Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF RegistryResearch in context
Summary: Background: Patients with atrial fibrillation (AF) frequently experience multimorbidity. Cluster analysis, a machine learning method for classifying patients with similar phenotypes, has not yet been used in South Asian AF patients. Methods: The Kerala Atrial Fibrillation Registry is a pro...
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Elsevier
2024-12-01
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| Series: | The Lancet Regional Health - Southeast Asia |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772368224001574 |
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| author | Yang Chen Bi Huang Peter Calvert Yang Liu Ying Gue Dhiraj Gupta Garry McDowell Jinbert Lordson Azariah Narayanan Namboodiri Govindan Unni Jayagopal Pathiyil Balagopalan Gregory Yoke Hong Lip Bahuleyan Charantharayil Gopalan Narayanan Namboodiri A. Jabir A. George Koshy Geevar Zachariah M. Shifas Babu K. Venugopal Eapen Punnose K.U. Natarajan Johny Joseph C. Ashokan Nambiar P.B. Jayagopal P.P. Mohanan Raju George Govindan Unni C.G. Sajeev N. Syam Anil Roby Rachel Daniel V.V. Krishnakumar Anand M. Pillai Stigi Joseph G.K. Mini Shaffi Fazaludeen Koya Koshy Eapen Raghu Ram Cibu Mathew Ali Faizal Biju Issac Sujay Renga Jaideep Menon D. Harikrishna K. Suresh Tiny Nair S.S. Susanth R.Anil Kumar T.P. Abilash P. Sreekala E. Rajeev Arun Raj Ramdas Naik S. Rajalekshmi Anoop Gopinath R. Binu Jossy Chacko P.T. Iqbal N.M. Sudhir Madhu Sreedharan N. Balakrishnan Muhammed Musthaffa B. Jayakumar Sheeba George Anand Kumar Thomas Mathew V.K. Pramod Muhammed Shaloob Madhu Paulose Chandy K.R. Vinod Karuana Das Z.Sajan Ahamad Pramod Mathew |
| author_facet | Yang Chen Bi Huang Peter Calvert Yang Liu Ying Gue Dhiraj Gupta Garry McDowell Jinbert Lordson Azariah Narayanan Namboodiri Govindan Unni Jayagopal Pathiyil Balagopalan Gregory Yoke Hong Lip Bahuleyan Charantharayil Gopalan Narayanan Namboodiri A. Jabir A. George Koshy Geevar Zachariah M. Shifas Babu K. Venugopal Eapen Punnose K.U. Natarajan Johny Joseph C. Ashokan Nambiar P.B. Jayagopal P.P. Mohanan Raju George Govindan Unni C.G. Sajeev N. Syam Anil Roby Rachel Daniel V.V. Krishnakumar Anand M. Pillai Stigi Joseph G.K. Mini Shaffi Fazaludeen Koya Koshy Eapen Raghu Ram Cibu Mathew Ali Faizal Biju Issac Sujay Renga Jaideep Menon D. Harikrishna K. Suresh Tiny Nair S.S. Susanth R.Anil Kumar T.P. Abilash P. Sreekala E. Rajeev Arun Raj Ramdas Naik S. Rajalekshmi Anoop Gopinath R. Binu Jossy Chacko P.T. Iqbal N.M. Sudhir Madhu Sreedharan N. Balakrishnan Muhammed Musthaffa B. Jayakumar Sheeba George Anand Kumar Thomas Mathew V.K. Pramod Muhammed Shaloob Madhu Paulose Chandy K.R. Vinod Karuana Das Z.Sajan Ahamad Pramod Mathew |
| author_sort | Yang Chen |
| collection | DOAJ |
| description | Summary: Background: Patients with atrial fibrillation (AF) frequently experience multimorbidity. Cluster analysis, a machine learning method for classifying patients with similar phenotypes, has not yet been used in South Asian AF patients. Methods: The Kerala Atrial Fibrillation Registry is a prospective multicentre cohort study in Kerala, India, and the largest prospective AF registry in South Asia. Hierarchical clustering was used to identify different phenotypic clusters. Outcomes were all-cause mortality, major adverse cardiovascular events (MACE), and composite bleeding events within one-year follow-up. Findings: 3348 patients were included (median age 65.0 [56.0–74.0] years; 48.8% male; median CHA2DS2-VASc 3.0 [2.0–4.0]). Five clusters were identified. Cluster 1: patients aged ≤65 years with rheumatic conditions; Cluster 2: patients aged >65 years with multi-comorbidities, suggestive of cardiovascular-kidney-metabolic syndrome; Cluster 3: patients aged ≤65 years with fewer comorbidities; Cluster 4: heart failure patients with multiple comorbidities; Cluster 5: male patients with lifestyle-related risk factors. Cluster 1, 2 and 4 had significantly higher MACE risk compared to Cluster 3 (Cluster 1: OR 1.36, 95% CI 