ObjectiveTo construct and validate a clinical prediction model for inflammatory remission outcomes in rheumatoid arthritis （RA） patients with liver and kidney deficiency syndrome treated with Bushen Zhiwang Decoction （补肾治尪汤， BZD） based on metabolomics. MethodsA prospective cohort study was conducted， enrol-ling 60 RA patients with liver and kidney deficiency syndrome. All patients were treated with BZD and conventional-dose oral conventional synthetic disease-modifying antirheumatic drugs （csDMARDs） for 12 months. Clinical data were collected， and the change in disease activity score in 28 joints （DAS28） after treatment compared with baseline （△DAS28） was used as the primary outcome and grouping criterion. Peripheral blood samples were collected before treatment to analyze plasma metabolites. Differential analysis and least absolute shrinkage and selection operator （LASSO） regression were used to preliminarily screen differential metabolites， followed by machine learning algorithms to further identify a core metabolite combination. Based on the expression levels of the core metabolite combination， a novel metabolite index， namely the metabolomics-based inflammatory remission score （Met-IRS）， was calculated using standar-dized metabolite values， and its clinical applicability was evaluated. A clinical prediction model was constructed by integrating clinical characteristics and Met-IRS， and the model performance was assessed. ResultsAmong the 60 patients， those with △DAS28 ≥ 0.27 were assigned to the high inflammatory remission group， while those with △DAS28 ＜ 0.27 were assigned to the low inflammatory remission group， with 30 cases in each group. Compared to the low inflammatory remission group， the high inflammatory remission group showed a higher frequency of methotrexate use and a lower positive rate of rheumatoid factor （RF） （P＜0.05）. Seven core metabolites were identified as the optimal combination， including mangiferic acid， fatty acid-hydroxy fatty acid ester 40∶6， fatty acid-hydroxy fatty acid ester 18∶0， fatty acid-hydroxy fatty acid ester 36∶1， glucosylceramide， lysophosphatidylcholine 22∶5， and pregnanetriol ketone. The calculated Met-IRS comprehensively reflected the characteristics of differential metabolites and demonstrated clinical applicability. Met-IRS was significantly higher in the high inflammatory remission group than in the low inflammatory remission group， and was positively correlated with high inflammatory remission outcomes （P＜0.05）. Based on the variables Met-IRS， methotrexate use， leflunomide use， and RF positivity， a clinical prediction model for inflammatory remission in RA treatment （Cj-RTRM） was constructed. Model performance evaluation demonstrated that the model had good clinical predictive ability， with an area under the receiver operating characteristic curve （AUC） of 0.880， sensitivity 0.967， specificity 0.700 and Youden's index 0.667. ConclusionThe clinical prediction model Cj-RTRM constructed based on the metabolomics-based inflammatory remission score Met-IRS can effectively predict clinical inflammatory remission outcomes in RA patients treated with BZD and accurately identify the advantageous population for this treatment. This model provides guiding evidence for dynamic inflammation monitoring， targeted management， and identification of populations with advantages in traditional Chinese medicine.

OBJECTIVE To analyze the selection and rationales of comparators for pharmaceutical enterprises in their medical insurance access application， so as to provide a reference for promoting communication and consensus between enterprises and medical insurance authorities in this process. METHODS The application materials for drugs outside the catalogue that passed formal review published by the National Healthcare Security Administration from 2021 to 2025 were extracted， and then content analysis was used to systematically sort out relevant information of the declared drugs and comparators； the specific situations and rationales of pharmaceutical enterprises’ selection of comparators were analyzed. RESULTS A total of 1 341 declared drug documents were collected. Data analysis showed that 1 035 （77.18%） were submitted with positive comparators and 306 （22.82%） used blank comparators； 58 drugs （4.33%） took combination therapy as the reference， and 5 drugs （0.37%） referred to non-pharmacological （or non-single pharmacological） treatment regimens. Among competitive drugs declared by multiple enterprises， 50.00% of the enterprises submitted different comparators. A total of 4 basic conditions and 39 additional conditions were extracted as the rationales for selecting positive comparators. For blank comparators， 12 drug-related factors， 2 administrative factors， and 1 other factor were identified. More than 10% of the drugs did not state the rationale for comparator selection， and over 44% of drugs using blank comparators provided only one justification. CONCLUSIONS Pharmaceutical enterprises mainly select comparators based on their own interests in the medical insurance access application， and there are deficiencies in the adequacy and standardization of their selection basis and reasoning. It is recommended that enterprises follow the principled requirements of medical insurance authorities， and fully and normatively explain the reasons for selecting comparators in combination with the characteristics of their own products. Meanwhile， it is advisable to change the current open-ended statement form of selection reasons into a closed-ended answering mode， so as to highlight the priority of selection， standardize the declaration behavior of enterprises， and reduce communication divergences between the two parties.

