Objective To propose a whole-life cycle management model for valvular heart disease (VHD), systematically elucidate its underlying logic and implementation pathways, and concurrently review and analyze its preliminary application outcomes. Methods Since 2020, West China Hospital of Sichuan University has established a management system encompassing "assessment-decision-intervention-follow-up", including: (1) a risk-stratified, tiered management pathway; (2) six core functions ("promotion, screening, prevention, diagnosis, treatment, and rehabilitation") coordinated by disease-specific managers; (3) an intelligent decision support information platform; and (4) a collaborative network of multidisciplinary teams and regional academic alliances. To evaluate the effectiveness of this management model, we retrospectively included three cohorts: (1) the population screened by echocardiography from 2020 to 2024, analyzing the detection rate of aortic valve disease and risk stratification; (2) patients enrolled in the whole-life cycle management from April 2021 to December 2024, assessing follow-up outcomes, hospital satisfaction, and changes in quality of life; (3) patients who underwent transcatheter aortic valve replacement (TAVR) from January 2022 to January 2024, evaluating the one-year all-cause mortality rate, perioperative complications, and improvements in New York Heart Association (NYHA) classification. Results Between 2020 and 2024, a total of 583 874 individuals underwent echocardiographic screening. A total of 48 089 patients with aortic valve disease were identified, including 3 401 (7.1%) high-risk patients, 18 657 (38.8%) moderate-risk patients, and 26 031 (54.1%) low-risk patients. Among them, 2 417 patients were enrolled in whole-life cycle management. Patient satisfaction scores showed a yearly increase, rising from 73.89 points before 2020 to 93.74 points in 2024. The 1-year mortality rate in the TAVR cohort decreased to 5.3%, significantly lower than the 8.2% observed under early standard management between 2014 and 2019 (P<0.01). Conclusion Through process optimization and resource integration, the VHD whole-life cycle management model has demonstrated significant effectiveness in standardizing diagnostic and follow-up procedures, enhancing patient satisfaction and quality of life, and reducing mortality. These outcomes highlight its practical value for broader implementation in China.

Recent studies have shown that an imbalance in iron metabolism can affect the composition and microstructural changes of bone, disrupting bone homeostasis and leading to osteoporosis(OP). The imbalance in iron metabolism, along with its induced local abnormal microenvironment and cellular iron death, has become a new focal point in OP research, drawing increasing attention from the academic community regarding the regulation of iron metabolism to prevent and manage OP. From the perspective of traditional Chinese medicine(TCM), iron metabolism imbalance has potential connections to TCM theories regarding internal organs, as well as treatments aimed at tonifying the kidney, strengthening the spleen, and activating blood circulation. Evidence is continually emerging that TCMs and effective components that tonify the kidney, strengthen the spleen, and activate blood circulation can prevent and manage OP by regulating iron metabolism. This article analyzes the relationship between iron and bone, as well as the effects of TCM formulations on improving iron metabolism and influencing bone metabolism, from the perspectives of iron metabolism mechanisms and TCM interventions, aiming to broaden existing clinical strategies for prevention and treatment and inject new momentum into the field of OP as it moves into a new era.
Osteoporosis/drug therapy* ; Humans ; Iron/metabolism* ; Drugs, Chinese Herbal/pharmacology* ; Animals ; Medicine, Chinese Traditional ; Bone and Bones/drug effects*

