Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis
Background: Atrial fibrillation Better Care (ABC) pathway is recommended by guidelines on atrial fibrillation (AF) and exerts a protective role against adverse outcomes of AF patients. But the possible differences in its effectiveness across the diverse range of patients in China have not been syste...
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| Format: | Article |
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Elsevier
2025-04-01
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| Series: | International Journal of Cardiology: Heart & Vasculature |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352906725000247 |
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| author | Jingyang Wang Haiyang Bian Jiangshan Tan Jun Zhu Lulu Wang Wei Xu Lei Wei Xuegong Zhang Yanmin Yang |
| author_facet | Jingyang Wang Haiyang Bian Jiangshan Tan Jun Zhu Lulu Wang Wei Xu Lei Wei Xuegong Zhang Yanmin Yang |
| author_sort | Jingyang Wang |
| collection | DOAJ |
| description | Background: Atrial fibrillation Better Care (ABC) pathway is recommended by guidelines on atrial fibrillation (AF) and exerts a protective role against adverse outcomes of AF patients. But the possible differences in its effectiveness across the diverse range of patients in China have not been systematically evaluated. We aim to comprehensively evaluate multiple clinical characteristics of patients, and probe clusters of ABC criteria efficacy in patients with AF. Methods: We used data from an observational cohort that included 2,016 patients with AF. We utilized 45 baseline variables for cluster analysis. We evaluated the management patterns and adverse outcomes of identified phenotypes. We assessed the effectiveness of adherence to the ABC criteria at reducing adverse outcomes of phenotypes. Results: Cluster analysis identified AF patients into three distinct groups. The clusters include Cluster 1: old patients with the highest prevalence rates of atherosclerotic and/or other comorbidities (n = 964), Cluster 2: valve-comorbidities AF in young females (n = 407), and Cluster 3: low comorbidity patients with paroxysmal AF (n = 644). The clusters showed significant differences in MACNE, all-cause death, stroke, and cardiovascular death. All clusters showed that full adherence to the ABC pathway was associated with a significant reduction in the risk of MACNE (all P < 0.05). For three clusters, adherence to the different ‘A’/‘B’/‘C’ criterion alone showed differential clinic impact. Conclusion: Our study suggested specific optimization strategies of risk stratification and integrated management for different groups of AF patients considering multiple clinical, genetic and socioeconomic factors. |
| format | Article |
| id | doaj-art-543f62d4de6c47a5ad88db279b05a959 |
| institution | Kabale University |
| issn | 2352-9067 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Cardiology: Heart & Vasculature |
| spelling | doaj-art-543f62d4de6c47a5ad88db279b05a9592025-02-07T04:47:50ZengElsevierInternational Journal of Cardiology: Heart & Vasculature2352-90672025-04-0157101621Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysisJingyang Wang0Haiyang Bian1Jiangshan Tan2Jun Zhu3Lulu Wang4Wei Xu5Lei Wei6Xuegong Zhang7Yanmin Yang8Emergency and Critical Care Center, Fuwai Hospital, National Center for Cardiovascular Diseases of China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaMOE Key Lab for Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, ChinaEmergency and Critical Care Center, Fuwai Hospital, National Center for Cardiovascular Diseases of China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaEmergency and Critical Care Center, Fuwai Hospital, National Center for Cardiovascular Diseases of China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaEmergency and Critical Care Center, Fuwai Hospital, National Center for Cardiovascular Diseases of China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaEmergency and Critical Care Center, Fuwai Hospital, National Center for Cardiovascular Diseases of China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, ChinaMOE Key Lab for Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, ChinaMOE Key Lab for Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China; Center for Synthetic and Systems Biology, School of Life Sciences and School of Medicine, Tsinghua University, Beijing 100084, China; Corresponding author at: MOE Key Lab for Bioinformatics and Bioinformatics Division of BNRIST, Department of Automation, Tsinghua University, Beijing 100084, China.Emergency and Critical Care Center, Fuwai Hospital, National Center for Cardiovascular Diseases of China, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China; Corresponding author at: Emergency and Intensive Care Center, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, 167 Beilishilu, Xicheng District, Beijing 100037, China.Background: Atrial fibrillation Better Care (ABC) pathway is recommended by guidelines on atrial fibrillation (AF) and exerts a protective role against adverse outcomes of AF patients. But the possible differences in its effectiveness across the diverse range of patients in China have not been systematically evaluated. We aim to comprehensively evaluate multiple clinical characteristics of patients, and probe clusters of ABC criteria efficacy in patients with AF. Methods: We used data from an observational cohort that included 2,016 patients with AF. We utilized 45 baseline variables for cluster analysis. We evaluated the management patterns and adverse outcomes of identified phenotypes. We assessed the effectiveness of adherence to the ABC criteria at reducing adverse outcomes of phenotypes. Results: Cluster analysis identified AF patients into three distinct groups. The clusters include Cluster 1: old patients with the highest prevalence rates of atherosclerotic and/or other comorbidities (n = 964), Cluster 2: valve-comorbidities AF in young females (n = 407), and Cluster 3: low comorbidity patients with paroxysmal AF (n = 644). The clusters showed significant differences in MACNE, all-cause death, stroke, and cardiovascular death. All clusters showed that full adherence to the ABC pathway was associated with a significant reduction in the risk of MACNE (all P < 0.05). For three clusters, adherence to the different ‘A’/‘B’/‘C’ criterion alone showed differential clinic impact. Conclusion: Our study suggested specific optimization strategies of risk stratification and integrated management for different groups of AF patients considering multiple clinical, genetic and socioeconomic factors.http://www.sciencedirect.com/science/article/pii/S2352906725000247Atrial fibrillationCluster analysisManagement patternMACNEABC |
| spellingShingle | Jingyang Wang Haiyang Bian Jiangshan Tan Jun Zhu Lulu Wang Wei Xu Lei Wei Xuegong Zhang Yanmin Yang Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis International Journal of Cardiology: Heart & Vasculature Atrial fibrillation Cluster analysis Management pattern MACNE ABC |
| title | Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis |
| title_full | Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis |
| title_fullStr | Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis |
| title_full_unstemmed | Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis |
| title_short | Evaluation of the ABC pathway in patients with atrial fibrillation: A machine learning cluster analysis |
| title_sort | evaluation of the abc pathway in patients with atrial fibrillation a machine learning cluster analysis |
| topic | Atrial fibrillation Cluster analysis Management pattern MACNE ABC |
| url | http://www.sciencedirect.com/science/article/pii/S2352906725000247 |
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