Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective: To assess the nutritional supply status of school meals for primary and secondary school students in Henan Province, so as to provide a basis for scientific guidance of school meals. Methods: During 2021-2023, 115 lunch and dinner samples were collected from 25 primary and secondary schools in Zhoukou, Anyang and Luoyang of Henan Province by a direct selection method, and 13 nutrients were determined for each sample. The nutrient supply was evaluated based on Nutrition Guidelines of School Meals and Reference Intake of Dietary Nutrients for Chinese Residents (2023 Edition). Mann-Whitney U test was used to compare the differences of nutritional supply between urban and rural schools. Results: The median values for energy (709.77 kcal,1 kcal=4.18 kJ), fat energy supply ratio (0.27) and carbohydrate energy supply ratio (0.55) in the 66 lunches and dinners from primary school were within the recommended range. The supply of protein (28.39 g) and sodium (1 464.59 mg) was excessive. The median values of zinc (2.62 mg) and dietary fiber (5.19 g) were lower than the reference values. No statistically significant differences were observed in the supply of 13 nutrients between urban and rural primary schools( U = 427.00 -633.00, P > 0.05 ). Among 49 samples from secondary schools, the median value of energy supply (930.02 kcal), carbohydrate energy ratio ( 0.54 ) and fat energy supply ratio(0.25) were within the recommended range; and the median values of protein (38.82 g) and sodium (2 556.80 mg) were higher than the standard; and the median values of calcium (250.32 mg) and vitamin B1 (0.16 mg) were lower than the standard. Additionally, the differences in the level of vitamin B2 ( U =372.00) and zinc ( U =375.00) between the urban and rural secondary schools were statistically significant ( P <0.05). Conclusion Nutrient supply of primary and secondary school meals in three cities of Henan Province is inadequate and imbalanced, and the recipe need to be further optimized and improved.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Objective To investigate the association between high-density lipoprotein cholesterol(HDL-C)levels and all-cause and cause-specific mortality in the elderly population,and to determine the optimal HDL-C concentration range associated with the lowest mortality risk.Methods This prospective cohort study included 16,632 elderly individuals aged≥60 years from the National Health and Nutrition Examination Survey(NHANES)between 1999 and 2018.Participants were divided into 5 groups based on HDL-C levels:<40 mg/dl group(n=2933),40-49 mg/dl group(n=4602),50-59 mg/dl group(n=3824),60-79 mg/dl group(n=3988),and≥80 mg/dl group(n=1285).Mortality data were obtained by linking records with the National Death Index(NDI).Cox proportional hazards regression and restricted cubic spline curves were used to analyze the association between HDL-C and mortality risk.Subgroup and sensitivity analyses were conducted.Results Multivariable-adjusted Cox models showed that,compared with 50-59 mg/dl HDL-C group,HDL-C<40 mg/dl group had significantly higher risks of all-cause mortality(HR=1.17,95%CI 1.05-1.31,P=0.006),cardiovascular mortality(HR=1.32,95%CI 1.08-1.58,P=0.001),and non-cardiovascular mortality(HR=1.14,95%CI 1.02-1.27,P=0.013).HDL-C≥80 mg/dl group had significantly higher risks of all-cause mortality(HR=1.18,95%CI 1.03-1.35,P=0.027)and non-cardiovascular mortality(HR=1.23,95%CI 1.03-1.46,P=0.029),but no significant effect on cardiovascular mortality(HR=1.07,95%CI 0.88-1.28,P=0.492).Restricted cubic spline curves revealed U-shaped associations between HDL-C and all-cause/non-cardiovascular mortality,and an inverse J-shaped association with cardiovascular mortality.These patterns were consistent in both elderly men and women.The HDL-C levels associated with the lowest risk were 56 mg/dl(95%CI 53-59 mg/dl)for all-cause mortality,54 mg/dl(95%CI 51-57 mg/dl)for cardiovascular mortality,and 59 mg/dl(95%CI 56-62 mg/dl)for non-cardiovascular mortality.Subgroup analyses showed no significant effect modifications.Sensitivity analyses confirmed the primary findings.Conclusions In the elderly population,extremely high HDL-C levels increase the risks of all-cause mortality and non-cardiovascular mortality,but do not elevate cardiovascular mortality.The risk of all-cause mortality is the lowest at an HDL-C concentration of 56 mg/dl.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective To understand the current situation of reprocessing reusable sterilized items in the central sterile supply departments(CSSD)of hospitals and provide a scientific basis for further standardized management.Methods Using the convenience sampling method,a self-designed questionnaire on the current situation of reprocessing of reusable disinfection items in CSSDs was used to conduct a survey in 31 provinces(autonomous regions and municipalities)from June 11th to 23rd,2024.The questionnaire covered 2 aspects,including the basic situation of the hospital and the reprocessing of disinfection items.Results A total of 1 835 questionnaires were distributed,and 1 603 valid questionnaires were retrieved.Regarding the use of reusable disinfection items,with the most frequently used items being humidification bottles,tourniquets,breathing bags and their accessories,and ventilator tubing systems etc.Among them,1 558 hospitals(97.19％)established standardized disposal procedures,and 996 hospitals(62.13％)assigned dedica-ted personnel to be responsible.In terms of the reprocessing procedures of reusable disinfection items,all 1 603 hospitals carried out drying process for the items;the main packaging method was plastic self-sealing bags(59.95％),and 541 hospitals(33.75％)sterilized the packaging materials.Additionally,935 hospitals(58.33％)used oil-free air compressors;among them,51 hospitals(58.62％)installed activated carbon filters at the end of the compressors.Conclusion The current situation of reprocessing reusable sterilized items in CSSDs of nationwide hospitals needs improvement.It is recommended to strengthen the training of professional disinfection knowledge,unify the sterilization standards for plastic self-sealing bags,develop a storage management system,and adopt a medical air compressor system without oil and water to reduce the risk of hospital infections.

