Objective To explore the effects of muscle mass and muscle strength on frailty in patients with maintenance hemodialysis (MHD).Methods MHD patients at The First Affiliated Hospital of Xi’an Jiaotong University were investigated from January to May 2024. Their frailty was evaluated using the Frailty Assessment Scale, and muscle mass (SMI) and muscle strength (HGS) were assessed with a body composition analyzer and a grip strength meter. Relevant data including sociodemographic characteristics, clinical medical information, and personal lifestyle habits were collected. Logistic regression analysis was utilized to analyze the impacts of muscle mass and muscle strength on frailty, and the masking effect of muscle strength between muscle mass and frailty was explored based on the path analysis method.Results A total of 340 MHD patients were surveyed. The prevalence rates of frailty, low SMI, and low HGS were 43.5%, 41.8%, and 39.1%, respectively. Among the 148 frail MHD patients, 45.3% had both low HGS and low SMI. After adjusting for confounding factors, muscle strength was a protective factor against frailty. In the overall population, the masking effect between SMI→HGS→frailty was significant, with a masking effect value of −0.068 (95% CI: −0.102 to −0.041). Subgroup analysis showed a masking effect value of −0.069 (95% CI: −0.112 to −0.035) in males; in females, the effect of muscle mass on frailty was completely masked by muscle strength, with a masking effect value of −0.074 (95% CI: −0.149 to −0.007).Conclusion Increasing muscle strength is significantly associated with reduced frailty in MHD patients. Grip strength, as a marker of muscle strength, is more significant than muscle mass. Muscle strength plays a masking role between muscle mass and frailty.

Objective To understand the construction and operation of Internet hospitals in China and provide a reference for improving relevant policies and developing Internet hospitals.Methods A questionnaire survey was conducted on 136 medical institutions in 8 provinces in China to collect operational data in 2022 of Internet hospitals and entity hospitals they were based on. SPSS 26.0 software was used to analyze the data, and the Mann-Whitney U test was used to compare Internet hospitals of different grades.Results Among the 136 medical institutions, 54 have established Internet hospitals, accounting for 39.7%. Among the 54 Internet hospitals, 27.8% have received government investment, 40.7% have set up independent management departments, and 85.2% have reached effective operation. In 2022, the median number of Internet hospitals in effective operation was 20000, which had a large gap compared with the median number of entity hospitals, which was 1.37 million. The top three business systems with the highest construction ratio in Internet hospitals were online diagnosis and treatment system, e-prescription and online medical order system, prescription review and drug distribution system, accounting for 83.3%, 75.9% and 75.9%, respectively.Conclusion In China, Internet hospitals have not established a relatively mature management and operation system, and the financial support is insufficient. Compared with the scale of offline hospitals, the promotion and application of Internet hospitals in China need to be further strengthened. It is necessary to strengthen policy guidance, give full play to the subjective initiative of hospitals, ensure capital investment, standardize the Internet operation and management system, and optimize the construction of information platforms, so as to enhance patients’ experience and satisfaction of Internet hospitals.

Objective To systematically evaluate the efficacy and safety of Chinese herbal decoctions containing Astragalus membranaceus and Poria cocos (HF-D) in the treatment of nephrotic syndrome (NS). 

Methods Databases including VIP, CNKI, WanFang, CBM, PubMed, Embase, and Cochrane were searched for clinical randomized controlled trials (RCTs) on HF-D for NS published between 2010 and 2023. Literature quality was assessed using the Cochrane Risk of Bias Tool, and data were analyzed with ReviewManager 5.3, Stata 12.0, and R software. 

Results Following PRISMA 2020 guidelines, 15 RCTs involving 1,538 patients were included. Meta-analysis showed: Efficacy outcomes: - The trial group had a significantly higher total response rate than the control group [RR=1.21, 95%CI: (1.13, 1.29), P<0.01].  The recurrence rate was significantly lower in the trial group [RR=0.38, 95%CI: (0.22, 0.66), P<0.01]. Laboratory indices: The trial group exhibited significantly lower levels of 24-hour urinary protein [MD=−1.20, 95%CI: (−1.43, −0.97), P<0.01], serum creatinine [MD=−7.13, 95%CI: (−13.15, −1.10), P<0.05], blood urea nitrogen [MD=−0.55, 95%CI: (−1.04, −0.06), P<0.05], total cholesterol [MD=−1.51, 95%CI: (−2.11, −0.91), *P*<0.01], and triglycerides [MD=−0.74, 95%CI: (−1.11, −0.36), P<0.01] compared to the control group. Serum albumin levels were significantly higher in the trial group [MD=5.65, 95%CI: (4.03, 7.27), P<0.01]. Safety outcomes: The incidence of adverse events was significantly lower in the trial group [RR=0.41, 95%CI: (0.28, 0.62), P<0.01]. Publication bias analysis: Egger’s test showed good symmetry in the funnel plot for 24-hour urinary protein (P>|t|=0.7), but trim-and-fill analysis for total response rate indicated potential unavoidable publication bias. 

Conclusion HF-D demonstrates promising efficacy and safety in treating NS, though further large-sample, high-quality RCTs are needed for validation.

Objective To construct a nomogram model to predict the risk of delirium during ICU admission in alcohol withdrawal patients.Methods Data were extracted from the Medical Information Mart for Intensive Care (MIMIC-IV version 2.2) on patients admitted to the ICU for alcohol withdrawal and were randomly divided into training and validation sets based on a 7:3 ratio. Lasso regression analysis combined with logistic regression analysis was used to select the best variables to construct the model for visualization in the form of the nomogram.Results Five optimal variables were screened as independent risk factors for delirium during ICU admission in alcohol withdrawal patients in this study, which were mean heart rate (OR=0.967, 95%CI: 0.953-0.982), SOFA score (OR=1.151, 95%CI: 1.054-1.257), OASIS score (OR=1.131, 95%CI: 1.089-1.174), use of propofol (OR=2.453, 95%CI: 1.187-5.071) and LMR (OR=0.876, 95%CI: 0.774-0.991). The area under the ROC curve (AUC) of the column-line graphical model constructed on the basis of these five optimal variables was 0.852 (95%CI: 0.820-0.885); the accuracy of the model was 0.706, the precision was 0.743, the recall was 0.690, the F1 score was 0.716, and the Brier score was 0.157; the mean absolute error of the calibration curve was 0.014; clinical decision curve analysis (DCA) showed a large net benefit at threshold probabilities of 0.07-0.95.Conclusion The nomogram model developed in this study can accurately predict the risk of delirium during ICU admission in patients with alcohol withdrawal and can be useful for early clinical prevention, intervention, and graded care.

Objective To analyze the effect of group narrative psychological intervention on non-suicidal self-injury behavior (NSSI) in adolescent depression patients.Method A total of 118 adolescent depression patients treated in a certain hospital from January 2022 to June 2023 were randomly divided into two groups: the control group of 59 patients and the observation group of 59 patients. The control group received routine nursing care, while the observation group received group narrative psychological intervention besides routine nursing care. The occurrence of NSSI in two groups of patients was statistically analyzed to compare their levels of depression, psychological resilience, and self-esteem before and after the intervention.Results Three months after the intervention, the frequency of NSSI and the degree of injury in the observation group patients were significantly reduced and lower than those in the control group patients (P<0.05). Before and after the intervention, the differences in SDS score, HAMD score, CD-RISC score, and SES score in the control group were 10.01±2.15, 6.91±1.78, 9.45±2.54, and 7.08±1.53, respectively, while the differences in the observation group were 16.14±3.02, 10.08±2.06, 15.10±2.86, and 11.75±2.12, respectively. The differences of the observation group were higher than those of the control group (P<0.05).Conclusion Group narrative psychological intervention can improve the depression situation of adolescent depression patients, reduce NSSI, and is worthy of clinical application.

