- Clinical Assistant Professorship in 2016 at Chinese University of Hong Kong
Reader (Clinical), Kent and Medway Medical School, University of Kent and Canterbury Christ Church University, Kent
Public Health Physician, Medway Council Public Health Directorate, Kent
Research Fellow, Institute of Advanced Studies, University of Surrey, Guildford
Visiting Professor, Faculty of Health and Medical Sciences, University of Surrey, Guildford
Senior Epidemiologist, Observational and Pragmatic Research Institute, Singapore and Optimum Patient Care, Cambridge
Professor, Department of Cardiology, The Second Hospital, Tianjin Medical University, Tianjin
Principal Investigator, Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Tianjin Institute of Cardiology, Tianjin
Honorary Professor, Xiamen Cardiovascular Hospital, Xiamen University, Xiamen, Fujian
Clinical Fellow, Department of Cardiac Electrophysiology, Xiamen Cardiovascular Hospital, Xiamen University, Xiamen, Fujian
Guest Professor, Department of Cardiology, The First Affiliated Hospital, Dalian Medical University, Dalian, Liaoning
People's Republic of China
Secretary, Cardiovascular Analytics Group (CVAG), Hong Kong, China-UK Collaboration
BA (Hons.) (Cantab.), MBBS (Imperial), MA (Cantab.), MPH (Manc.), MHM (UNSW), MD (Cantab.), PhD (Cantab.), RCPathME (UK), MFPH (UK), FAcadMEd, FESC, FACC, FSCAI, FHRS, FRCP (Glasg.), FRCP (Edin,), FRCP (Lond.), FRCPCH, FFPH
Committee Appointments (委员会任职)
Nucleus Committee Member, Young Community (2018-present), Board Member (2019-present), and Secretary to the Council (2021-present), International Society of Electrocardiology
Member, Board of Trustees, International Society for Noninvasive Electrocardiology (ISHNE) (2021-2024)
Awards and Prizes (奖项)
Stanford University The World’s Top 2% Scientists, Cardiovascular System & Hematology subfield, Year 2020 (2021)
European Society of Cardiology Asia Young Investigator Award First Prize (corresponding author) (2021)
International Congress of Electrocardiology Young Investigator Award First Prize (corresponding author) (2021)
The International Society for Noninvasive Electrocardiology (ISHNE) Bruce del Mar Junior Investigator Award (2021)
Stanford University The World’s Top 2% Scientists, Cardiovascular System & Hematology subfield, Year 2019 (2020)
International Congress of Electrocardiology ECG Bayés Award (2018)
Biography (English Only)
Tse attended secondary school in the United Kingdom, where he read Biology, Chemistry, Physics, Mathematics and Further Mathematics at Advanced Level (A-Levels). He achieved the best results in the School's history, with the highest ever UCAS Tariff Point Score of 695 points and two Advanced Extension Awards in Chemistry and Physics, receiving mostly 100% at AS Level and 100% in 11 papers in total for A-level.
In 2005, he matriculated at Trinity Hall of The University of Cambridge, where he read undergraduate Pre-Clinical Medicine and completed, successively, Clinical Medicine at The Imperial College London, Doctor of Philosophy (Cardiac Electrophysiology) at The School of Biological Sciences, and Doctor of Medicine (Cardiovascular Medicine) at the School of Clinical Medicine, of The University of Cambridge.
He is now a fully trained clinician-scientist with additional accreditations in Public Health Medicine from The University of Manchester, UK and Healthcare Management from The University of New South Wales, Australia. He started computer programming at the age of 12, writing programs such as a school-wide chatroom and a 2D racing game. With a strong interest in physics, mathematics and information technology since a young age, his current strategy is the application of advanced mathematical and statistical analyses to improve risk stratification in cardiovascular diseases.
Tse went to China in 2015 to establish the Laboratory of Cardiovascular Physiology, Cardiovascular Analytics Group and International Health Informatics Network for pre-clinical, clinical and meta-analysis research, respectively. He served, successively, as an Affiliated Investigator (2015), Principal Investigator (2016), and Professor (2019) at the Tianjin Institute of Cardiology, The Second Hospital of Tianjin Medical University. He was also appointed to a Guest Professorship at The First Affiliated Hospital of Dalian Medical University (2018) and an Honorary Professorship at the Xiamen Cardiovascular Hospital of Xiamen University (2019).
He subsequently took a sabbatical to pursue a six-month advanced fellowship training at the Department of Cardiac Electrophysiology of the Xiamen Cardiovascular Hospital, where he focused on catheter ablation of atrial and supraventricular arrhythmias.
