Turakhia, Mintu P

AbstractIntroductionCatheter ablation of atrial fibrillation (AF) is frequently studied in randomized trials, observational and registry studies. The aim of this expert opinion is to provide guidance for clinicians and industry regarding the development of future clinical studies on catheter ablation of AF, implement lessons learned from previous studies, and promote a higher degree of consistency across studies.BackgroundStudies on catheter ablation of AF may benefit from well‐described definitions of endpoints and consistent methodology and documentation of outcomes related to efficacy, safety and cost‐effectiveness. The availably of new, innovative technologies warrants further consideration about their application and impact on study design and the choice of endpoints. Moreover, recent insights gained from AF ablation studies suggest a reconsideration of some methodological aspects.MethodsA panel of clinical experts on catheter ablation of AF and designing and conducting clinical studies developed an expert opinion on the design and endpoints for studies on catheter ablation of AF. Discussions within the expert panel with the aim to reach consensus on predefined topics were based on outcomes reported in the literature and experiences from recent clinical trials.ResultsA comprehensive set of recommendations is presented. Key elements include the documentation of clinical AF, medication during the study, repeated ablations and their effect on endpoint assessments, postablation blanking and the choice of rhythm‐related and other endpoints.ConclusionThis expert opinion provides guidance and promotes consistency regarding design of AF catheter ablation studies and identified aspects requiring further research to optimize study design and methodology.CONDENSED ABSTRACTRecent insights from studies on catheter ablation of atrial fibrillation (AF) and the availability of new innovative technologies warrant reconsideration of methodological aspects related to study design and the choice and assessment of endpoints. This expert opinion, developed by clinical experts on catheter ablation of AF provides a comprehensive set of recommendations related to these methodological aspects. The aim of this expert opinion is to provide guidance for clinicians and industry regarding the development of clinical studies, implement lessons learned from previous studies, and promote a higher degree of consistency across studies.

Hypertension is among the most important risk factors for cardiovascular disease, chronic kidney disease, and dementia. The artificial intelligence (AI) field is advancing quickly, and there has been little discussion on how AI could be leveraged for improving the diagnosis and management of hypertension. AI technologies, including machine learning tools, could alter the way we diagnose and manage hypertension, with potential impacts for improving individual and population health. The development of successful AI tools in public health and health care systems requires diverse types of expertise with collaborative relationships between clinicians, engineers, and data scientists. Unbiased data sources, management, and analyses remain a foundational challenge. From a diagnostic standpoint, machine learning tools may improve the measurement of blood pressure and be useful in the prediction of incident hypertension. To advance the management of hypertension, machine learning tools may be useful to find personalized treatments for patients using analytics to predict response to antihypertension medications and the risk for hypertension-related complications. However, there are real-world implementation challenges to using AI tools in hypertension. Herein, we summarize key findings from a diverse group of stakeholders who participated in a workshop held by the National Heart, Lung, and Blood Institute in March 2023. Workshop participants presented information on communication gaps between clinical medicine, data science, and engineering in health care; novel approaches to estimating BP, hypertension risk, and BP control; and real-world implementation challenges and issues.

Atrial fibrillation (AF) is one of the strongest risk factors for ischemic stroke, which is a leading cause of disability and death. Given the aging population, increasing prevalence of AF risk factors, and improved survival in those with cardiovascular disease, the number of individuals affected by AF will continue increasing over time. While multiple proven stroke prevention therapies exist, important questions remain about the optimal approach to stroke prevention at the population and individual patient levels. Our report summarizes the National Heart, Lung, and Blood Institute virtual workshop focused on identifying key research opportunities related to stroke prevention in AF. The workshop reviewed major knowledge gaps and identified targeted research opportunities to advance stroke prevention in AF in the following areas: (1) improving risk stratification tools for stroke and intracranial hemorrhage; (2) addressing challenges with oral anticoagulants; and (3) delineating the optimal roles of percutaneous left atrial appendage occlusion and surgical left atrial appendage closure/excision. This report aims to promote innovative, impactful research that will lead to more personalized, effective use of stroke prevention strategies in people with AF.

Abstract Digital technology is now an integral part of medicine. Tools for detecting, screening, diagnosis, and monitoring health-related parameters have improved patient care and enabled individuals to identify issues leading to better management of their own health. Wearable technologies have integrated sensors and can measure physical activity, heart rate and rhythm, and glucose and electrolytes. For individuals at risk, wearables or other devices may be useful for early detection of atrial fibrillation or sub-clinical states of cardiovascular disease, disease management of cardiovascular diseases such as hypertension and heart failure, and lifestyle modification. Health data are available from a multitude of sources, namely clinical, laboratory and imaging data, genetic profiles, wearables, implantable devices, patient-generated measurements, and social and environmental data. Artificial intelligence is needed to efficiently extract value from this constantly increasing volume and variety of data and to help in its interpretation. Indeed, it is not the acquisition of digital information, but rather the smart handling and analysis that is challenging. There are multiple stakeholder groups involved in the development and effective implementation of digital tools. While the needs of these groups may vary, they also have many commonalities, including the following: a desire for data privacy and security; the need for understandable, trustworthy, and transparent systems; standardized processes for regulatory and reimbursement assessments; and better ways of rapidly assessing value.