Objective To study the fairness of health resource allocation in China's maternal and child health hospitals and provide a reference for further optimizing resource allocation, as maternal and child health hospitals have gradually become an influential part of the medical institutions in China and play a significant role in comprehensively improving the health level of women and children.Methods The Lorenz curve, Gini coefficient, and agglomeration degree were used to analyze the fairness of health resource allocation in China's maternal and child health hospitals from three dimensions: population, economy, and geography.Results (1) The total amount of health resources in China's maternal and child health hospitals is showing an increasing trend; (2) the fairness of resource allocation based on population is better than that based on economy and geographic area; (3) there is a significant regional gap in the health resources of maternal and child health hospitals in China. In terms of geographical distribution fairness, densely populated areas are superior to areas with average or sparse populations; in terms of population distribution fairness, areas with an average population are better than areas with dense or sparse populations.
Conclusion The gap between the supply and demand of health resources in China's maternal and child health hospitals still needs to be filled; it is necessary to focus on improving the fairness of health resource allocation in China's maternal and child health hospitals in terms of geographic dimensions and narrowing the gap in regional health resource allocation.

Objective To investigate the impact of medication adherence on blood pressure control in community-dwelling hypertensive patients. 

Methods A follow-up study was conducted on hypertensive patients managed by the Community Health Service Center of Sanxiang Town, Zhongshan City, Guangdong Province, from January 2022 to December 2023. Participants were provided with home blood pressure monitors to collect self-measured blood pressure data, and antihypertensive prescription information was obtained from local community clinics. Two time periods were set: T₁ (2022) and T₂ (2023). Annual medication adherence indicators and blood pressure control effect indices were extracted for each participant. A cross-lagged model was used to analyze the longitudinal associations and predictive relationships between medication adherence and blood pressure control. 

Results A total of 305 patients were included. In the autoregressive path, the associations between medication adherence and blood pressure control at T₁ and T₂ were statistically significant (P < 0.01), both showing positive correlations. In the cross-lagged path, medication adherence at T₁ predicted blood pressure control at T₂ (β₁ = -0.068, P < 0.05), indicating that better medication adherence was associated with reduced severity of blood pressure exceeding the target level. 

Conclusion Both medication adherence and blood pressure control exhibited certain stability, and medication adherence had a definite predictive significance for subsequent good blood pressure control.

Objective To explore the potential profile classification of cognitive function in patients with obstructive sleep apnea hypopnea syndrome (OSAHS), and analyze the influencing factors of different cognitive function classifications.Methods A convenient sampling of 210 OSAHS patients treated in the First People’s Hospital of Yongkang City from April 2021 to April 2024 was conducted to score cognitive function by using the Montreal Cognitive Assessment Scale (MoCA), and the optimal number of cognitive function categories were analyzed and named by potential profile. Clinical data of patients were collected by using hospital electronic medical record system, and the influencing factors of cognitive function classification were analyzed with univariate analysis and multifactor logistic regression.Results The cognitive function of 210 active OSAHS patients could be classified into three potential categories: 54 cases (25.71%) of low cognitive function, 86 cases (40.95%) of middle cognitive function-low memory, 70 cases (33.33%) of high cognitive function. Multivariate logistic regression analysis showed that age (OR=1.043, 95%CI=1.001-1.088), OSAHS severity (OR=1.694, 95%CI=1.069-2.685), and HSP70 (OR=1.589, 95%CI=1.006-2.509) were independent factors of middle cognitive function-low memory type in OSAHS patients with high cognitive function as reference. Age (OR=1.117, 95%CI=1.055-1.183), smoking history (OR=6.893, 95%CI=2.518-18.867), drinking history (OR=4.972, 95%CI=1.893-13.064), OSAHS severity (OR=2.668, 95%CI=1.441-4.942), HSP70 (OR=2.769, 95%CI=1.484-5.168), GABA (OR=0.940, 95%CI=0.910-0.970) were independent factors of low cognitive function type in OSAHS patients (P<0.05).Conclusion There is a certain heterogeneity in the classification of potential profiles of cognitive function in OSAHS patients. Medical staff can focus on OSAHS patients’ age, lifestyle, condition, HSP70 and GABA levels to classify patients and take appropriate measures to reduce cognitive function impairment.

Objective To analyze the effect of tobacco control interventions among rural residents in Tongxiang City, Zhejiang Province, and provide a basis for formulating tobacco control strategies in Tongxiang City. Methods A stratified multi-stage random sampling method was used to select 640 rural permanent residents aged 15–69 years from 4 towns in Tongxiang City as the survey subjects. Tobacco control health interventions were implemented, and the intervention effects were analyzed. 

Results Correct rate of knowledge about tobacco hazards: The correct rate increased from 43.29% before intervention to 68.32% after intervention, with a statistically significant difference (χ²=81.297, P<0.001).Correct rate of knowledge about smoking cessation skills: The correct rate increased from 55.98% before intervention to 72.39% after intervention, with a statistically significant difference (χ²=37.469, P<0.001). Correct rate of knowledge about secondhand smoke handling: The correct rate increased from 29.69% before intervention to 54.69% after intervention, with a statistically significant difference (χ²=82.002, P<0.001). Smoking rate: The smoking rate decreased from 22.97% before intervention to 17.34% after intervention, with a statistically significant difference (χ²=6.291, P=0.012). 

Conclusion After health interventions, the awareness rate of tobacco control knowledge among rural residents significantly increased, and the smoking rate significantly decreased, indicating that the intervention measures are effective and worthy of further promotion.

Objective To investigate the association between the triglyceride-glucose index (TyG) and renal function with the incidence of depression. 

Methods The study population comprised middle-aged and older adults without baseline depression from the UK Biobank database. Exposure variables were the TyG index and renal function. Traditional and additive Cox proportional hazards models were used to estimate the associations between the TyG index, renal function, and depression incidence. 

Results A total of 383,860 eligible participants were included, among whom 20,711 (5.40%) were diagnosed with depression during follow-up. The results showed: - Compared with the lowest TyG quartile (Q1), higher TyG quartiles were associated with a significantly increased risk of depression (HR_Q4 = 1.42, 95%CI: 1.36–1.47; HR_Q3 = 1.27, 95%CI: 1.22–1.32; HR_Q2 = 1.13, 95%CI: 1.09–1.18). - Compared with the normal renal function group, the renal dysfunction group had a significantly higher risk of incident depression (HR_{moderate-severe dysfunction} = 2.14, 95%CI: 1.96–2.33; HR_{mild dysfunction} = 1.32, 95%CI: 1.28–1.36). - No additive (RERI = 0.01, 95%CI: –0.05–0.07) or multiplicative (HR = 0.99, 95%CI: 0.95–1.04) interaction was observed between the TyG index and renal function in relation to depression incidence. 

Conclusion Middle-aged and older adults with a high baseline TyG index or renal dysfunction have a higher risk of depression. Monitoring the TyG index and estimated glomerular filtration rate (eGFR) is recommended for early prevention of depression in this population.

Objective：To explore methods for accurately describing the transition state of disease patterns, facilitating the analysis of related impacts caused by disease pattern transitions. 

Methods：Based on the theory of multidimensional vector similarity and distance metrics, a Disease Pattern Distance Index (DPI) was constructed. Using the disease structure data of inpatients in Shenzhen from 2007 to 2022, the study analyzed the changes in disease patterns and their relationship with the average cost per hospitalization. 

