Does early detection of atrial fibrillation reduce the risk of thromboembolic events? Rationale and design of the Heartline study.

BACKGROUND: The impact of using direct-to-consumer wearable devices as a means to timely detect atrial fibrillation (AF) and to improve clinical outcomes is unknown. METHODS: Heartline is a pragmatic, randomized, and decentralized application-based trial of US participants aged ≥65 years. Two randomized cohorts include adults with possession of an iPhone and without a history of AF and those with a diagnosis of AF taking a direct oral anticoagulant (DOAC) for ≥30 days. Participants within each cohort are randomized (3:1) to either a core digital engagement program (CDEP) via iPhone application (Heartline application) and an Apple Watch (Apple Watch Group) or CDEP alone (iPhone-only Group). The Apple Watch Group has the watch irregular rhythm notification (IRN) feature enabled and access to the ECG application on the Apple Watch. If an IRN notification is issued for suspected AF then the study application instructs participants in the Apple Watch Group to seek medical care. All participants were "watch-naïve" at time of enrollment and have an option to either buy or loan an Apple Watch as part of this study. The primary end point is time from randomization to clinical diagnosis of AF, with confirmation by health care claims. Key secondary endpoint are claims-based incidence of a 6-component composite cardiovascular/systemic embolism/mortality event, DOAC medication use and adherence, costs/health resource utilization, and frequency of hospitalizations for bleeding. All study assessments, including patient-reported outcomes, are conducted through the study application. The target study enrollment is approximately 28,000 participants in total; at time of manuscript submission, a total of 26,485 participants have been enrolled into the study. CONCLUSION: The Heartline Study will assess if an Apple Watch with the IRN and ECG application, along with application-facilitated digital health engagement modules, improves time to AF diagnosis and cardiovascular outcomes in a real-world environment. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT04276441.

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.

A cardiovascular clinic patients' survey to assess challenges and opportunities of digital health adoption during the COVID-19 pandemic.

BACKGROUND: COVID-19 boosted healthcare digitalization and personalization in cardiology. However, understanding patient attitudes and engagement behaviors is essential to achieve successful acceptance and implementation of digital health technologies in personalized care. OBJECTIVE: This study aims to understand current and future trends in wearable device and telemedicine use in the cardiology clinic patient population, recognize patients' attitude towards digital health before and after COVID-19, and identify potential socioeconomic and racial/ethnic differences in adoption of digital health tools in a New Orleans patient population. METHODS: A cross-sectional survey was distributed to Tulane Cardiology Clinic patients between September 2020 and January 2021. Basic demographic information, medical comorbidities, device usage, and opinions on digital health tools were collected. RESULTS: Survey responses from 299 participants (average age = 54 years, 50.8% female, 24.4% African American) showed that digital health use was more prevalent in younger, healthier, and more educated individuals. Wearable use was also higher among White patients compared to African American patients. Patients cited costs and technology knowledge as primary deterrents for using wearables, despite being more inclined to use wearables for disease monitoring (41%). While wearable use did not increase after COVID-19 (36.6% pre-COVID vs 35.4% post-COVID, P = .77), telemedicine use rose significantly (10.8% pre-COVID vs 24.3% during COVID, P < .0001). Patients mostly noted telemedicine's effectiveness in overcoming difficult healthcare access barriers. Additionally, most patients are in support of wearables and telemedicine either complementing or replacing routine tests and traditional clinical visits. CONCLUSION: Demographic and socioeconomic disparities negatively impact wearable health device and telemedicine adoption within cardiovascular clinic patients. Although telemedicine use increased after COVID-19, this effect was not observed for wearables, reflecting significant economic and digital literacy challenges underlying wearable acceptance.

New-onset atrial arrhythmias associated with mortality in black and white patients hospitalized with COVID-19.