Objective To develop measurement instruments of dynamic capabilities for public hospitals,providing a reference for the evaluation and enhancement of their dynamic capabilities.Methods The initial construction of the measurement instruments of dynamic capabilities for public hospitals was based on literature review and focused group discussions.Two rounds of expert consultation were conducted using the Delphi method,and the reliability and validity of the questionnaire was initially examined through pre-survey.Results The effective response rates for both rounds of consultation were above 85％,with high positive coefficient,the authority coefficients greater than 0.8,and variation coefficients less than 0.25,indicating a high level of consensus among experts.Furthermore,the reliability and validity are acceptable.After integrating expert feedback,the measurement instruments of dynamic capabilities for public hospitals were finalized,encompassing four dimensions:sensing,learning,integration capability,and innovation capabilities.Conclusion The measurement instruments of dynamic capabilities for public hospitals,developed based on the Delphi method,can be applied in the practice of evaluating and enhancing the dynamic capabilities of public hospitals.

Objective To develop measurement instruments of dynamic capabilities for public hospitals,providing a reference for the evaluation and enhancement of their dynamic capabilities.Methods The initial construction of the measurement instruments of dynamic capabilities for public hospitals was based on literature review and focused group discussions.Two rounds of expert consultation were conducted using the Delphi method,and the reliability and validity of the questionnaire was initially examined through pre-survey.Results The effective response rates for both rounds of consultation were above 85％,with high positive coefficient,the authority coefficients greater than 0.8,and variation coefficients less than 0.25,indicating a high level of consensus among experts.Furthermore,the reliability and validity are acceptable.After integrating expert feedback,the measurement instruments of dynamic capabilities for public hospitals were finalized,encompassing four dimensions:sensing,learning,integration capability,and innovation capabilities.Conclusion The measurement instruments of dynamic capabilities for public hospitals,developed based on the Delphi method,can be applied in the practice of evaluating and enhancing the dynamic capabilities of public hospitals.

BACKGROUND:The patient underwent multiple hypoproteinemia after total hip arthroplasty,which affected postoperative healing and rehabilitation.OBJECTIVE:To investigate and screen the risk factors for hypoproteinemia after total hip arthroplasty,and to establish a nomogram prediction model so as to provide guidance for judging whether hypoproteinemia occurs after total hip arthroplasty.METHODS:A total of 355 patients who underwent total hip arthroplasty were included,and according to whether hypoproteinemia occurred on the first day after surgery,they were divided into 238 cases in the hypoproteinemia group and 117 cases in the normal group,with a hypoproteinemia rate of 67％.Data were collected,including age,gender,diabetes mellitus,hypertension,hyperuricemia,hyperlipidemia,anesthesia method,preoperative leukocytes,preoperative erythrocytes,preoperative hemoglobin,preoperative platelets,preoperative plasma prothrombin time,preoperative activated partial prothrombin time,preoperative international normalized ratio,preoperative thrombin time,preoperative fibrinogen,preoperative erythrocyte sedimentation rate,preoperative C-reactive protein,preoperative D-dimer,preoperative mean corpuscular hemoglobin content,preoperative mean corpuscular volume,operation time,body mass index,preoperative procalcitonin,and preoperative hematocrit.SPSS 27.0 software was used for univariate analysis,followed by R language(4.3.1)to perform least absolute shrinkage and selection operator regression and 10-fold cross-validation of the observation indicators to obtain the intersection of the two risk factors.SPSS 27.0 software was used to perform multivariate binary logistic regression to obtain the final risk factors.The prediction model of hypoproteinemia after total hip arthroplasty was constructed by R language.The receiver operating characteristic curve,calibration curve,and clinical decision curve were constructed to assess the predictive model predictive ability.RESULTS AND CONCLUSION:(1)Univariate analysis,least absolute shrinkage and selection operator regression,and multivariate logistic regression were used to screen out significant differences in age(OR=1.024,P=0.023),preoperative platelets(OR=0.995,P=0.028),and preoperative erythrocyte sedimentation rate(OR=1.031,P=0.045)in judging whether hypoproteinemia would occur after surgery(P＜0.05).(2)The nomogram prediction model was constructed based on the final risk factors screened by multivariate Logistic regression,and the prediction ability of the model was evaluated by constructing the receiver operating characteristic curve,and the area under the calculated receiver operating characteristic curve reached 0.835(95％CI=0.779-0.891),C-index=0.835.A threshold of 0-0.83 could bring better clinical efficacy calculated by the decision curve analysis.The model has good sensitivity and accuracy,which can better identify the risk of postoperative hypoproteinemia for medical staff and patients before total hip arthroplasty.

