BACKGROUND: Valvular heart disease (VHD) has become increasingly common with the aging in China. This study aimed to evaluate regional differences in the clinical features, management strategies, and outcomes of patients with VHD across different regions in China. METHODS: Data were collected from the China-VHD Study. From April 2018 to June 2018, 12,347 patients who presented with moderate or severe native VHD with a median of 2 years of follow-up from 46 centers at certified tertiary hospitals across 31 provinces, autonomous regions, and municipalities in Chinese mainland were included in this study. According to the locations of the research centers, patients were divided into five regional groups: eastern, southern, western, northern, and central China. The clinical features of VHD patients were compared among the five geographical regions. The primary outcome was all-cause mortality or rehospitalization for heart failure. Kaplan-Meier survival analysis was used to compare the cumulative incidence rate. RESULTS: Among the enrolled patients (mean age, 61.96 years; 6877 [55.70%] male), multiple VHD was the most frequent type (4042, 32.74%), which was mainly found in eastern China, followed by isolated mitral regurgitation (3044, 24.65%), which was mainly found in northern China. The etiology of VHD varied significantly across different regions of China. The overall rate of valve interventions was 32.67% (4008/12,268), with the highest rate in southern China at 48.46% (205/423). In terms of procedure, the proportion of transcatheter valve intervention was relatively low compared to that of surgical treatment. Patients with VHD in western China had the highest incidence of all-cause mortality or rehospitalization for heart failure. Valve intervention significantly improved the outcome of patients with VHD in all five regions (all P  <0.05). CONCLUSIONS: This study revealed that patients with VHD in China are characterized by significant geographic disparities in clinical features, treatment, and clinical outcomes. Targeted efforts are needed to improve the management and prognosis of patients with VHD in China according to differences in geographical characteristics. REGISTRATION ClinicalTrials.gov , NCT03484806.
Aged ; Female ; Humans ; Male ; Middle Aged ; China/epidemiology* ; Heart Valve Diseases/therapy* ; Kaplan-Meier Estimate ; Tertiary Care Centers ; Treatment Outcome

OBJECTIVES: To create a mixed valvular heart disease (MVHD)-related age-adjusted comorbidity index (MVACI) model for predicting mortality risk of patients with MVHD. METHODS: A total of 4080 patients with moderate or severe MVHD in the China-VHD study were included. The primary endpoint was 2-year all-cause mortality. A MVACI model prediction model was constructed based on the mortality risk factors identified by univariate and multivariate Cox regression analysis. Restricted cubic splines were used to assess the relationship between MVACI scores and 2-year all-cause mortality. The optimal threshold, determined by the maximum Youden index from receiver operator characteristic (ROC) curve analysis, was used to stratify patients. Kaplan-Meier method was used to calculate 2-year all-cause mortality and compared using the Log-rank test. Univariate and multivariate Cox proportional hazards models were employed to calculate hazard ratios (HR) and 95% confidence intervals (CI), evaluating the association between MVACI scores and mortality. Paired ROC curves were used to compare the discriminative ability of MVACI scores with the European System for Cardiac Operative Risk Evaluation Ⅱ(EuroSCORE Ⅱ) or the age-adjusted Charlson comorbidity index (ACCI) in predicting 2-year clinical outcomes, while calibration curves assessed the calibration of these models. Internal validation was performed using the Bootstrap method. Subgroup analyses were conducted based on etiology, treatment strategies, and disease severity. RESULTS: Multivariate analysis identified the following variables independently associated with 2-year all-cause mortality in patients: pulmonary hypertension, myocardiopathy, heart failure, low body weight (body mass index <18.5 kg/m²), anaemia, hypoalbuminemia, renal insufficiency, cancer, New York Heart Association (NYHA) class and age. The score was independently associated with the risk of all-cause mortality, and exhibited good discrimination (AUC=0.777, 95%CI: 0.755-0.799) and calibration (Brier score 0.062), with significantly better predictive performance than EuroSCORE Ⅱ or ACCI (both adjusted P<0.01). The internal validation showed that the MVACI model's predicted probability of 2-year all-cause mortality was generally consistent with the actual probability. The AUCs for predicting all-cause mortality risk were all above 0.750, and those for predicting adverse events were all above 0.630. The prognostic value of the score remained consistent in patients regardless of their etiology, therapeutic option, and disease severity. CONCLUSIONS The MVACI was constructed in this study based on age and comorbidities, and can be used for mortality risk prediction and risk stratification of MVHD patients. It is a simple algorithmic index and easy to use.
Humans ; Prognosis ; Comorbidity ; Heart Valve Diseases/epidemiology* ; Female ; Male ; Middle Aged ; Aged ; Proportional Hazards Models ; Risk Factors ; China/epidemiology* ; Age Factors ; Risk Assessment ; Adult ; ROC Curve