Following the core outcome set standards for development(COS-STAD), this study aims to construct core outcome set(COS) for clinical research on traditional Chinese medicine(TCM) treatment of simple obesity. Firstly, a comprehensive review was conducted on the randomized controlled trial(RCT) and systematic review(SR) about TCM treatment of simple obesity that were published in Chinese and English databases to collect reported outcomes. Additional outcomes were obtained through semi-structured interviews with patients and open-ended questionnaire surveys for clinicians. All the collected outcomes were then merged and organized as an initial outcome pool, and then a preliminary list of outcomes was formed after discussion by the working group. Subsequently, two rounds of Delphi surveys were conducted with clinicians, methodology experts, and patients to score the importance of outcomes in the list. Finally, a consensus meeting was held to establish the COS for clinical research on TCM treatment of simple obesity. A total of 221 RCTs and 12 SRs were included, and after integration of supplementary outcomes, an initial outcome pool of 141 outcomes were formed. Following discussions in the steering advisory group meeting, a preliminary list of 33 outcomes was finalized, encompassing 9 domains. Through two rounds of Delphi surveys and a consensus meeting, the final COS for clinical research on TCM treatment of simple obesity was determined to include 8 outcomes: TCM symptom scores, body mass index(BMI), waist-hip ratio, waist circumference, visceral fat index, body fat rate, quality of life, and safety, which were classified into 4 domains: TCM-related outcomes, anthropometric measurements, quality of life, and safety. This study has preliminarily established a COS for clinical research on TCM treatment of simple obesity. It helps reduce the heterogeneity in the selection and reporting of outcomes in similar clinical studies, thereby improving the comparability of research results and the feasibility of meta-analysis and providing higher-level evidence support for clinical practice.
Humans ; Obesity/therapy* ; Medicine, Chinese Traditional ; Randomized Controlled Trials as Topic ; Treatment Outcome ; Drugs, Chinese Herbal/therapeutic use*

As a new type of production factor that can empower the development of new quality productivity, the data element is an important engine to promote the high quality development of the industry. Traditional Chinese medicine(TCM) dispensing is the most basic work of TCM clinical pharmacy, and its quality directly affects the clinical efficacy of TCM. The integration of data elements and TCM dispensing can stimulate the innovation and vitality of the TCM dispensing industry and promote the high-quality and sustainable development of the industry. A large-scale, detailed, and systematic study on TCM dispensing was conducted. The innovative practice path of data fusion construction in the whole process of TCM dispensing was investigated by integrating the digital resources "nine full activities" of TCM dispensing, creating the digital dictionary of "TCM clinical information data elements", and exploring innovative applications of TCM dispensing driven by data and technology, so as to promote the standardized, digital, and intelligent development of TCM dispensing in medical health services. The research content of this project was successfully selected as the second batch of "Data element×" typical cases of National Data Administration in 2024, which is the only selected case in the field of TCM.
Medicine, Chinese Traditional/methods* ; Drugs, Chinese Herbal ; Humans

OBJECTIVE: The results of limited studies on the relationship between environmental pollution and dementia have been contradictory. We analyzed the combined effects of PM _2.5 and smoking on the prevalence of dementia and cognitive impairment in an elderly community-dwelling Chinese population. METHODS: We assessed 24,117 individuals along with the annual average PM _2.5 concentrations from 2012 to 2016. Dementia was confirmed in the baseline survey at a qualified clinical facility, and newly suspected dementia was assessed in 2017, after excluding cases of suspected dementia in 2015. National census data were used to weight the sample data to reflect the entire population in China, with multiple logistic regression performed to analyze the combined effects of PM _2.5 and smoking frequency on dementia and cognitive impairment. RESULTS: Individuals exposed to the highest PM _2.5 concentration and smoked daily were at higher risk of dementia than those in the lowest PM _2.5 concentration group ( OR, 1.603; 95% CI [1.626-1.635], P < 0.0001) and in the nonsmoking group ( OR, 1.248; 95% CI [1.244-1.252]; P < 0.0001). Moderate PM _2.5 exposure and occasional smoking together increased the short-term risk of cognitive impairment. High-level PM _2.5 exposure and smoking were associated with an increased risk of dementia, so more efforts are needed to reduce this risk through environmental protection and antismoking campaigns. CONCLUSION High-level PM _2.5 exposure and smoking were associated with an increased risk of dementia. Lowering the ambient PM _2.5, and smoking cessation are recommended to promote health.
Humans ; Dementia/etiology* ; Male ; Aged ; Female ; Cognitive Dysfunction/etiology* ; China/epidemiology* ; Particulate Matter/analysis* ; Smoking/epidemiology* ; Air Pollutants/analysis* ; Aged, 80 and over ; Environmental Exposure/adverse effects* ; Prevalence ; Middle Aged

