Comprehensive atrial fibrillation burden and symptom reduction post-ablation: insights from DECAAF II.

AIMS: Traditional atrial fibrillation (AF) recurrence after catheter ablation is reported as a binary outcome. However, a paradigm shift towards a more granular definition, considering arrhythmic or symptomatic burden, is emerging. We hypothesize that ablation reduces AF burden independently of conventional recurrence status in patients with persistent AF, correlating with symptom burden reduction. METHODS AND RESULTS: Ninety-eight patients with persistent AF from the DECAAF II trial with pre-ablation follow-up were included. Patients recorded daily single-lead electrocardiogram (ECG) strips, defining AF burden as the proportion of AF days among total submitted ECG days. The primary outcome was atrial arrhythmia recurrence. The AF severity scale was administered pre-ablation and at 12 months post-ablation. At follow-up, 69 patients had atrial arrhythmia recurrence and 29 remained in sinus rhythm. These patients were categorized into a recurrence (n = 69) and a no-recurrence group (n = 29). Both groups had similar baseline characteristics, but recurrence patients were older (P = 0.005), had a higher prevalence of hyperlipidaemia (P = 0.007), and had a larger left atrial (LA) volume (P = 0.01). There was a reduction in AF burden in the recurrence group when compared with their pre-ablation burden (65 vs. 15%, P < 0.0001). Utah Stage 4 fibrosis and diabetes predicted less improvement in AF burden. The symptom severity score at 12 months post-ablation was significantly reduced compared with the pre-ablation score in the recurrence group, and there was a significant correlation between the reduction in symptom severity score and the reduction in AF burden (R = 0.39, P = 0.001). CONCLUSION: Catheter ablation reduces AF burden, irrespective of arrhythmia recurrence post-procedure. There is a strong correlation between AF burden reduction and symptom improvement post-ablation. Notably, elevated LA fibrosis impedes AF burden decrease following catheter ablation.

Rationale and Design of the mTECH-Rehab Randomized Controlled Trial: Impact of a Mobile Technology Enabled Corrie Cardiac Rehabilitation Program on Functional Status and Cardiovascular Health.

BACKGROUND: Cardiac rehabilitation (CR) is an evidence-based, guideline-recommended intervention for patients recovering from a cardiac event, surgery or procedure that improves morbidity, mortality, and functional status. CR is traditionally provided in-center, which limits access and engagement, most notably among underrepresented racial and ethnic groups due to barriers including cost, scheduling, and transportation access. This study is designed to evaluate the Corrie Hybrid CR, a technology-based, multicomponent health equity-focused intervention as an alternative to traditional in-center CR among patients recovering from a cardiac event, surgery, or procedure compared with usual care alone. METHODS: The mTECH-Rehab (Impact of a Mobile Technology Enabled Corrie CR Program) trial will randomize 200 patients who either have diagnosis of myocardial infarction or who undergo coronary artery bypass grafting surgery, percutaneous coronary intervention, heart valve repair, or replacement presenting to 4 hospitals in a large academic health system in Maryland, United States, to the Corrie Hybrid CR program combined with usual care CR (intervention group) or usual care CR alone (control group) in a parallel arm, randomized controlled trial. The Corrie Hybrid CR program leverages 5 components: (1) a patient-facing mobile application that encourages behavior change, patient empowerment, and engagement with guideline-directed therapy; (2) Food and Drug Administration-approved smart devices that collect health metrics; (3) 2 upfront in-center CR sessions to facilitate personalization, self-efficacy, and evaluation for the safety of home exercise, followed by a combination of in-center and home-based sessions per participant preference; (4) a clinician dashboard to track health data; and (5) weekly virtual coaching sessions delivered over 12 weeks for education, encouragement, and risk factor modification. The primary outcome is the mean difference between the intervention versus control groups in distance walked on the 6-minute walk test (ie, functional capacity) at 12 weeks post randomization. Key secondary and exploratory outcomes include improvement in a composite cardiovascular health metric, CR engagement, quality of life, health factors (including low-density lipoprotein-cholesterol, hemoglobin A1c, weight, diet, smoking cessation, blood pressure), and psychosocial factors. Approval for the study was granted by the local institutional review board. Results of the trial will be published once data collection and analysis have been completed. CONCLUSIONS: The Corrie Hybrid CR program has the potential to improve functional status, cardiovascular health, and CR engagement and advance equity in access to cardiac rehabilitation. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT05238103.