OBJECTIVES: To analyze the efficacy and safety of concomitant left atrial appendage clipping during heart valve surgery for valvular heart disease patients with atrial fibrillation. METHODS: Fifty-eight patients who underwent concomitant left atrial appendage clipping during cardiac valve surgery in the Second Affiliated Hospital of Nanchang University from January 2017 to June 2023 were included in the analysis, including 1 case who underwent aortic valve replacement, 49 cases who underwent mitral valve replace-ment (or valvuloplasty)+tricuspid valvuloplasty, and 8 cases who underwent double valve replacement+tricuspid valvuloplasty (3 cases combined with coronary artery bypass grafting). The patients were followed up for 3-36 months [(16.69±6.61) months] after operation, and the changes of cardiac function and the occurrence of serious adverse complications were evaluated. RESULTS: The cardiopulmonary bypass time ranged from 75 to 145 min [(102.50±21.03) min], and the aortic cross-clamp time ranged from 35 to 80 min [(58.02±14.63) min]. The length of postoperative intensive care unit stay was 1 to 5 days [(2.47±0.82) d], and the length of postoperative hospital stay was 7 to 22 days [(10.84±2.69) d]. Cardiac ultrasound indicated complete closure of the left atrial appendage in all cases. During the follow-up, New York Heart Association (NYHA) functional classifications were improved in 54 patients. No left atrial appendage-related bleeding events or other perioperative complications were observed; and no cerebral infarction, limb embolism events, or mortality cases occurred during the follow-up. CONCLUSIONS For valvular heart disease patients with atrial fibrillation, concomitant left atrial appendage clipping during cardiac valve surgery demonstrates efficacy and safety, with no severe adverse events during a medium-term follow-up.
Humans ; Atrial Appendage/surgery* ; Atrial Fibrillation/complications* ; Male ; Female ; Heart Valve Diseases/complications* ; Aged ; Middle Aged ; Heart Valve Prosthesis Implantation/methods* ; Treatment Outcome ; Cardiac Surgical Procedures/methods* ; Mitral Valve/surgery*

OBJECTIVES: To analyze the association between biological aging markers (phenotypic age and phenotypic age acceleration) and valvular heart diseases. METHODS: Research subjects who met the inclusion and exclusion criteria were selected from the UK Biobank from 2006 to 2010. The phenotypic age and phenotypic age acceleration were calculated. Cox multivariate analysis was used to examine the relationship between the aging markers and valvular heart diseases. Sensitivity analysis was conducted by removing missing values and subgroup analysis. The predictive accuracy of phenotypic age and phenotypic age acceleration for valvular heart diseases was analyzed using receiver operating characteristic (ROC) curves, and a clinical decision curve was generated based on logistic regression. RESULTS: A total of 411 687 subjects were included in the study, among whom there were 14 258 patients with valvular heart diseases. The overall median follow-up time was 12.80 years, the median follow-up time for patients with non-rheumatic aortic valve diseases (n=5238), non-rheumatic mitral valve diseases (n=4558), and non-rheumatic tricuspid valve diseases (n=411) were 12.82 years, 12.83 years and 12.84 years, respectively. After adjusting for demographic factors (gender, race, education, Townsend deprivation index), anthropometric factors (body mass index), lifestyle factors (smoking, alcohol consumption, Dietary Approaches to Stop Hypertension score), hypertension and hyperlipidemia, Cox multivariate analysis showed phenotypic age and phenotypic age acceleration were independent risk factors for valvular heart diseases, including non-rheumatic aortic valve diseases, non-rheumatic mitral valve diseases, and non-rheumatic tricuspid valve diseases (phenotypic age: corrected HR=1.04, P<0.01; phenotypic age acceleration: corrected HR=1.03, P<0.01), which was also confirmed by sensitivity analysis. ROC curves and clinical decision curves demonstrated that compared with the phenotypic age acceleration, phenotypic age had higher accuracy (the areas and the curves were 0.721 and 0.599) and higher net benefit in predicting valvular heart diseases. Moreover, compared with a single indicator, the combination of the two indicators had higher accuracy (the area under the curve was 0.725) and higher net benefit. CONCLUSIONS Phenotypic age and phenotypic age acceleration,as markers of biological aging, are independent risk factors for valvular heart diseases. Compared with phenotypic age acceleration, phenotypic age has a greater advantage in predicting valvular heart diseases. Overall, the combination of the two indicators offers a more effective approach for predicting valvular heart diseases.
Humans ; Male ; Female ; Heart Valve Diseases/epidemiology* ; Middle Aged ; Aged ; Aging ; Adult ; Biomarkers ; Phenotype ; Risk Factors ; Aged, 80 and over