1.08–1.71; Cluster 2: OR 1.79, 95% CI 1.42–2.25; Cluster 4: OR 1.76, 95% CI 1.31–2.36). The results for other outcomes were similar. Atrial fibrillation Better Care (ABC) pathway in the whole cohort was low (10.1%), especially in Cluster 4 (1.9%). Overall adherence to the ABC pathway was associated with reduced all-cause mortality (OR 0.26, 95% CI 0.15–0.46) and MACE (OR 0.45, 95% CI 0.31–0.46), similar trends were evident in different clusters. Interpretation: Cluster analysis identified distinct phenotypes with implications for outcomes. There was poor ABC pathway adherence overall, but adherence to such integrated care was associated with improved outcomes. Funding: Kerala Chapter of Cardiological Society of India. |
| format | Article |
| id | doaj-art-f94aa368a2c64b99891e60c7a9b203ed |
| institution | Kabale University |
| issn | 2772-3682 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | The Lancet Regional Health - Southeast Asia |
| spelling | doaj-art-f94aa368a2c64b99891e60c7a9b203ed2024-11-15T06:15:31ZengElsevierThe Lancet Regional Health - Southeast Asia2772-36822024-12-0131100507Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF RegistryResearch in contextYang Chen0Bi Huang1Peter Calvert2Yang Liu3Ying Gue4Dhiraj Gupta5Garry McDowell6Jinbert Lordson Azariah7Narayanan Namboodiri8Govindan Unni9Jayagopal Pathiyil Balagopalan10Gregory Yoke Hong Lip11Bahuleyan Charantharayil Gopalan12Narayanan NamboodiriA. JabirA. George KoshyGeevar ZachariahM. Shifas BabuK. VenugopalEapen PunnoseK.U. NatarajanJohny JosephC. Ashokan NambiarP.B. JayagopalP.P. MohananRaju GeorgeGovindan UnniC.G. SajeevN. SyamAnil RobyRachel DanielV.V. KrishnakumarAnand M. PillaiStigi JosephG.K. MiniShaffi Fazaludeen KoyaKoshy EapenRaghu RamCibu MathewAli FaizalBiju IssacSujay RengaJaideep MenonD. HarikrishnaK. SureshTiny NairS.S. SusanthR.Anil KumarT.P. AbilashP. SreekalaE. RajeevArun RajRamdas NaikS. RajalekshmiAnoop GopinathR. BinuJossy ChackoP.T. IqbalN.M. SudhirMadhu SreedharanN. BalakrishnanMuhammed MusthaffaB. JayakumarSheeba GeorgeAnand KumarThomas MathewV.K. PramodMuhammed ShaloobMadhu Paulose ChandyK.R. VinodKaruana DasZ.Sajan AhamadPramod MathewLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom; Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom; Corresponding author. Liverpool Centre for Cardiovascular Science, William Henry Duncan Building, 6 West Derby St, Liverpool, Merseyside, L7 8TX, United Kingdom.Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom; Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of ChinaLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, United KingdomLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom; Department of Cardiovascular Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of ChinaLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, United KingdomLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, United KingdomLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom; School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United KingdomDepartment of Clinical Research, Ananthapuri Hospitals and Research Institute, Thiruvananthapuram, India; Department of Research, Global Institute of Public Health, Trivandrum, IndiaSree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, IndiaDepartment of Cardiology, Jubilee Hridhayalaya, Jubilee Mission Medical College Hospital, Thrissur, IndiaDepartment of Cardiology, Lakshmi Hospital, Kochi, IndiaLiverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom; Danish Centre for Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, DK-9220, Denmark; Corresponding author. Liverpool Centre for Cardiovascular Science, William Henry Duncan Building, 6 West Derby St, Liverpool, Merseyside, L7 8TX, United Kingdom.Department of Cardiology, Ananthapuri Hospitals and Research Institute, Thiruvananthapuram, IndiaSummary: Background: Patients with atrial fibrillation (AF) frequently experience multimorbidity. Cluster analysis, a machine learning method for classifying patients with similar phenotypes, has not yet been used in South Asian