Since then, he has returned to Tianjin Medical University, where he holds a full professorial appointment, directing cardiovascular research as a Principal Investigator at its Key Laboratory. Tse has provided input on strategic planning at the institutional level under the direction of senior management. This has led to the successful applications of local, national and international grant awards. Through successfully leading and managing international studies with collaborative efforts, his team continues to deliver sustained and impactful research output. He has also developed experience in project and personnel management. In particular, his team's ethos focuses on the bilateral nature of mentorship, where each team member plays an important role and is encouraged to develop his or her full potential.
He is also a Senior Epidemiologist at the Observational and Pragmatic Research Institute, Singapore and Optimum Patient Care, Cambridge (2020), Visiting Professor at the Faculty of Health and Medical Sciences (2020) and a Fellow of the Institute of Advanced Studies (2021), University of Surrey, Guildford, a Reader in Public Health at the Kent and Medway Medical School, University of Kent and Canterbury Christ Church University with a concurrent appointment as a Public Health Physician in the Medway Council Public Directorate (2021), United Kingdom.
In recognition of contributions to the understanding of cardiac electrophysiology, Tse was selected for the ECG Bayés Award by the International Congress of Electrocardiology for the best research output by a young investigator in 2018, The Bruce del Mar Junior Investigator Award by the International Society for Noninvasive Electrocardiology (ISHNE) in 2021, and senior author of the work selected as First Prize for the Young Investigator Award (first author Dr. Sharen Lee) of the International Congress of Electrocardiology in 2021 and for the Young Investigator Award (first author Dr. Sharen Lee) at the European Society of Cardiology Asia Congress 2021. Tse was 1 of the 33 researchers from China listed on the World’s Top 2% Scientists Released by Stanford University for the Cardiovascular System & Hematology subfield (excluding self-citation) for the Year 2019 and 1 of 57 researchers for the Year 2020.
He is the Secretary of The Cardiovascular Analytics Group (CVAG), Hong Kong, China-UK Collaboration, a highly active research group comprising of more than 100 members globally. With a collective team effort, The CVAG has established more than 20 longitudinal cohorts, allowing the application of data- and hypothesis-driven methodologies to investigate the epidemiology, risk factors, and outcomes of a number of rare and common cardiovascular diseases. Using these cohorts, it was possible to develop predictive risk models that were enhanced by artificial intelligence and machine learning algorithms.
Tse a Medical Examiner Member of the Royal College of Pathologists, received his Membership of the Faculty of Public Health (UK), and has been elected a Fellow of the European Society of Cardiology (2016), American College of Cardiology (2017), Society for Cardiovascular Angiography and Interventions (2019), Heart Rhythm Society (2018), Royal College of Physicians and Surgeons of Glasgow (Fellowship qua Physician) (2017), Royal College of Physicians of Edinburgh (2018), Royal College of Physicians of London (2019), Royal College of Paediatrics and Child Health (2019) and Faculty of Public Health (UK) (2019).
For excellence in teaching and medical education, Tse was voted by medical students for the Medical Clerkship Exemplary Teaching Award, and subsequently elected to the Fellowship of the UK Academy of Medical Educators.
H-index: 43 (Google Scholar); 38 (ResearchGate); 33 (Scopus); 33 (Web of Science)
Tse's Research pages are detailed below:
His Research Team's websites:
Tse's major preoccupation has been research into the pathophysiology of cardiac arrhythmogenesis. His work has led to the identification of novel mechanisms by which immunosuppressive, anti-lipidemic and anti-diabetic medications exert protective effects against adverse remodelling, and of electrophysiological substrates underlying cardiac arrhythmias. These pre-clinical findings have provided opportunities for translational application, improving risk stratification for patients suffering from rare cardiac ion channelopathies and common cardiovascular diseases. Tse serves as the Principal Investigator of population-based studies into the epidemiology of Brugada syndrome, long QT syndrome, catecholaminergic polymorphic ventricular tachycardia, arrhythmogenic right ventricular dysplasia / cardiomyopathy, ischaemic heart disease, valvular heart disease, heart failure, myocarditis, diabetes mellitus, hypertension, stroke, gout and COVID-19. His team established the international Brugada Electrocardiographic Indices Consortium and published multi-national studies into Brugada syndrome. From these projects, the team has successfully developed risk models to stratify patients who are at risk of adverse events and mortality. These models have been significantly improved by the application of cutting-edge machine learning algorithms that can extract details on latent interactions between risk variables.