Results：Taking 2007 as the baseline, the in-hospital disease pattern distance index in Shenzhen gradually increased, reaching 20.93 by 2022, indicating a gradual change in disease patterns. Among them, the proportions of trauma/toxicity, pregnancy/delivery, and infectious diseases decreased, while the proportions of tumors, urogenital diseases, and musculoskeletal and connective tissue diseases significantly increased. A positive correlation was found between the Disease Pattern Distance Index and the average cost per hospitalization (r = 0.9929, P < 0.01). 

Conclusion：Disease patterns can be characterized by multidimensional vectors, and the method of measuring vector distances can describe the relative relationships between disease patterns.

Objectives To explore the long-term trends in demographic characteristics and survival rates of lung cancer patients in a Grade A tertiary oncology hospital in Guangdong Province from 2000 to 2015, so as to provide epidemiological evidence for lung cancer prevention and control strategies. Methods Information of patients first diagnosed with lung cancer in a large Grade A tertiary oncology hospital from 2000 to 2015 was extracted from the hospital's medical record information system, including demographic data, diagnosis and treatment information, and follow-up data up to December 31, 2020. Stratified analysis was conducted according to diagnosis year (2000-2003, 2004-2006, 2007-2009, 2010-2012, and 2013-2015), gender, and age (<45 years, 45-54 years, 55-64 years, 65-74 years, and ≥75 years). Trend chi-square test was used to analyze the changing trends of the composition ratio of patients in different groups during the observation period; life table method was adopted for survival analysis to evaluate the 5-year survival rate and median survival time of patients; weighted least squares regression model was used to analyze the changing trends of survival rate and median survival time. Results A total of 20,685 patients were included in the final analysis, with 71.0% being male and 29.0% being female. The composition ratio of female patients gradually increased from 24.9% in 2000-2003 to 31.5% in 2013-2015 (χ²=49.449, P<0.001). The overall 5-year survival rate of lung cancer patients increased from 20.3% (95%CI: 18.7%-21.9%) in 2000-2003 to 47.6% (95%CI: 46.0%-49.2%) in 2013-2015, with an average increase of 6.8% every 3 years (95%CI: 4.1%-9.4%). The 5-year survival rate of female patients increased more significantly, from 21.4% (95%CI: 18.1%-24.7%) to 56.3% (95%CI: 53.7%-59.0%), with an average increase of 8.8% every 3 years (95%CI: 6.1%-11.6%); the survival rate of male patients increased from 19.9% (95%CI: 18.1%-21.7%) to 43.3% (95%CI: 41.4%-45.2%), with an average increase of 5.5% every 3 years. Age-stratified analysis showed that the 5-year survival rate of young patients increased more significantly; the median survival time of lung cancer patients in different gender and age groups showed an upward trend, with the median survival time increasing from 1.58 years (2000-2003) to 4.90 years (2013-2015) (P<0.05). Conclusions From 2000 to 2015, the 5-year survival rate and median survival time of lung cancer patients in a Grade A tertiary oncology hospital in Guangdong Province increased significantly. Among them, female and young lung cancer patients had higher 5-year survival rates and a greater annual increase, which was attributed to the continuous improvement of lung cancer diagnosis and treatment quality in Guangdong Province. However, the gradual increase in the composition ratio of female lung cancer patients suggests that further research on the pathogenesis of lung cancer and related influencing factors is needed to provide a basis for the formulation of lung cancer prevention and control strategies.

Objective To conduct a retrospective analysis of 2,789 death cases in a tertiary grade A hospital in a certain prefecture-level city from 2019 to 2023, explore the factors related to deceased patients, and provide a reference basis for continuously improving the level of treatment and reducing the mortality rate. Methods The first-page information of inpatient medical records from 2019 to 2023 was collected using the medical record statistics system. Combined with the International Classification of Diseases (ICD) rules, Excel 2007 and SPSS 19.0 software were used to conduct statistical analysis on the data of 2,789 deceased patients. Statistical charts were used to describe the distribution differences in mortality rates across different years, genders, age groups, emergency/critical care patients, and low-risk groups. The composition and ranking of the main causes of death (by disease category) and the composition and ranking of specific disease types of the main causes of death among inpatient deceased patients were analyzed, and the chi-square test (χ² test) was used for inter-group comparison.Results Over the 5-year period from 2019 to 2023, the mortality rate of the hospital was 0.61%, and the male-to-female ratio of inpatients was 1:1.07. The mortality rate was 0.80% for male patients and 0.43% for female patients. The age group with the highest mortality rate among deceased patients was those aged 81 years and above (3.2%), followed by the 71-80 years group (1.11%). In terms of the proportion of deaths by age group, the 71-80 years group accounted for the highest proportion (24.60%), followed by the 61-70 years group (22.30%). The top 3 disease categories in terms of the proportion of causes of death were tumors (29.94%), circulatory system diseases (28.11%), and respiratory system diseases (11.98%). The top 3 specific disease types causing death were acute myocardial infarction, lung cancer, and intracranial injury. Patients admitted with acute conditions had the highest mortality rate (5.51%), followed by those admitted with critical conditions (0.67%). A total of 88 patients died in the low-risk group over the 5 years, with a low-risk group mortality rate of 0.36‰. The highest low-risk group mortality rate in the 5 years was in 2022 (0.64‰), and the lowest was in 2023 (0.11‰).Conclusion To improve the comprehensive service capacity of disease diagnosis and treatment in tertiary public hospitals, it is necessary to optimize the allocation of hospital resources, strengthen the discipline construction and specialized service capacity of departments such as oncology, cardiovascular medicine, and critical care medicine, continuously enhance the ability and level of diagnosis and treatment of difficult and critical diseases, and promote the high-quality development of the hospital.

Objective To analyze the correlation between psychological resilience and job burnout of nursing staff in the rehabilitation department. Methods From February 26 to May 31, 2024, 102 nursing staff engaged in nursing work in the rehabilitation department were selected as the research subjects. A general information questionnaire, Connor-Davidson Resilience Scale (CD-RISC), and Maslach Burnout Inventory-General Survey (MBI-GS) were used for the survey. Spearman correlation analysis (rs value) was applied to explore the correlation between psychological resilience and job burnout of the nursing staff. Results Among the 102 nursing staff in the rehabilitation department, the total score of CD-RISC (psychological resilience) was 69 (64, 81) [median (interquartile range)], and the total score of MBI-GS (job burnout) was 3 (3, 4) [median (interquartile range)]. The total score of CD-RISC and its dimensions were negatively correlated with the total score of MBI-GS and its dimensions (P < 0.05). Conclusion Psychological resilience of nursing staff in the rehabilitation department is negatively correlated with their job burnout.

