Objectives: There is limited knowledge regarding the impact of lifestyle changes on cardiovascular events and mortality among individuals with prehypertension or prediabetes. Methods: This was a serial retrospective cohort study utilizing data from the Korean National Health Insurance Service Health Screening Cohort. The primary outcome considered in the study was major adverse cardiovascular events (MACE). Results: A higher risk of MACE was found in men with prehypertension whose unhealthy lifestyle deteriorated (hazard ratio [HR], 1.13; 95% CI, 1.04–1.23; P=0.004), those who gained weight (HR, 1.15;95% CI, 1.03–1.28; P=0.010), and those who began smoking (HR, 1.34; 95% CI, 1.17–1.55; P<0.001).Conversely, a reduced risk of MACE was observed in men with prehypertension who improved their unhealthy lifestyle, quit smoking, reduced alcohol consumption, or increased the frequency of physical activity. In men with prediabetes, the risk of MACE was higher in those whose unhealthy lifestyle worsened (HR, 1.23; 95% CI, 1.12–1.35; P<0.001), those who gained weight (HR, 1.19; 95% CI, 1.06–1.33; P=0.003), those who started smoking (HR, 1.41; 95% CI, 1.22–1.64; P<0.001), and those who decreased their physical activity frequency (HR, 1.21; 95% CI, 1.09–1.35; P<0.001). Conclusion Preventive lifestyle changes reduce cardiovascular events and mortality, particularly in men at risk of developing hypertension or type 2 diabetes.

Objectives: There is limited knowledge regarding the impact of lifestyle changes on cardiovascular events and mortality among individuals with prehypertension or prediabetes. Methods: This was a serial retrospective cohort study utilizing data from the Korean National Health Insurance Service Health Screening Cohort. The primary outcome considered in the study was major adverse cardiovascular events (MACE). Results: A higher risk of MACE was found in men with prehypertension whose unhealthy lifestyle deteriorated (hazard ratio [HR], 1.13; 95% CI, 1.04–1.23; P=0.004), those who gained weight (HR, 1.15;95% CI, 1.03–1.28; P=0.010), and those who began smoking (HR, 1.34; 95% CI, 1.17–1.55; P<0.001).Conversely, a reduced risk of MACE was observed in men with prehypertension who improved their unhealthy lifestyle, quit smoking, reduced alcohol consumption, or increased the frequency of physical activity. In men with prediabetes, the risk of MACE was higher in those whose unhealthy lifestyle worsened (HR, 1.23; 95% CI, 1.12–1.35; P<0.001), those who gained weight (HR, 1.19; 95% CI, 1.06–1.33; P=0.003), those who started smoking (HR, 1.41; 95% CI, 1.22–1.64; P<0.001), and those who decreased their physical activity frequency (HR, 1.21; 95% CI, 1.09–1.35; P<0.001). Conclusion Preventive lifestyle changes reduce cardiovascular events and mortality, particularly in men at risk of developing hypertension or type 2 diabetes.

Objectives: There is limited knowledge regarding the impact of lifestyle changes on cardiovascular events and mortality among individuals with prehypertension or prediabetes. Methods: This was a serial retrospective cohort study utilizing data from the Korean National Health Insurance Service Health Screening Cohort. The primary outcome considered in the study was major adverse cardiovascular events (MACE). Results: A higher risk of MACE was found in men with prehypertension whose unhealthy lifestyle deteriorated (hazard ratio [HR], 1.13; 95% CI, 1.04–1.23; P=0.004), those who gained weight (HR, 1.15;95% CI, 1.03–1.28; P=0.010), and those who began smoking (HR, 1.34; 95% CI, 1.17–1.55; P<0.001).Conversely, a reduced risk of MACE was observed in men with prehypertension who improved their unhealthy lifestyle, quit smoking, reduced alcohol consumption, or increased the frequency of physical activity. In men with prediabetes, the risk of MACE was higher in those whose unhealthy lifestyle worsened (HR, 1.23; 95% CI, 1.12–1.35; P<0.001), those who gained weight (HR, 1.19; 95% CI, 1.06–1.33; P=0.003), those who started smoking (HR, 1.41; 95% CI, 1.22–1.64; P<0.001), and those who decreased their physical activity frequency (HR, 1.21; 95% CI, 1.09–1.35; P<0.001). Conclusion Preventive lifestyle changes reduce cardiovascular events and mortality, particularly in men at risk of developing hypertension or type 2 diabetes.