2017-present: Comparative drug outcomes and development of predictive models to improve risk stratification in diabetes mellitus, hypertension, atrial fibrillation, stroke, myocardial infarction, valvular heart disease, heart failure, myocarditis, pericarditis and COVID-19 using population-based datasets
2016-present: Cardiac remodelling in cardio-metabolic disorders
2008-present: Mechanisms underlying cardiac arrhythmogenesis and risk stratification in Brugada syndrome, long QT syndrome, catecholaminergic polymorphic ventricular tachycardia and arrhythmogenic right ventricular dysplasia/cardiomyopathy
Impact case studies
Research Mentorship Platform
The Medical Education Research Unit of the Cardiovascular Analytics Group is responsible for the organization and coordination of its mentorship platform. The group places a strong emphasis on mentoring young researchers and our goal is to make research accessible to all interested students. Recently, the findings of the mentorship model, specifically its benefits on gender equity and leadership development, have been published in the leading journal, Diabetes and European Heart Journal:
1. Tse, G.*, Liu, T., Roever, L., Lee, S. (2022) The Women’s Leadership Gap in Diabetes: A Call for Equity and Excellence. Diabetes. 71(1):e1–e2. PMID: 34995349. https://doi.org/10.2337/db21-0686. 5-year impact factor: 7.9.
2. Chan, J.S.K., Lau, D.H.H., King, E., Shum, Y.K.L., Roever, L., Liu, T., Ng, K., Dee, E.C., Ciobanu, A., Bazoukis, G., Mahmoudi, E., Satti, D.I., Jeevaratnam, K., Baranchuk, A., Tse, G. (2021) Virtual medical research mentoring and collaboration: breaking the bounds of nationality during the COVID-19 pandemic. ESC Asia 2021 with APSC & AFC Virtual Congress (poster presentation). European Heart Journal. 43(Suppl_1): ehab849.179. https://doi.org/10.1093/eurheartj/ehab849.179.
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 the 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.
Tse's 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.
Heart failure is the common final pathway of many cardiovascular conditions, with an estimated 70 million people worldwide suffering from it. Many of the heart failure patients are frail, and are at greater risks of acute decompensation events. Therefore, there is a pressing need to accurately forecast such events before they occur, which would allow timely intervention to improve clinical outcomes and quality of life. With this in mind, the team has worked towards greater precision and accuracy in phenotyping and prediction of adverse outcomes in heart failure. An exemplar is the development of a multimodality risk score, which included atrial and ventricular measurements from electrocardiography and echocardiography, blood inflammatory marker [neutrophil-to-lymphocyte ratio (NLR)], and prognostic nutritional index (PNI) and comorbidity records, to improve the prediction of atrial fibrillation, stroke and mortality. The application of multilayer perceptron and multi-task learning improved the F1-score from 0.81 to 0.89 and 0.94, respectively.
Moreover, frailty assessment is highly time-consuming and there have been increasing efforts globally to develop surrogate markers of frailty, as exemplified by electronic frailty indices. Subsequent data-driven explorations in a larger cohort resulted in the development of a heart failure-specific electronic frailty index, which did not require clinical assessment, electrocardiographic or echocardiographic testing. This index, computed from comorbidity and laboratory data, showed an excellent performance with an area under the receiver operating characteristic curve of 0.86 with logistic regression, which was significantly improved to 0.88 and 0.91 by decision tree and gradient boosting methods for short-term mortality prediction.
1. Ju, C., Zhou, J., Lee, S., Tan, M.S., Liu, T., Bazoukis, G., Jeevaratnam, K., Chan, E.W.Y., Wong, I.C.K., Wei, L., Zhang, Q.*, Tse, G.* (2021) Derivation of an electronic frailty index for predicting short-term mortality in heart failure: a machine learning approach. ESC Heart Failure. 8(4):2837-2845. PMID: 34080784. https://doi.org/10.1002/ehf2.13358. Impact factor: 4.4.
2. Sun, Y., Wang, N., Zhang, Y., Yang, J., Tse, G.*, Liu, Y.* (2021) Predictive value of H2FPEF score in patients with heart failure with preserved ejection fraction. ESC Heart Failure. 8(2):1244-1252. PMID: 33403825. https://doi.org/10.1002/ehf2.13187. Impact factor: 4.4.
3. Tse, G., Zhou, J., Woo, S.W.D., Ko, C.H., Lai, R.W.C., Liu, T., Liu, Y., Leung, K.S.K., Li, A., Lee, S., Li, K.H.C., Lakhani, I., Zhang, Q. (2020) Multi-modality machine-learning approach for risk stratification in heart failure with left ventricular ejection fraction ≤ 45%. ESC Heart Failure. 7(6):3716-25. PMID: 33094925. https://doi.org/10.1002/ehf2.12929. Impact factor: 4.4.