Objective To determine the effect of non-high-density lipoprotein cholesterol(non-HDL-C)on all-cause and cause-specific mortality in an ≥60-year-old elderly population.Methods A total of 16 642 older adults(≥60 years)were subjected from the National Health and Nutrition Examination Survey between 1999 and 2018.According to the tertile of non-HDL-C level,the par-ticipants were divided into tertile 1(＜3.15 mmol/L,n=5499),tertile 2(3.15-4.06 mmol/L,n=5499),and tertile 3 groups(＞4.06 mmol/L,n=5644).The occurrences of all-cause,cardiovascu-lar,and non-cardiovascular death were identified as the study endpoint.Cox proportional hazards regression,Kaplan-Meier survival and restricted cubic spline curve analyses were applied for sta-tistical study.Results An obvious L-shaped associations were observed in non-HDL-C level with risks for all-cause,cardiovascular,and non-cardiovascular death.After adjusting multivariable,the tertile 1 group had significantly higher risks for all-cause,cardiovascular,and non-cardiovascular death than the tertile 2 group(HR=1.123,95％CI:1.054-1.200,P=0.000;HR=1.142,95％CI:1.024-1.292,P=0.027;HR=1.113,95％CI:1.033-1.210,P=0.011).Kaplan-Meier survival analysis showed that the tertile 1 group had notably lower survival rate than the tertile 2 group and the tertile 3 group(P＜0.01).Threshold effect analysis revealed that when non-HDL-C level was lower than 3.36,3.18 and 3.59 mmol/L,respectively,the risk of all-cause,cardiovascular,and noncardiovascular mortality was increased.Conclusion In the elderly ≥60-year-old population,non-HDL-C level exhibited a L-shaped association with all-cause and cause-specific mortality,and＞3.18 mmol/L is regarded as a rational range.

BACKGROUND: Medical informatics accumulated vast amounts of data for clinical diagnosis and treatment. However, limited access to follow-up data and the difficulty in integrating data across diverse platforms continue to pose significant barriers to clinical research progress. In response, our research team has embarked on the development of a specialized clinical research database for cardiology, thereby establishing a comprehensive digital platform that facilitates both clinical decision-making and research endeavors. METHODS: The database incorporated actual clinical data from patients who received treatment at the Cardiovascular Medicine Department of Chinese PLA General Hospital from 2012 to 2021. It included comprehensive data on patients' basic information, medical history, non-invasive imaging studies, laboratory test results, as well as peri-procedural information related to interventional surgeries, extracted from the Hospital Information System. Additionally, an innovative artificial intelligence (AI)-powered interactive follow-up system had been developed, ensuring that nearly all myocardial infarction patients received at least one post-discharge follow-up, thereby achieving comprehensive data management throughout the entire care continuum for high-risk patients. RESULTS: This database integrates extensive cross-sectional and longitudinal patient data, with a focus on higher-risk acute coronary syndrome patients. It achieves the integration of structured and unstructured clinical data, while innovatively incorporating AI and automatic speech recognition technologies to enhance data integration and workflow efficiency. It creates a comprehensive patient view, thereby improving diagnostic and follow-up quality, and provides high-quality data to support clinical research. Despite limitations in unstructured data standardization and biological sample integrity, the database's development is accompanied by ongoing optimization efforts. CONCLUSION The cardiovascular specialty clinical database is a comprehensive digital archive integrating clinical treatment and research, which facilitates the digital and intelligent transformation of clinical diagnosis and treatment processes. It supports clinical decision-making and offers data support and potential research directions for the specialized management of cardiovascular diseases.