Objective Atrial fibrillation(AF)is a disease of high heterogeneity,and the association between AF phenotypes and the outcome of different catheter ablation strategies remains unclear.Conventional classification of AF(e.g.according to duration,atrial size,and thromboembolism risk)fails to provide reference for the optimal stratification of the prognostic risks or to guide individualized treatment plan.In recent years,research on machine learning has found that cluster analysis,an unsupervised data-driven approach,can uncover the intrinsic structure of data and identify clusters of patients with pathophysiological similarity.It has been demonstrated that cluster analysis helps improve the characterization of AF phenotypes and provide valuable prognostic information.In our cohort of AF inpatients undergoing radiofrequency catheter ablation,we used unsupervised cluster analysis to identify patient subgroups,to compare them with previous studies,and to evaluate their association with different suitable ablation patterns and outcomes.Methods The participants were AF patients undergoing radiofrequency catheter ablation at West China Hospital between October 2015 and December 2017.All participants were aged 18 years or older.They underwent radiofrequency catheter ablation during their hospitalization.They completed the follow-up process under explicit informed consent.Patients with AF of a reversible cause,severe mitral stenosis or prosthetic heart valve,congenital heart disease,new-onset acute coronary syndrome within three months prior to the surgery,or a life expectancy less than 12 months were excluded according to the exclusion criteria.The cohort consisted of 1 102 participants with paroxysmal or persistent/long-standing persistent AF.Data on 59 variables representing demographics,AF type,comorbidities,therapeutic history,vital signs,electrocardiographic and echocardiographic findings,and laboratory findings were collected.Overall,data for the variables were rarely missing(<5% ),and multiple imputation was used for correction of missing data.Follow-up surveys were conducted through outpatient clinic visits or by telephone.Patients were scheduled for follow-up with 12-lead resting electrocardiography and 24-hours Holter monitoring at 3 months and 6 months after the ablation procedure.Early ablation success was defined as the absence of documented AF,atrial flutter,or atrial tachycardia>30 seconds at 6-month follow-up.Hierarchical clustering was performed on the 59 baseline variables.All characteristic variables were standardized to have a mean of zero and a standard deviation of one.Initially,each patient was regarded as a separate cluster,and the distance between these clusters was calculated.Then,the Ward minimum variance method of clustering was used to merge the pair of clusters with the minimum total variance.This process continued until all patients formed one whole cluster.The"NbClust"package in R software,capable of calculating various statistical indices,including pseudo t2 index,cubic clustering criterion,silhouette index etc,was applied to determine the optimal number of clusters.The most frequently chosen number of clusters by these indices was selected.A heatmap was generated to illustrate the clinical features of clusters,while a tree diagram was used to depict the clustering process and the heterogeneity among clusters.Ablation strategies were compared within each cluster regarding ablation efficacy.Results Five statistically driven clusters were identified:1)the younger age cluster(n=404),characterized by the lowest prevalence of cardiovascular and cerebrovascular comorbidities but the highest prevalence of obstructive sleep apnea syndrome(14.4% );2)a cluster of elderly adults with chronic diseases(n=438),the largest cluster,showing relatively higher rates of hypertension,diabetes,stroke,and chronic obstructive pulmonary disease;3)a cluster with high prevalence of sinus node dysfunction(n=160),with patients showing the highest prevalence of sick sinus syndrome and pacemaker implantation;4)the heart failure cluster(n=80),with the highest prevalence of heart failure(58.8% )and persistent/long-standing persistent AF(73.7% );5)prior coronary artery revascularization cluster(n=20),with patients of the most advanced age(median:69.0 years old)and predominantly male patients,all of whom had prior myocardial infarction and coronary artery revascularization.Patients in cluster 2 achieved higher early ablation success with pulmonary veins isolation alone compared to extensive ablation strategies(79.6% vs.66.5%;odds ratio[OR]=1.97,95% confidence interval[CI]:1.28-3.03).Although extensive ablation strategies had a slightly higher success rate in the heart failure group,the difference was not statistically significant.Conclusions This study provided a unique classification of AF patients undergoing catheter ablation by cluster analysis.Age,chronic disease,sinus node dysfunction,heart failure and history of coronary artery revascularization contributed to the formation of the five clinically relevant subtypes.These subtypes showed differences in ablation success rates,highlighting the potential of cluster analysis in guiding individualized risk stratification and treatment decisions for AF patients.

Objective    To compare the in-hospital outcomes of transapical transcatheter aortic valve replacement (TA-TAVR) for bicuspid aortic valve (BAV) patients and tricuspid aortic valve (TAV) patients. Methods    Patients (including BAV and TAV patients) who underwent TA-TAVR with the J-ValveTM in West China Hospital from July 2014 to July 2020 were included consecutively. The clinical outcomes of the patients were analyzed. Results    A total of 354 patients were included in the study, 75 in the BAV group and 279 in the TAV group. There were 229 males and 125 females with a mean age of 72.2±6.0 years. No death occurred during the procedure, and the overall technical success rate was 97.7%. The all-cause in-hospital mortality rate was 1.4%. Twenty (26.7%) patients with BAV and 46 (16.5%) patients with TAV had mild or higher perivalvular leaks immediately after the procedure. No patients with BAV required permanent pacemaker implantation postoperatively, while 13 (4.7%) TAV patients required permanent pacemaker implantation, with an overall pacemaker implantation rate of 3.7%. One (1.3%) BAV patient and 7 (2.5%) TAV patients developed acute kidney injury postoperatively. One (1.3%) BAV patient and 1 (0.4%) TAV patient developed peri-operative myocardial infarction. The average postoperative hospital stay was 7.6±3.6 d for BAV patients and 8.6±6.1 d for TAV patients. There was no statistical difference in primary or secondary in-hospital outcomes between BAV and TAV patients (P>0.05). Conclusion    Compared to TAV patients, BAV patients have similar in-hospital outcomes, with a low incidence of adverse clinical outcomes, which provides preliminary evidence for its implementation in Chinese patients with a high proportion of BAV.