The Lancet Regional Health - Southeast Asia (Dec 2024)

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

  • 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

Journal volume & issue
Vol. 31
p. 100507

Abstract

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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.

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