Free Consultation and Collaborations 

Dr. Tse's Team has developed a number of predictive risk models based on big data research studies from Hong Kong. These models are published in leading international, high impact factor and high ranking peer-reviewed journals. As the research findings are already published in the public domain, they are available for both commercial and non-commercial use with or without adaptation. Please attribute the authors by citing the appropriate references. As a courtesy, please inform Dr. Tse if you implement any of the models, so that the societal impact can be known and shared with the team.

Disclaimer: Consultations on model design, development, implementation, interpretation and use are available free of charge from Dr. Tse and his team members. For enquiries, please email garytse86@gmail.com. The models were specifically designed and validated for use in Chinese subjects. However, the computed risks are available for the cohort level. Whilst they can be individualised and tailor-made for each person, interpretation of findings needs to be done in consultation with a trained healthcare professional. Although Dr. Tse is fully registered with a license to practice medicine in the United Kingdom (Pre-clinical Training:  University of Cambridge, Clinical Training: Imperial College London), with Master-level training in Precision Medicine (Genetics and Genomics from the University of Oxford) and Doctoral level training (Doctor of Philosophy in Biochemistry, Doctor of Medicine in Cardiovascular Genetics and Epidemiology, both from the University of Cambridge), advice sessions offered by him are not medical consultations and should not be interpreted as such.

Disease-specific models

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., 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.

5. 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.

6. 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.

7. Chung, C.T., Lee, S., Zhou, J., Chou, O., Lee, T.T.L., Leung, K.S.K., Jeevaratnam, K., Wong, W.T., Liu, T., Tse, G.* (2022) Clinical characteristics, healthcare resource utilisation and costs in patients with catecholaminergic polymorphic ventricular tachycardia: a retrospective cohort study. Reviews in Cardiovascular Medicine. 23(8), 276. https://doi.org/10.31083/j.rcm2308276. Impact factor: 4.4.

8. 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.

9. 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.

10. Tse, G.*, Lee, S., Bin Waleed, K., Hui, J.M.H., Lakhani, I. (2022) Analysis of clinical characteristics, genetic basis, management and arrhythmic outcomes of patients with catecholaminergic polymorphic ventricular tachycardia from a Chinese City. British Cardiovascular Society Annual Conference 2022. Manchester, United Kingdom. Oral presentation. Published in Heart. http://dx.doi.org/10.1136/heartjnl-2022-BCS.104.

Heart failure

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. Chan, J.S.K., Satti, D.I., Lee, Y.H.A., Hui, J.M.H., Lee, T.T.L., Chou, O.H.I., Wai, A.K.C., Ciobanu, A., Liu, Y., Liu, T., Roever, L., Biondi-Zoccai, G., Zhang, Q., Cheung, B.M.Y., Zhou, J.*, Tse, G.* (2022) High visit-to-visit cholesterol variability predicts new-onset heart failure and adverse cardiovascular events: a retrospective population-based cohort study. European Journal of Preventive Cardiology. PMID: 35653641. https://doi.org/10.1093/eurjpc/zwac097. 5-year impact factor: 5.5.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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. Lee, Y.H.A., Zhou, J., Hui, J.M.H., Liu, X., Lee, T.T.L., Hui, K., Chan, J.S.K., Wai, A.K.C., Wong, W.T., Liu, T., Ng, K., Lee, S., Dee, E.C., Zhang, Q.*, Tse, G.* (2022) Risk of new-onset prostate cancer for metformin versus sulphonylurea use in type 2 diabetes mellitus: a propensity score-matched study. Journal of the National Comprehensive Cancer Network. 20(6):674-682.e15. PMID: 35714677 https://doi.org/10.6004/jnccn.2022.7010. 5-Year impact factor: 8.3.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

8. 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.

COVID-19

The coronavirus (COVID-19) has led to a global pandemic, which has placed an overwhelming burden on healthcare, economic and social systems worldwide. Tse leads the Hong Kong COVID-19 cohort study. His 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.

Impact Case

Our research on cardiovascular medications has been incorporated into the evidence base of various international guidelines. These include the benefits of novel oral anticoagulants (1, 2)in the 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation (3), 2020 Canadian Cardiovascular Society/Canadian Heart Rhythm Society Comprehensive Guidelines for the Management of Atrial Fibrillation (4), benefits of on cognitive impairment and dementia in the 2021 European Heart Rhythm Association Practical Guide on the Use of Non-Vitamin K Antagonist Oral Anticoagulants in Patients with Atrial Fibrillation (5), 2022 Expert Recommendations on the Usage of Non-vitamin K Antagonist Oral Anticoagulants (NOACs) from India (6) and 2023 EHRA expert consensus document on the management of arrhythmias in frailty syndrome (7), endorsed by the Heart Rhythm Society (HRS), Asia Pacific Heart Rhythm Society (APHRS), Latin America Heart Rhythm Society (LAHRS), and Cardiac Arrhythmia Society of Southern Africa (CASSA). Similarly, our work on statins has led to incorporation of pre-loading of statins to improve clinical outcomes after PCI (8). For anti-hypertensive medications (9), cited in Chinese General Practice Experts Consensus on the Use of Benazepril Hydrochloride and Hydrochlorothiazide Tablets in Primary Care (10).