Objectives: There is limited knowledge regarding the impact of lifestyle changes on cardiovascular events and mortality among individuals with prehypertension or prediabetes. Methods: This was a serial retrospective cohort study utilizing data from the Korean National Health Insurance Service Health Screening Cohort. The primary outcome considered in the study was major adverse cardiovascular events (MACE). Results: A higher risk of MACE was found in men with prehypertension whose unhealthy lifestyle deteriorated (hazard ratio [HR], 1.13; 95% CI, 1.04–1.23; P=0.004), those who gained weight (HR, 1.15;95% CI, 1.03–1.28; P=0.010), and those who began smoking (HR, 1.34; 95% CI, 1.17–1.55; P<0.001).Conversely, a reduced risk of MACE was observed in men with prehypertension who improved their unhealthy lifestyle, quit smoking, reduced alcohol consumption, or increased the frequency of physical activity. In men with prediabetes, the risk of MACE was higher in those whose unhealthy lifestyle worsened (HR, 1.23; 95% CI, 1.12–1.35; P<0.001), those who gained weight (HR, 1.19; 95% CI, 1.06–1.33; P=0.003), those who started smoking (HR, 1.41; 95% CI, 1.22–1.64; P<0.001), and those who decreased their physical activity frequency (HR, 1.21; 95% CI, 1.09–1.35; P<0.001). Conclusion Preventive lifestyle changes reduce cardiovascular events and mortality, particularly in men at risk of developing hypertension or type 2 diabetes.

Objectives: There is limited knowledge regarding the impact of lifestyle changes on cardiovascular events and mortality among individuals with prehypertension or prediabetes. Methods: This was a serial retrospective cohort study utilizing data from the Korean National Health Insurance Service Health Screening Cohort. The primary outcome considered in the study was major adverse cardiovascular events (MACE). Results: A higher risk of MACE was found in men with prehypertension whose unhealthy lifestyle deteriorated (hazard ratio [HR], 1.13; 95% CI, 1.04–1.23; P=0.004), those who gained weight (HR, 1.15;95% CI, 1.03–1.28; P=0.010), and those who began smoking (HR, 1.34; 95% CI, 1.17–1.55; P<0.001).Conversely, a reduced risk of MACE was observed in men with prehypertension who improved their unhealthy lifestyle, quit smoking, reduced alcohol consumption, or increased the frequency of physical activity. In men with prediabetes, the risk of MACE was higher in those whose unhealthy lifestyle worsened (HR, 1.23; 95% CI, 1.12–1.35; P<0.001), those who gained weight (HR, 1.19; 95% CI, 1.06–1.33; P=0.003), those who started smoking (HR, 1.41; 95% CI, 1.22–1.64; P<0.001), and those who decreased their physical activity frequency (HR, 1.21; 95% CI, 1.09–1.35; P<0.001). Conclusion Preventive lifestyle changes reduce cardiovascular events and mortality, particularly in men at risk of developing hypertension or type 2 diabetes.

Objective: Atrial fibrillation (AF) increases the risk of thromboembolic events, making oral anticoagulants (OACs) essential for high-risk patients. This fact sheet provides nationwide statistics on AF management for stroke prevention in Korea. We aimed to evaluate current anticoagulation treatment trends and strategies in Korea.MethodThe Korean national health claims database from the National Health Insurance Service was used. AF patients aged ≥ 18 years from 2013 to 2022 were included. OAC use, including warfarin and non-vitamin K antagonist OACs (NOACs), was tracked through prescription data. The rates of OAC use were calculated based on continued use, considering prescription dates and amounts. For patients with multiple encounters, the last encounter was used for analysis. Results: During the study, 20.4% of strokes were accompanied by AF, with AF diagnosed within 6 months before or after the stroke. The number of patients diagnosed with AF after a stroke increased from 4893 in 2013 to 6978 in 2022. Among newly diagnosed AF patients requiring OACs, 51% were not prescribed OACs within 6 months. OAC treatment rates for high-risk AF patients increased from 44.6% in 2013 to 77.5% in 2022, with NOAC prescriptions rising significantly after 2015. Regional variations in OAC prescription rates were observed, with lower rates in suburban/rural areas than in urban regions (76.0% vs. 79.6%, p < 0.001). Conclusions Considerable strokes could have been prevented with earlier AF detection and OAC treatment through more intensive electrocardiogram screening.

Purpose: Advanced chronic kidney disease (CKD), including end-stage renal disease (ESRD) on dialysis, increases thromboembolic risk among patients with atrial fibrillation (AF). This study examined the comparative safety and efficacy of direct-acting oral anticoagulant (DOAC) compared to warfarin or no oral anticoagulant (OAC) in AF patients with advanced CKD or ESRD on dialysis. Materials and Methods: Using data from the COmparison study of Drugs for symptom control and complication prEvention of AF (CODE-AF) registry, 260 non-valvular AF patients with advanced CKD (defined as estimated glomerular filtration rate <30 mL/min per 1.73/m2 ) or ESRD on dialysis were enrolled from June 2016 to July 2020. The study population was categorized into DOAC, warfarin, and no OAC groups; and differences in major or clinically relevant non-major (CRNM) bleeding, stroke/systemic embolism (SE), myocardial infarction/critical limb ischemia (CLI), and death were assessed. Results: During a median 24 months of follow-up, major or CRNM bleeding risk was significantly reduced in the DOAC group compared to the warfarin group [hazard ratio (HR) 0.11, 95% confidence interval (CI) 0.01 to 0.93, p=0.043]. In addition, the risk of composite adverse clinical outcomes (major or CRNM bleeding, stroke/SE, myocardial infarction/CLI, and death) was significantly reduced in the DOAC group compared to the no OAC group (HR 0.16, 95% CI 0.03 to 0.91, p=0.039). Conclusion Among AF patients with advanced CKD or ESRD on dialysis, DOAC was associated with a lower risk of major or CRNM bleeding compared to warfarin and a lower risk of composite adverse clinical outcomes compared to no OAC.ClinicalTrials.gov (NCT02786095)