AF patients. Methods: The Kerala Atrial Fibrillation Registry is a prospective multicentre cohort study in Kerala, India, and the largest prospective AF registry in South Asia. Hierarchical clustering was used to identify different phenotypic clusters. Outcomes were all-cause mortality, major adverse cardiovascular events (MACE), and composite bleeding events within one-year follow-up. Findings: 3348 patients were included (median age 65.0 [56.0–74.0] years; 48.8% male; median CHA2DS2-VASc 3.0 [2.0–4.0]). Five clusters were identified. Cluster 1: patients aged ≤65 years with rheumatic conditions; Cluster 2: patients aged >65 years with multi-comorbidities, suggestive of cardiovascular-kidney-metabolic syndrome; Cluster 3: patients aged ≤65 years with fewer comorbidities; Cluster 4: heart failure patients with multiple comorbidities; Cluster 5: male patients with lifestyle-related risk factors. Cluster 1, 2 and 4 had significantly higher MACE risk compared to Cluster 3 (Cluster 1: OR 1.36, 95% CI 1.08–1.71; Cluster 2: OR 1.79, 95% CI 1.42–2.25; Cluster 4: OR 1.76, 95% CI 1.31–2.36). The results for other outcomes were similar. Atrial fibrillation Better Care (ABC) pathway in the whole cohort was low (10.1%), especially in Cluster 4 (1.9%). Overall adherence to the ABC pathway was associated with reduced all-cause mortality (OR 0.26, 95% CI 0.15–0.46) and MACE (OR 0.45, 95% CI 0.31–0.46), similar trends were evident in different clusters. Interpretation: Cluster analysis identified distinct phenotypes with implications for outcomes. There was poor ABC pathway adherence overall, but adherence to such integrated care was associated with improved outcomes. Funding: Kerala Chapter of Cardiological Society of India.http://www.sciencedirect.com/science/article/pii/S2772368224001574Atrial fibrillationClustering analysisPhenotype classificationABC pathwayKeralaSouth Asia |
| spellingShingle | Yang Chen Bi Huang Peter Calvert Yang Liu Ying Gue Dhiraj Gupta Garry McDowell Jinbert Lordson Azariah Narayanan Namboodiri Govindan Unni Jayagopal Pathiyil Balagopalan Gregory Yoke Hong Lip Bahuleyan Charantharayil Gopalan Narayanan Namboodiri A. Jabir A. George Koshy Geevar Zachariah M. Shifas Babu K. Venugopal Eapen Punnose K.U. Natarajan Johny Joseph C. Ashokan Nambiar P.B. Jayagopal P.P. Mohanan Raju George Govindan Unni C.G. Sajeev N. Syam Anil Roby Rachel Daniel V.V. Krishnakumar Anand M. Pillai Stigi Joseph G.K. Mini Shaffi Fazaludeen Koya Koshy Eapen Raghu Ram Cibu Mathew Ali Faizal Biju Issac Sujay Renga Jaideep Menon D. Harikrishna K. Suresh Tiny Nair S.S. Susanth R.Anil Kumar T.P. Abilash P. Sreekala E. Rajeev Arun Raj Ramdas Naik S. Rajalekshmi Anoop Gopinath R. Binu Jossy Chacko P.T. Iqbal N.M. Sudhir Madhu Sreedharan N. Balakrishnan Muhammed Musthaffa B. Jayakumar Sheeba George Anand Kumar Thomas Mathew V.K. Pramod Muhammed Shaloob Madhu Paulose Chandy K.R. Vinod Karuana Das Z.Sajan Ahamad Pramod Mathew Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF RegistryResearch in context The Lancet Regional Health - Southeast Asia Atrial fibrillation Clustering analysis Phenotype classification ABC pathway Kerala South Asia |
| title | Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF RegistryResearch in context |
| title_full | Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF RegistryResearch in context |
| title_fullStr | Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF RegistryResearch in context |
| title_full_unstemmed | Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF RegistryResearch in context |
| title_short | Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF RegistryResearch in context |
| title_sort | phenotypes of south asian patients with atrial fibrillation and holistic integrated care management cluster analysis of data from kerala af registryresearch in context |
| topic | Atrial fibrillation Clustering analysis Phenotype classification ABC pathway Kerala South Asia |
| url | http://www.sciencedirect.com/science/article/pii/S2772368224001574 |
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