Objective To evaluate the research status of chronic disease comorbidity trajectories, and systematically sort out the influence mechanisms of health behavioral factors on the development trajectories of chronic diseases and their comorbidities. Methods By retrieving relevant literatures published from 2000 to 2024 in databases such as CNKI, PubMed and Web of Science, we systematically sorted out the methodological progress of chronic disease comorbidity trajectory modeling at home and abroad, and focused on analyzing the impacts of health behavioral factors (smoking, physical activity, sleep quality, dietary patterns, and body weight management) on the accumulation of chronic diseases and the evolution of comorbidity trajectories. We comprehensively used evidence from cross-sectional studies and longitudinal cohort studies to explore the dynamic process of transition from single disease to multi-morbidity. Results Trajectory modeling studies showed that 4–6 types of comorbidity progression trajectories with significant heterogeneity could be identified based on advanced methods such as latent class growth models. Health behavior interventions had significant effects on the prevention and control of chronic disease comorbidities. Regular physical activity could serve as a primary prevention measure for 35 types of chronic diseases, significantly reducing the cumulative risk of chronic diseases; smoking cessation interventions effectively blocked the progression of cancers and cardiovascular diseases; adequate sleep (≥7 h/night) and high-quality dietary patterns were both significantly associated with a reduced risk of an increase in the number of chronic diseases. Longitudinal cohort studies further confirmed that individuals with low-level physical activity had a 2–3 times higher risk of being classified into the rapid progression trajectory group, and smoking combined with high-risk drinking significantly increased the probability of transitioning to a high-risk comorbidity state. Conclusion Health behaviors play a crucial regulatory role in the whole-course evolution of chronic diseases. Future prevention and control strategies should shift from single-behavior interventions to dynamic management of aggregated behavior patterns, so as to promote the construction of an individualized prevention and intervention system oriented by comorbidity trajectories.

Objective To evaluate the operational efficiency of surgical departments in a tertiary specialized public hospital, establish an Autoregressive Integrated Moving Average (ARIMA) multiplicative seasonal model to predict surgical volume, and provide references for the hospital's operational decision-making. Methods Data Envelopment Analysis (DEA) and Malmquist index method were used for static and dynamic analysis of 13 surgical departments in the target hospital from 2020 to 2023. The ARIMA combined with multiplicative seasonal model was applied to predict the short-term future surgical volume. Results From 2020 to 2023, the proportions of non-DEA efficient surgical departments in the hospital were 30.8%, 46.2%, 23.1% and 46.2% respectively, with accurate recommended values for adjusting input and output quantities. The average values of total factor productivity change index for each year and the four-year period were 0.957, 0.937, 1.051 and 0.980 respectively. The ARIMA multiplicative seasonal model predicted that the hospital's short-term future surgical volume would continue to increase, and the hospital should adjust resource allocation reasonably in advance. Conclusion There are differences in operational efficiency among the surgical departments of the hospital, so differentiated management should be well implemented. It is recommended to flexibly adjust health resource allocation by combining scientific analysis tools such as DEA and ARIMA multiplicative seasonal model. 

Objective To scientifically formulate budget targets, integrate the Diagnosis-Related Groups (DRG) tool into budget management, develop a budget preparation method that matches the medical insurance payment method, and improve the level of budget management. Methods Based on 282,701 DRG-related cases from 2022 to 2023, SPSS 25.0 software was used for statistical analysis. An income budget prediction model was established through analysis of variance and regression analysis. Results According to the model prediction, the Case Mix Index (CMI) in 2024 could reach 1.31, the total weight increased by 9.8%, the unit price per weight decreased from 24,662 yuan to 21,570 yuan, the total budgeted income increased by 6.90%, and the average cost increased by 4.67%. Conclusion Integrating medical services into comprehensive budget management in the form of DRG data realizes the refinement of budget management, promotes the transformation of public hospitals' operation mode to value-based healthcare, and contributes to the achievement of strategic goals. 

The concept of Outcome-Based Education (OBE) is an advanced educational philosophy. In the design of the teaching syllabus for Medical Statistics based on the OBE concept, students should be the main focus of teaching objectives, and teaching activities should be centered on students. Attention should be paid not only to "cultivating professional talents" but also to "fostering moral integrity". On one hand, it is necessary to add competence objectives, deepen affective objectives, and strengthen the integration of ideological and political education into the curriculum within the connotation of course objectives. On the other hand, it is essential to incorporate higher-order thinking skill objectives to enhance students' ability to solve practical problems using statistical thinking and methods. In the design of teaching activities, the amount of assignments should be increased—requiring students to complete relevant research designs and the writing of papers/reports—so that students can "stay engaged" in "doing assignments" during their spare time.

Objective To analyze the early warning value of glycated hemoglobin (HbA1c) and high-sensitivity C-reactive protein (hs-CRP) in the differential diagnosis of type 2 diabetic proliferative retinopathy (PDR). Methods A total of 338 patients with diabetic retinopathy (DR) discharged from hospital between 2019 and 2023 were selected as research subjects. According to the coding rules of the International Classification of Diseases, 10th Revision (ICD-10), they were divided into two groups: the type 2 diabetic non-proliferative retinopathy (NPDR) group (230 cases) and the type 2 diabetic proliferative retinopathy (PDR) group (108 cases).A PDR prediction model was established using logistic regression. The receiver operating characteristic (ROC) curve was adopted to analyze the cut-off points and early warning values of HbA1c and hs-CRP for PDR screening, respectively. Additionally, the decision curve analysis (DCA) was used to compare the fitting effect differences between the initial model and the final model after adding HbA1c and hs-CRP. Results Independent samples t-test results showed that the levels of HbA1c and hs-CRP in the PDR group were significantly higher than those in the type 2 diabetic non-proliferative retinopathy group (P<0.01). ROC curve analysis indicated that the cut-off points of HbA1c and hs-CRP for PDR diagnosis were 9.510% and 1.675 mg/L, respectively, with the areas under the ROC curve (AUC) reaching 0.826 and 0.929 (P<0.01). DCA results revealed that the net benefit of the final model established after including HbA1c and hs-CRP was higher than that of the initial model. The clinical impact curve (CIC) showed that when the threshold probability (Pt) > 0.4, the actual distribution of the final model was close to its predicted distribution, and the fitting degree was significantly improved. Logistic regression results of the final model demonstrated that both HbA1c (OR=6.052, 95% CI: 2.745–13.346) and hs-CRP (OR=1.835, 95% CI: 1.001–3.367) were risk factors for PDR (P<0.05). Conclusion HbA1c and hs-CRP have high early warning value for PDR and are expected to become auxiliary indicators for clinical early warning of DR lesions and differential diagnosis of PDR.

Objective To explore the patterns and context of research in the field of patients' medical care choice, and provide reference for future studies. Methods Using the visual analysis tool CiteSpace, a visual analysis was conducted on domestic literature related to patients' offline medical care choice, which was published between 2013 and 2023 and included in databases such as CNKI, SinoMed, Wanfang Data Knowledge Service Platform, and VIP Chinese Journal Service Platform. Results A total of 80 literatures were included. The number of literatures on patients' offline medical care choice showed an overall upward trend, but the total quantity was relatively small. Jiang Jinxing, Zhuo Lang, Zhang Yan, Miao Chunxia, etc., were high-yield authors; Sichuan University, Xuzhou Medical University, and Capital Medical University published a relatively large number of papers. The keyword co-occurrence map showed that keywords such as "medical care choice", "influencing factors", and "hierarchical medical system" had high frequencies; the keyword timeline map was mainly clustered into 9 major thematic clusters, including "primary care first consultation", "medical care choice", "medical care preference", "medical-seeking behavior", "hierarchical medical system", "patients", "health education", "rural patients", and "dermatology". Conclusions It is suggested to strengthen and promote cooperation among scholars, institutions, and regions, continuously supplement and revise existing conclusions based on dynamic changes, provide theoretical support for the formulation of national health policies, and promote the construction of a more reasonable medical system.