PURPOSE: Intertrochanteric fracture (ITF) classification is crucial for surgical decision-making. However, orthopedic trauma surgeons have shown lower accuracy in ITF classification than expected. The objective of this study was to utilize an artificial intelligence (AI) method to improve the accuracy of ITF classification. METHODS: We trained a network called YOLOX-SwinT, which is based on the You Only Look Once X (YOLOX) object detection network with Swin Transformer (SwinT) as the backbone architecture, using 762 radiographic ITF examinations as the training set. Subsequently, we recruited 5 senior orthopedic trauma surgeons (SOTS) and 5 junior orthopedic trauma surgeons (JOTS) to classify the 85 original images in the test set, as well as the images with the prediction results of the network model in sequence. Statistical analysis was performed using the SPSS 20.0 (IBM Corp., Armonk, NY, USA) to compare the differences among the SOTS, JOTS, SOTS + AI, JOTS + AI, SOTS + JOTS, and SOTS + JOTS + AI groups. All images were classified according to the AO/OTA 2018 classification system by 2 experienced trauma surgeons and verified by another expert in this field. Based on the actual clinical needs, after discussion, we integrated 8 subgroups into 5 new subgroups, and the dataset was divided into training, validation, and test sets by the ratio of 8:1:1. RESULTS: The mean average precision at the intersection over union (IoU) of 0.5 (mAP50) for subgroup detection reached 90.29%. The classification accuracy values of SOTS, JOTS, SOTS + AI, and JOTS + AI groups were 56.24% ± 4.02%, 35.29% ± 18.07%, 79.53% ± 7.14%, and 71.53% ± 5.22%, respectively. The paired t-test results showed that the difference between the SOTS and SOTS + AI groups was statistically significant, as well as the difference between the JOTS and JOTS + AI groups, and the SOTS + JOTS and SOTS + JOTS + AI groups. Moreover, the difference between the SOTS + JOTS and SOTS + JOTS + AI groups in each subgroup was statistically significant, with all p < 0.05. The independent samples t-test results showed that the difference between the SOTS and JOTS groups was statistically significant, while the difference between the SOTS + AI and JOTS + AI groups was not statistically significant. With the assistance of AI, the subgroup classification accuracy of both SOTS and JOTS was significantly improved, and JOTS achieved the same level as SOTS. CONCLUSION In conclusion, the YOLOX-SwinT network algorithm enhances the accuracy of AO/OTA subgroups classification of ITF by orthopedic trauma surgeons.
Humans ; Hip Fractures/diagnostic imaging* ; Orthopedic Surgeons ; Algorithms ; Artificial Intelligence

OBJECTIVES: To analyze the early clinical outcomes of the Xcor^TM transcatheter aortic valve replacement (TAVR) system in treating severe aortic stenosis. This study has been registered at Chinese Clinical Trial Registry (ChiCTR2200065593). METHODS: This single-arm, prospective clinical trial enrolled patients with severe aortic stenosis treated with the Xcor^TM TAVR system at the Section of Heart Valve & Coronary Artery Surgery, Guangdong Provincial People's Hospital. Perioperative and follow-up parameters were compared to evaluate differences in hemodynamic outcomes. All-cause mortality, aortic regurgitation, paravalvular leakage, cerebrovascular events, and reoperation were analyzed. RESULTS: Thirty-two patients with severe aortic stenosis were included (20 males, 12 females), with (70.9±4.3) years old and a Society of Thoracic Surgeons (STS) score of 6.45% (6.07%, 7.28%). Notably, 87.5% of patients had New York Heart Association (NYHA) class≥Ⅲ. All patients underwent successful Xcor^TM bioprosthesis implantation, achieving an immediate technical success rate of 100.0% and device success rate of 96.9%. Mean aortic valve gradient decreased from (55.21±23.17) mmHg (1 mmHg=0.133 kPa) to (8.45±5.30) mmHg, peak aortic jet velocity decreased from (4.66±0.85) m/s to (1.99±0.48) m/s, aortic valve area increased from (0.66±0.21) cm² to (2.09±0.67) cm² (all P<0.01). Intraoperative ventricular fibrillation occurred in one patient, while one case exhibited moderate prosthetic valve regurgitation and paravalvular leakage post-procedure. At 12-month follow-up, sustained improvements were observed in cardiac function, left ventricular ejection fraction, hemodynamic parameters, and SF-12 quality-of-life scores (all P<0.01). All-cause mortality was 12.5% (4/32), with 13.8% (4/29) developing moderate paravalvular leakage. CONCLUSIONS The Xcor^TM TAVR system demonstrated favorable early outcomes in severe aortic stenosis patients, significantly improving symptoms and hemodynamics while exhibiting excellent performance in preventing malignant arrhythmias and coronary obstruction.
Humans ; Male ; Female ; Aortic Valve Stenosis/surgery* ; Transcatheter Aortic Valve Replacement/methods* ; Aged ; Follow-Up Studies ; Prospective Studies ; Treatment Outcome ; Aged, 80 and over ; Heart Valve Prosthesis ; Middle Aged

