Our Unit is at the forefront of international research in cardiovascular medicine, collaborating with physicians and researchers from Asia, Europe, North America and South America. Currently, we focus on the following major directions.

Direction 1: Elucidating the mechanisms underlying cardiac arrhythmogenesis and risk stratification in Brugada syndrome, long QT syndrome, catecholaminergic polymorphic ventricular tachycardia and arrhythmogenic right ventricular dysplasia/cardiomyopathy

Direction 2: Incorporating spatial and temporal heterogeneities in electrocardiographic conduction and repolarization indices and latent interactions between risk variables to better predict adverse outcomes in cardiovascular diseases

Ventricular Arrhythmias and Sudden Cardiac Death

Sudden cardiac death (SCD) is a significant problem globally, requiring the use of implantable cardioverter-defibrillators to prevent ventricular arrhythmic events. However, sudden death may be the first presenting events and as such, these patients do not receive the adequate medical assessment in time for SCD prevention. Moreover, the selection of patients requiring ICDs is currently suboptimal. Some patients never suffer from arrhythmic events post-ICD insertion, whereas others do so despite being labelled as low risk. Of the different causes of SCD, Brugada syndrome is a cardiac ion channelopathy that has a higher disease prevalence of Asia compared to Western countries. Therefore, the team is uniquely placed to deliver research objectives for this condition. Whilst other inherited arrhythmic syndromes such as long QT syndrome (LQTS) and catecholaminergic polymorphic ventricular tachycardia (CPVT) are rarer in Asia, the team has studied the genetic and clinical epidemiology of these conditions in collaboration with local genetic and cardiology experts, leading to significant advances in the understanding of inherited arrhythmia-related SCD in the Asia Pacific region.

The novel findings include improving risk stratification strategies for BrS, LQTS and CPVT by machine learning-driven methods, which can incorporate non-linear interactions between risk variables. Thus, the use of non-negative matrix factorisation and random survival forest analyses provided more accurate predictive models for forecasting future arrhythmic events. The team identified 19 novel pathogenic variants in putative genes encoding for protein subunits of cardiac ion channels or for proteins that constitute downstream signalling pathways. Current efforts focus on cooperation with international investigators to test the hypothesis that the incorporation of genetic information for personalised risk prediction. In collaboration with basic science teams, the team is utilising tissue engineered models using stem cell-derived cardiomyocytes, which allows functional characterisation of activation, inactivation and and recovery from inactivation at the single cell level and the investigations of electrophysiological and arrhythmic phenotypes using state-of-the-art platforms for disease modelling.

Our team is leading the Brugada Electrocardiographic Indices Registry (BEIR) Consortium, which bring together more than 63 investigators from 43 international centres in 21 countries. This constitutes one of the largest cohorts for Brugada syndrome, enabling not only the validation of existing score models published by international investigators, but also refinement of risk stratification by ethnicity, age of diagnosis and sex using machine learning.

1. Zhang, Z.H., Barajas-Martinez, H., Xia, H., Li, B., Capra, J.A., Clatot, J., Chen, G.X., Yang, B., Jiang, H., Tse, G., Aizawa, Y., Gollob, M.H., Scheinman, M., Antzelevitch, C., Hu, D. (2021) Distinct features of patients with early repolarization and Brugada syndromes carrying SCN5A pathogenic variants. Journal of the American College of Cardiology. 78(16): 1603-1617. PMID: 34649698. https://doi.org/10.1016/j.jacc.2021.08.024. 5-year impact factor: 20.0.

2. Tse, G.*, Zhou, J., Lee, S., Liu, T., Bazoukis, G., Mililis, P., Wong, I.C.K., Chen, C., Xia, Y., Kamakura, T., Aiba, T., Kusano, K., Zhang, Q., Letsas, K.P. (2020) Incorporating latent variables using nonnegative matrix factorization improves risk stratification in Brugada syndrome. Journal of the American Heart Association. e012714. PMID: 33170070. https://doi.org/10.1161/JAHA.119.012714. 5-year impact factor: 5.1.

3. Lee, S., Zhou, J., Li, K.H.C., Leung, K.S.K., Lakhani, I., Liu, T., Wong, I.C.K., Mok, N.S., Mak, C., Jeevaratnam, K., Zhang, Q.*, Tse, G.* (2021) Territory-wide Cohort Study of Brugada Syndrome in Hong Kong: Predictors of Long-Term Outcomes Using Random Survival Forests and Non-Negative Matrix Factorisation. Open Heart. 8(1):e001505. PMID: 33547222. https://doi.org/10.1136/openhrt-2020-001505. 5-year impact factor: 2.6.