Objectives To retrospectively analyze the changes in the disease spectrum and hospitalization expenses of elderly discharged patients from a tertiary hospital in Taizhou City, and to provide data support for hospital management and disease control. Methods Clinical data and hospitalization expense records of patients aged 60 years and above from 2018 to 2023 were extracted from the medical record system. Discharge diagnoses were grouped in accordance with the International Classification of Diseases, 10th Revision (ICD-10). The hospitalization expenses and disease prevalence were described statistically. Results A total of 194,242 patients aged 60 years and above were included in this study from 2018 to 2023, among whom 61.94% were male and 38.06% were female, with an average age of (71.89 ± 7.68) years. The top 4 disease categories based on the primary diagnosis were as follows: Factors influencing health status and contact with health services (23.00%); Diseases of the circulatory system (14.23%); Diseases of the digestive system (12.35%); Diseases of the respiratory system (10.04%). Hospitalization expenses increased with the extension of length of stay and the increase in the proportion of surgical procedures. Pharmaceutical fees, diagnostic fees, and consumable fees accounted for the dominant proportion of total hospitalization expenses. Conclusions Disease control departments should focus on the prevention and control of tumors, circulatory system diseases, and digestive system diseases. Medical institutions should optimize the structure of hospitalization expenses to reflect the labor and technical value of medical staff. Meanwhile, they should actively promote day surgery and give full play to the role of medical consortia in patient triage, so as to reasonably reduce the average length of hospital stay.

Objective To study the influencing factors of in-hospital hemorrhagic transformation in elderly patients with acute ischemic stroke undergoing intravenous thrombolysis, construct a risk prediction model, and explore related countermeasures for early warning and prevention.Methods A total of 195 elderly patients with acute ischemic stroke undergoing intravenous thrombolysis from May 2018 to May 2023 were selected retrospectively as research subjects. They were divided into non-hemorrhagic transformation group (140 cases) and hemorrhagic transformation group (55 cases) according to whether hemorrhagic transformation occurred. General data of patients were collected for comparison; a hemorrhagic transformation prediction model was constructed via multivariate logistic regression analysis, and the receiver operating characteristic (ROC) curve was plotted to analyze the predictive efficacy of the model.Results There were statistically significant differences between the two groups in age, presence of hyperlipidemia, presence of atrial fibrillation, time from onset to intravenous thrombolysis, admission NIHSS score, NLR, FPG level, and family history of stroke (P < 0.05). Multivariate logistic regression analysis identified 7 independent risk factors (P < 0.05). For the prediction model, the Hosmer-Lemeshow test showed χ² = 6.536 and P = 0.587. Nomogram validation indicated that the model had an AUC of 0.909 (good discrimination), with a maximum Youden index of 0.659, sensitivity of 0.909, and specificity of 0.750. The theoretical and actual values of the calibration curve showed good consistency.Conclusion The Nomogram model for in-hospital hemorrhagic transformation in elderly patients with acute ischemic stroke undergoing intravenous thrombolysis has good predictive value. It can provide a reference for early screening of high-risk groups and further help formulate more accurate prevention and treatment plans. 

Objective To analyze the nursing demands of stroke patients with aphasia during the rehabilitation period based on the KANO model. Methods A questionnaire was designed by combining the KANO model with the Delphi expert consultation method. A total of 200 stroke patients with aphasia who were in the rehabilitation period in the Department of Neurology of a municipal people's hospital from January 2023 to January 2024 were randomly selected as the research subjects. The attributes of patients' nursing demands were determined through KANO model demand attribute classification, better-worse coefficient analysis and two-dimensional matrix analysis. The most sensitive and need-to-improve elements were identified via demand element screening. Results After the classification of demand attributes by the KANO model, combined with better-worse coefficient analysis and two-dimensional matrix analysis, the index classification results showed that the expected demands accounted for 50.00% (8/16), attractive demands accounted for 18.75% (3/16), and must-be demands accounted for 31.25% (5/15). Through the KANO model factor selection line, the top 5 key factors affecting the nursing demands of stroke patients with aphasia during the rehabilitation period were screened out, which were post-discharge speech medical referral service, breaking the daily routine, relatives' companionship, adjustment of negative psychological state, and doctor-patient communication channels. Conclusion For the nursing service of stroke patients with aphasia during the rehabilitation period, on the basis of meeting the must-be attributes, efforts should be made to improve the expected attributes and strive to satisfy the attractive attributes. Hospitals can invest resources in these items to provide better medical services for patients.

Objective To explore the impact of pharmaceutical care under the physician-pharmacist collaboration model on disease control and health behaviors in elderly patients with chronic diseases in the community. Methods A total of 110 elderly patients with chronic diseases in the community were selected and randomly divided into two groups. Patients in the control group were provided with routine medication consultation and guidance, while those in the observation group received pharmaceutical care intervention under the physician-pharmacist collaboration model. The disease control status and health behaviors of patients in the two groups were compared. Results After the intervention, the compliance rates of blood glucose, blood lipid, and blood pressure reaching the standard in the observation group were higher than those in the control group (P<0.05); the rate of good medication adherence in the observation group was higher than that in the control group (P<0.05); and the completion rates of health behaviors such as abiding by the doctor's advice for medication, regular reexamination, healthy diet, and regular work and rest in the observation group were higher than those in the control group (P<0.05). Conclusions For elderly patients with chronic diseases in the community, the application of pharmaceutical care under the physician-pharmacist collaboration model for nursing intervention can effectively control the disease, improve health behaviors, and enhance medication adherence.

Objective: To construct a WeChat-based remote rehabilitation nursing program for patients with Alzheimer's disease and evaluate its effect, so as to provide a reference for the rehabilitation nursing of Alzheimer's disease. Method: From June 2022 to June 2024, based on literature analysis, two rounds of expert letter consultations were conducted to revise the items and construct the WeChat-based remote rehabilitation nursing program; the program was initially applied in 300 patients with Alzheimer's disease, and its effect was evaluated. Result: The effective recovery rate of the two rounds of expert letter consultation questionnaires was 100%. The expert authority coefficient was 0.88, the coefficient of variation was 0.05-0.21, and the Kendall's coordination coefficients were 0.211 and 0.095 respectively. After the application of the program, the patients' depression level, quality of life and language function were all improved compared with those before the application, and the differences were statistically significant (P < 0.05). Conclusion: The WeChat-based remote rehabilitation nursing program for patients with Alzheimer's disease constructed in this study has good scientificity and practicability, and can provide a reference for the implementation of rehabilitation nursing for patients with Alzheimer's disease.

Objective To group patients with gastric malignant tumors into Diagnosis-Related Groups (DRGs) using the Exhaustive CHAID (ECHAID) decision tree model, and empirically analyze the influencing factors of inpatient expenses.Methods Retrospectively extract the first-page data of medical records of inpatients with gastric malignant tumors who received treatment in our hospital from June 2022 to August 2024. Taking inpatient expenses as the dependent variable, 16 clinical and expense-related variables including age, gender, main diagnosis, surgical method, and Comorbidity and Complication Index (CCI) were selected as independent variables to construct the ECHAID decision tree model. The model parameters were set as follows: maximum tree depth of 5 layers, minimum sample size of 100 for parent nodes, minimum sample size of 50 for child nodes, and significance level α = 0.05. The average expense and Case Mix Index (CMI) of the terminal DRGs nodes were calculated to form the final DRGs grouping scheme. The 10-fold cross-validation method was used to evaluate the stability of the model.Results The binary logistic multivariate regression model showed that the tolerance and Variance Inflation Factor (VIF) of each index had good discrimination, and there was no multicollinearity. Surgical method, chemotherapy regimen, and Charlson Comorbidity Index were the main factors affecting the median inpatient expenses (P < 0.001). By incorporating surgical method, chemotherapy regimen, and Charlson Comorbidity Index as independent variables into the ECHAID decision model and establishing tree-like nodes, a total of 4 layers with 27 nodes were generated, including 15 DRGs grouping nodes. The range of Coefficient of Variation (CV) was 0.17-0.31, and the range of Relative Weight (RW) was 1.05-3.26, indicating good discrimination between groups. The median of average inpatient expenses among the 15 DRGs groups ranged from 40,321.46 yuan to 128,565.90 yuan, and the over-limit range between groups was 2.27%-3.76%.Conclusion The DRGs grouping scheme for gastric malignant tumors based on the ECHAID decision tree model has good homogeneity and heterogeneity, which can provide a reference for the formulation of relevant payment policies.