OBJECTIVES: The Charlson comorbidity index reflects overall comorbidity burden and has been applied in cardiovascular medicine. However, its role in predicting in-hospital mortality in patients with acute myocardial infarction (AMI) complicated by ventricular arrhythmias (VA) remains unclear. This study aims to evaluate the predictive value of the Charlson comorbidity index in this setting and to construct a nomogram model for early risk identification and individualized management to improve outcomes. METHODS: Using the open-access critical care database MIMIC-IV (Medical Information Mart for Intensive Care IV), we identified intensive care unit (ICU) patients diagnosed with AMI complicated by VA. Patients were grouped according to in-hospital survival. The predictive performance of the Charlson comorbidity index and other clinical variables for in-hospital mortality was analyzed. Key predictors were selected using the least absolute shrinkage and selection operator (LASSO) regression, followed by multivariable Logistic regression. A nomogram model was constructed based on the regression results. Model performance was assessed using receiver operating characteristic (ROC) curves and calibration plots. RESULTS: A total of 1 492 patients with AMI and VA were included, of whom 340 died and 1 152 survived during hospitalization. Significant differences were observed between survivors and non-survivors in sex distribution, vital signs, comorbidity burden, organ function, and laboratory parameters (all P<0.05). The area under the curve (AUC) of the Charlson comorbidity index for predicting in-hospital mortality was 0.712 (95% CI 0.681 to 0.742), significantly higher than albumin, international normalized ratio (INR), hemoglobin, body temperature, and platelet count (all P<0.001), but comparable to Sequential Organ Failure Assessment (SOFA) score (P>0.05). LASSO regression identified seven key predictors: the Charlson comorbidity index (quartile groups: T1, <6; T2, ≥6-<7; T3, ≥7-<9; T4, ≥9), ventricular fibrillation, age, systolic blood pressure, respiratory rate, body temperature, and SOFA score. Multivariate Logistic regression showed that compared with T1, mortality risk increased significantly in T2 (OR=1.996, 95% CI 1.135 to 3.486, P=0.016), T3 (OR=3.386, 95% CI 2.192 to 5.302, P<0.001), and T4 (OR=5.679, 95% CI 3.711 to 8.842, P<0.001). Age (OR=1.056, P<0.001), respiratory rate (OR=1.069, P<0.001), SOFA score (OR=1.223, P<0.001), and ventricular fibrillation (OR=2.174, P<0.001) were independent risk factors, while systolic blood pressure (OR=0.984, P<0.001) and body temperature (OR=0.648, P<0.001) were protective factors. The nomogram incorporating these predictors achieved an AUC of 0.849 (95% CI 0.826 to 0.871) with high discrimination and good calibration (mean absolute error=0.014). CONCLUSIONS The Charlson comorbidity index is an independent predictor of in-hospital mortality in AMI patients complicated by VA, with performance comparable to the SOFA score. The nomogram model based on the Charlson comorbidity index and additional clinical variables effectively estimates mortality risk and provides a valuable reference for clinical decision-making.
Humans ; Nomograms ; Hospital Mortality ; Myocardial Infarction/complications* ; Male ; Female ; Comorbidity ; Middle Aged ; Aged ; Arrhythmias, Cardiac/complications* ; ROC Curve ; Intensive Care Units