PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■心房颤动（AF）是一种高度异质性的疾病，房颤表型与不同导管消融策略结果之间的关联尚不清楚.AF的常规分类(例如，根据持续时间，心房大小,和血栓栓塞风险）未能为预后风险的最佳分层或指导个体化治疗计划提供参考。近年来,关于机器学习的研究发现，聚类分析，一种无监督的数据驱动方法，可以揭示数据的内在结构，并识别具有病理生理相似性的患者群。已经证明，聚类分析有助于改善AF表型的表征并提供有价值的预后信息。在我们接受射频导管消融术的房颤住院患者队列中,我们使用无监督聚类分析来识别患者亚组，为了将它们与以前的研究进行比较，并评估它们与不同合适的消融模式和结果的关联。
参与者是2015年10月至2017年12月在华西医院接受射频导管消融的房颤患者。所有参与者年龄均为18岁或以上。他们在住院期间接受了射频导管消融。他们在明确的知情同意下完成了后续过程。有可逆性原因的房颤患者，严重二尖瓣狭窄或人工心脏瓣膜,先天性心脏病,在手术前三个月内新发急性冠脉综合征,根据排除标准,或预期寿命小于12个月被排除.该队列由1102名阵发性或持续性/长期持续性房颤患者组成。代表人口统计的59个变量的数据，AF类型,合并症,治疗史,生命体征,心电图和超声心动图检查结果，并收集了实验室发现。总的来说,变量的数据很少丢失(<5%)，并使用多重插补校正缺失数据。后续调查是通过门诊就诊或电话进行的。患者计划在消融手术后3个月和6个月进行12导联静息心电图和24小时动态心电图监测的随访。早期消融成功定义为无房颤记录,房扑,或6个月随访时房性心动过速>30秒。对59个基线变量进行分层聚类。将所有特征变量标准化为具有零的平均值和一的标准偏差。最初，每个患者被视为一个单独的集群，并计算了这些簇之间的距离。然后，使用Ward最小方差聚类方法合并总方差最小的一对聚类.这个过程一直持续到所有患者形成一个完整的集群。R软件中的“NbClust”软件包，能够计算各种统计指标，包括伪t2索引，立方聚类标准，轮廓指数等，用于确定最佳的聚类数量。选择了这些索引最频繁选择的聚类数量。生成了一个热图来说明集群的临床特征，而树形图用于描述聚类过程和聚类之间的异质性。在每个集群内比较消融策略的消融疗效。
■确定了五个统计驱动的集群：1）年龄较小的集群（n=404），以心脑血管合并症患病率最低，阻塞性睡眠呼吸暂停综合征患病率最高（14.4%）；2）一组患有慢性疾病的老年成年人（n=438），最大的集群，显示相对较高的高血压发病率，糖尿病,中风,和慢性阻塞性肺疾病；3）窦房结功能障碍患病率高的集群（n=160），病窦综合征和起搏器植入患病率最高的患者；4）心力衰竭群（n=80），心力衰竭(58.8%)和持续性/长期持续性房颤(73.7%)患病率最高;5)冠状动脉血运重建前组(n=20),高龄患者（中位数：69.0岁），主要为男性患者，所有患者均曾发生过心肌梗死和冠状动脉血运重建.与广泛的消融策略相比，第2组患者仅采用肺静脉隔离即可实现更高的早期消融成功率（79.6％vs.66.5%；比值比[OR]=1.97，95%置信区间[CI]：1.28-3.03）。尽管广泛的消融策略在心力衰竭组中的成功率略高，差异无统计学意义。
■本研究通过聚类分析对接受导管消融的房颤患者进行了独特的分类。年龄,慢性疾病,窦房结功能障碍,心力衰竭和冠状动脉血运重建史促成了5种临床相关亚型的形成.这些亚型显示消融成功率不同，强调聚类分析在指导房颤患者个体化风险分层和治疗决策方面的潜力。
参与者是2015年10月至2017年12月在华西医院接受射频导管消融的房颤患者。所有参与者年龄均为18岁或以上。他们在住院期间接受了射频导管消融。他们在明确的知情同意下完成了后续过程。有可逆性原因的房颤患者，严重二尖瓣狭窄或人工心脏瓣膜,先天性心脏病,在手术前三个月内新发急性冠脉综合征,根据排除标准,或预期寿命小于12个月被排除.该队列由1102名阵发性或持续性/长期持续性房颤患者组成。代表人口统计的59个变量的数据，AF类型,合并症,治疗史,生命体征,心电图和超声心动图检查结果，并收集了实验室发现。总的来说,变量的数据很少丢失(<5%)，并使用多重插补校正缺失数据。后续调查是通过门诊就诊或电话进行的。患者计划在消融手术后3个月和6个月进行12导联静息心电图和24小时动态心电图监测的随访。早期消融成功定义为无房颤记录,房扑,或6个月随访时房性心动过速>30秒。对59个基线变量进行分层聚类。将所有特征变量标准化为具有零的平均值和一的标准偏差。最初，每个患者被视为一个单独的集群，并计算了这些簇之间的距离。然后，使用Ward最小方差聚类方法合并总方差最小的一对聚类.这个过程一直持续到所有患者形成一个完整的集群。R软件中的“NbClust”软件包，能够计算各种统计指标，包括伪t2索引，立方聚类标准，轮廓指数等，用于确定最佳的聚类数量。选择了这些索引最频繁选择的聚类数量。生成了一个热图来说明集群的临床特征，而树形图用于描述聚类过程和聚类之间的异质性。在每个集群内比较消融策略的消融疗效。
■确定了五个统计驱动的集群：1）年龄较小的集群（n=404），以心脑血管合并症患病率最低，阻塞性睡眠呼吸暂停综合征患病率最高（14.4%）；2）一组患有慢性疾病的老年成年人（n=438），最大的集群，显示相对较高的高血压发病率，糖尿病,中风,和慢性阻塞性肺疾病；3）窦房结功能障碍患病率高的集群（n=160），病窦综合征和起搏器植入患病率最高的患者；4）心力衰竭群（n=80），心力衰竭(58.8%)和持续性/长期持续性房颤(73.7%)患病率最高;5)冠状动脉血运重建前组(n=20),高龄患者（中位数：69.0岁），主要为男性患者，所有患者均曾发生过心肌梗死和冠状动脉血运重建.与广泛的消融策略相比，第2组患者仅采用肺静脉隔离即可实现更高的早期消融成功率（79.6％vs.66.5%；比值比[OR]=1.97，95%置信区间[CI]：1.28-3.03）。尽管广泛的消融策略在心力衰竭组中的成功率略高，差异无统计学意义。
■本研究通过聚类分析对接受导管消融的房颤患者进行了独特的分类。年龄,慢性疾病,窦房结功能障碍,心力衰竭和冠状动脉血运重建史促成了5种临床相关亚型的形成.这些亚型显示消融成功率不同，强调聚类分析在指导房颤患者个体化风险分层和治疗决策方面的潜力。
