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Patients with atrial fibrillation are known to have a high risk of mortality. There is a paucity of population-based studies about the impact of atrial fibrillation on the mortality risk stratified by age, sex, and detailed causes of death. A total of 15,411 patients with atrial fibrillation from the Korean National Health Insurance Service-National Sample Cohort were enrolled, and causes of death were identified according to codes of the 10th revision of the International Classification of Diseases. From 2002 to 2013, a total of 4,479 (29%) deaths were confirmed, and the crude mortality rate for all-cause death was 63.3 per 1,000 patient-years. Patients with atrial fibrillation had a 3.7-fold increased risk of all-cause death compared with the general population. The standardized mortality ratio for all-cause death was the highest in young patients and decreased with increasing age (standardized mortality ratio 21.93, 95% confidence interval 7.60-26.26 in patients aged <20 years; standardized mortality ratio 2.77, 95% confidence interval 2.63-2.91 in patients aged ≥80 years). Women with atrial fibrillation exhibited a greater excess mortality risk than men (standardized mortality ratio 3.81, 95% confidence interval 3.65-3.98 in women; standardized mortality ratio 3.35, 95% confidence interval 3.21-3.48 in men). Cardiovascular disease was the leading cause of death (38.5%), and cerebral infarction was the most common specific disease. Patients with atrial fibrillation had an about 5 times increased risk of death due to cardiovascular disease compared with the general population. Patients with atrial fibrillation had a 4 times increased risk of mortality compared with the general population. However, the impact of atrial fibrillation on mortality decreased with age and in men. Cerebral infarction was the most common cause of death, and more attention should be paid to reducing the risk of stroke.

Following their introduction, the non-vitamin K antagonist oral anticoagulants (NOACs) are increasingly prescribed in Asia for stroke prevention in patients with non-valvular atrial fibrillation (AF). Few contemporary data are available on temporal trends in antithrombotic therapy use in Asian countries, in the era of NOACs. Using the National Health Insurance Service database of the entire Korean adult AF population, the use of aspirin, vitamin K antagonist, and NOACs between 2008 and 2015 were analyzed (n = 276,246 in 2015). Most of the included cohort had CHA2DS2-VASc score ≥ 2 (78.2% in 2008 and 83.2% in 2015), yet approximately 17% were prescribed no antithrombotic therapy throughout the study period. Aspirin prescription consistently decreased (from 48.2% to 31.5%) over time, while OAC prescription significantly increased from 34.7% to 50.6%. NOAC prescriptions accounted for 50% of total OAC prescription in 2015. Similar trends in antithrombotic therapy were found both in men and in women, but women were more likely to be undertreated with OAC. Female gender, presence of vascular disease and prior intracranial hemorrhage were associated with OAC underuse. Between 2008 and 2015, a greater proportion of AF patients received OAC treatment with increasing NOAC prescription trends in the recent 3 years. A substantial proportion (approx. 50%) of Korean patients with AF still remain undertreated.

R-peak detection is an essential step in analyzing electrocardiograms (ECGs). Previous deep learning models reported their performance primarily in a single database, and some models did not perform at the highest levels when applied to a database different from the testing database. To achieve high performances in cross-database validations, we developed a novel deep learning model for R-peak detection using stationary wavelet transform (SWT) and separable convolution. Three databases (i.e., the MIT-BIH Arrhythmia [MIT-BIH], the Institute of Cardiological Technics [INCART], and the QT) were used in both the training and testing models, and the MIT-BIH ST Change (MIT-BIH-ST), European ST-T, TELE and MIT-BIH Noise Stress Test (MIT-BIH-NST) databases were further used for testing. The detail coefficient of level 4 decomposition by SWT and the first derivative from filtered ECGs were used for model inputs, and the interval of 150 ms centered at marked peaks was used for labels. Separable convolution with atrous spatial pyramidal pooling was selected as the model’s architecture, and noise-augmented waveforms of 5.69 s duration (2048 size in 360 Hz) were used in training. The model performance was evaluated using cross-database validation. The F1 scores of the peak detection model were 0.9994, 0.9985, and 0.9999 in the MIT-BIH, INCART, and QT databases, respectively. When the above three databases were pooled, the F1 scores were 0.9993 for fivefold cross-validation and 0.9991 for cross-database validation. The model performance remained high for MIT-BIH-ST, European ST-T, and TELE, with F1 scores of 0.9995, 0.9988, and 0.9790, respectively. The model performance when trained by severe noise augmentation increased for the MIT-BIH-NST database (F1 scores from 0.9504 to 0.9759) and decreased for the MIT-BIH database (F1 scores from 0.9994 to 0.9991). The present SWT and separable convolution-based model for R-peak detection yields a high performance even for cross-database validations.