Clinically recognized atrial fibrillation (AF) is associated with higher risk of complications, including ischemic stroke, cognitive decline, heart failure, myocardial infarction, and death. It is increasingly recognized that AF frequently is undetected until complications such as stroke or heart failure occur. Hence, the public and clinicians have an intense interest in detecting AF earlier. However, the most appropriate strategies to detect undiagnosed AF (sometimes referred to as subclinical AF) and the prognostic and therapeutic implications of AF detected by screening are uncertain. Our report summarizes the National Heart, Lung, and Blood Institute’s virtual workshop focused on identifying key research priorities related to AF screening. Global experts reviewed major knowledge gaps and identified critical research priorities in the following areas: (1) role of opportunistic screening; (2) AF as a risk factor, risk marker, or both; (3) relationship between AF burden detected with long-term monitoring and outcomes/treatments; (4) designs of potential randomized trials of systematic AF screening with clinically relevant outcomes; and (5) role of AF screening after ischemic stroke. Our report aims to inform and catalyze AF screening research that will advance innovative, resource-efficient, and clinically relevant studies in diverse populations to improve the diagnosis, management, and prognosis of patients with undiagnosed AF.

The field of cardiac electrophysiology has been on the cutting edge of advanced digital technologies for many years. More recently, medical device development through traditional clinical trials has been supplemented by direct to consumer products with advancement of wearables and health care apps. The rapid growth of innovation along with the mega-data generated has created challenges and opportunities. This review summarizes the regulatory landscape, applications to clinical practice, opportunities for virtual clinical trials, the use of artificial intelligence to streamline and interpret data, and integration into the electronic medical records and medical practice. Preparation of the new generation of physicians, guidance and promotion by professional societies, and advancement of research in the interpretation and application of big data and the impact of digital technologies on health outcomes will help to advance the adoption and the future of digital health care.

Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.

Background: Atrial fibrillation (AF) adversely impacts health-related quality of life (hrQoL). While some patients demonstrate improvements in hrQoL, the factors associated with large improvements in hrQoL are not well described. Methods: We assessed factors associated with a 1-year increase in the Atrial Fibrillation Effect on Quality-of-Life score of 1 SD (≥18 points; 3× clinically important difference), among outpatients in the Outcomes Registry for Better Informed Treatment of Atrial Fibrillation I registry. Results: Overall, 28% (181/636) of patients had such a hrQoL improvement. Compared with patients not showing large hrQoL improvement, they were of similar age (median 73 versus 74, P =0.3), equally likely to be female (44% versus 48%, P =0.3), but more likely to have newly diagnosed AF at baseline (18% versus 8%; P =0.0004), prior antiarrhythmic drug use (52% versus 40%, P =0.005), baseline antiarrhythmic drug use (34.8% versus 26.8%, P =0.045), and more likely to undergo AF-related procedures during follow-up (AF ablation: 6.6% versus 2.0%, P =0.003; cardioversion: 12.2% versus 5.9%, P =0.008). In multivariable analysis, a history of alcohol abuse (adjusted OR, 2.41; P =0.01) and increased baseline diastolic blood pressure (adjusted OR, 1.23 per 10-point increase and >65 mm Hg; P =0.04) were associated with large improvements in hrQoL at 1 year, whereas patients with prior stroke/transient ischemic attack, chronic obstructive pulmonary disease, and peripheral arterial disease were less likely to improve ( P <0.05 for each). Conclusions: In this national registry of patients with AF, potentially treatable AF risk factors are associated with large hrQoL improvement, whereas less reversible conditions appeared negatively associated with hrQoL improvement. Understanding which patients are most likely to have large hrQoL improvement may facilitate targeting interventions for high-value care that optimizes patient-reported outcomes in AF. Registration: URL: http://www.clinicaltrials.gov . Unique identifier: NCT01165710.

Background Patient satisfaction with therapy is an important metric of care quality and has been associated with greater medication persistence. We evaluated the association of patient satisfaction with warfarin therapy to other metrics of anticoagulation care quality and clinical outcomes among patients with atrial fibrillation ( AF ). Methods and Results Using data from the ORBIT ‐ AF (Outcomes Registry for Better Informed Treatment of Atrial Fibrillation) registry, patients were identified with AF who were taking warfarin and had completed an Anti‐Clot Treatment Scale ( ACTS ) questionnaire, a validated metric of patient‐reported burden and benefit of oral anticoagulation. Multivariate regressions were used to determine association of ACTS burden and benefit scores with time in therapeutic international normalized ratio range ( TTR ; both ≥75% and ≥60%), warfarin discontinuation, and clinical outcomes (death, stroke, major bleed, and all‐cause hospitalization). Among 1514 patients with AF on warfarin therapy (75±10 years; 42% women; CHA 2 DS 2 ‐ VAS c 3.9±1.7), those most burdened with warfarin therapy were younger and more likely to be women, have paroxysmal AF , and to be treated with antiarrhythmic drugs. After adjustment for covariates, ACTS burden scores were independent of TTR ( TTR ≥75%: odds ratio, 1.01 [95% CI , 0.99–1.03]; TTR ≥60%: odds ratio, 1.01 [95% CI , 0.98–1.05]), warfarin discontinuation (odds ratio, 0.99; 95% CI , 0.97–1.01), or clinical outcomes. ACTS benefit scores were also not associated with TTR , warfarin discontinuation, or clinical outcomes. Conclusions In a large registry of patients with AF taking warfarin, ACTS scores provided independent information beyond other traditional metrics of oral anticoagulation care quality and identified patient groups at high risk for dissatisfaction with warfarin therapy.