Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF Registry

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; KERALA-AF Registry Investigators

doi:10.1016/j.lansea.2024.100507

Phenotypes of South Asian patients with atrial fibrillation and holistic integrated care management: cluster analysis of data from KERALA-AF Registry

Lancet Reg Health Southeast Asia. 2024 Nov 14:31:100507. doi: 10.1016/j.lansea.2024.100507. eCollection 2024 Dec.

Authors

Yang Chen^{1

2}, Bi Huang^{1

3}, Peter Calvert¹, Yang Liu^{1

4}, Ying Gue¹, Dhiraj Gupta¹, Garry McDowell^{1

5}, Jinbert Lordson Azariah^{6

7}, Narayanan Namboodiri⁸, Govindan Unni⁹, Jayagopal Pathiyil Balagopalan¹⁰, Gregory Yoke Hong Lip^{1

11}, Bahuleyan Charantharayil Gopalan¹²; KERALA-AF Registry Investigators

Collaborators

KERALA-AF Registry Investigators:
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

Affiliations

¹ Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart and Chest Hospital, Liverpool, United Kingdom.
² Department of Cardiovascular and Metabolic Medicine, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.
³ Department of Cardiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
⁴ Department of Cardiovascular Medicine, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China.
⁵ School of Pharmacy and Biomolecular Sciences, Liverpool John Moores University, Liverpool, United Kingdom.
⁶ Department of Clinical Research, Ananthapuri Hospitals and Research Institute, Thiruvananthapuram, India.
⁷ Department of Research, Global Institute of Public Health, Trivandrum, India.
⁸ Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum, India.
⁹ Department of Cardiology, Jubilee Hridhayalaya, Jubilee Mission Medical College Hospital, Thrissur, India.
¹⁰ Department of Cardiology, Lakshmi Hospital, Kochi, India.
¹¹ Danish Centre for Health Services Research, Department of Clinical Medicine, Aalborg University, Aalborg, DK-9220, Denmark.
¹² Department of Cardiology, Ananthapuri Hospitals and Research Institute, Thiruvananthapuram, India.

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.

Keywords: ABC pathway; Atrial fibrillation; Clustering analysis; Kerala; Phenotype classification; South Asia.