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To investigate whether specific time series patterns for blood pressure (BP), heart rate (HR), and sympathetic tone are associated with metabolic factors and the 10-year risk of atherosclerotic cardiovascular disease (ASCVD). A total of 989 patients who underwent simultaneous 24-hour ambulatory BP and Holter electrocardiogram monitoring were enrolled. The patients were categorized into sixteen groups according to their circadian patterns using the consensus clustering analysis method. Metabolic factors, including cholesterol profiles and apolipoprotein, were compared. The 10-year ASCVD risk was estimated based on the Framingham risk model. Overall, 16 significant associations were found between the clinical variables and cluster groups. Age was commonly associated with all clusters in systolic BP (SBP), diastolic BP (DBP), HR, and sympathetic tone. Metabolic indicators, including diabetes, body mass index, total cholesterol, high-density lipoprotein, and apolipoprotein, were associated with the four sympathetic tone clusters. In the crude analysis, the ASCVD risk increased incrementally from clusters 1 to 4 across SBP, DBP, HR, and sympathetic tone. After adjustment for multiple variables, however, only sympathetic tone clusters 3 and 4 showed a significantly high proportion of patients at high risk (≥7.5%) of 10-year ASCVD (odds ratio (OR) = 5.90, 95% confidential interval (CI) = 1.27-27.46, and P value = 0.024 and OR = 15.28, 95% CI = 3.59-65.11, and P value < 0.001, respectively). Time series patterns of BP, HR, and sympathetic tone can serve as an indicator of aging. Circadian variations in sympathetic tone can provide prognostic information about patient metabolic profiles and indicate future ASCVD risk., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2020 Myung Han Hyun et al.)

Purpose: Many studies have proposed predictive models for type 2 diabetes mellitus (T2DM). However, these predictive models have several limitations, such as user convenience and reproducibility. The purpose of this study was to develop a T2DM predictive model using electronic medical records (EMRs) and machine learning and to compare the performance of this model with traditional statistical methods., Materials and Methods: In this study, a total of available 8454 patients who had no history of diabetes and were treated at the cardiovascular center of Korea University Guro Hospital were enrolled. All subjects completed 5 years of follow up. The prevalence of T2DM during follow up was 4.78% (404/8454). A total of 28 variables were extracted from the EMRs. In order to verify the cross-validation test according to the prediction model, logistic regression (LR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), and K-nearest neighbor (KNN) algorithm models were generated. The LR model was considered as the existing statistical analysis method., Results: All predictive models maintained a change within the standard deviation of area under the curve (AUC) <0.01 in the analysis after a 10-fold cross-validation test. Among all predictive models, the LR learning model showed the highest prediction performance, with an AUC of 0.78. However, compared to the LR model, the LDA, QDA, and KNN models did not show a statistically significant difference., Conclusion: We successfully developed and verified a T2DM prediction system using machine learning and an EMR database, and it predicted the 5-year occurrence of T2DM similarly to with a traditional prediction model. In further study, it is necessary to apply and verify the prediction model through clinical research., Competing Interests: The authors have no potential conflicts of interest to disclose., (© Copyright: Yonsei University College of Medicine 2019.)

LA Functional Remodeling After AF Ablation Background Functional remodeling of left atrium (LA) after radiofrequency catheter ablation (RFCA) for atrial fibrillation (AF) has not been fully elucidated. This study aimed to determine the impact of RFCA on LA transport function in patients who maintained sinus rhythm (SR) after AF ablation. Methods and Results A total of 96 patients (paroxysmal AF [PAF] = 52) who maintained SR during 1 year after AF ablation were enrolled. Multislice computed tomography was performed to determine LA volume (LAV) and LA emptying fraction (LAEF) at pre-RFCA and 1-year post-RFCA. Creatine kinase-MB (CK-MB) and troponin-T levels were analyzed 1-day post-RFCA. At 1-year post-RFCA, mean LAV and LAEF decreased in overall patients. Based on LAEF change (ΔLAEF) cutoff of 5.0%, LAEF reduced in 41 patients (worsened group) and improved or showed no change in 55 patients (preserved group). Compared with preserved group, worsened group had a higher proportion of PAF, higher levels of CK-MB and troponin-T, and additional LA ablation. ΔLAEF was inversely correlated with CK-MB and troponin-T levels. Subgroup analysis showed that LAEF significantly decreased in PAF patients who underwent additional LA ablation. Multivariate analysis revealed that high baseline LAEF and additional LA ablation were independent predictors for worsened LAEF. Conclusions Although SR was maintained for 1 year after AF ablation, LAEF as well as LAV decreased. The extent of LAEF deterioration was significantly associated with the amount of iatrogenic myocardial damage. Our data indicate that extensive atrial ablation may lead to LA functional deterioration, especially in patients with PAF. [ABSTRACT FROM AUTHOR]