Objective This study aims to develop a Nemenyi test program for pairwise comparisons following the Kruskal-Wallis test of multiple independent samples using Python, and provide an efficient and reusable nonparametric test tool for medical researchers. Methods Based on Python, a directly callable function program named "kruskal_nemenyi_test" was designed and developed to realize the automated processing of the Kruskal-Wallis test and Nemenyi test. The correctness of the program was verified using the sample data from the textbook Medical Statistics. Results A complete program for the Kruskal-Wallis test and Nemenyi test was successfully developed. Verification tests showed that the test statistics and P-values output by the program were consistent with the standard sample results, and the program code has been open-sourced and shared on the GitHub platform. Conclusion The Python-based Nemenyi test program features high efficiency, accuracy and ease of invocation. It provides a practical tool for nonparametric statistical analysis in medical research and helps improve the scientificity and reliability of statistical results.

Objective To analyze the influencing factors of depression risk among the urban and rural elderly in China, and conduct a comparison between urban and rural areas.Methods Based on the 2018 China Health and Retirement Longitudinal Study (CHARLS) data, a total of 7,690 elderly individuals aged 60 years and above were included as the research subjects. Binary logistic regression model and decision tree model were used to conduct multivariate analysis on the depression risk of the urban and rural elderly. Additionally, the prediction accuracy of the two models and the screened influencing factors were compared. Results (1) The detection rate of high depression risk among the 7,690 elderly individuals was 30.73%, with 21.5% in urban areas and 43.0% in rural areas.
(2) Gender, educational level, self-rated health, and chronic diseases were common influencing factors for depression risk among both urban and rural elderly. High depression risk in the urban elderly was associated with social activities, while depression risk in the rural elderly was associated with wage income and sleep.(3) The correct prediction rates of the logistic regression model for the influencing factors of depression in the urban and rural elderly were 82.5% and 74.3% respectively, and those of the decision tree model were 81.5% and 73.4% respectively. The prediction effect of the logistic regression model was better than that of the decision tree model.Conclusion There are significant urban-rural differences in the proportion of high depression risk and its influencing factors among the elderly in China.

Objective To explore the nomogram prediction model for lung cancer patients complicated with lower respiratory tract infection.Methods The data of lung cancer patients admitted to a cancer hospital in Jiangxi Province from July 2020 to July 2023 were analyzed retrospectively. Logistic regression was used to analyze the risk factors for lower respiratory tract infection in lung cancer patients, and R software was applied to construct a nomogram prediction model for the risk factors of lower respiratory tract infection in lung cancer patients.Results Among 200 lung cancer patients, 118 were complicated with lower respiratory tract infection. Multivariate logistic regression analysis showed that ≥3 underlying diseases, combined chemotherapeutic drugs, clinical stage (Stage III-IV), length of hospital stay ≥20 days, invasive procedures, and abuse of antibacterial drugs were independent risk factors for lower respiratory tract infection in lung cancer patients (P < 0.05). The calibration curve of the nomogram prediction model was close to the original curve and the ideal curve, with a C-index of 0.895 (95% CI: 0.851-0.938), indicating a high degree of model fit. When the threshold of the nomogram prediction model was > 0.17, it could provide clinical net benefits.Conclusion The occurrence of lower respiratory tract infection in lung cancer patients is related to factors such as the number of underlying diseases, combined use of chemotherapeutic drugs, and clinical stage (Stage III-IV). Constructing a personalized nomogram prediction model with these factors as predictors is helpful for the predictive assessment of lower respiratory tract infection in lung cancer patients.

Objective: Based on the Actor-Partner Interdependence Model (APIM), this study aims to analyze the relationships between dyadic coping, self-fatigue regulation ability, and quality of life in maintenance hemodialysis (MHD) patients and their caregivers, so as to provide a theoretical basis for clinical interventions. Method: A total of 150 pairs of MHD patients and their caregivers admitted to a hospital from January 2023 to February 2024 were selected as research subjects. General information questionnaires, Dyadic Coping Scale, Self-Regulation Fatigue Scale, and Quality of Life Scale were used for investigation. The collected data were analyzed using SPSS 25.0, and AMOS 24.0 software was used to construct the Actor-Partner Interdependence Model of dyadic coping, self-fatigue regulation ability, and quality of life. Result: The results of stratified chi-square test showed that males aged 50 years, males and females with low educational levels, married males and females, and those with a per capita monthly household income of less than 3,000 yuan were risk factors for MHD patients and caregivers, with statistically significant differences (P < 0.05). The self-fatigue regulation ability of MHD patients was higher than that of their caregivers, while their dyadic coping and quality of life were lower than those of their caregivers, with statistically significant differences (P < 0.05). Pearson correlation analysis showed that dyadic coping was negatively correlated with self-fatigue regulation ability and positively correlated with quality of life in both MHD patients and their caregivers (P < 0.05). In terms of actor effects, dyadic coping and self-fatigue regulation ability of both MHD patients and their caregivers could predict their own quality of life, showing a positive correlation (b = 0.681, 0.623, 0.604, 0.649, P < 0.001). In terms of partner effects, dyadic coping and self-fatigue regulation ability of MHD patients could predict the quality of life of caregivers, and dyadic coping and self-fatigue regulation ability of caregivers could also predict the quality of life of MHD patients, all showing a positive correlation (b = 0.623, 0.561, 0.604, 0.628, all P < 0.001). Conclusion: The quality of life of MHD patients is comprehensively affected by themselves and their caregivers. Paying attention to the interactive effects of dyadic coping level and self-fatigue regulation ability between MHD patients and their caregivers can improve the quality of life of both MHD patients and their caregivers.

Objective To construct a prediction model for prolonged hospitalization in patients with acute pancreatitis (AP) based on bedside simple indicators.Methods A total of 240 AP patients admitted to the Department of Gastroenterology of a certain hospital of integrated traditional Chinese and Western medicine from January 2020 to January 2024 were selected as the study subjects of the modeling set. According to whether the patients had prolonged hospitalization, the patients were divided into the prolonged group and the non-prolonged group. The general data, bedside simple indicator data, serological indicator data, Revised Atlanta Classification (RAC), and Bedside Index for Severity in Acute Pancreatitis (BISAP) of the patients were collected. Multivariate logistic regression analysis was used to screen the related factors for prolonged hospitalization in AP patients, and a risk prediction model was established and a nomogram was drawn for evaluation. According to the ratio of modeling set to validation set of 7:3, 103 AP patients admitted to another hospital from February 2024 to November 2024 were selected as the validation set for external validation of the model.Results The modeling set included all factors with statistically significant differences between groups in the univariate analysis (P < 0.05). Multivariate analysis results showed that heart rate, pain status, blood urea nitrogen, RAC, BISAP, and peritoneal irritation sign were all risk factors for prolonged hospitalization in AP patients (P < 0.05). The sensitivity was 0.957, specificity was 0.972, area under the curve was 0.982, Youden index was 0.884, and the Hosmer-Lemeshow goodness-of-fit test showed a good effect (χ² = 2.455, P = 0.964). For the validation set, the prediction model had a sensitivity of 1.000, specificity of 0.892, area under the curve of 0.985, Youden index of 0.892, and the Hosmer-Lemeshow goodness-of-fit test also showed a good effect (χ² = 1.538, P = 0.992). The calibration curves of the two groups of models were both close to the ideal curve, which verified that the model had good goodness-of-fit and relatively good predictive performance.Conclusion The prediction model for prolonged hospitalization in AP patients constructed based on bedside simple indicators has good predictive performance, which can provide a reference for predicting the prolonged hospitalization of AP patients. In clinical practice, it can be used as an evaluation method to assess whether the hospitalization time of AP patients is prolonged according to the actual use timing and needs.