Use of Human-Centered Design Methodology to Develop a Digital Toolkit to Optimize Heart Failure Guideline-Directed Medical Therapy.

BACKGROUND: Guideline-directed medical therapies (GDMTs) improve quality of life and health outcomes for patients with heart failure (HF). However, GDMT utilization is suboptimal among patients with HF. OBJECTIVE: The aims of this study were to engage key stakeholders in semistructured, virtual human-centered design sessions to identify challenges in GDMT optimization posthospitalization and inform the development of a digital toolkit aimed at optimizing HF GDMTs. METHODS: For the human-centered design sessions, we recruited (a) clinicians who care for patients with HF across 3 hospital systems, (b) patients with HF with reduced ejection fraction (ejection fraction ≤ 40%) discharged from the hospital within 30 days of enrollment, and (c) caregivers. All participants were 18 years or older, English speaking, with Internet access. RESULTS: A total of 10 clinicians (median age, 37 years [interquartile range, 35-41], 12 years [interquartile range, 10-14] of experience caring for patients with HF, 80% women, 50% White, 50% nurse practitioners) and three patients and one caregiver (median age 57 years [IQR: 53-60], 75% men, 50% Black, 75% married) were included. Five themes emerged from the clinician sessions on challenges to GDMT optimization (eg, barriers to patient buy-in). Six themes on challenges (eg, managing medications), 4 themes on motivators (eg, regaining independence), and 3 themes on facilitators (eg, social support) to HF management arose from the patient and caregiver sessions. CONCLUSIONS: The clinician, patient, and caregiver insights identified through human-centered design will inform a digital toolkit aimed at optimizing HF GDMTs, including a patient-facing smartphone application and clinician dashboard. This digital toolkit will be evaluated in a multicenter, clinical trial.

The Virtual Inclusive Digital Health Intervention Design to Promote Health Equity (iDesign) Framework for Atrial Fibrillation: Co-design and Development Study.

BACKGROUND: Smartphone ownership and mobile app use are steadily increasing in individuals of diverse racial and ethnic backgrounds living in the United States. Growing adoption of technology creates a perfect opportunity for digital health interventions to increase access to health care. To successfully implement digital health interventions and engage users, intervention development should be guided by user input, which is best achieved by the process of co-design. Digital health interventions co-designed with the active engagement of users have the potential to increase the uptake of guideline recommendations, which can reduce morbidity and mortality and advance health equity. OBJECTIVE: We aimed to co-design a digital health intervention for patients with atrial fibrillation, the most common cardiac arrhythmia, with patient, caregiver, and clinician feedback and to describe our approach to human-centered design for building digital health interventions. METHODS: We conducted virtual meetings with patients with atrial fibrillation (n=8), their caregivers, and clinicians (n=8). We used the following 7 steps in our co-design process: step 1, a virtual meeting focused on defining challenges and empathizing with problems that are faced in daily life by individuals with atrial fibrillation and clinicians; step 2, a virtual meeting focused on ideation and brainstorming the top challenges identified during the first meeting; step 3, individualized onboarding of patients with an existing minimally viable version of the atrial fibrillation app; step 4, virtual prototyping of the top 3 ideas generated during ideation; step 5, further ranking by the study investigators and engineers of the ideas that were generated during ideation but were not chosen as top-3 solutions to be prototyped in step 4; step 6, ongoing engineering work to incorporate top-priority features in the app; and step 7, obtaining further feedback from patients and testing the atrial fibrillation digital health intervention in a pilot clinical study. RESULTS: The top challenges identified by patients and caregivers included addressing risk factor modification, medication adherence, and guidance during atrial fibrillation episodes. Challenges identified by clinicians were complementary and included patient education, addressing modifiable atrial fibrillation risk factors, and remote atrial fibrillation episode management. Patients brainstormed more than 30 ideas to address the top challenges, and the clinicians generated more than 20 ideas. Ranking of the ideas informed several novel or modified features aligned with the Theory of Health Behavior Change, features that were geared toward risk factor modification; patient education; rhythm, symptom, and trigger correlation for remote atrial fibrillation management; and social support. CONCLUSIONS: We co-designed an atrial fibrillation digital health intervention in partnership with patients, caregivers, and clinicians by virtually engaging in collaborative creation through the design process. We summarize our experience and describe a flexible approach to human-centered design for digital health intervention development that can guide innovative clinical investigators.

Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology.

Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.

C-reactive Protein Elevation Is Associated With QTc Interval Prolongation in Patients Hospitalized With COVID-19.

Background: The relationship between inflammation and corrected QT (QTc) interval prolongation is currently not well defined in patients with COVID-19. Objective: This study aimed to assess the effect of marked interval changes in the inflammatory marker C-reactive protein (CRP) on QTc interval in patients hospitalized with COVID-19. Methods: In this retrospective cohort study of hospitalized adult patients admitted with COVID-19 infection, we identified 85 patients who had markedly elevated CRP levels and serial measurements of an ECG and CRP during the same admission. We compared mean QTc interval duration, and other clinical and ECG characteristics between times when CRP values were high and low. We performed mixed-effects linear regression analysis to identify associations between CRP levels and QTc interval in univariable and adjusted models. Results: Mean age was 58 ± 16 years, of which 39% were women, 41% were Black, and 25% were White. On average, the QTc interval calculated via the Bazett formula was 15 ms higher when the CRP values were "high" vs. "low" [447 ms (IQR 427-472 ms) and 432 ms (IQR 412-452 ms), respectively]. A 100 mg/L increase in CRP was associated with a 1.5 ms increase in QTc interval [ß coefficient 0.15, 95% CI (0.06-0.24). In a fully adjusted model for sociodemographic, ECG, and clinical factors, the association remained significant (ß coefficient 0.14, 95% CI 0.05-0.23). Conclusion: An interval QTc interval prolongation is observed with a marked elevation in CRP levels in patients with COVID-19.

Secular Trends in Risk Profiles Among Adults With Cardiovascular Disease in the United States.

BACKGROUND: Documenting trends in risk factors among individuals with cardiovascular disease (CVD) may inform policy and secondary prevention initiatives. OBJECTIVES: This study aimed to examine 20-year trends in risk profiles among U.S. adults with CVD and any racial/ethnic disparities. METHODS: In this serial cross-sectional analysis of 6,335 adults with self-reported CVD participating in the National Health and Nutrition Examination Survey from 1999 through 2018, we calculated age- and sex-adjusted proportions with ideal risk factor attainment. RESULTS: The proportions with ideal hemoglobin A1c (<7% if diabetes or <5.7% if not) and body mass index (<25 kg/m2) worsened from 58.7% (95% CI: 55.2%-62.1%) to 52.4% (95% CI: 48.2%-56.6%) and 23.9% (95% CI: 21.5%-26.4%) to 18.2% (95% CI: 15.6%-21.2%) from 1999-2002 to 2015-2018, respectively. After initial improvement, the proportion with blood pressure <130/80 mm Hg declined from 52.1% (95% CI: 48.9%-55.4%) in 2007-2010 to 48.6% (95% CI: 44.2%-52.7%) in 2015-2018. The proportion with non-high-density lipoprotein cholesterol levels <100 mg/dL increased from 7.3% (95% CI: 5.6%-9.5%) in 1999-2002 to 30.3% (95% CI: 25.7%-35.5%) in 2015-2018. The proportions with ideal smoking, physical activity, and diet profiles were unchanged over time, and in 2015-2018 were 77.8% (95% CI: 73.6%-81.4%), 22.4% (95% CI: 19.3%-25.9%), and 1.3% (95% CI: 0.7%-2.6%). Worsening trends were observed in Hispanic adults for cholesterol, and in Black adults for smoking (both P < 0.05 for nonlinear and linear trends). Persistently lower ideal risk factor attainment was observed for blood pressure in Black adults and for hemoglobin A1c levels in Asian adults compared with White adults (all P < 0.05 for differences). CONCLUSIONS: Trends in cardiovascular risk factor profiles in U.S. adults with CVD were suboptimal from 1999 through 2018, with persistent racial/ethnic disparities.