4. Wang, Y., Xiao, G., Zhang, G., Wang, B., Lin, Z., Saiwha, H.D., You, H., Lai, K., Su, M., Wen, H., Wang, J., Annest, L., Tse, G. (2020) Early Results of the Revivent TC Procedure for Treatment of Left Ventricular Aneurysm and Heart Failure Due to Ischemic Cardiomyopathy. EuroIntervention. 2020 Jan 28:EIJ-D-19-00225. PMID: 31985453. https://doi.org/10.4244/eij-d-19-00225. 5-year impact factor: 3.8.
5. Zhang, Y., Yuan, M., Gong, M., Li, G., Liu, T., Tse, G. (2018) Letter to the Editor: Associations between prefrailty or frailty components and clinical outcomes in heart failure: a follow-up meta-analysis. Journal of the American Medical Directors Association. pii: S1525-8610(18)30609-1. PMID: 30541690. https://doi.org/10.1016/j.jamda.2018.10.029. 5-year impact factor: 6.3.
6. Zhang, Y., Yuan, M., Gong, M., Tse, G., Li, G., Liu, T. (2018) Frailty and clinical outcomes in heart failure: a systematic review with meta-analysis. Journal of the American Medical Directors Association. S1525-8610(18)30329-3. PMID: 30076123. https://doi.org/10.1016/j.jamda.2018.06.009. 5-year impact factor: 6.3.
7. Tse, G.*, Gong, M., Wong, S.H., Wu, W.K.K., Bazoukis, G., Lampropoulos, K., Wong, W.T., Xia, Y., Wong, M.C.S., Liu, T., Woo, J. (2017) Frailty and clinical outcomes in advanced heart failure patients undergoing left ventricular assist device implantation: a systematic review and meta-analysis. Journal of the American Medical Directors Association. pii: S1525-8610(17)30545-5. PMID: 29129497.http://dx.doi.org/10.1016/j.jamda.2017.09.022. Impact factor: 5.
Diabetes and hypertension
Diabetes and hypertension are the most prevalent cardio-metabolic conditions that can lead to debilitating complications. For type 2 diabetic patients in whom lifestyle modification strategies and oral pharmacotherapy are not adequate for glycemic control, insulin injection is the final life-saving intervention. This selected subgroup of patients is at much higher risks of adverse cardiovascular events compared to those who are not receiving insulin, partly owing to their poorer glycemic control. Moreover, the use of insulin itself increases the risk of potentially lethal, hypoglycemic events.
The team currently leads the Hong Kong Diabetes Study, a longitudinal cohort study that allows the application of machine learning allowed more accurate prediction of arrhythmic and cause-specific mortality outcomes. Recently, the team found that measures of glycemic and lipid variability as well as those of chronic inflammation can be used for enhancing risk prediction in type 2 diabetes patients receiving insulin therapy. Such efforts have been extended to type 2 patients who are not receiving any medical therapy or those managed pharmacologically with oral medications. In doing so, the aim is to produce accurate yet computationally efficient models that can be generalized to all diabetic patients. Moreover, the team showed that higher baseline, maximum, minimum, standard deviation, coefficient of variation, and variability score of systolic/diastolic blood pressure significantly predicted incident anxiety in both normotensive and hypertensive cohorts of patients attending family medicine clinics.
1. Zhou, J., Lee, S., Wong, W.T., Bin Waleed, K., Leung, K.S.K., Lee, T.T.L., Wai, A.K.C., Liu, T., Chang, C., Cheung, B.M., Zhang, Q.*, Tse, G. (2021) Gender-specific clinical risk scores incorporating blood pressure variability for predicting incident dementia. Journal of the American Medical Informatics Association. ocab173. PMID: 34643701. https://doi.org/10.1093/jamia/ocab173. 5-year impact factor: 5.2.
2. Zhou, J., Li, H., Chang, C., Wu, W.K.K., Wang, X., Liu, T., Cheung, B.M.Y., Zhang, Q., Lee, S., Tse, G. (2021) The association between blood pressure variability and hip or vertebral fracture risk: A population-based study. Bone. 116015. PMID: 34029778. https://doi.org/10.1016/j.bone.2021.116015. 5-year impact factor: 4.4.
3. Zhou, J., Lee, S., Wong, W.T., Leung, K.S.K., Nam, R.H.K., Leung, P.S.H., Chau, Y.L.A., Liu, T., Chang, C., Cheung, B.M., Tse, G.*, Zhang, Q.* (2021) Gender-and Age-Specific Associations of Visit-To-Visit Blood Pressure Variability with Incident Anxiety. Frontiers in Cardiovascular Medicine. 8:650852. PMID: 34026870. https://doi.org/10.3389/fcvm.2021.650852. Impact factor: 6.1.
