Effects of Time-Restricted Eating on Cardiometabolic and Cardiovascular Health: Study Protocol (TRES)

This study aims to assess the safety, feasibility, and effectiveness of 10-hr Time-Restricted Eating (TRE) compared to ad libitum eating on anthropometric measurements, cardiometabolic and cardiovascular health in patients with Acute Coronary Syndrome (ACS). The Time-Restricted Eating Study (TRES) is a single-centre, pragmatic, prospective, randomised controlled trial that will include 48 patients with ACS. Participants will be randomised in a 1:1 ratio to the intervention group where eating duration is restricted to 10 hours per day or control group with no limitation of eating duration imposed. Testing is scheduled at baseline and after four weeks of intervention. The primary outcome is change in body weight after four weeks of intervention. Secondary outcomes include changes in body composition, glycaemic and lipid profiles, inflammatory markers, oxidative stress, endothelial function, arterial stiffness, blood pressure, heart rate, safety, and feasibility of TRE on patients with ACS. The study was approved by the UiTM Research Ethics Committee. Findings will be disseminated through manuscripts, reports, and presentations. Findings on the feasibility and effectiveness of TRE in patients with ACS may broaden the body of evidence for implementing TRE as a dietary intervention to prevent secondary cardiovascular diseases

A case report of heterozygous familial hypercholesterolaemia with LDLR gene mutation complicated by premature coronary artery disease detected in primary care

BackgroundFamilial Hypercholesterolemia (FH) is an autosomal dominant genetic condition predominantly caused by the low-density lipoprotein receptor (LDLR) gene mutation.Case SummaryThis is the case of a 54-year-old Malay woman with genetically confirmed FH complicated by premature coronary artery disease (PCAD). She was clinically diagnosed in primary care at 52 years old, fulfilling the Simon Broome Criteria (possible FH), Dutch Lipid Clinic Criteria (score of 8: probable FH) and Familial Hypercholesterolemia Case Ascertainment Tool (FAMCAT relative risk score of 9.51). Subsequently, she was confirmed to have a heterozygous LDLR c.190+4A>T intron 2 pathogenic variant at the age of 53 years. She was known to have hypercholesterolemia and was treated with statin since the age of 25. However, the lipid-lowering agent was not intensified to achieve the recommended treatment target. The delayed FH diagnosis has caused this patient to have PCAD and percutaneous coronary intervention (PCI) at the age of 29 years and a second PCI at the age of 49 years. She also has a very strong family history of hypercholesterolemia and PCAD, where seven out of eight of her siblings were affected. Despite this, FH was not diagnosed early and cascade screening of family members was not conducted, resulting in a missed opportunity to prevent PCAD.DiscussionFH can be clinically diagnosed in primary care to identify those who may require genetic testing. Multidisciplinary care focuses on improving identification, cascade screening and management of FH is vital to improving prognosis and ultimately preventing PCAD

Cocaine-Associated Myocardial Infarction: Should They All Be Stented?

Cocaine use is a known cause of chest pain and acute myocardial infarction and frequently leads to cardiac catheterization procedure. The treatment of cocaine-related acute coronary syndromes presents unique challenges because a variety of mechanisms including atherosclerotic plaque rupture, platelet activation, and coronary vasospasm may contribute to the pathogenesis. Our case highlights important considerations taken in dealing with this acute scenari

Vulnerable plaque: From bench to bedside; local pacification versus systemic therapy

Critical coronary stenoses accounts for a small proportion of acute coronary syndromes and sudden death. The majority are caused by coronary thromboses that arise from a nonangiographically obstructive atheroma. Recent developments in noninvasive imaging of so-called vulnerable plaques created opportunities to direct treatment to prevent morbidity and mortality associated with these high-risk lesions. This review covers therapy employed in the past, present, and potentially in the future as the natural history of plaque assessment unfolds

Predicting 30-day mortality after an acute coronary syndrome (ACS) using machine learning methods for feature selection, classification and visualization

Hybrid combinations of feature selection, classification and visualisation using machine learning (ML) methods have the potential for enhanced understanding and 30-day mortality prediction of patients with cardiovascular disease using population-specific data. Identifying a feature selection method with a classifier algorithm that produces high performance in mortality studies is essential and has not been reported before. Feature selection methods such as Boruta, Random Forest (RF), Elastic Net (EN), Recursive Feature Elimination (RFE), learning vector quantization (LVQ), Genetic Algorithm (GA), Cluster Dendrogram (CD), Support Vector Machine (SVM) and Logistic Regression (LR) were combined with RF, SVM, LR, and EN classifiers for 30-day mortality prediction. ML models were constructed using 302 patients and 54 input variables from the Malaysian National Cardiovascular Disease Database. Validation of the best ML model was performed against Thrombolysis in Myocardial Infarction (TIMI) using an additional dataset of 102 patients. The Self-Organising Feature Map (SOM) was used to visualise mortality-related factors post-ACS. The performance of ML models using the area under the curve (AUC) ranged from 0.48 to 0.80. The best-performing model (AUC = 0.80) was a hybrid combination of the RF variable importance method, the sequential backward selection and the RF classifier using five predictors (age, triglyceride, creatinine, troponin, and total cholesterol). Comparison with TIMI using an additional dataset resulted in the best ML model outperforming the TIMI score (AUC = 0.75 vs. AUC = 0.60). The findings of this study will provide a basis for developing an online ML-based population-specific risk scoring calculator

Data analytics approach for short- and long-term mortality prediction following acute non-ST-elevation myocardial infarction (NSTEMI) and Unstable Angina (UA) in Asians.

BackgroundTraditional 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.ObjectiveTo 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.MethodsWe 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.ResultsUsing 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 ConclusionsIn 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

Pattern and predictors of outcomes for infective endocarditis in North Kuala Lumpur

Context: Infective endocarditis (IE) still carries significant mortality and morbidity ever since 1835. Despite improvement in medical technologies, mortality outcome remains unchanged. We sought to analyze the pattern of presentation, treatment, and outcomes predictors for patient presenting to our hospital. This study will explore some of the factors that could be associated with the outcome of a patient diagnosed with IE for a better guidance in management. Subjects and Methods: This is a retrospective dual center cohort study from North Kuala Lumpur from January 2012 to December 2013. Fifty patients with definite IE based on modified Duke's criteria were recruited into the study. Clinical presentation, risk factors, biochemical markers, echocardiography, and outcome were obtained through chart review, clinic data, and telephone call. Simple logistic regression was utilized for inferential statistic. Results: A total of 50 patients, 37 (74%) males and 13 (26%) females were included in the study. The mean age was 42 ± 16.4. Most patients (80.39%) were diagnosed within the 1st week of admission. Staphylococcus aureus was the most common pathogen (38%) and the mitral valve was predominantly affected (68%). Complication was common and in-hospital mortality remains high (28%). Nearly 20% of the patients who had surgical intervention survived and discharged alive. Presence of complications predicts poor outcome (odds ratio [OR]: 5.5 P = 0.02), whereas surgical intervention predicts good outcome (OR: 1.56 P = 0.027). Conclusions: Mortality remains relatively high in patient with IE. Those who presented with complications are at 5.5-fold risk of mortality. Surgical intervention showed an association with good outcome within this cohort