Arrhythmia & Electrophysiology Review最新文献

Research examining the utility of cardiac device data to manage patients with heart failure (HF) is rapidly evolving. COVID-19 has reignited interest in remote monitoring, with manufacturers each developing and testing new ways to detect acute HF episodes, risk stratify patients and support self-care. As standalone diagnostic tools, individual physiological metrics and algorithm-based systems have demonstrated utility in predicting future events, but the integration of remote monitoring data with existing clinical care pathways for device HF patients is not well described. This narrative review provides an overview of device-based HF diagnostics available to care providers in the UK, and describes the current state of play with regard to how these systems fit in with current HF management.

Leadless pacing is a rapidly growing field. Initially designed to provide right ventricular pacing for those who were contraindicated for conventional devices, the technology is growing to explore the potential benefit of avoiding long-term transvenous leads in any patient who requires pacing. In this review, we first examine the safety and performance of leadless pacing devices. We then review the evidence for their use in special populations, such as patients with high risk of device infection, patients on haemodialysis, and patients with vasovagal syncope who represent a younger population who may wish to avoid transvenous pacing. We also summarise the evidence for leadless cardiac resynchronisation therapy and conduction system pacing and discuss the challenges of managing issues, such as system revisions, end of battery life and extractions. Finally, we discuss future directions in the field, such as completely leadless cardiac resynchronisation therapy-defibrillator devices and whether leadless pacing has the potential to become a first-line therapy in the near future.

Stroke is a leading cause of death worldwide. With escalating healthcare costs, early non-invasive stroke risk stratification is vital. The current paradigm of stroke risk assessment and mitigation is focused on clinical risk factors and comorbidities. Standard algorithms predict risk using regression-based statistical associations, which, while useful and easy to use, have moderate predictive accuracy. This review summarises recent efforts to deploy machine learning (ML) to predict stroke risk and enrich the understanding of the mechanisms underlying stroke. The surveyed body of literature includes studies comparing ML algorithms with conventional statistical models for predicting cardiovascular disease and, in particular, different stroke subtypes. Another avenue of research explored is ML as a means of enriching multiscale computational modelling, which holds great promise for revealing thrombogenesis mechanisms. Overall, ML offers a new approach to stroke risk stratification that accounts for subtle physiologic variants between patients, potentially leading to more reliable and personalised predictions than standard regression-based statistical associations.

Radiation-associated cardiovascular disease, an increasingly recognised disease process, is a significant adverse effect of radiation therapy for common malignancies that involve the chest, and include lymphomas, lung, mediastinal and breast cancers. Two factors contribute to the increasing incidence of radiation-associated cardiovascular disease: advances in malignancy detection and the improved survival of cancer patients, by which many symptoms of radiation-associated cardiovascular disease, specifically radiation-associated arrhythmias, present years and/or decades following initial radiotherapy. We present a focused overview of the currently understood pathophysiology, prevalence and management strategies of radiation-associated arrhythmias, which include bradyarrhythmias, tachyarrhythmias and autonomic dysfunction.

Over the past decade there has been an interest in understanding the role of gut microbiota in the pathogenesis of AF. A number of studies have linked the gut microbiota to the occurrence of traditional AF risk factors such as hypertension and obesity. However, it remains unclear whether gut dysbiosis has a direct effect on arrhythmogenesis in AF. This article describes the current understanding of the effect of gut dysbiosis and associated metabolites on AF. In addition, current therapeutic strategies and future directions are discussed.

Aims: In patients with a low AF burden and long periods of sinus rhythm, 'pill-in-the-pocket' oral anticoagulation (OAC) may, taken as needed in response to AF episodes, offer the same thromboembolic protection as continuous, life-long OAC, while reducing bleeding complications at the same time. The purpose of this study is to systematically summarise available evidence pertaining to the feasibility, safety and efficacy of pill-in-the-pocket OAC.

Methods: Medline and Embase were searched from inception to July 2022 for studies adopting a pill-in-the-pocket OAC strategy in AF patients guided by daily rhythm monitoring (PROSPERO/CRD42020209564). Outcomes of interest were extracted and event rates per patient-years of follow-up were calculated. A random effects model was used for pooled estimates.

Results: Eight studies were included (711 patients). Daily rhythm monitoring was continuous in six studies and intermittent in two (pulse checks or smartphone single-lead electrocardiograms were used). Anticoagulation criteria varied across studies, reflecting the uncertainty regarding the AF burden that warrants anticoagulation. The mean time from AF meeting OAC criteria to its initiation was not reported. Adopting pill-in-the-pocket OAC led to 390 (54.7%) patients stopping OAC, 85 (12.0%) patients taking pill-in-the-pocket OAC and 237 (33.3%) patients remaining on or returning to continuous OAC. Overall, annualised ischaemic stroke and major bleeding rates per patient-year of follow-up were low at 0.005 (95% CI [0.002-0.012]) and 0.024 (95% CI [0.013-0.043]), respectively.

Conclusion: Current evidence, although encouraging, is insufficient to inform practice. Additional studies are required to improve our understanding of the relationships between AF burden and thromboembolic risk to help define anticoagulation criteria and appropriate monitoring strategies.

AF is the most common clinically relevant cardiac arrhythmia associated with multiple comorbidities, cardiovascular complications (e.g. stroke) and increased mortality. As artificial intelligence (AI) continues to transform the practice of medicine, this review article highlights specific applications of AI for the screening, diagnosis and treatment of AF. Routinely used digital devices and diagnostic technology have been significantly enhanced by these AI algorithms, increasing the potential for large-scale population-based screening and improved diagnostic assessments. These technologies have similarly impacted the treatment pathway of AF, identifying patients who may benefit from specific therapeutic interventions. While the application of AI to the diagnostic and therapeutic pathway of AF has been tremendously successful, the pitfalls and limitations of these algorithms must be thoroughly considered. Overall, the multifaceted applications of AI for AF are a hallmark of this emerging era of medicine.

Sudden cardiac arrest due to lethal ventricular arrhythmias is a major cause of mortality worldwide and results in more years of potential life lost than any individual cancer. Most of these sudden cardiac arrest events occur unexpectedly in individuals who have not been identified as high-risk due to the inadequacy of current risk stratification tools. Artificial intelligence tools are increasingly being used to solve complex problems and are poised to help with this major unmet need in the field of clinical electrophysiology. By leveraging large and detailed datasets, artificial intelligence-based prediction models have the potential to enhance the risk stratification of lethal ventricular arrhythmias. This review presents a synthesis of the published literature and a discussion of future directions in this field.

[This corrects the article DOI: 10.15420/aer.2021.30.].