Int J Arrhythm.  2023 Jun;24(2):9. 10.1186/s42444-023-00092-4.

Historical perspective and recent progress in cardiac ion channelopathies research and clinical practice in Hong Kong

Affiliations
  • 1Cardiac Electrophysiology Unit, Cardiovascular Analytics Group, Hong Kong, China
  • 2Kent and Medway Medical School, Canterbury, Kent, UK
  • 3Sabah Al Ahmad Cardiac Center, Amiri Hospital, Ministry of Health, Kuwait City, Kuwait
  • 4Departamento de Pesquisa Clinica, Universidade Federal de Uberlândia, Uberlândia, MG, Brazil
  • 5Cardiac Intensive Care Unit, Department of Cardiology, Delta Clinic, Rosario, Santa Fe, Argentina
  • 6Department of Cardiology, Medical School, University of Nicosia, 2408 Nicosia, Cyprus
  • 7Tianjin Key Laboratory of Ionic‑Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, Second Hospital of Tianjin Medical University, Tianjin 300211, China
  • 8Faculty of Health and Life Sciences, Aston University Medical School, Birmingham, UK
  • 9Faculty of Medicine and Health Sciences, Royal College of Surgeons in Ireland, Dublin, Ireland

Abstract

Cardiac ion channelopathies encompass a set of inherited or acquired conditions that are due to dysfunction in ion channels or their associated proteins, typically in the presence of structurally normal hearts. They are associated with the development of ventricular arrhythmias and sudden cardiac death. The aim of this review is to provide a historical perspective and recent advances in the research of the cardiac ion channelopathies, Brugada syndrome, long QT syn‑ drome and catecholaminergic polymorphic ventricular tachycardia, in Hong Kong, China. In particular, recent works on the development of novel predictive models incorporating machine learning techniques to improve risk strati‑ fication are outlined. The availability of linked records of affected patients with good longitudinal data in the public sector, together with multidisciplinary collaborations, implies that ion channelopathy research efforts have advanced significantly.

Keyword

Ion channelopathies; Brugada syndrome; Catecholaminergic polymorphic ventricular tachycardia; Long QT syndrome; Sudden cardiac death; Risk stratification; Machine learning
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