[Cluster Analysis and Ablation Success Rate in Atrial Fibrillation Patients Undergoing Catheter Ablation]. / æˆ¿é¢¤å°„é¢‘æ¶ˆèžæ‚£è€ çš„èšç±»åˆ†æžåŠæ¶ˆèžæˆåŠŸçŽ‡è¯„ä»·.

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 1102 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.

Psychosocial Factors Associated With Long-Term Cognitive Impairment Among COVID-19 Survivors: A Cross-Sectional Study.

ABSTRACT: COVID-19 survivors complained of the experience of cognitive impairments, which also called "brain fog" even recovered. The study aimed to describe long-term cognitive change and determine psychosocial factors in COVID-19 survivors. A cross-sectional study was recruited 285 participants from February 2020 to April 2020 in 17 hospitals in Sichuan Province. Cognitive function, variables indicative of the virus infection itself, and psychosocial variables were collected by telephone interview. Univariate logistic regression and Lasso logistic regression models were used for variable selection which plugged into a multiple logistics model. Overall prevalence of moderate or severe cognitive impairment was 6.3%. Logistic regression showed that sex, religion, smoking status, occupation, self-perceived severity of illness, sleep quality, perceived mental distress after COVID-19, perceived discrimination from relatives and friends, and suffered abuse were associated with cognitive impairment. The long-term consequences of cognitive function are related to multiple domains, in which psychosocial factors should be taken into consideration.

Fractal Analysis: Prognostic Value of Left Ventricular Trabecular Complexity Cardiovascular MRI in Participants with Hypertrophic Cardiomyopathy.

Background The prognostic value of myocardial trabecular complexity in patients with hypertrophic cardiomyopathy (HCM) is unknown. Purpose To explore the prognostic value of myocardial trabecular complexity using fractal analysis in participants with HCM. Materials and Methods The authors prospectively enrolled participants with HCM who underwent 3.0-T cardiovascular MRI from August 2011 to October 2017. The authors also enrolled 100 age- and sex-matched healthy participants to form a comparison group. Trabeculae were quantified with fractal analysis of cine slices to estimate the fractal dimension (FD). Participants with HCM were divided into normal and high FD groups according to the upper limit of normal reference value from the healthy group. The primary end point was defined as all-cause mortality and aborted sudden cardiac death. The secondary end point was the composite of the primary end point and readmission to the hospital owing to heart failure. Internal validation was performed using the bootstrapping method. Results A total of 378 participants with HCM (median age, 50 years; age range, 40-61 years; 207 men) and 100 healthy participants (median age, 46 years; age range, 36-59 years; 55 women) were included in this study. During the median follow-up of 33 months ± 18 (standard deviation), the increased maximal apical FD (≥1.325) had a higher risk of the primary and secondary end points than those with a normal FD (<1.325) (P = .01 and P = .04, respectively). Furthermore, Cox analysis revealed that left ventricular maximal apical FD (hazard ratio range, 1.001-1.008; all P < .05) provided significant prognostic value to predict the primary and secondary end points after adjustment for the European Society of Cardiology predictors and late gadolinium enhancement. Internal validation showed that left ventricular maximal apical FD retained a good performance in predicting the primary end points with an area under the curve of 0.70 ± 0.03. Conclusion Left ventricular apical fractal dimension, which reflects myocardial trabecular complexity, was an independent predictor of the primary and secondary end points in patients with hypertrophic cardiomyopathy. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Captur and Moon in this issue.