Identifying the prognostic significance of early arrhythmia recurrence during the blanking period and the optimal blanking period duration: insights from the DECAAF II study.

OBJECTIVE: Early atrial arrhythmia recurrence following atrial fibrillation (AF) ablation is common. Current guidelines promulgate a 3-month blanking period. We hypothesize that early atrial arrhythmia recurrence during the blanking period may predict longer-term ablation outcomes. METHODS AND RESULTS: A total of 688 patients with persistent AF undergoing catheter ablation were included in the DECAAF II trial database. The primary endpoint of the study was the first confirmed recurrence of atrial arrhythmia. Recurrence was also monitored during the 90-day blanking period. A total of 287 patients experienced recurrent atrial arrhythmia during the blanking period, while 401 remained in sinus rhythm. Rates of longer-term arrhythmia recurrence were substantially higher among those who developed recurrence during the blanking period compared to those who remained in sinus rhythm throughout the blanking period (68% vs. 32%, P < 0.001). The study cohort was divided into three groups according to the timing of arrhythmia recurrence during the blanking period. Of those who had recurrent arrhythmia during the first month of the blanking period (Group 1), 43.9% experienced longer-term recurrence, compared to 61.6% who recurred during the second month of the blanking period (Group 2), and 93.3% of those who had arrhythmia recurrence during the third month (Group 3, P < 0.001). The risk of recurrent arrhythmia was highest in Group 3 (HR = 10.15), followed by Group 2 (HR = 2.35) and Group 1 (HR = 1.5). Receiver operating characteristic analysis was performed to assess the relationship between the timing of arrhythmia recurrence and the primary outcome (AUC = 0.746, P < 0.001). The optimal blanking period duration was identified as 34 days. Atrial fibrillation burden determined by smartphone electrocardiogram technology over the 18 months follow-up period was significantly higher in Group 3 (29%) compared to Groups 1 (6%) and 2 (7%) and in patients who stayed in sinus rhythm during the blanking period (5%) (P < 0.0001). CONCLUSION: Early atrial arrhythmia recurrence during the blanking period, particularly during the third month, is significantly associated with later recurrence. Although a blanking period is warranted, it should be abbreviated.

Compliance Challenges in a Longitudinal COVID-19 Cohort Using Wearables for Continuous Monitoring: Observational Study.

BACKGROUND: The WEAICOR (Wearables to Investigate the Long Term Cardiovascular and Behavioral Impacts of COVID-19) study was a prospective observational study that used continuous monitoring to detect and analyze biometrics. Compliance to wearables was a major challenge when conducting the study and was crucial for the results. OBJECTIVE: The aim of this study was to evaluate patients' compliance to wearable wristbands and determinants of compliance in a prospective COVID-19 cohort. METHODS: The Biostrap (Biostrap USA LLC) wearable device was used to monitor participants' biometric data. Compliance was calculated by dividing the total number of days in which transmissions were sent by the total number of days spent in the WEAICOR study. Univariate correlation analyses were performed, with compliance and days spent in the study as dependent variables and age, BMI, sex, symptom severity, and the number of complications or comorbidities as independent variables. Multivariate linear regression was then performed, with days spent in the study as a dependent variable, to assess the power of different parameters in determining the number of days patients spent in the study. RESULTS: A total of 122 patients were included in this study. Patients were on average aged 41.32 years, and 46 (38%) were female. Age was found to correlate with compliance (r=0.23; P=.01). In addition, age (r=0.30; P=.001), BMI (r=0.19; P=.03), and the severity of symptoms (r=0.19; P=.03) were found to correlate with days spent in the WEAICOR study. Per our multivariate analysis, in which days spent in the study was a dependent variable, only increased age was a significant determinant of compliance with wearables (adjusted R2=0.1; ß=1.6; P=.01). CONCLUSIONS: Compliance is a major obstacle in remote monitoring studies, and the reasons for a lack of compliance are multifactorial. Patient factors such as age, in addition to environmental factors, can affect compliance to wearables.

Studying the Effect of Long COVID-19 Infection on Sleep Quality Using Wearable Health Devices: Observational Study.

BACKGROUND: Patients with COVID-19 have increased sleep disturbances and decreased sleep quality during and after the infection. The current published literature focuses mainly on qualitative analyses based on surveys and subjective measurements rather than quantitative data. OBJECTIVE: In this paper, we assessed the long-term effects of COVID-19 through sleep patterns from continuous signals collected via wearable wristbands. METHODS: Patients with a history of COVID-19 were compared to a control arm of individuals who never had COVID-19. Baseline demographics were collected for each subject. Linear correlations among the mean duration of each sleep phase and the mean daily biometrics were performed. The average duration for each subject's total sleep time and sleep phases per night was calculated and compared between the 2 groups. RESULTS: This study includes 122 patients with COVID-19 and 588 controls (N=710). Total sleep time was positively correlated with respiratory rate (RR) and oxygen saturation (SpO2). Increased awake sleep phase was correlated with increased heart rate, decreased RR, heart rate variability (HRV), and SpO2. Increased light sleep time was correlated with increased RR and SpO2 in the group with COVID-19. Deep sleep duration was correlated with decreased heart rate as well as increased RR and SpO2. When comparing different sleep phases, patients with long COVID-19 had decreased light sleep (244, SD 67 vs 258, SD 67; P=.003) and decreased deep sleep time (123, SD 66 vs 128, SD 58; P=.02). CONCLUSIONS: Regardless of the demographic background and symptom levels, patients with a history of COVID-19 infection demonstrated altered sleep architecture when compared to matched controls. The sleep of patients with COVID-19 was characterized by decreased total sleep and deep sleep.