4. Lee, S., Zhou, J., Leung, K.S.K., Wu, W.K.K., Wong, W.T., Liu, T., Wong, I.C.K., Jeevaratnam, K., Zhang, Q.*, Tse, G.* (2021) Development of a predictive risk model for all-cause mortality in diabetic patients in Hong Kong. BMJ Open Diabetes Research & Care. https://doi.org/10.1136/bmjdrc-2020-001950. Impact factor: 3.4.
5. Lee, S., Zhou, J., Wong, W.T., Liu, T., Wu, W.K.K., Wong, I.C.K., Zhang, Q.*, Tse, G.* (2021) Glycemic and lipid variability for predicting complications and mortality in diabetes mellitus using machine learning. BMC Endocrine Disorders. 21(1):94. PMID: 33947391. http://dx.doi.org/10.1186/s12902-021-00751-4. 5-year impact factor: 2.8.
6. Lee, S., Zhou, J., Guo, C.L., Wong, W.T., Liu, T., Wong, I.C.K., Jeevaratnam, K., Zhang, Q.*, Tse, G.* (2021) Predictive scores for identifying type 2 diabetes mellitus patients at risk of acute myocardial infarction and sudden cardiac death. Endocrinology, Diabetes & Metabolism. https://doi.org/10.1002/edm2.240.
7. Lee, S., Liu, T., Zhou, J., Zhang, Q., Wong, W.T., Tse, G.* (2020) Predictions of diabetes complications and mortality using hba1c variability: a 10-year observational cohort study. Acta Diabetologica. 58(2): 171-180. PMID: 32939583. https://doi.org/10.1007/s00592-020-01605-6. 5-year impact factor: 4.3.
The coronavirus (COVID-19) has led to a global pandemic, which has placed an overwhelming burden on healthcare, economic and social systems worldwide. Tse's team has leveraged their expertise on pharmacoepidemiology, and collaborated with local clinicians and researchers to study the risk factors, epidemiology and outcomes in COVID-19. This has led to a development of predictive risk model for forecasting severe outcomes, defined as admission to the intensive care unit, need for invasive ventilation or 30-day mortality. This model was published in Nature's npj Digital Medicine, has been translated into clinical use. The easy-to-use risk score, now available on QxMD, a subsidiary of WebMD, can serve as an accessible screening tool for the early direction of resources to these high-risk individuals and improve their prognosis.
1. Tse, G.*, Zhou, J.*, Lee, S., Wong, W.T., Li, X., Liu, T., Cao, Z., Zeng, D.D., Wai, A.K.C., Wong, I.C.K., Cheung, B.M.Y., Zhang, Q. (2021) Relationship between angiotensin-converting enzyme inhibitors or angiotensin receptor blockers and COVID-19 incidence or severe disease. Journal of Hypertension. https://doi.org/10.1097/HJH.0000000000002866. 5-year impact factor: 4.8.
2. Zhou, J., Lee, S., Wang, X., Li, Y., Wu, W.K.K., Liu, T., Cao, Z., Zeng, D.D., Leung, K.S.K., Wai, A.K.C., Wong, I.C.K., Cheung, B.M.Y., Zhang, Q.*, Tse, G.* (2021) Development of a multivariable prediction model for severe COVID-19 disease: a population-based study from Hong Kong. NPJ Digital Medicine. https://doi.org/10.1038/s41746-021-00433-4. Impact factor: 11.7.
3. Zhou, J., Lee, S., Guo, C.L., Chang, C., Liu, T., Leung, K.S.K., Wai, A.K.C., Cheung, B.M.Y., Tse, G.*, Zhang, Q.* (2021) Anticoagulant or antiplatelet use and severe COVID-19 disease: a propensity score matched territory-wide study. Pharmacological Research. 105473. PMID: 33524539. https://doi.org/10.1016/j.phrs.2021.105473. 5-year impact factor: 7.7.
4. Zhou, J., Wang, X., Lee, S., Wu, W.K.K., Cheung, B.M.Y., Zhang, Q.*, Tse, G.* (2020) Proton pump inhibitor or famotidine use and severe COVID-19 disease: a propensity score-matched territory-wide study. Gut. gutjnl-2020-323668. PMID: 33277346. https://doi.org/10.1136/gutjnl-2020-323668. 5-year impact factor: 17.8.