Our studies on diabetes (11) and heart failure (12) have been incorporated into the 2020 ESC Guidelines on sports cardiology and exercise in patients with cardiovascular disease (13) and the 2023 Scientific Statement From the American Heart Association and American College of Cardiology (14), respectively. The works on laboratory predictors (15) were also cited by the A Scientific Statement From the American Heart Association (16). Our work on obesity has been incorporated into the OBEDIS Core Variables Project: European Expert Guidelines (17).

Our arrhythmia research (18) has been incorporated into 2022 Expert Consensus Statement on the state of genetic testing for cardiac diseases (19). 2019 HRS expert consensus statement on evaluation, risk stratification, and management of arrhythmogenic cardiomyopathy (20). Our ECG research has been incorporated into Consensus Document Endorsed by the International Society of Electrocardiology and the International Society for Holter and Noninvasive Electrocardiology (21). For predicting response to cardiac resynchronization therapy (22), our work was cited by the 2023 HRS/APHRS/LAHRS guideline on cardiac physiologic pacing for the avoidance and mitigation of heart failure (23). Our contributions to AF burden in COVID-19 (24)has been incorporated into the 2021 consensus paper on Cardiovascular disease and COVID-19 from the ESC Working Group on Coronary Pathophysiology & Microcirculation, ESC Working Group on Thrombosis and the Association for Acute CardioVascular Care (ACVC), in collaboration with the European Heart Rhythm Association (EHRA) (25) and 2021 Consensus statement on cardiac electrophysiology practices during the coronavirus disease 2019 (COVID-19) pandemic by the Indian Heart Rhythm Society (26). For cardiovascular disease burden (27), this was incorporated into 2023 Chinese Expert Consensus for Management of Myocardial Injury, Myocarditis, and Post-infection Condition with Coronavirus Disease 2019 (28).

Our studies on frailty in patients with cardiovascular disease (29, 30) have been incorporated into the 2021 ACC/AHA/SCAI Guideline for Coronary Artery Revascularization (31), 2022 Japan Circulation Society’s Guideline Focused Update on Diagnosis and Treatment in Patients With Stable Coronary Artery Disease (32) and 2023 EHRA expert consensus document on the management of arrhythmias in frailty syndrome (7). They have also been incorporated into the 2019 recommendation document “Transforming the future of Aging” by SAPEA (Science Advice for Policy by European Academies), the body that informs the European Commission Group of Chief Scientific Advisors (33) as well as being cited by Fried, the pioneer who conceptualised frailty syndrome, and by another leading group in their reviews in the Lancet (34, 35).

Our quantification of risks of adverse outcomes for frail patients with advanced heart failure (36) has informed the evidence base for recommendations of clinical decision making regarding the use of mechanical circulatory support devices in the 2019 EACTS Expert Consensus on long-term mechanical circulatory support (37), by the 2021 Consensus Statement of the Frailty Heart Workgroup (American Society of Transplantation’s Thoracic and Critical Care Community of Practice) (38), 2022 Statement from a panel of multidisciplinary experts on behalf the Heart Failure Working Group of the French Society of Cardiology and on behalf French Society of Geriatrics and Gerontology (39), and 2023 International Society for Heart and Lung Transplantation/Heart Failure Society of America Guideline on Acute Mechanical Circulatory Support (40).

Our investigations of cardiac imaging (41) has been incorporated into the 2022 Intracardiac echocardiography Chinese expert consensus (42).

Our investigations of cardiovascular risk in cancer patients have bene incorporated into 2022 ESC Guidelines on cardio-oncology (43). For AF risk in cancer patients (44), this was cited by Drug-Induced Arrhythmias: A Scientific Statement From the American Heart Association (45). Risk reduction strategies for cancer prevention have been incorporated into the 2019 European Society of Gastrointestinal Endoscopy (ESGE) Guideline (46), 2020 China guideline for the screening, early detection and early treatment of colorectal cancer (47), 2021 Chinese consensus on prevention of colorectal neoplasia (48), 2021 ACG Clinical Guidelines: Colorectal Cancer Screening (49) and 2023 International Management Guidelines (50).

Our cohort study and meta-analysis on intramural haematomas have provided the evidence base for the 2021 The American Association for Thoracic Surgery expert consensus document (51) and the 2022 ACC/AHA Guideline for the Diagnosis and Management of Aortic Disease: A Report of the American Heart Association/American College of Cardiology Joint Committee on Clinical Practice Guidelines (52).

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