Starting a Research Career in Cardiology: Advice for Fellows in Training and Early-Career Cardiologists.

Launching an academic career in cardiology can be challenging. Mentorship has long been considered a core component in the academic career advancement of trainees across different disciplines and career stages, including cardiovascular disease. But simply having a mentor may not be sufficient to embark on a successful academic journey in cardiology. In this paper, we share advice on starting a research career in cardiology from both the mentee and mentor viewpoints. These perspectives reflect academic career guidance models developed at the Johns Hopkins Center for Mobile Technologies to Achieve Equity in Cardiovascular Health, which is funded by an American Heart Association Strategic Focused Network grant, to emphasize training. Core principles include encouraging mentees to develop a unique professional identity cultivated by a diverse, collaborative, and effective mentorship and sponsorship team.

Application of the very high risk criterion and evaluation of cholesterol guideline adherence in acute myocardial infarction patients at an urban academic medical center.

Objective: The 2018 AHA/ACC cholesterol guidelines recommend considering non-statin agents among very high-risk (VHR) patients with LDL-C ≥ 70 mg/dL after maximizing statin therapy. We aimed to evaluate the prevalence of VHR status in acute myocardial infarction (AMI) patients at hospital discharge and the adherence to guideline-directed cholesterol therapy (GDCT) within one-year follow-up post-AMI. Methods: We performed a retrospective analysis of patients who suffered a type 1 AMI between October 2015 and March 2019, and then were followed at our institution for 1 year after hospital discharge. We calculated the percentage of patients at VHR and among those with follow up lipid panels, we determined the proportion able to achieve GDCT. Results: The mean age of the 331 AMI patients was 61.0 (SD 11.9) years and 33.6% were women. Overall, 268 (81.0%) patients were categorized as having VHR at discharge. Among patients at VHR, a lipid panel was rechecked in 153 individuals (57.1%) within 1 year of discharge, with the median time to lipid recheck being 22.4 weeks (interquartile range: 10.9-40.7 weeks). Among those with a lipid panel re-check, 100 (65.4%) of patients achieved GDCT. Conclusions: Approximately 4 out of 5 AMI patients were considered VHR per the 2018 AHA/ACC guidelines, only about half had follow up lipid panels in the year following AMI, and about two-thirds of those with follow up lipid panels achieved GDCT.

Acquired Long QT and Ventricular Arrhythmias in the Setting of Acute Inflammation: A Case Series.

We report a case series of 4 patients with transient marked QTc prolongation and ventricular arrhythmias in the setting of inflammation with very high ferritin levels. Three patients were positive for coronavirus disease-2019. In the setting of an acute rise in inflammatory markers, electrocardiography screening for QTc prolongation is warranted. (Level of Difficulty: Beginner.).

Mobile health in preventive cardiology: current status and future perspective.