5. Li, X., Guan, B., Su, T., Liu, W., Chen, M., Bin Waleed, K., Guan, X., Tse, G.*, Zhu, Z.* Impact of cardiovascular disease and cardiac injury on in-hospital mortality in patients with COVID-19: a systematic review and meta-analysis. (2020) Heart. 27:heartjnl-2020-317062. PMID: 32461330. https://doi.org/10.1136/heartjnl-2020-317062. 5-year impact factor: 5.4.
6. Wang, Y., Tse, G., Li, G., Lip, G.Y.H., Liu, T. (2020) ACE Inhibitors and Angiotensin II Receptor Blockers May Have Different Impact on Prognosis of COVID-19. Journal of the American College of Cardiology. 76(17):2041. PMID: 33092742. https://doi.org/10.1016/j.jacc.2020.07.068. 5-year impact factor: 19.0.
Cardiovascular risk and its modification by pharmacological agents
The group has studied cardiovascular risk in a variety of conditions and the comparative drug actions on adverse cardiovascular outcomes:
1. Mui, J.V., Zhou, J., Lee, S., Leung, K.S.K., Lee, T.T.L., Chou, O.H.I., Tsang, S.L., Wai, A.K.C., Liu, T., Wong, W.T., Chang, C., Tse, G.*, Zhang, Q.* (2021) Sodium glucose cotransporter 2 (SGLT2) inhibitors versus dipeptidyl peptidase-4 (DPP4) inhibitors for new onset dementia: a propensity score-matched population-based study with competing risk analysis. Frontiers in Cardiovascular Medicine. 8:747620. PMID: 34746262. https://doi.org/10.3389/fcvm.2021.747620. Impact factor: 6.1.
2. Lee, S., Zhou, J., Leung, K.S.K., Wai, A.K.C., Jeevaratnam, K., King, E., Liu, T., Wong, W.T., Chang, C., Wong, I.C.K., Cheung, B.M., Tse, G.*, Zhang, Q.* (2021) Comparison of sodium glucose cotransporter-2 inhibitor and dipeptidyl peptidase-4 inhibitor on the risks of new-onset atrial fibrillation, stroke and mortality in diabetic patients: a propensity score-matched study in Hong Kong. Cardiovascular Drugs and Therapy. https://doi.org/10.1007/s10557-022-07319-x. 5-year impact factor: 4.1.
3. Zhou, J., Lee, S., Leung, K.S.K., Wai, A.K.C., Liu, T., Liu, Y., Chang, D., Wong, W.T., Wong, I.C.K., Cheung, B.M., Zhang, Q.*, Tse, G.* (2022) Incident heart failure and myocardial infarction in sodium-glucose cotransporter-2 versus dipeptidyl peptidase-4 inhibitor users. ESC Heart Failure. PMID: 35132823. https://doi.org/10.1002/ehf2.13830. Impact factor: 4.4.
4. Sfairopoulos, D., Zhang, N., Wang, Y., Chen, Z., Letsas, K.P., Tse, G., Li, G., Lip, G.Y.H., Liu, T., Korantzopoulos, P. (2021) Association between sodium-glucose cotransporter-2 inhibitors and risk of sudden cardiac death or ventricular arrhythmias: a meta-analysis of randomized controlled trials. Europace. https://doi.org/10.1093/europace/euab177. 5-year impact factor: 5.3.
5. Zhang, N, Tse, G., Liu, T. (2021) Neutrophil-lymphocyte ratio in the immune checkpoint inhibitors-related atherosclerosis. European Heart Journal. PMID: 33748846. https://doi.org/10.1093/eurheartj/ehab158. 5-year impact factor: 30.0.
6. Guo, S., Tse, G., Liu, T. (2020) Cardioprotective strategies to prevent trastuzumab-induced cardiotoxicity. Lancet. 15;395(10223):491-492. PMID: 32061289. https://doi.org/10.1016/S0140- 6736(19)32549-8. Impact factor: 59.
7. Ju, C., Lai, R.W.C., Li, K.H.C., Hung, J.K.F., Lai, J.C.N., Ho, J., Liu, Y., Tsoi, M.F., Liu, T., Cheung, B.M.Y., Wong, I.C.K., Tam, L.S., Tse, G.* (2019) Comparative cardiovascular risk in users versus non- users of xanthine oxidase inhibitors and febuxostat versus allopurinol users. Rheumatology. PMID: 31873735. https://doi.org/10.1093/rheumatology/kez576. Impact factor: 5.7.