In recent years, the field of heart valve interventional treatment has developed rapidly, and various new technologies have been continuously applied. The study provides a comprehensive review and analysis of the research progress and challenges related to interventional treatment products for the aortic valve, pulmonary valve, mitral valve, and tricuspid valve both domestically and internationally. The study also summarizes the status of heart valve interventional devices entering Special Review of Innovative Medical Device of National Medical Products Administration (NMPA). The results show that the field of valve interventional treatment is developing rapidly, but there is still room for improvement in terms of valve durability, operational suitability, and complication reduction. Lastly, the study looks forward to the future development trend of new technologies for heart valve interventional therapy, and puts forward suggestions for the promotion and application of new technologies of valve interventional therapy from the perspective of supervision.
Humans ; Heart Valve Prosthesis ; Heart Valves/surgery* ; Heart Valve Diseases ; Heart Valve Prosthesis Implantation/instrumentation*

Heart valve disease (HVD) is one of the common cardiovascular diseases. Heart sound is an important physiological signal for diagnosing HVDs. This paper proposed a model based on combination of basic component features and envelope autocorrelation features to detect early HVDs. Initially, heart sound signals lasting 5 minutes were denoised by empirical mode decomposition (EMD) algorithm and segmented. Then the basic component features and envelope autocorrelation features of heart sound segments were extracted to construct heart sound feature set. Then the max-relevance and min-redundancy (MRMR) algorithm was utilized to select the optimal mixed feature subset. Finally, decision tree, support vector machine (SVM) and k-nearest neighbor (KNN) classifiers were trained to detect the early HVDs from the normal heart sounds and obtained the best accuracy of 99.9% in clinical database. Normal valve, abnormal semilunar valve and abnormal atrioventricular valve heart sounds were classified and the best accuracy was 99.8%. Moreover, normal valve, single-valve abnormal and multi-valve abnormal heart sounds were classified and the best accuracy was 98.2%. In public database, this method also obtained the good overall accuracy. The result demonstrated this proposed method had important value for the clinical diagnosis of early HVDs.
Humans ; Heart Sounds ; Heart Valve Diseases/diagnosis* ; Algorithms ; Support Vector Machine ; Signal Processing, Computer-Assisted

Humans ; Aortic Valve/surgery* ; Heart Valve Diseases ; China ; Transcatheter Aortic Valve Replacement

Humans ; Aortic Valve/surgery* ; Heart Valve Diseases ; China ; Transcatheter Aortic Valve Replacement

Heart Valve Diseases ; Heart Valve Prosthesis ; Humans ; Hypereosinophilic Syndrome/complications* ; Mitral Valve ; Thrombosis/etiology*

Aortic Valve ; Aortic Valve Stenosis/surgery* ; China ; Heart Valve Diseases/surgery* ; Heart Valve Prosthesis ; Heart Valve Prosthesis Implantation ; Humans ; Transcatheter Aortic Valve Replacement ; Treatment Outcome