There is a paucity of information about cardiovascular outcomes related to exercise habit change after a new diagnosis of atrial fibrillation (AF). We investigated the association between exercise habits after a new AF diagnosis and ischemic stroke, heart failure (HF), and all-cause death. This is a nationwide population-based cohort study using data from the Korea National Health Insurance Service. A retrospective analysis was performed for 66,692 patients with newly diagnosed AF between 2010 and 2016 who underwent 2 serial health examinations within 2 years before and after their AF diagnosis. Individuals were divided into 4 categories according to performance of regular exercise, which was investigated by a self-reported questionnaire in each health examination, before and after their AF diagnosis: persistent non-exercisers (30.5%), new exercisers (17.8%), exercise dropouts (17.4%), and exercise maintainers (34.2%). The primary outcomes were incidence of ischemic stroke, HF, and all-cause death. Differences in baseline characteristics among groups were balanced considering demographics, comorbidities, medications, lifestyle behaviors, and income status. The risks of the outcomes were computed by weighted Cox proportional hazards models with inverse probability of treatment weighting (IPTW) during a mean follow-up of 3.4 ± 2.0 years. The new exerciser and exercise maintainer groups were associated with a lower risk of HF compared to the persistent non-exerciser group: the hazard ratios (HRs) (95% CIs) were 0.95 (0.90-0.99) and 0.92 (0.88-0.96), respectively (p < 0.001). Also, performing exercise any time before or after AF diagnosis was associated with a lower risk of mortality compared to persistent non-exercising: the HR (95% CI) was 0.82 (0.73-0.91) for new exercisers, 0.83 (0.74-0.93) for exercise dropouts, and 0.61 (0.55-0.67) for exercise maintainers (p < 0.001). For ischemic stroke, the estimates of HRs were 10%-14% lower in patients of the exercise groups, yet differences were statistically insignificant (p = 0.057). Energy expenditure of 1,000-1,499 MET-min/wk (regular moderate exercise 170-240 min/wk) was consistently associated with a lower risk of each outcome based on a subgroup analysis of the new exerciser group. Study limitations include recall bias introduced due to the nature of the self-reported questionnaire and restricted external generalizability to other ethnic groups. Initiating or continuing regular exercise after AF diagnosis was associated with lower risks of HF and mortality. The promotion of exercise might reduce the future risk of adverse outcomes in patients with AF.

The optimal anesthetic agent for radiofrequency catheter ablation (RFCA) of atrial fibrillation (AF) and its impact on the recovery profiles remain uncertain. We compared the recovery and hemodynamic parameters between the remimazolam-flumazenil and propofol groups during RFCA. Patients were randomized into the remimazolam-flumazenil and propofol groups. The primary outcome measure was the time to eye opening following the discontinuation of anesthetic agents. Secondary outcomes included time to extubation, time to discharge from the operating room, intraprocedural hemodynamic variables and postoperative quality outcomes. Fifty-three patients were included in the final analysis (n = 26 in the remimazolam-flumazenil and n = 27 in the propofol group). The time to eye opening was significantly shorter in the remimazolam-flumazenil group compared to the propofol group (median [interquartile range]: 174 [157–216] vs. 353 [230–483] s, P  < 0.001). The mean blood pressure and bispectral index were significantly higher in the remimazolam-flumazenil group compared to the propofol group (mean difference [95% CI], 7.2 [1.7–12.7] mmHg and 6 [3–8]; P  = 0.011 and < 0.001, respectively), which were within target ranges in both groups. Other secondary outcomes were comparable between the groups. Consequently, remimazolam emerges as a promising anesthetic agent, characterized by rapid recovery and stable hemodynamics, during RFCA of AF. Trial registration: NCT05397886.

