients were divided into energy-deficient group and energy-sufficient group. And according to whether early enteral nutrition (EEN) was initiated, the patients were divided into EEN group and non-EEN group. The differences of clinical outcomes between energy-deficient group and energy-sufficient group, EEN group and non-EEN group were compared.Results:There was no significant difference in age, sex, BMI, primary disease and ECMO pattern between energy-deficient group and energy-sufficient group, EEN group and non-EEN group. The ECMO conversion time (days) and hospitalization time (days) in the energy-deficient group were significantly lower than those in the energy-sufficient group, and the survival rate in the energy-deficient group was significantly lower than that in the energy-sufficient group [43.2% (19/44) vs. 66.0% (31/47), P=0.029]. Kaplan-Meier survival analysis showed that the 28-day survival rate in the energy-deficient group was significantly lower than that in the energy-sufficient group, and the risk of death was 2.595 times higher than that in the energy-sufficient group. The conversion time (days), hospital stay (days) and average daily energy intake [kcal/(kg·d)] in the EEN group were higher than those in the non-EEN group ( P<0.05), and the survival rate in the non-EEN group was significantly higher than that in the non-EEN group [66.1% (41/62) vs. 31.0% (9/29), P<0.002]. Kaplan-Meier survival analysis showed that the 28-day survival rate in the non-EEN group was significantly lower than that in the EEN group, and the risk of death was 2.981 times higher than that in the EEN group ( P<0.001). Conclusions:The energy intake of patients with ECMO above 16.94 kcal/ (kg·d) is a protective factor for prognosis. EEN helps to increase early energy intake and improve prognosis in patients during ECMO.

To effectively introduce and utilize global advanced educational resources, the implementation of Chinese-foreign joint education program in medical universities and the establishment of an internal quality assurance system with Chinese characteristics and substantially equivalent to international standards of medical education is crucial for improving the quality of Chinese medical talent cultivation and achieving high-quality development of education opening up to the outside world in the new era. Using the Chinese-Foreign Joint Education Program jointly run by Chongqing Medical University and University of Leicester as an example, this paper proposes a plan for the development of an internal quality assurance system with three core indicators of management mechanism, team building, and student services. This plan provides experience and a reference for domestic universities to carry out cross-border medical education and improve the quality and efficiency of Chinese-foreign joint education programs.

Objective:To explore the pathogenic agents of acute respiratory infection (ARI) in children in Beijing.Methods:In the cross-sectional study, 3 groups of children from different departments were enrolled from Feb 6 th, 2023 (6 th week) to May 28 th (21 th week), 2023, including influenza-like case group from emergency department for nucleic acid testing of influenza virus (Flu) and human metapneumovirus (HMPV), the outpatient ARI group under nucleic acid testing for Flu, respiratory syncytial virus (RSV), adenovirus (ADV), and parainfluenza virus (PIV), and the inpatient ARI group under nucleic acid testing for Flu, RSV, HMPV, ADV, human bocavirus (HBoV), Rhinovirus (Rh), PIV, coronavirus (HCoV), Mycoplasma pneumoniae (Mp) and Chlamydia pneumonia (Cp). Results:There were 320 influenza-like cases enrolled, including 192 males and 128 females, aged 4.7 (3.6, 6.9) years, and 117 cases (36.6%) positive for Flu A, which contained similar proportion of pandemic H1N1 (H1N1) 47.0% (55/117) and H3N2 53.0% (62/117), and 13 cases for HMPV 4.1% (13/320). The rate of Flu reached its peak at the 10 th week, with H1N1 as the predominant one from the 6 th to 9 th week (10.0%-50.0%) and then H3N2 from the 10 th to 16 th week (15.0%-90.0%). HMPV was detected from the 15 th week 5.0% (1/20), and then reached to 30.0% (6/20) at the 20 th week. In the outpatient ARI group, 7 573 were enrolled, including 4 131 males and 3 442 females, aged 4.0 (2.1, 5.3) years, and the highest positive rate for RSV 32.9% (2 491/7 573), followed by Flu A 12.1% (915/7 573). The dominant one was Flu A in weeks 6-14 (23.2%-74.7%), then RSV in the 15 th week 24.8% (36/145). In the inpatient ARI group, 1 391 patients were enrolled, including 804 males and 587 females, aged 3.3 (0.4, 5.8) years, and the highest positive rate for Rh 18.7% (260/1 391), followed by RSV 12.4% (173/1 391), Flu A 10.2% (142/1 391, of which 116 cases (81.7%) were H1N1, and 26 cases (18.3%) were H3N2) and HMPV 3.1% (43/1 391). H1N1 was detected from the 7 th week 10% (6/60), to peak in the 11 th week 31.8% (21/66). H3N2 was detected from the 8 th week 1.5% (1/68), and then kept in low level. The proportion of H1N1 among Flu was 81.7% (116/142) in the inpatient ARI group. RSV was detected from 12 th week 1.3% (1/80), reaching 30.4% (35/115) at 19 th week. The positive rate of HMPV reached 12.1% (14/116) at 21 th week. Conclusions:In the spring of 2023, the first one in Beijing is the Flu epidemic, with H1N1 being the predominant one in the early stage and H3N2 in the later stage. Then, there is a postponed RSV epidemic and an increased HMPV detection. In addition, nucleic acid testing for outpatient children should be strengthened to provide early warning of epidemics.

Objective This study aims to analyze the factors influencing benefit finding among cervical cancer patients and their spouses,as well as the interconnections between these factors.The goal is to provide a foundation for developing targeted clinical interventions.Methods Using the convenience sampling method,cervical cancer patients and spouses of 245 pairs who attended or were hospitalized in a tertiary-level hospital in Taiyuan City from October 2022 to July 2023 were selected as study subjects.Data were collected using a general information questionnaire,the Distress Disclosure Index,the Connor-Davidson Resilience Scale,and the Benefit Finding Scale.Univariate analysis,Pearson correlation analysis,and multiple linear regression were employed to scrutinize the data,leading to the establishment of Actor-Partner Interdependence Model.Results Benefit finding scores for cervical cancer patients and their spouses were(65.31±7.94)and(69.87±9.63),respectively.Multiple linear regression revealed that the educational level of patients and their spouses,whether or not they received chemotherapy or radiotherapy,self-disclosure and psychological resilience were the factors that affected patients'benefit finding.Spouse's education level,occupation,self-disclosure,psychological resilience and patients'self-disclosure and psychological resilience were the influencing factors of spouse's benefit finding.The Actor-Partner Interdependence Model analysis indicated that the self-disclosure and psychological resilience of cervical cancer patients positively predicted their own benefit finding and that of their spouses(path coefficients were 0.415,0.501,0.216,and 0.168,respectively,all P＜0.05).However,spouses'self-disclosure and psychological resilience could only positively predict their own benefit finding(path coefficients were 0.188 and 0.254,respectively,all P＜0.05).Conclusion Benefit finding among cervical cancer patients and their spouses is moderate and influenced by various factors.Both self-disclosure and psychological resilience of cervical cancer patients and their spouses have positive subjective effects on their own benefit finding.Healthcare professionals should encourage both parties to engage in healthy interactions about the disease,take steps to increase the level of psychological resilience of both,and ultimately tap into a higher level of benefit finding.