Background: It has become important to identify and manage risk factors for subclinical atrial fibrillation (AF) with an increase in its detection rate. Thus, this research aimed to investigate whether alcohol consumption contrib‑ utes to the development of subclinical AF. Methods: This prospective study enrolled 467 patients without AF from a multicenter pacemaker registry. The incidence of subclinical AF (episodes of atrial rate > 220 beats per minute without symptoms) was compared between alcohol-drinking and non-drinking groups. Results: During followup (median 18 months), the incidence and risk of long-duration atrial high-rate episodes (AHRE) ≥ 24 h were increased in the alcohol group compared to the non-alcohol group [5.47 vs. 2.10 per 100 personyears, adjusted hazard ratio (HR), 2.83; 95% confidence interval (CI), 1.14–7.04; P = 0.03]. After propensity score match‑ ing, the incidence and risk of long-duration AHRE were higher in the alcohol group (6.97 vs. 1.27 per 100 personyears, adjusted HR, 7.84; 95% CI, 1.21–50.93; P = 0.03). The mean burden of long-duration subclinical AF was higher in the alcohol group than in the non-alcohol group (0.18 vs. 1.61% during follow-up, P = 0.08). Conclusion Alcohol consumption was associated with an increased risk of subclinical AF. Long-duration AHRE inci‑ dence and AHRE burden were higher in alcohol drinkers than in non-drinkers.

Background: The prognostic significance of resting heart rate and its therapeutic target in atrial fibrillation (AF) is uncertain. We sought to investigate the relationship between resting heart rate and cardiovascular outcomes in patients with non-paroxysmal AF (non-PAF). Methods: In this propensity score-weighted, multi-center prospective cohort study, 3217 patients with non-PAF were analyzed. Patients were categorized according to the baseline resting heart rate and cardiovascular outcomes were accessed for a median follow-up of 30 months. The primary outcome was a composite of cardiovascular death, heart failure hospitalization, and myocardial infarction/critical limb ischemia. Results: Freedom from primary outcome was longest among patients with resting heart rate 80–99 beats per minute (bpm) whereas shortest among those with ≤ 59 bpm (weighted log rank, p = 0.008). Compared with heart rate ≥ 100 bpm, resting heart rate 80–99 and 60–79 bpm was associated with reduced risk of primary outcome (weighted hazard ratio [WHR] 0.52, 95% confidence interval [CI] 0.32–0.84, p = 0.008 and WHR 0.58, 95% CI 0.37–0.92, p = 0.021 for heart rate 80–99 and 60–79 bpm, respectively). Using weighted restricted cubic spline curves, there was a U-shaped association between the resting heart rate and primary outcome with reduced risk of primary outcome in heart rate range of 68–99 bpm. This association was maintained regardless of atrioventricular node (AVN) blocker use or persistent/permanent AF (p for interaction 0.767 for AVN blocker use and 0.720 for AF type). Conclusion Resting heart rate was associated with cardiovascular outcomes in patients with non-PAF and those with resting heart rate between 68 and 99 bpm had lower risk of adverse cardiovascular events regardless of AVN blocker use or persistent/permanent AF.

Background: Conventional modality requires several days observation by Holter monitor to differentiate atrial fibril‑ lation (AF) between Paroxysmal atrial fibrillation (PAF) and Non-paroxysmal atrial fibrillation (Non-PAF). Rapid and practical differentiating approach is needed. Objective: To develop a machine learning model that observes 10-s of standard 12-lead electrocardiograph (ECG) for real-time classification of AF between PAF versus Non-PAF. Methods: In this multicenter, retrospective cohort study, the model training and cross-validation was performed on a dataset consisting of 741 patients enrolled from Severance Hospital, South Korea. For cross-institutional validation, the trained model was applied to an independent data set of 600 patients enrolled from Ewha University Hospital, South Korea. Lasso regression was applied to develop the model. Results: In the primary analysis, the Area Under the Receiver Operating Characteristic Curve (AUC) on the test set for the model that predicted AF subtype only using ECG was 0.72 (95% CI 0.65–0.80). In the secondary analysis, AUC only using baseline characteristics was 0.53 (95% CI 0.45–0.61), while the model that employed both baseline characteris‑ tics and ECG parameters was 0.72 (95% CI 0.65–0.80). Moreover, the model that incorporated baseline characteristics, ECG, and Echocardiographic parameters achieved an AUC of 0.76 (95% CI 0.678–0.855) on the test set. Conclusions Our machine learning model using ECG has potential for automatic differentiation of AF between PAF versus Non-PAF achieving high accuracy. The inclusion of  Echocardiographic parameters further increases model per‑ formance. Further studies are needed to clarify the next steps towards clinical translation of the proposed algorithm.