Background: Obesity is one principle risk factor increasing the risk of noncommunicable diseases including diabetes, hypertension and atherosclerosis. In Thailand, a 2014 study reported obesity (BMI ≥25 kg/m 2) in a Thai population aged ≥15 years was 37.5, 32.9 and 41.8% overall and among males and females, respectively. The study aimed to determine trends in the prevalence of obesity among adults residing in a Thai rural community between 2012 and 2018 and investigate the associations between obesity and behavioral factors. Methods: Serial cross-sectional studies were conducted in 2012 and 2018 among adults in Na-Ngam rural community. In 2012 and 2018, all 635 and 627 individuals, respectively, were interviewed using structured questionnaires related to demographics, risk behaviors, comorbidities and arthrometric measurement. Spot urine was collected by participants and obesity was defined as BMI ≥25 kg/m 2. The risk factors for obesity were analyzed in the 2018 survey. Results: A total of 1262 adults in Na-Ngam rural community were included in the study. The prevalence of obesity was 33.9% in 2012 and 44.8% in 2018 (P < 0.001). The average BMI increased from 23.9 ± 4.2 kg/m 2 in 2012 to 25.0 ± 4.52 kg/m 2 in 2018 (P < 0.001). Obesity was associated with higher age (AOR 0.99; 95%CI 0.97-0.99), smoking (AOR 0.52; 95%CI 0.28-0.94), instant coffee-mix consumption > 1 cup/week (AOR 1.44; 95%CI 1.02-2.04), higher number of chronic diseases (≥1 disease AOR 1.82; 95%CI 1.01-2.68, > 2 diseases AOR 2.15; 95%CI 1.32-3.50), and higher spot urine sodium level (AOR 1.002; 95%CI 0.99-1.01).

Apixaban can significantly prevent stroke events in patients with non-valvular atrial fibrillation (NVAF), as can be observed from the large, randomized, controlled trial conducted in the present study. However, the real-world evidence of bleeding events related to the apixaban plasma levels in Asian populations is limited. This study aimed to investigate the apixaban plasma levels and clinical outcomes among NVAF patients receiving apixaban, including determining the risk factors associated with bleeding during routine care. Seventy-one patients were included in the study. The median values were 112.79 (5–95th percentiles: 68.69–207.8) μg/L and 185.62 (5–95th percentiles: 124.06–384.34) μg/L for the apixaban trough (Ctrough) and apixaban peak plasma levels (Cpeak), respectively. Stroke and bleeding were found in 8 (11.27%) and 14 patients (19.72%), respectively. There was no statistical significance for Ctrough and Cpeak in the stroke and non-stroke groups, respectively. The median of Ctrough (139.15 μg/L) in patients with bleeding was higher than that in the non-bleeding group (108.14 μg/L), but there was no statistical significance. However, multivariate analyses showed that bleeding history (odds ratio (OR): 17.62; 95% confidence interval (CI): 3.54–176.64; and p-value = 0.002) and Ctrough (OR: 1.01; 95%: CI 1.00–1.03; and p-value = 0.038) were related to bleeding events. Almost all of the patients presented apixaban plasma levels within the expected range. Interestingly, bleeding events were associated with the troughs of the apixaban plasma levels and bleeding history.

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Objective: This study aimed to report the efficacy and safety of 1-year outcome for single-procedure radiofrequency catheter ablation (RFCA) at Phramongkutklao Hospital. Methods: Review of medical records was carried out on consecutive patients with symptomatic atrial fibrillation (AF) who had undergone first-time RFCA in Phramongkutklao Hospital between January 2009 and December 2018. The efficacy and safety of outcomes after 1 year of RFCA were collected, analyzed, and validated using descriptive data. Results: 61 patients underwent RFCA for the first time. 77.05% were male, with a mean age of 58.31 ± 10.83 years. Paroxysmal AF presented in 65.57%. 49.18% had hypertension, 9.84% had a history of ischemic stroke or transient ischemic attack, 6.56% had diabetes, 6.56% had coronary artery disease, and 4.92% had heart failure. 96.72% of RFCA procedures were performed under local anesthesia and conscious sedation. Pulmonary vein isolation was performed in all patients. Roofline, mitral isthmus line, and posterior wall isolation were created in 27.87%, 13.11%, and 3.28%, respectively. Additional complex fractionated atrial electrograms (CFAEs) were targeted in 19.67%. After 12 months, 45.45% remained in sinus rhythm, with only one patient experiencing a procedure-related complication with cardiac tamponade. Conclusion: The 1-year results of single-procedure RFCA for treating AF at our center, while not highly successful in our first decade, were comparable to other series. Notably, there was a relatively low rate of complications.

Objectives: This study compared feature selection by machine learning or expert recommendation in the performance of classification models for in-hospital mortality among patients with acute coronary syndrome (ACS) who underwent percutaneous coronary intervention (PCI).Methods: A dataset of 1,123 patients with ACS who underwent PCI was analyzed. After assigning 80% of instances to the training set through random splitting, we performed feature scaling and resampling with the synthetic minority over-sampling technique and Tomek link method. We compared two feature selection methods: recursive feature elimination with cross-validation (RFECV) and selection by interventional cardiologists. We used five simple models: support vector machine (SVM), random forest, decision tree, logistic regression, and artificial neural network. The performance metrics were accuracy, recall, and the false-negative rate, measured with 10-fold cross-validation in the training set and validated in the test set.Results: Patients’ mean age was 66.22 ± 12.88 years, and 33.63% had ST-elevation ACS. Fifteen of 34 features were selected as important with the RFECV method, while the experts chose 11 features. All models with feature selection by RFECV had higher accuracy than the models with expert-chosen features. In the training set, the random forest model had the highest accuracy (0.96 ± 0.01) and recall (0.97 ± 0.02). After validation in the test set, the SVM model displayed the highest accuracy (0.81) and a recall of 0.61.Conclusions: Models with feature selection by RFECV had higher accuracy than those with feature selection by experts in identifying patients with ACS at high risk for in-hospital mortality.