PURPOSE OF REVIEW: Despite cutting edge acute interventions and growing preventive strategies supported by robust clinical trials, cardiovascular disease (CVD) has stubbornly persisted as a leading cause of death in the United States and globally. The American Heart Association recognizes mobile health technologies (mHealth) as an emerging strategy in the mitigation of CVD risk factors, with significant potential for improving population health. The purpose of this review is to highlight and summarize the latest available literature on mHealth applications and provide perspective on future directions and barriers to implementation. RECENT FINDINGS: While available randomized controlled trials and systematic reviews tend to support efficacy of mHealth, published literature includes heterogenous approaches to similar problems with inconsistent results. Some of the strongest recent evidence has been focused on the use of wearables in arrhythmia detection. Systematic reviews of mHealth approaches demonstrate benefit when applied to risk factor modification in diabetes, cigarette smoking cessation, and physical activity/weight loss, while also showing promise in multi risk factor modification via cardiac rehabilitation. SUMMARY: Evidence supports efficacy of mHealth in a variety of applications for CVD prevention and management, but continued work is needed for further validation and scaling. Future directions will focus on platform optimization, data and sensor consolidation, and clinical workflow integration. Barriers include application heterogeneity, lack of reimbursement structures, and inequitable access to technology. Policies to promote access to technology will be critical to evidence-based mHealth technologies reaching diverse populations and advancing health equity.

Strengthening the Learning Health System in Cardiovascular Disease Prevention: Time to Leverage Big Data and Digital Solutions.

PURPOSE OF REVIEW: The past few decades have seen significant technologic innovation for the treatment and diagnosis of cardiovascular diseases. The subsequent growing complexity of modern medicine, however, is causing fundamental challenges in our healthcare system primarily in the spheres of patient involvement, data generation, and timely clinical implementation. The Institute of Medicine advocated for a learning health system (LHS) in which knowledge generation and patient care are inherently symbiotic. The purpose of this paper is to review how the advances in technology and big data have been used to further patient care and data generation and what future steps will need to occur to develop a LHS in cardiovascular disease. RECENT FINDINGS: Patient-centered care has progressed from technologic advances yielding resources like decision aids. LHS can also incorporate patient preferences by increasing and standardizing patient-reported information collection. Additionally, data generation can be optimized using big data analytics by developing large interoperable datasets from multiple sources to allow for real-time data feedback. Developing a LHS will require innovative technologic solutions with a patient-centered lens to facilitate symbiosis in data generation and clinical practice.

Heart Rate Measurements in Patients with Obstructive Sleep Apnea and Atrial Fibrillation: Prospective Pilot Study Assessing Apple Watch's Agreement With Telemetry Data.

BACKGROUND: Patients with obstructive sleep apnea (OSA) are at a higher risk for atrial fibrillation (AF). Consumer wearable heart rate (HR) sensors may be a means for passive HR monitoring in patients with AF. OBJECTIVE: The aim of this study was to assess the Apple Watch's agreement with telemetry in measuring HR in patients with OSA in AF. METHODS: Patients with OSA in AF were prospectively recruited prior to cardioversion/ablation procedures. HR was sampled every 10 seconds for 60 seconds using telemetry and an Apple Watch concomitantly. Agreement of Apple Watch with telemetry, which is the current gold-standard device for measuring HR, was assessed using mixed effects limits agreement and Lin's concordance correlation coefficient. RESULTS: A total of 20 patients (mean 66 [SD 6.5] years, 85% [n=17] male) participated in this study, yielding 134 HR observations per device. Modified Bland-Altman plot revealed that the variability of the paired difference of the Apple Watch compared with telemetry increased as the magnitude of HR measurements increased. The Apple Watch produced regression-based 95% limits of agreement of 27.8 - 0.3 × average HR - 15.0 to 27.8 - 0.3 × average HR + 15.0 beats per minute (bpm) with a mean bias of 27.8 - 0.33 × average HR bpm. Lin's concordance correlation coefficient was 0.88 (95% CI 0.85-0.91), suggesting acceptable agreement between the Apple Watch and telemetry. CONCLUSIONS: In patients with OSA in AF, the Apple Watch provided acceptable agreement with HR measurements by telemetry. Further studies with larger sample populations and wider range of HR are needed to confirm these findings.