There are few reports on head-to-head comparisons of electrocardiogram (ECG) monitoring between adhesive single-lead and Holter devices for arrhythmias other than atrial fibrillation (AF). This study aimed to compare 24 h ECG monitoring between the two devices in patients with general arrhythmia. Twenty-nine non-AF patients with a workup of pre-diagnosed arrhythmias or suspicious arrhythmic episodes were evaluated. Each participant wore both devices simultaneously, and the cardiac rhythm was monitored for 24 h. Selective ECG parameters were compared between the two devices. Two cardiologists independently compared the diagnoses of each device. The two most frequent monitoring indications were workup of premature atrial contractions (41.4%) and suspicious arrhythmia-related symptoms (37.9%). The single-lead device had a higher noise burden than the Holter device (0.04 ± 0.05% vs. 0.01 ± 0.01%, p = 0.024). The number of total QRS complexes, ventricular ectopic beats, and supraventricular ectopic beats showed an excellent degree of agreement between the two devices (intraclass correlation coefficients = 0.991, 1.000, and 0.987, respectively). In addition, the minimum/average/maximum heart rates showed an excellent degree of agreement. The two cardiologists made coherent diagnoses for all 29 participants using both monitoring methods. In conclusion, the single-lead adhesive device could be an acceptable alternative for ambulatory ECG monitoring in patients with general arrhythmia.

There is insufficient evidence for the use of single-lead electrocardiogram (ECG) monitoring with an adhesive patch-type device (APD) over an extended period compared to that of the 24-hour Holter test for atrial fibrillation (AF) detection. In this paper, we aimed to compare AF detection by the 24-hour Holter test and 72-hour single-lead ECG monitoring using an APD among patients with AF. This was a prospective, single-center cohort study. A total of 210 patients with AF with clinical indications for the Holter test at cardiology outpatient clinics were enrolled in the study. The study participants were equipped with both the Holter device and APD for the first 24 hours. Subsequently, only the APD continued ECG monitoring for an additional 48 hours. AF detection during the first 24 hours was compared between the two devices. The diagnostic benefits of extended monitoring using the APD were evaluated. A total of 200 patients (mean age 60 years; n=141, 70.5% male; and n=59, 29.5% female) completed 72-hour ECG monitoring with the APD. During the first 24 hours, both monitoring methods detected AF in the same 40/200 (20%) patients (including 20 patients each with paroxysmal and persistent AF). Compared to the 24-hour Holter test, the APD increased the AF detection rate by 1.5-fold (58/200; 29%) and 1.6-fold (64/200; 32%) with 48- and 72-hour monitoring, respectively. With the APD, the number of newly discovered patients with paroxysmal AF was 20/44 (45.5%), 18/44 (40.9%), and 6/44 (13.6%) at 24-, 48-, and 72-hour monitoring, respectively. Compared with 24-hour Holter monitoring, 72-hour monitoring with the APD increased the detection rate of paroxysmal AF by 2.2-fold (44/20). Compared to the 24-hour Holter test, AF detection could be improved with 72-hour single-lead ECG monitoring with the APD.