ObjectiveTo study the clinical characteristics and influencing factors of rheumatoid arthritis （RA） in the patients with cold dampness obstruction syndrome. MethodsThe RA patients treated in the Department of Traditional Chinese Medicine and Rheumatology of the China-Japan Friendship Hospital from August 2022 to June 2024 were selected. The demographic information， clinical data， laboratory test results， and traditional Chinese medicine （TCM） symptom information were collected for syndrome differentiation， on the basis of which the characteristics and influencing factors of cold dampness obstruction syndrome were analyzed. ResultsA total of 258 RA patients were selected in this study， including 88 （34.1%） patients with cold dampness obstruction syndrome， 53 （20.5%） patients with dampness and heat obstruction syndrome， 31 （12.0%） patients with wind dampness obstruction syndrome， 29 （11.2%） patients with liver-kidney deficiency syndrome， 19 （7.4%） patients with Qi-blood deficiency syndrome， 14 （5.4%） patients with phlegm-stasis obstruction syndrome， 15 （5.8%） patients with stasis obstructing collateral syndrome and 9 （3.5%） patients with Qi-Yin deficiency syndrome. The patients were assigned into two groups of cold dampness obstruction syndrome and other syndromes. The group of cold dampness obstruction syndrome had lower joint fever， 28-tender joint count （TJC28）， and 28-joint disease activity score （DAS28）-C-reactive protein （CRP） and higher central sensitization， cold feeling of joints， fear of wind and cold， cold limbs， and abdominal distention than the group of other syndromes （P<0.05）. The binary logistic regression analysis showed that central sensitization （OR 5.749， 95%CI 2.116-15.616， P<0.001） and DAS28-CRP （OR 0.600， 95% CI 0.418-0.862， P=0.006） were the independent factors influencing cold dampness obstruction syndrome in RA. ConclusionCold dampness obstruction syndrome is a common syndrome in RA patients. It is associated with central sensitization， cold feeling of joints， abdominal distension and may be a clinical syndrome associated with central sensitization.

Based on 490 national-level policy documents directly related to hospital construction and development issued since the founding of the People's Republic of China,it divides the evolution of hospital construction and development into four stages:the foundational period,it the exploratory period of the healthcare reform,the acceleration period of the new healthcare reform,and the critical period of deepening medical reform.Applying on policy feedback theory,it analyzes the internal logic underlying the evolution of hospital construction and development in China.The findings reveal that past policies influence future policy-making through four mechanisms:interpretive,resource,evolutionary and learning effects.It suggests that policy optimization should integrate a historical perspective into the policy process to advance the modernization of hospital governance systems and capacities at the public policy level.

Objective:To evaluate the epidemiological characteristics and influencing factors of out-of-hospital sudden cardiac death (SCD) in Hangzhou from 2016 to 2019.Methods:SCD events recorded by Hangzhou Emergency Center from January 1, 2016 to December 31, 2019 were reviewed. Demographic and mortality data were recorded, and the distribution patterns of SCD events in terms of date, time, and population with different characteristics were observed. Time series analysis method and a distributed lag nonlinear model based on quasi-Poisson distribution were used to explore the possible nonlinear association between ambient temperature and SCD incidence.Results:A total of 4 744 out-of-hospital sudden death events were recorded by Hangzhou Emergency Center from January 1, 2016 to December 31, 2019. After excluding non-SCD events and observed events with missing items, 3 743 SCD events were finally included in the study. The survey results showed that the incidence of out-of-hospital SCD in Hangzhou was 96.5 cases per 100 000 person-years. Most of the people who experienced SCD were aged ≥60 years. The incidence in males (2 462 cases, 66%) was significantly higher than that in females (1 281 cases, 34%), and the proportion of events occurring during the day (2 737 cases, 73%) was significantly higher than that at night (1 006 cases, 27%), mainly occurring between 7: 00 and 9: 00. High temperature was associated with an increased risk of SCD. When the average daily temperature was higher than 25.5 ℃, the risk of SCD increased with the further increase of average daily temperature.Conclusions:SCD events mainly occur in the elderly population aged ≥60 years, with a significantly higher incidence in males than in females, and more frequently during the day than at night, mainly between 7: 00 and 9: 00 in the morning. High temperature is closely related to the risk of SCD. It is particularly important to carry out targeted SCD screening and prevention for different populations and implement appropriate prevention strategies for high-risk groups of SCD in high-temperature weather.

