Given the rapidly growing burden of cardiovascular disease (CVD) in Asia, this study forecasts the CVD burden and associated risk factors in Asia from 2025 to 2050. Data from the Global Burden of Disease 2019 study was used to construct regression models predicting prevalence, mortality, and disability-adjusted life years (DALYs) attributed to CVD and risk factors in Asia in the coming decades. Between 2025 and 2050, crude cardiovascular mortality is expected to rise 91.2% despite a 23.0% decrease in the age-standardised cardiovascular mortality rate (ASMR). Ischaemic heart disease (115 deaths per 100,000 population) and stroke (63 deaths per 100,000 population) will remain leading drivers of ASMR in 2050. Central Asia will have the highest ASMR (676 deaths per 100,000 population), more than three-fold that of Asia overall (186 deaths per 100,000 population), while high-income Asia sub-regions will incur an ASMR of 22 deaths per 100,000 in 2050. High systolic blood pressure will contribute the highest ASMR throughout Asia (105 deaths per 100,000 population), except in Central Asia where high fasting plasma glucose will dominate (546 deaths per 100,000 population). This forecast forewarns an almost doubling in crude cardiovascular mortality by 2050 in Asia, with marked heterogeneity across sub-regions. Atherosclerotic diseases will continue to dominate, while high systolic blood pressure will be the leading risk factor. This was supported by the 10.13039/501100011744 NUHS Seed Fund (NUHSRO/2022/058/RO5+6/Seed-Mar/03), 10.13039/501100001349 National Medical Research Council Research Training Fellowship (MH 095:003/008-303), 10.13039/501100001352 National University of Singapore Yong Loo Lin School of Medicine's Junior Academic Fellowship Scheme, 10.13039/501100011744 NUHS Clinician Scientist Program (NCSP2.0/2024/NUHS/NCWS) and the CArdiovascular DiseasE National Collaborative 10.13039/100005721 Enterprise (CADENCE) National Clinical Translational Program (MOH-001277-01).

Background: Sex and gender differences in acute coronary syndrome (ACS) have been well studied in the western population. However, limited studies have examined the trends of these differences in a multi-ethnic Asian population.Objectives: To study the trends in sex and gender differences in ACS using the Malaysian NCVD-ACS Registry.Methods: Data from 24 hospitals involving 35,232 ACS patients (79.44% men and 20.56% women) from 1^st.Jan 2012 to 31^st.Dec 2016 were analysed. Data were collected on demographic characteristics, coronary risk factors, anthropometrics, treatments and outcomes. Analyses were done for ACS as a whole and separately for ST-segment elevation myocardial infarction (STEMI), Non-STEMI and unstable angina. These were then compared to published data from March 2006 to February 2010 which included 13,591 ACS patients (75.8% men and 24.2% women).Results: Women were older and more likely to have diabetes mellitus, hypertension, dyslipidemia, previous heart failure and renal failure than men. Women remained less likely to receive aspirin, beta-blocker, angiotensin-converting enzyme inhibitor (ACE-I) and statin. Women were less likely to undergo angiography and percutaneous coronary intervention (PCI) despite an overall increase. In the STEMI cohort, despite a marked increase in presentation with Killip class IV, women were less likely to received primary PCI or fibrinolysis and had longer median door-to-needle and door-to-balloon time compared to men, although these had improved. Women had higher unadjusted in-hospital, 30-Day and 1-year mortality rates compared to men for the STEMI and NSTEMI cohorts. After multivariate adjustments, 1-year mortality remained significantly higher for women with STEMI (adjusted OR: 1.31 (1.09–1.57), p<0.003) but were no longer significant for NSTEMI cohort.Conclusion: Women continued to have longer system delays, receive less aggressive pharmacotherapies and invasive treatments with poorer outcome. There is an urgent need for increased effort from all stakeholders if we are to narrow this gap.