The association between atrial fibrillation (AF) and uterine fibroids (UF) is unclear, though UF are linked to an elevated risk of cardiovascular disease. This study aimed to investigate the AF risk in UF patients. Females aged 20–39 who received health examinations from 2009 to 2012 were included. UF were defined using the ICD-10 codes, and surgical treatment status was defined by procedural codes. The primary outcome was incident AF, evaluated using a Cox regression model, which also assessed AF risk according to surgical treatment status. Among 2,574,349 participants (UF 20,682 [0.8%], mean age 29.8 ± 4.3 years, mean follow-up duration 7.3 ± 1.1 years), 3,868 patients developed AF ( n = 61, UF; n = 3,807, control). AF incidence was higher in the UF group than the control (0.41 and 0.20 per 1,000 person-years, respectively). Multivariate Cox-regression analysis identified UF as an independent risk factor for AF (aHR 1.50, 95% CI 1.16–1.93, p = 0.002). Compared to the control group, UF group with surgery had similar AF risk (aHR 1.22, 95% CI 0.79–1.90), however, those without surgery had higher AF risk (aHR 1.69, 95% CI 1.24–2.30). UF patients were associated with increased risk of AF. Careful monitoring of arrhythmia development is warranted in women with UF.

We evaluated the association between nonalcoholic fatty liver disease (NAFLD) and incident atrial fibrillation (AF) and analyzed the impact of NAFLD on AF risk in relation to body mass index (BMI). A total of 8,048,055 subjects without significant liver disease who were available fatty liver index (FLI) values were included. Subjects were categorized into 3 groups based on FLI: < 30, 30 to < 60, and ≥ 60. During a median 8-year of follow-up, 534,442 subjects were newly diagnosed as AF (8.27 per 1000 person-years). Higher FLI was associated with an increased risk of AF (hazard ratio [HR] 1.053, 95% confidence interval [CI] 1.046–1.060 in 30 ≤ FLI < 60, and HR 1.115, 95% CI 1.106–1.125 in FLI ≥ 60). In underweight subjects (BMI < 18.5 kg/m 2 ), higher FLI raised the risk of AF (by 1.6-fold in 30 ≤ FLI < 60 and by twofold in FLI ≥ 60). In normal- and overweight subjects, higher FLI was associated with an increased risk of AF, but the HRs were attenuated. In obese subjects, higher FLI was not associated with higher risk of AF. NAFLD as assessed by FLI was independently associated with an increased risk of AF in nonobese subjects with BMI < 25 kg/m 2 . The impact of NAFLD on AF risk was accentuated in lean subjects with underweight.

Background Patients with diabetes mellitus have an increased risk of incident atrial fibrillation (AF). The effect of accumulated hypertension burden is a less well-known modifiable risk factor. We explored the relationship between accumulated hypertension burden and incident AF in these patients. Methods We evaluated data for 526,384 patients with diabetes who underwent three consecutive health examinations, between 2009 and 2012, from the Korean National Health Insurance Service. Hypertension burden was calculated by assigning points to each stage of hypertension in each health examination: 1 for stage 1 hypertension (systolic blood pressure [SBP] 130–139 mmHg; diastolic blood pressure [DBP] 80–89 mmHg); 2 for stage 2 (SBP 140–159 mmHg and DBP 90–99 mmHg); and 3 for stage 3 (SBP ≥ 160 mmHg or DBP ≥ 100 mmHg). Patients were categorized into 10 hypertensive burden groups (0–9). Groups 1–9 were then clustered into 1–3, 4–6, and 7–9. Results During a mean follow-up duration of 6.7 ± 1.7 years, AF was newly diagnosed in 18,561 (3.5%) patients. Compared to patients with hypertension burden 0, those with burden 1 to 9 showed a progressively increasing risk of incident AF: 6%, 11%, 16%, 24%, 28%, 41%, 46%, 57%, and 67% respectively. Clusters 1–3, 4–6, and 7–9 showed increased risks by 10%, 26%, and 45%, respectively, when compared to a hypertension burden of 0. Conclusions Accumulated hypertension burden was associated with an increased risk of incident AF in patients with diabetes. Strict BP control should be emphasized for these patients.