Background: Conventional risk score for predicting short and long-term mortality following an ST-segment elevation myocardial infarction (STEMI) is often not population specific. Objective: Apply machine learning for the prediction and identification of factors associated with short and long-term mortality in Asian STEMI patients and compare with a conventional risk score. Methods: The National Cardiovascular Disease Database for Malaysia registry, of a multi-ethnic, heterogeneous Asian population was used for in-hospital (6299 patients), 30-days (3130 patients), and 1-year (2939 patients) model development. 50 variables were considered. Mortality prediction was analysed using feature selection methods with machine learning algorithms and compared to Thrombolysis in Myocardial Infarction (TIMI) score. Invasive management of varying degrees was selected as important variables that improved mortality prediction. Results: Model performance using a complete and reduced variable produced an area under the receiver operating characteristic curve (AUC) from 0.73 to 0.90. The best machine learning model for in-hospital, 30 days, and 1-year outperformed TIMI risk score (AUC = 0.88, 95% CI: 0.846–0.910; vs AUC = 0.81, 95% CI:0.772–0.845, AUC = 0.90, 95% CI: 0.870–0.935; vs AUC = 0.80, 95% CI: 0.746–0.838, AUC = 0.84, 95% CI: 0.798–0.872; vs AUC = 0.76, 95% CI: 0.715–0.802, p < 0.0001 for all). TIMI score underestimates patients’ risk of mortality. 90% of non-survival patients are classified as high risk (>50%) by machine learning algorithm compared to 10–30% non-survival patients by TIMI. Common predictors identified for short- and long-term mortality were age, heart rate, Killip class, fasting blood glucose, prior primary PCI or pharmaco-invasive therapy and diuretics. The final algorithm was converted into an online tool with a database for continuous data archiving for algorithm validation. Conclusions: In a multi-ethnic population, patients with STEMI were better classified using the machine learning method compared to TIMI scoring. Machine learning allows for the identification of distinct factors in individual Asian populations for better mortality prediction. Ongoing continuous testing and validation will allow for better risk stratification and potentially alter management and outcomes in the future.

Background: Traditional risk assessment tools often lack accuracy when predicting the short- and long-term mortality following a non-ST-segment elevation myocardial infarction (NSTEMI) or Unstable Angina (UA) in specific population. Objective: To employ machine learning (ML) and stacked ensemble learning (EL) methods in predicting short- and long-term mortality in Asian patients diagnosed with NSTEMI/UA and to identify the associated features, subsequently evaluating these findings against established risk scores. Methods: We analyzed data from the National Cardiovascular Disease Database for Malaysia (2006–2019), representing a diverse NSTEMI/UA Asian cohort. Algorithm development utilized in-hospital records of 9,518 patients, 30-day data from 7,133 patients, and 1-year data from 7,031 patients. This study utilized 39 features, including demographic, cardiovascular risk, medication, and clinical features. In the development of the stacked EL model, four base learner algorithms were employed: eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Naive Bayes (NB), and Random Forest (RF), with the Generalized Linear Model (GLM) serving as the meta learner. Significant features were chosen and ranked using ML feature importance with backward elimination. The predictive performance of the algorithms was assessed using the area under the curve (AUC) as a metric. Validation of the algorithms was conducted against the TIMI for NSTEMI/UA using a separate validation dataset, and the net reclassification index (NRI) was subsequently determined. Results: Using both complete and reduced features, the algorithm performance achieved an AUC ranging from 0.73 to 0.89. The top-performing ML algorithm consistently surpassed the TIMI risk score for in-hospital, 30-day, and 1-year predictions (with AUC values of 0.88, 0.88, and 0.81, respectively, all p < 0.001), while the TIMI scores registered significantly lower at 0.55, 0.54, and 0.61. This suggests the TIMI score tends to underestimate patient mortality risk. The net reclassification index (NRI) of the best ML algorithm for NSTEMI/UA patients across these periods yielded an NRI between 40–60% (p < 0.001) relative to the TIMI NSTEMI/UA risk score. Key features identified for both short- and long-term mortality included age, Killip class, heart rate, and Low-Molecular-Weight Heparin (LMWH) administration. Conclusions: In a broad multi-ethnic population, ML approaches outperformed conventional TIMI scoring in classifying patients with NSTEMI and UA. ML allows for the precise identification of unique characteristics within individual Asian populations, improving the accuracy of mortality predictions. Continuous development, testing, and validation of these ML algorithms holds the promise of enhanced risk stratification, thereby revolutionizing future management strategies and patient outcomes.

Asia is home to 60% of the world's population, including the world's 2 most populous countries, India (1.1B) and China (1.2B). With cardiovascular disease burden and mortality increasing, the role of preventive cardiology is increasingly important. The challenges in addressing the cardiovascular disease burden in Asia include unique cardiometabolic features of the different populations, heterogeneity of risk factors among Asian countries, differing levels of health literacy and socioeconomic status, suboptimal infrastructure to support preventive care especially in the primary care sector, high out-of-pocket costs, and environmental pollution. Asia is a large continent that comprises diverse populations with varying cultures, socioeconomic status, and health literacy levels. Effective preventive cardiology may require differential health care resource allocation and financing models.

Transcatheter structural heart intervention (TSHI) has gained popularity over the past decade as a means of cardiac intervention in patients with prohibitive surgical risks. Following the exponential rise in cases and devices developed over the period, there has been increased focus on developing the role of “structural imagers” amongst cardiologists. This review, as part of a growing initiative to develop the field of interventional echocardiography, aims to highlight the role of echocardiography in myriad TSHIs available within Asia. We first discuss the various echocardiography-based imaging modalities, including 3-dimensional echocardiography, fusion imaging, and intracardiac echocardiography. We then highlight a selected list of structural interventions available in the region—a combination of established interventions alongside novel approaches—describing key anatomic and pathologic characteristics related to the relevant structural heart diseases, before delving into various aspects of echocardiography imaging for each TSHI.

There remains great heterogeneity in the availability of data and approaches in managing hypertension in Asia-Pacific. A narrative review was performed to better understand the epidemiology of hypertension and challenges in hypertension care provision regionally. The data obtained were discussed under five domains: epidemiology; hospitalisation and financial impact; medication prescription; national guidelines, societies and measures; and barriers and challenges in managing hypertension. Sixteen countries were included, with the prevalence of hypertension ranging between 10.6% and 48.3%. Rates of undiagnosed, untreated and uncontrolled hypertension were alarming throughout the region, with various factors from the five major domains contributing to this, such as urbanisation, gender and socioeconomic status. Data on care provision were generally sparse. Although various measures and policies exist in individual countries to help combat hypertension, challenges have also been identified: some are systemic, while others appear more granular and culturally influenced. Collaboration between stakeholders between countries is crucial to effectively tackle these issues.

Asia faces a rapidly rising burden of cardiovascular disease (CVD). Preventive cardiology efforts may help address the CVD epidemic. Solutions to address the CVD burden include a cardiovascular risk assessment framework, improving health screening efforts, better cardiovascular risk factor management, novel innovation strategies encompassing targeted lifestyle measures, and strengthening governmental efforts. With the region's wide socioeconomic and other disparities, contextualizing and practical adaptation of various strategies into local practices, especially in low-middle-income countries, will determine the success of CVD prevention efforts. A differential approach addressing cardiovascular risk factor screening, prevention, and management that considers the context-specific socioeconomic, governmental, and cultural aspects in diverse Asian populations may help reduce the rapidly rising CVD trajectory in Asia.