BACKGROUND:The patient underwent multiple hypoproteinemia after total hip arthroplasty,which affected postoperative healing and rehabilitation.OBJECTIVE:To investigate and screen the risk factors for hypoproteinemia after total hip arthroplasty,and to establish a nomogram prediction model so as to provide guidance for judging whether hypoproteinemia occurs after total hip arthroplasty.METHODS:A total of 355 patients who underwent total hip arthroplasty were included,and according to whether hypoproteinemia occurred on the first day after surgery,they were divided into 238 cases in the hypoproteinemia group and 117 cases in the normal group,with a hypoproteinemia rate of 67％.Data were collected,including age,gender,diabetes mellitus,hypertension,hyperuricemia,hyperlipidemia,anesthesia method,preoperative leukocytes,preoperative erythrocytes,preoperative hemoglobin,preoperative platelets,preoperative plasma prothrombin time,preoperative activated partial prothrombin time,preoperative international normalized ratio,preoperative thrombin time,preoperative fibrinogen,preoperative erythrocyte sedimentation rate,preoperative C-reactive protein,preoperative D-dimer,preoperative mean corpuscular hemoglobin content,preoperative mean corpuscular volume,operation time,body mass index,preoperative procalcitonin,and preoperative hematocrit.SPSS 27.0 software was used for univariate analysis,followed by R language(4.3.1)to perform least absolute shrinkage and selection operator regression and 10-fold cross-validation of the observation indicators to obtain the intersection of the two risk factors.SPSS 27.0 software was used to perform multivariate binary logistic regression to obtain the final risk factors.The prediction model of hypoproteinemia after total hip arthroplasty was constructed by R language.The receiver operating characteristic curve,calibration curve,and clinical decision curve were constructed to assess the predictive model predictive ability.RESULTS AND CONCLUSION:(1)Univariate analysis,least absolute shrinkage and selection operator regression,and multivariate logistic regression were used to screen out significant differences in age(OR=1.024,P=0.023),preoperative platelets(OR=0.995,P=0.028),and preoperative erythrocyte sedimentation rate(OR=1.031,P=0.045)in judging whether hypoproteinemia would occur after surgery(P＜0.05).(2)The nomogram prediction model was constructed based on the final risk factors screened by multivariate Logistic regression,and the prediction ability of the model was evaluated by constructing the receiver operating characteristic curve,and the area under the calculated receiver operating characteristic curve reached 0.835(95％CI=0.779-0.891),C-index=0.835.A threshold of 0-0.83 could bring better clinical efficacy calculated by the decision curve analysis.The model has good sensitivity and accuracy,which can better identify the risk of postoperative hypoproteinemia for medical staff and patients before total hip arthroplasty.

Systemic lupus erythematosus （SLE）， a refractory autoimmune disease， is among the dominant diseases where traditional Chinese medicine （TCM） shows advantages in the field of rheumatology and immunology. The China-Japan Friendship Hospital hosted the "46^th Youth Salon on Dominant Diseases （Systemic Lupus Erythematosus）" organized by the China Association of Chinese Medicine， which led to a consensus on "the advantages， challenges， interdisciplinary approaches， and translational achievements of integrated TCM and Western medical approaches in the diagnosis and treatment of SLE." The diagnosis and treatment of SLE currently face several challenges， such as frequent misdiagnosis and missed diagnosis in the early stages， difficulty in achieving treatment targets， multiple side effects from pharmacotherapy， and the lack of management strategies for special populations， all of which hinder the fulfillment of the clinical needs of patients. Integrated TCM and Western medical approaches can improve clinical symptoms such as skin erythema， aversion to cold and cold limbs， fatigue， dry mouth， restlessness， and heat sensation in the palms and soles， thereby improving patients' quality of life. The approaches also help consolidate the efficacy of conventional Western medicine， slow disease progression， reduce relapse rates， address multi-organ involvement， and prevent or treat complications. Additionally， they enhance efficacy and reduce toxicity， prevent the side effects of Western medications， help reduce hormone use， and offer distinct advantages in the individualized intervention of special populations， contributing to the whole-process management of the disease. However， evidence-based medical support for this integrated approach remains limited， and the quality of available evidence is generally low. Common evaluation systems and modern research methodologies should be adopted to clarify the efficacy of TCM in SLE treatment. Efforts should be made to carry out high-quality evidence-based medical research， strengthen the development of fundamental and pharmacological research， and further explain the distinct advantages of TCM in the diagnosis and treatment of SLE. Future efforts should focus on advancing the integration of TCM and modern medicine， incorporating multi-omics technologies， individualized stratification， and other precision medicine concepts， in combination with artificial intelligence. Moreover， interdisciplinary collaboration should be promoted to utilize modern technology in exploring the essence of TCM theories and screening effective formulae， thereby comprehensively improving the diagnosis and treatment of SLE through integrated TCM and Western medical approaches.