Obesity has become a pandemic in the Asia-Pacific region, with an alarming rate of increase in the prevalence of obesity. The impact of obesity and its associated comorbidities goes beyond the healthcare system, posing a substantial burden not only on the individual, but also on the society and the economy. In this review, we focus on the latest situation in the Asia-Pacific region, and review the diagnostic criteria of obesity, management strategies and access to treatment in the region. National guidelines on obesity management are available in some countries, reflecting the recognition of the problem of obesity by healthcare professionals and government bodies. Although a range of anti-obesity pharmacotherapies and bariatric surgeries are available, access remains restricted and reimbursement criteria vary in different parts of the Asia-Pacific. Prevention is key, as treatment alone cannot reverse the tide of obesity, and a sustained partnership between the patient and the clinician is necessary to manage the chronic disease of obesity.

Background: Conventional risk score for predicting in-hospital mortality following Acute Coronary Syndrome (ACS) is not catered for Asian patients and requires different types of scoring algorithms for STEMI and NSTEMI patients. Objective: To derive a single algorithm using deep learning and machine learning for the prediction and identification of factors associated with in-hospital mortality in Asian patients with ACS and to compare performance to a conventional risk score. Methods: The Malaysian National Cardiovascular Disease Database (NCVD) registry, is a multi-ethnic, heterogeneous database spanning from 2006–2017. It was used for in-hospital mortality model development with 54 variables considered for patients with STEMI and Non-STEMI (NSTEMI). Mortality prediction was analyzed using feature selection methods with machine learning algorithms. Deep learning algorithm using features selected from machine learning was compared to Thrombolysis in Myocardial Infarction (TIMI) score. Results: A total of 68528 patients were included in the analysis. Deep learning models constructed using all features and selected features from machine learning resulted in higher performance than machine learning and TIMI risk score (p < 0.0001 for all). The best model in this study is the combination of features selected from the SVM algorithm with a deep learning classifier. The DL (SVM selected var) algorithm demonstrated the highest predictive performance with the least number of predictors (14 predictors) for in-hospital prediction of STEMI patients (AUC = 0.96, 95% CI: 0.95–0.96). In NSTEMI in-hospital prediction, DL (RF selected var) (AUC = 0.96, 95% CI: 0.95–0.96, reported slightly higher AUC compared to DL (SVM selected var) (AUC = 0.95, 95% CI: 0.94–0.95). There was no significant difference between DL (SVM selected var) algorithm and DL (RF selected var) algorithm (p = 0.5). When compared to the DL (SVM selected var) model, the TIMI score underestimates patients’ risk of mortality. TIMI risk score correctly identified 13.08% of the high-risk patient’s non-survival vs 24.7% for the DL model and 4.65% vs 19.7% of the high-risk patient’s non-survival for NSTEMI. Age, heart rate, Killip class, cardiac catheterization, oral hypoglycemia use and antiarrhythmic agent were found to be common predictors of in-hospital mortality across all ML feature selection models in this study. The final algorithm was converted into an online tool with a database for continuous data archiving for prospective validation. Conclusions: ACS patients were better classified using a combination of machine learning and deep learning in a multi-ethnic Asian population when compared to TIMI scoring. Machine learning enables the identification of distinct factors in individual Asian populations to improve mortality prediction. Continuous testing and validation will allow for better risk stratification in the future, potentially altering management and outcomes.

Routine coagulation tests do not enable rapid, accurate determination of direct oral anticoagulant (DOAC) therapy. The ecarin clotting assay (ECA), performed on the ClotPro viscoelastic testing device, may enable sensitive and specific detection of dabigatran. We assessed the association between trough plasma dabigatran concentration and clotting time (CT) in the ClotPro ECA, in patients with non-valvular atrial fibrillation (NVAF). Each patient provided a single venous blood sample, ∼1 hour before dabigatran dosing. The study included 118 patients, of whom 64 were receiving dabigatran 110 mg twice daily and 54 were receiving 150 mg twice daily. ECA CT was moderately correlated with trough plasma dabigatran concentration (r = 0.80, p < 0.001). Slight trends toward increased plasma dabigatran concentration and prolonged ECA CT were apparent with 150 mg versus the 110 mg dose (differences not statistically significant). Individuals with creatinine clearance below 50 mL/minute had significantly higher plasma dabigatran concentrations and significantly prolonged ECA CT versus those with creatinine clearance ≥50 mL/minute. In conclusion, this preliminary study has demonstrated that CT in the ClotPro ECA reflects the plasma concentration of dabigatran in patients with NVAF. The ECA could potentially be used to assess the impact of dabigatran on a patient’s coagulation status.