Detecting heart failure using wearables: a pilot study.

OBJECTIVE: Heart failure (HF) can be difficult to diagnose by physical examination alone. We examined whether wristband technologies may facilitate more accurate bedside testing. APPROACH: We studied on a cohort of 97 monitored in-patients and performed a cross-sectional analysis to predict HF with data from the wearable and other clinically available data. We recorded photoplethysmography (PPG) and accelerometry data using the wearable at 128 samples per second for 5 min. HF diagnosis was ascertained via chart review. We extracted four features of beat-to-beat variability and signal quality, and used them as inputs to a machine learning classification algorithm. MAIN RESULTS: The median [interquartile] age was 60 [51 68] years, 65% were men, and 54% had heart failure; in addition, 30% had acutely decompensated HF. The best 10-fold cross-validated testing performance for the diagnosis of HF was achieved using a support vector machine. The waveform-based features alone achieved a pooled test area under the curve (AUC) of 0.80; when a high-sensitivity cut-point (90%) was chosen, the specificity was 50%. When adding demographics, medical history, and vital signs, the AUC improved to 0.87, and specificity improved to 72% (90% sensitivity). SIGNIFICANCE: In a cohort of monitored in-patients, we were able to build an HF classifier from data gathered on a wristband wearable. To our knowledge, this is the first study to demonstrate an algorithm using wristband technology to classify HF patients. This supports the use of such a device as an adjunct tool in bedside diagnostic evaluation and risk stratification.

How useful is the smartwatch ECG?

Apple launched a novel feature of the Apple Watch (Apple Inc.) series 4 that enables consumers to record a rhythm strip and assist with self-diagnosis of atrial fibrillation (AF). The watch is paired with an app that provides automatic classification of the rhythm. Ability of the algorithm to identify AF has received Food and Drug Administration clearance. Given increasing use of direct-to-consumer devices, important questions regarding the utilization of such devices and their features in clinical practice arise. It is unclear how the data obtained from these devices can be optimally incorporated in patient care and what it means for patients. Safety and security of using wearables are also of concern. Furthermore, whether data generated from the Electrocardiogram (ECG) feature will be beneficial to public health is to be determined. We discuss possible uses and challenges of Apple's (Apple Inc.) newly launched ECG feature and review an upcoming trial looking at clinical applications and outcomes using this technology. We also review the literature on the Kardia (AliveCor Inc.) mobile and smartwatch ECG technology and briefly discuss Apple Watch irregular heartbeat notifications along with the Apple Heart Study.

Addressing the Gap in Physician Preparedness To Assess Cardiovascular Risk in Women: a Comprehensive Approach to Cardiovascular Risk Assessment in Women.

PURPOSE OF REVIEW: Increased recognition of risk factors and improved knowledge of sex-specific presentations has led to improved clinical outcomes for women with cardiovascular disease (CVD) compared to two decades ago. Yet, CVD remains the leading cause of death for women in the USA. Women have unique risk factors for CVD that continue to go under-recognized by their physicians. RECENT FINDINGS: In a nationwide survey of primary care physicians (PCPs) and cardiologists, only 22% of PCPs and 42% of cardiologists reported being extremely well prepared to assess CVD risk in women. A presidential advisory from the American Heart Association (AHA) and American College of Obstetrics and Gynecologist (ACOG) recommends that cardiologists and obstetricians and gynecologists (Ob/Gyns) collaborate to promote CVD risk identification and reduction throughout a woman's lifetime. We suggest a comprehensive approach to identify unique and traditional risk factors for CVD in women, address the gap in physician knowledge, and improve cardiovascular care for women.