8. Roever, L., Tse, G., Versaci, F., Biondi-Zoccai, G. (2019) Admission glucagon-like peptide-1 levels in acute myocardial infarction: is this really a new biomarker of cardiovascular risk? European Heart Journal. PMID: 31834367. https://doi.org/10.1093/eurheartj/ehz868. Impact factor: 25.
9. Tse, G., Gong, M., Li, G., Wong, S.H., Wu, W.K.K., Wong, W.T., Roever, L., Lee, A.P.W., Lip, G.Y.H., Wong, M.C.S., Liu, T. (2018) Genotype-guided warfarin dosing vs. conventional dosing strategies: a systematic review and meta-analysis of randomized controlled trials. British Journal of Clinical Pharmacology. 84(9):1868-1882. PMID: 29704269. https://doi.org/10.1111/bcp.13621. 5-year impact factor: 4.2.
Pre-clinical research summary
In collaboration with Prof. Jack Wong and Prof. Tong Liu's research groups, Tse's team focuses on investigating the pathophysiology of atrial and ventricular arrhythmias, which are important conditions leading to thromboembolic events and sudden cardiac death, respectively. They have unravelled the novel mechanisms by which immunosuppressive, anti-lipidemic and anti-diabetic medications exert protective effects against atrial fibrillation (Liu,..., Tse, Li and Liu, Cardiovasc Ther. 2017 Oct;35(5)). Using a combination of genetic, pharmacological, biochemical and imaging approaches, the team has identified reductions in oxidative stress and prevention of mitochondrial dysfunction are critical to achieve reverse remodelling of the atria (Zhang,...Tse, Li and Liu, J Am Heart Assoc. 2017 May 15;6(5). pii: e005945; Yang,...Tse, Li and Liu, J Am Heart Assoc. 2018 May 2;7(10):e008807; Shao et al., Cardiovasc Diabetol. 2019,18(1):165. doi: 10.1186/s12933-019-0964-4; Wang...Tse, Li, Liu, Fu., J Physiol Biochem. 2020 Oct 21. doi: 10.1007/s13105-020-00769-7).
Using Langendorff mouse models (reviewed in Yeo,...and Tse, J Basic Clin Physiol Pharmacol. 2017 May 1;28(3):191-200), his team has elucidated the relative contributions of gap junction and sodium channel function to cardiac conduction, and the relationship between conduction, repolarization, their heterogeneities, and dynamic substrates of electrical restitution to atrial and ventricular arrhythmogenicity. Of note, his team was the first to validate the use of the S1S2 protocol against the gold standard of dynamic pacing for assessing electrical restitution in mice. With The team's recent efforts have focused on the use of time-domain, frequency-domain and non-linear analysis to interrogate data derived from action potential time series (Tse et al., Front Physiol. 2018 9:1578). Under the directions of Prof. Kamalan Jeevaratnam's group in the United Kingdom, it was found that three dimensional restitution analysis of sinus rhythm electrocardiograms followed by application of the k-NN classifier algorithm with matching using synthetic minority oversampling technique could be used to predict paroxysmal atrial fibrillation in equine athletes (Huang, Alexeenko, Huang, Tse, Marr and Jeevaratnam, Function. 2020. doi:10.1093/function/zqaa031).
Clinical research summary
In collaboration with local and international groups, Tse's team utilizes clinical data to better define disease life course and epidemiology of cardiac arrhythmias. Using insights from their pre-clinical programme, they have identified key electrocardiographic (ECG) predictors that can aid risk stratification in stroke. They have validated that atrial remodelling can be detected using the ECG and P-wave parameters reflecting atrial electrical dysfunction can predict future onset and recurrence of atrial fibrillation (AF) (Tse et al., Int J Cardiol. 2017), and more importantly stroke events independently of AF (He, Tse et al., Stroke. 2017 Aug;48(8):2066-2072). Recently, the team applied a decision tree learning approach, demonstrating that incorporation of individual and non-linear interaction variables between P-wave area and age significantly improved prediction of incident AF (Tse et al., Front. Bioeng. Biotechnol. 2020). They subsequently discovered that a multi-task Gaussian process learning model significantly improved the predictive performance for adverse outcomes such as atrial fibrillation, transient ischaemic attack/stroke and all-cause mortality compared to logistic regression and decision tree learning (Tse et al., Eur J Clin Invest, 2020; Tse et al., ESC Heart Failure, 2020).
