Precision Medicine for Cardiac Ion Channelopathies in Hong Kong: From Case Reports to Identification of Novel Genetic Variants and Development of Risk Prediction Tools using Population-based Datasets
Congenital cardiac ion channelopathies refer to a set of inherited conditions characterized by abnormalities in the structure and/or function of ion channels, their associated proteins or other signalling components, predisposing affected individuals to life-threatening ventricular tachyarrhythmias and therefore sudden cardiac death. This is a literature review focusing on the progress of clinical research on congenital cardiac ion channelopathies in Hong Kong, from case reports in the 1990s to population-based studies in the 2020s. Locally, patients with Brugada syndrome, long QT syndrome and catecholaminergic polymorphic ventricular tachycardia have been studied. Leveraging the power of linked electronic health records data in the public sector, the epidemiology, clinical characteristics, genetics, genotype-phenotype relationship and predictive factors of arrhythmic events have received attention. Future efforts should focus on multidisciplinary collaborations between clinicians, scientists and data scientists the use of genomic data combined with clinical data for personalised risk prediction. With the Government’s drive for innovations and recent announcement of the Strategic Development of Genomic Medicine in Hong Kong, future efforts should be focused on the development of a national registry linking the databases and standardizing the data fields and reporting in different centres in Hong Kong, other cities in the Greater Bay Area and the wider mainland. Eventually the goal is to incorporate the vast amount of genomic information with clinical details to achieve personalised risk prediction through multidisciplinary collaborations.
Sharen Lee, Ngai Shing Mok, Gary Tse, Precision Medicine for Cardiac Ion Channelopathies in Hong Kong: From Case Reports to Identification of Novel Genetic Variants and Development of Risk Prediction Tools using Population-based Datasets Journal of the Hong Kong College of Cardiology 2023;30(3) https://doi.org/10.55503/2790-6744.1506
Dec 19, 2022
Mar 23, 2023