Objective To explore the influencing factors of medication adherence in female patients with schizophrenia in the recovery period and construct a risk prediction model. Methods A total of 140 female patients with schizophrenia in the recovery period admitted to Hengfeng Branch of Shangrao Third People's Hospital from April 2022 to October 2023 were selected as the modeling set, and another 60 female patients with schizophrenia in the recovery period admitted from November 2023 to October 2024 were selected as the validation set. The Morisky Medication Adherence Scale was used to assess the patients' medication adherence. Patient data were collected, and univariate analysis and multivariate logistic regression analysis were used to screen the independent influencing factors of patients' medication adherence. R software was used to draw a nomogram. Calibration curves were used to evaluate the consistency of the model; ROC curves were used to evaluate the predictive efficacy of the model; and the Hosmer-Lemeshow (H-L) test was used to judge the goodness of fit of the model. Results Among the 140 patients in the modeling set, 67 cases (47.86%) had poor medication adherence. Univariate analysis showed that age, educational level, personal monthly income, course of disease, disease awareness, social support, and family care were influencing factors of medication adherence (P < 0.05). Multivariate logistic regression analysis showed that age, educational level, personal monthly income, disease awareness, social support, and family care were independent influencing factors of medication adherence. A risk prediction model was constructed based on the above 6 factors. For the modeling set, the area under the ROC curve (AUC) was 0.836 (95% CI: 0.768-0.903), with a sensitivity of 89.6% and a specificity of 69.9%. The calibration curve of the prediction model for the modeling set was close to the standard curve, indicating good consistency of the model. The results of the H-L goodness-of-fit test showed χ² = 11.323 and P = 0.184. For the external validation set, the AUC was 0.990 (95% CI: 0.972-1.000), with a sensitivity of 96.4% and a specificity of 96.9%. Conclusion Age, educational level, personal monthly income, disease awareness, social support, and family care are influencing factors of medication adherence. The risk prediction model constructed based on these factors has good clinical predictive value.

Objectives This study aimed to explore the safety and efficacy of ciprofol in general anesthesia induction for pediatric patients undergoing orthopedic surgery by comparing it with propofol. Methods Propensity score matching was used to retrospectively analyze the clinical data of pediatric patients who underwent orthopedic surgery at the Affiliated Hospital of Binzhou Medical University from May 2024 to November 2024. According to the different intravenous general anesthetics used in anesthesia induction, the cases were divided into the ciprofol group and the propofol group. The primary outcome measures included hemodynamic changes such as blood pressure and heart rate of the children after entering the operating room, during induction, during intubation, and 10 minutes after intubation; the secondary outcome measures included adverse reactions such as postoperative nausea and vomiting. Results A total of 35 patients in the ciprofol group and 45 patients in the propofol group who met the inclusion and exclusion criteria were enrolled. After propensity score matching, 32 patients in the ciprofol group and 32 patients in the propofol group were finally included. There were no statistically significant differences in baseline indicators such as age, gender, height, and body weight between the ciprofol group and the propofol group (P>0.05). The mean arterial pressure of the ciprofol group was higher than that of the propofol group during anesthesia induction, intubation, and 10 minutes after intubation, with statistically significant differences (P<0.05); the heart rates of the two groups were similar during anesthesia induction, intubation, and 10 minutes after intubation, with no statistically significant differences (P>0.05). There was no statistically significant difference in the incidence of adverse reactions such as postoperative nausea and vomiting between the two groups (P>0.05). Conclusions Compared with propofol, ciprofol has more stable hemodynamic indicators during anesthesia induction and can better complete general anesthesia induction in children. 

Objective To explore the relationship between healthy sleep patterns and the risk of peptic ulcer, with a view to early prevention and relevant interventions.Methods A total of 357,636 participants from the UK Biobank were included in this study. Healthy sleep patterns were defined to include sleep duration, sleep chronotype, insomnia, snoring, and daytime napping. The Cox proportional hazards model was used to assess the association between healthy sleep patterns and the risk of peptic ulcer.Results Healthy sleep patterns were associated with a reduced risk of peptic ulcer (HR=0.88, 95% CI: 0.86–0.89), gastric ulcer (HR=0.86, 95% CI: 0.84–0.89), and duodenal ulcer (HR=0.89, 95% CI: 0.86–0.92). Specifically, sleep duration of 7–8 hours per day (HR=0.82, 95% CI: 0.79–0.85), morning chronotype (HR=0.91, 95% CI: 0.87–0.94), infrequent insomnia (HR=0.78, 95% CI: 0.74–0.81), and infrequent daytime napping (HR=0.75, 95% CI: 0.68–0.82) were all associated with a reduced risk of peptic ulcer. The results of subgroup analysis stratified by baseline characteristics were consistent with the overall model.
Conclusion Having healthy sleep patterns is associated with a reduced risk of peptic ulcer, gastric ulcer, and duodenal ulcer. Therefore, taking effective measures to improve sleep behavior is of positive significance for the public to prevent peptic ulcer.

Objective To understand the status of TORCH infection in reproductive-age women in Binzhou Area and provide reference for infection prevention and prenatal and postnatal care. Methods Retrospectively collect TORCH test results of reproductive-age women, analyze from the aspects of year, age and season, and use SPSS statistical software for chi-square test (χ² test). Results In the serum TORCH test of reproductive-age women, the highest IgM positive rate was for herpes simplex virus (HSV₁₊₂) (14.45%), followed by rubella virus (RV) (1.51%), cytomegalovirus (CMV) (0.53%) and Toxoplasma gondii (TOX) (0.37%); the highest IgG positive rate was for CMV (96.36%), followed by HSV₁₊₂(94.08%) and RV (81.09%), with the lowest being TOX (0.66%). There were statistically significant differences in the positive rates of HSV₁₊₂-IgM, RV-IgM/IgG and CMV-IgG among different years (P＜0.05); there were statistically significant differences in the positive rates of HSV₁₊₂-IgM, CMV-IgG and RV-IgG among different age groups (P＜0.05); there were no statistically significant differences in the positive rates of TORCH IgM and IgG among different seasons (P＞0.05). Conclusion Acute infection in Binzhou Area is mainly caused by HSV₁₊₂ and shows an increasing trend year by year; previous infections are mainly caused by CMV and HSV₁₊₂, neither of which has seasonal distribution characteristics, and the infection is mainly in women of appropriate childbearing age. To reduce adverse pregnancy outcomes and improve the quality of newborns, it is still necessary to strengthen TORCH screening for reproductive-age women. 

