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Chen, Y, Huang, B, Calvert, P, Liu, Y, Gue, Y, Gupta, D, McDowell, G, Azariah, JL, Namboodiri, N, Unni, G, Balagopalan, JP, Lip, GYH, Gopalan, BC, Jabir, A, George Koshy, A, Zachariah, G, Shifas Babu, M, Venugopal, K, Punnose, E, Natarajan, KU , Joseph, J, Ashokan Nambiar, C, Mohanan, PP, George, R, Sajeev, CG, Syam, N, Roby, A, Daniel, R, Krishnakumar, VV, Pillai, AM, Joseph, S, Mini, GK, Koya, SF, Eapen, K, Ram, R, Mathew, C, Faizal, A, Issac, B, Renga, S, Menon, J, Harikrishna, D, Suresh, K, Nair, T, Susanth, SS, Kumar, RA, Abilash, TP, Sreekala, P, Rajeev, E, Raj, A, Naik, R, Rajalekshmi, S, Gopinath, A, Binu, R, Chacko, J, Iqbal, PT, Sudhir, NM, Sreedharan, M, Balakrishnan, N, Musthaffa, M, Jayakumar, B, George, S, Kumar, A, Mathew, T, Pramod, VK, Shaloob, M, Chandy, MP, Vinod, KR, Das, K, Ahamad, ZS and Mathew, P (2024) Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF Registry. The Lancet Regional Health - Southeast Asia, 31. ISSN 2772-3682
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Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management cluster analysis of data from KERALA-AF Registry.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview |
Abstract
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.
| Item Type: | Article |
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| Uncontrolled Keywords: | Cardiovascular; Clinical Research; Heart Disease; 2.4 Surveillance and distribution; 4.2 Evaluation of markers and technologies; Cardiovascular; 3 Good Health and Well Being |
| Subjects: | Q Science > QH Natural history > QH301 Biology R Medicine > R Medicine (General) |
| Divisions: | Pharmacy and Biomolecular Sciences |
| Publisher: | Elsevier |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 03 Dec 2024 12:27 |
| Last Modified: | 03 Dec 2024 12:30 |
| DOI or ID number: | 10.1016/j.lansea.2024.100507 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/24995 |
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