BACKGROUND: Specific details about cardiovascular complications, especially arrhythmias, related to the coronavirus disease of 2019 (COVID-19) are not well described. OBJECTIVE: We sought to evaluate the incidence and predictive factors of cardiovascular complications and new-onset arrhythmias in Black and White hospitalized COVID-19 patients and determine the impact of new-onset arrhythmia on outcomes. METHODS: We collected and analyzed baseline demographic and clinical data from COVID-19 patients hospitalized at the Tulane Medical Center in New Orleans, Louisiana, between March 1 and May 1, 2020. RESULTS: Among 310 hospitalized COVID-19 patients, the mean age was 61.4 ± 16.5 years, with 58,7% females, and 67% Black patients. Black patients were more likely to be younger, have diabetes and obesity. The incidence of cardiac complications was 20%, with 9% of patients having new-onset arrhythmia. There was no significant difference in cardiovascular outcomes between Black and White patients. A multivariate analysis determined age ≥60 years to be a predictor of new-onset arrhythmia (OR = 7.36, 95% CI [1.95;27.76], p = .003). D-dimer levels positively correlated with cardiac and new-onset arrhythmic event. New onset atrial arrhythmias predicted in-hospital mortality (OR = 2.99 95% CI [1.35;6.63], p = .007), a longer intensive care unit length of stay (mean of 6.14 days, 95% CI [2.51;9.77], p = .001) and mechanical ventilation duration(mean of 9.08 days, 95% CI [3.75;14.40], p = .001). CONCLUSION: Our results indicate that new onset atrial arrhythmias are commonly encountered in COVID-19 patients and can predict in-hospital mortality. Early elevation in D-dimer in COVID-19 patients is a significant predictor of new onset arrhythmias. Our finding suggest continuous rhythm monitoring should be adopted in this patient population during hospitalization to better risk stratify hospitalized patients and prompt earlier intervention.

High-degree atrioventricular block in COVID-19 hospitalized patients.

AIMS: The novel coronavirus SARS-CoV-2 has shown the potential to significantly affect the cardiovascular system. Cardiac arrhythmias are commonly reported complications in COVID-19 hospitalized patients. METHODS AND RESULTS: While tachyarrhythmias seem most common, we describe four cases of COVID-19 patients who developed a transient high-degree atrioventricular (AV) block during the course of their hospitalization. All four patients who developed a high-degree AV block during their hospitalization with COVID-19 did not require permanent pacing. CONCLUSION: Similarly to most AV blocks associated with infectious organisms and given its transient nature, this case series suggests that conservative management strategies should be preferred in COVID-19 patients who develop complete heart block.

Metabolic Syndrome and COVID-19 Mortality Among Adult Black Patients in New Orleans.

OBJECTIVE: Coronavirus disease 2019 (COVID-19) mortality is high in patients with hypertension, obesity, and diabetes. We examined the association between hypertension, obesity, and diabetes, individually and clustered as metabolic syndrome (MetS), and COVID-19 outcomes in patients hospitalized in New Orleans during the peak of the outbreak. RESEARCH DESIGN AND METHODS: Data were collected from 287 consecutive patients with COVID-19 hospitalized at two hospitals in New Orleans, LA from 30 March to 5 April 2020. MetS was identified per World Health Organization criteria. RESULTS: Among 287 patients (mean age 61.5 years; female, 56.8%; non-Hispanic black, 85.4%), MetS was present in 188 (66%). MetS was significantly associated with mortality (adjusted odds ratio [aOR] 3.42 [95% CI 1.52-7.69]), intensive care unit (ICU) (aOR 4.59 [CI 2.53-8.32]), invasive mechanical ventilation (IMV) (aOR 4.71 [CI 2.50-8.87]), and acute respiratory distress syndrome (ARDS) (aOR 4.70 [CI 2.25-9.82]) compared with non-MetS. Multivariable analyses of hypertension, obesity, and diabetes individually showed no association with mortality. Obesity was associated with ICU (aOR 2.18 [CI, 1.25-3.81]), ARDS (aOR 2.44 [CI 1.28-4.65]), and IMV (aOR 2.36 [CI 1.33-4.21]). Diabetes was associated with ICU (aOR 2.22 [CI 1.24-3.98]) and IMV (aOR 2.12 [CI 1.16-3.89]). Hypertension was not significantly associated with any outcome. Inflammatory biomarkers associated with MetS, CRP, and lactate dehydrogenase (LDH) were associated with mortality (CRP [aOR 3.66] [CI 1.22-10.97] and LDH [aOR 3.49] [CI 1.78-6.83]). CONCLUSIONS: In predominantly black patients hospitalized for COVID-19, the clustering of hypertension, obesity, and diabetes as MetS increased the odds of mortality compared with these comorbidities individually.