Moreover, his team has identified ECG predictors for ventricular arrhythmic and sudden cardiac death events in rare congenital ion channelopathies and more prevalent conditions of ischemic heart disease and myocardial infarction. These include fragmentation of the QRS complex (Meng et al., Front Physiol. 2017 Sep 12;8:678) and prolonged Tpeak-Tend intervals (Tse et al. Heart Rhythm. 2017 Aug;14(8):1131-1137), reflecting increased dispersion of conduction velocities and increased spatial dispersion of repolarization, respectively (reviewed in Tse et al., Europace. 2017 May 1;19(5):712-721). Tse is currently working with his team to establish multi-national arrhythmia cohorts, such as Brugada syndrome and long QT syndrome, utilizing the full capabilities of cutting edge machine learning techniques to perform higher dimensional analysis on multi-modality data sets. In a multi-national study involving China, Japan and Greece, the team found that incorporating latent features between risk variables significantly improved arrhythmic risk prediction in Brugada syndrome (Tse et al., Journal of the American Heart Association 2020, DOI: 10.1161/JAHA.119.012714). In collaboration with Dalian Medical University, the team found that mortality prediction in patients with acquired long QT syndrome was more accurate using both random survival forests (RSF) and non-negative matrix factorization (NMF) compared to RSF and Cox regression models (Chen, ... Tse*, Xia*, Heart Rhythm 2020, S1547-5271(20)31033-X).
Tse's group makes use of population- and registry-based data to examine the epidemiology, disease outcomes (Wang...Tse, EuroIntervention. 2020 Jan 28. pii: EIJ-D-19-00225) and comparative drug effects in different cardiovascular conditions (Ju...Tse, Rheumatology (Oxford) 2020. pii: kez576) and to create score-based systems with advanced machine learning techniques for improving risk stratification (Li...Tse, Atherosclerosis 2020, 301:30-36). His team also further provided epidemiological evidence on the cancer-metabolic link, reporting important relationships between serum insulin levels and lymph node metastases in endometrial cancer (Mu...Tse, Cancer Medicine 2018. 7(4):1519-1527).
Finally, in collaboration with international investigators, Tse publishes high-quality systematic reviews and meta-analyses on cardiovascular epidemiology, thereby providing a scientific basis for evidence-based practice in clinical medicine (e.g. Tse et al., J Am Med Dir Assoc 2017, 18(12):1097.e1-1097; Tse et al., Int J Cardiol 2018. 250:152-156; Lakhani...Tse, Metabolism 2018. 83:11-17; Chi...Tse, JACC Clinical Electrophysiology 2018 4(9):1214-1223; Tse et al. Br J Clin Pharmacol. 2018 Sep;84(9):1868-1882).
Service and Output
Together, Tse and his team have published more than 250 publications (Google Scholar H-index: 43) in top journals such as the Lancet, European Heart Journal, Journal of the American College of Cardiology, Journal of the American Medical Directors Association, Nature npj Digital Medicine, Nature npj Regenerative Medicine, Circulation: Arrhythmia and Electrophysiology, Circulation: Cardiovascular Imaging, International Journal of Cardiology, European Journal of Clinical Investigation, ESC Heart Failure, Stroke, Gut, Heart, Journal of the American Heart Association, Heart Rhythm, Europace, Journal of Arrhythmia and JACC: Clinical Electrophysiology. Their research has been recognized by conferences organized by learned societies, including the Gordon Research Conferences, European Cardiac Arrhythmia Society, Heart Rhythm Society and Europace. Tse has served as chairman/co-chairman of five national meetings, such as the NSFC-RGC Conference in Calcium Signalling, and The Joint Inaugural Clinical and Translational Cardiology Conference).
Tse serves on the editorial board of Current Hypertension Reviews, Cardio-Oncology, Journal of Electrocardiology, Oxford Medical Case Reports, Biomedical Reports, International Journal of Cardiology Heart & Vasculature and Life. He is a regular academic reviewer for >50 international journals in cardiology, cardiac electrophysiology, cardiovascular biology and epidemiology. He served as Nucleus Committee Member of the Young Community (2018), International Council Member (2019) and Secretary (2021) of the International Society of Electrocardiology (ISE); Board of Trustees Member (2021), International Society for Holter and Noninvasive Electrocardiology (ISHNE), Co-Director (2018-), International Health Informatics Study Network (IHISN): a research collaborative network involving >25 academics from 6 countries. He also served as an Abstract Reviewer and a Faculty Panel Member for European Society of Cardiology Congress and an external expert reviewer for funding bodies from the UK (Medical Research Council, Rosetrees Trust) and New Zealand (Auckland Medical Research Foundation), as well as an External Assessor for Tenure Committee considering academic promotions to Associate Professorship. Tse has attracted approximately CNY$10.3 million research-related funding as a principal applicant and co-applicant from national grant agencies of China and the United Kingdom.