Objective To analyze the current status of medical and health resources in Henan Province and predict the changing trends of these resources.Methods Based on the data related to medical and health resources in Henan Province from 2012 to 2021, a grey GM(1,1) prediction model was constructed to predict and analyze the allocation of human, material, and financial resources of medical and health services in Henan Province from 2022 to 2025.Results By 2025, the number of medical institutions in Henan Province will increase to 77,659, the number of hospital beds will rise to 920,000, and the average number of beds per 1,000 people will be 9.40. The number of licensed (assistant) doctors and registered nurses will reach 306,000 and 447,200 respectively, with a doctor-nurse ratio of 1:1.46. The total health expenditure will increase to 664.668 billion yuan, with an average annual growth rate of 11.63%, among which government health expenditure, social health expenditure, and personal health expenditure will account for 24.17%, 50.96%, and 24.88% respectively.Conclusions The level of hospital bed allocation and health financing in Henan Province is gradually improving, but the number of medical institutions is growing slowly and there is still a shortage of registered nurses. It is urgent to take effective measures to rationally allocate health resources and accurately meet the differentiated and multi-level health needs of the people.

Objectives This study aimed to explore the risk factors for complicated shoulder-hand syndrome (SHS) in elderly stroke patients and construct a prediction model for SHS in elderly stroke patients. Methods A total of 252 elderly stroke patients treated in a hospital from May 2020 to November 2023 were selected as the training set, and they were divided into the SHS group and non-SHS group according to whether they had complicated SHS. Another 108 elderly stroke patients treated in the same hospital from December 2023 to October 2024 were selected as the test set. Data including age, gender, stroke type, stroke history, complicated hemiplegia, obesity, malnutrition, depression, alcohol consumption, smoking, dysphagia, sleep quality, hypertension and diabetes were collected from the two groups. LASSO analysis was used to screen the predictive factors for SHS in elderly stroke patients, logistic regression analysis was used to screen the risk factors for SHS in elderly stroke patients, R4.2.3 was used to establish a nomogram model for SHS in elderly stroke patients, and the predictive efficacy of the model was analyzed. Results Among the 252 elderly stroke patients, 78 developed SHS, with an incidence rate of 30.95%. There were no statistically significant differences in age, gender, stroke type, stroke history, obesity, malnutrition, alcohol consumption, dysphagia and hypertension between the SHS group and non-SHS group (P>0.05), while there were statistically significant differences in complicated hemiplegia, depression, smoking, sleep quality and diabetes between the two groups (P<0.05). Logistic regression analysis showed that complicated hemiplegia, depression, smoking, poor sleep quality and diabetes were risk factors for SHS in elderly stroke patients (P<0.05). The area under the ROC curve (AUC) of the training set was 0.719 (95% CI: 0.648-0.790), and the AUC of the test set was 0.728 (95% CI: 0.659-0.797). The bootstrap method was used to obtain the calibration curve, and the bias-corrected curve was used to evaluate the calibration curve. The actual values of the calibration curve in the training set were basically consistent with the predicted values, with a C-index of 0.736 (95% CI: 0.671-0.801) and a Hosmer-Lemeshow goodness-of-fit test result of χ²=1.642 (P=0.801). The actual values of the calibration curve in the test set were basically consistent with the predicted values, with a C-index of 0.743 (95% CI: 0.634-0.852) and a Hosmer-Lemeshow goodness-of-fit test result of χ²=0.630 (P=0.889). The decision curve of the training set showed that when the threshold probability was 15%-93%, the nomogram had a high net benefit value in predicting SHS in elderly stroke patients; the decision curve of the test set showed that when the threshold probability was 13%-85%, the nomogram had a high net benefit value in predicting SHS in elderly stroke patients. Conclusions Complicated hemiplegia, depression, smoking, poor sleep quality and diabetes are risk factors for SHS in elderly stroke patients, and the nomogram model for SHS in stroke patients has certain clinical practicability.

Objectives To understand the satisfaction level and influencing factors of outpatient pharmacy window services in hospitals nationwide, and to provide a basis for improving the quality of outpatient pharmacy services and optimizing pharmacy management work. Methods Questionnaire survey data on outpatient patient experience in hospitals from different regions, collected by the Patient Experience Research Base of the Medical Administration and Service Guidance Center of the National Health Commission between 2021 and 2024, were used. Variables including patients’ basic information, waiting time for medication pickup, satisfaction with medication usage explanation, and relevant hospital characteristics were collected. A mixed-effects model was adopted to analyze the influencing factors of waiting time for medication pickup and satisfaction with medication usage explanation. Results A total of 39,957 outpatients from 20 hospitals were surveyed, including 16,093 males (40.3%) and 23,864 females (59.7%). Among the outpatients, 36,914 cases (92.38%) had a waiting time for medication pickup of 20 minutes or less, and 3,043 cases (7.62%) had a waiting time of more than 20 minutes; 32,247 cases (80.70%) were satisfied with the medication usage explanation, and 7,710 cases (19.30%) were dissatisfied. Hospital grade (OR=2.70, 95%CI:1.45-5.02) and hospital location (OR=0.41, 95%CI:0.18-0.94) were the main factors affecting the waiting time for medication pickup. Age, gender, long-term residence, expense category, and reasons for hospital selection also had statistically significant effects on the waiting time for medication pickup (P<0.05). Waiting time for medication pickup (OR=0.20, 95%CI:0.18-0.21) and hospital grade (OR=0.66, 95%CI:0.54-0.82) were the main factors affecting satisfaction with medication usage explanation. Gender, long-term residence, expense category, reasons for hospital selection, hospital location, and average daily outpatient volume also had statistically significant effects on satisfaction with medication usage explanation (P<0.05). Conclusions Outpatient pharmacy window services urgently need further strengthening and refinement. It is necessary to enhance the awareness of active service, rationally allocate human resources, improve pharmacists’ professional level, advocate civilized and polite service, and popularize drug knowledge through multiple channels, so as to improve patients’ medical experience.

Objective To analyze the relevant risk factors for acute-on-chronic liver failure (ACLF) in patients with liver cirrhosis and construct an early warning model.Methods A total of 153 cirrhotic patients who were hospitalized and treated in a municipal hospital from May 2020 to July 2023 were retrospectively selected as the research subjects. They were randomly divided into a model set (102 cases) and a validation set (51 cases) at a ratio of 2:1. According to whether ACLF developed during the follow-up period, the model set was further divided into an ACLF group and a non-ACLF group. Multivariate logistic regression analysis was used to identify the influencing factors of ACLF occurrence, and a risk prediction model for ACLF in cirrhotic patients was constructed. The accuracy of the model was evaluated using the receiver operating characteristic (ROC) curve, and the goodness-of-fit of the model was assessed via the Hosmer-Lemeshow test.Results In the model set, there were 16 cases in the ACLF group (15.69%) and 86 cases in the non-ACLF group (84.31%). Multivariate analysis results showed that albumin (A_H) (OR=0.739, 95% CI: 0.576–0.949, P=0.018), prothrombin time (T_P) (OR=1.310, 95% CI: 1.015–1.691, P=0.038), and activated partial thromboplastin time (T_APT) (OR=1.177, 95% CI: 1.044–1.327, P=0.008) were all independent influencing factors for ACLF occurrence in the patients (P<0.05). On this basis, the risk prediction model was established with the equation: logitP＝-9.925-0.302×A_H+0.270×T_P+0.163×T_APT. The area under the ROC curve (AUC) of the model was 0.980 (95% CI: 0.958–1.000), with a sensitivity of 1.000, a specificity of 0.907, and a Youden index of 0.907. The Hosmer-Lemeshow test of the combined model showed that (X²=1.61)and P=0.991.Conclusion The risk prediction model for ACLF in cirrhotic patients has good predictive efficacy. Medical staff can use indicators such as A_H, T_P and T_APT to identify high-risk patients and take timely intervention measures.