4. Tse, G., Lee, S., Li, A., Chang, D., Li, G., Zhou, J., Liu, T., Zhang, Q. (2021). Automated electrocardiogram analysis identifies novel predictors of ventricular arrhythmias in Brugada syndrome. Frontiers in Cardiovascular Medicine. 7:618254. PMID: 33521066. https://doi.org/10.3389/fcvm.2020.618254. Impact factor: 6.1.

5. Tse, G., Lee, S., Mok, N.S., Liu, T., Chang, D. (2020) Incidence and Predictors of Atrial Fibrillation in a Chinese Cohort of Brugada Syndrome. International Journal of Cardiology. S0167-5273(20)31954-9. PMID: 32387420. https://doi.org/10.1016/j.ijcard.2020.05.007. 5-year impact factor: 4.0.

6. Tse, G.*, Lee, S., Liu, T., Yuen, H.C., Wong, I.C.K., Mak, C., Mok, N.S., Wong, W.T. (2020) Identification of novel SCN5A single nucleotide polymorphisms in Brugada syndrome: a territory-wide study from Hong Kong. Frontiers in Physiology. 11:574590. PMID: 33071830. https://doi.org/10.3389/fphys.2020.574590. 5-year impact factor: 3.9.

7. Lee, S., Zhou, J., Liu, T., Letsas, K.P., Hothi, S.S., Vassiliou, V., Li, G., Baranchuk, A., Chang, D., Zhang, Q., Tse, G.* (2020) Temporal variability in electrocardiographic indices in subjects with Brugada patterns. Frontiers in Physiology. 11:953. https://doi.org/10.3389/fphys.2020.00953. 5-year impact factor: 3.9.

8. Tse, G., Lee, S., Zhou, Liu, T., Wong, I.C.K., Mak, C., Mok, N.S., Jeevaratnam, K., Zhang, Q., Cheng, S.H., Wong, W.T. (2021) Territory-wide Chinese cohort of long QT syndrome: random survival forest and Cox analyses. Frontiers in Cardiovascular Medicine. 8:608592. PMID: 33614747. https://doi.org/10.3389/fcvm.2021.608592. Impact factor: 6.1.

9. Chen, C., Zhou, J., Yu, H., Zhang, Q., Lin, Y., Li, D., Yang, Y., Wang, Y., Tse, G.*, Xia, Y.* (2020) Identification of important risk factors for all-cause mortality of acquired long QT syndrome patients using random survival forests and non-negative matrix factorization. Heart Rhythm. S1547-5271(20)31033-X. PMID: 33127541. https://doi.org/10.1016/j.hrthm.2020.10.022. 5-year impact factor: 4.8.

10. Lee, S., Zhou, J., Jeevaratnam, K., Wong, W.T., Wong, I.C.K., Mak, C., Mok, N.S., Liu, T., Zhang, Q.*, Tse, G.* (2021) Paediatric/young versus adult patients with long QT syndrome. Open Heart. 8(2):e001671. PMID: 34518285. https://doi.org/10.1136/openhrt-2021-001671. 5-year impact factor: 2.6.

11. Lee, S., Zhou, Jeevaratnam, K., Wong, W.T., Wong, I.C.K., Mak, C., Mok, N.S., Liu, T., Zhang, Q., Tse, G.* (2021) Paediatric/young versus adult patients with congenital long QT syndrome or catecholaminergic polymorphic ventricular tachycardia. ESC Congress 2021 – The Digital Experience. Virtual Congress. Published in European Heart Journal 42(Supplement_1): October 2021, ehab724.1870, https://doi.org/10.1093/eurheartj/ehab724.1870.

12. Takla, M., Edling, C.E., Zhang, K., Saadeh, K., Tse, G., Salvage, S.C., Huang, C.L.H., Jeevaratnam, K. (2021) Transcriptional profiles of genes related to electrophysiological function in scn5a+/- murine hearts. ESC Congress 2021 – The Digital Experience. Virtual Congress. Published in European Heart Journal, 42(Supplement_1): October 2021, ehab724.3214, https://doi.org/10.1093/eurheartj/ehab724.3214.