Rapid Atrial Fibrillation in the Emergency Department.

Atrial fibrillation (AF) is the most common rhythm disorder seen in doctors' offices and emergency departments (EDs). In both settings, an AF holistic pathway including anticoagulation or stroke avoidance, better symptom management, and cardiovascular and comorbidity optimization should be followed. However, other considerations need to be assessed in the ED, such as haemodynamic instability, the onset of AF, the presence of acute heart failure and pre-excitation. Although the Advanced Cardiovascular Life Support guidelines (European Society of Cardiology guidelines, Acute Cardiac Care Association/European Heart Rhythm Association position statements) and several recent AF publications have greatly assisted physicians in treating AF with rapid ventricular response in the ED, further practical clinical guidance is required to improve physicians' skill and knowledge in providing the best treatment for patients. Herein, we combine multiple strategies with supporting evidence-based treatment and experiences encountered in clinical practice into practical stepwise approaches. We hope that the stepwise algorithm may assist residents and physicians in managing AF in the ED.

Advanced image-supported lead placement in cardiac resynchronisation therapy: protocol for the multicentre, randomised controlled ADVISE trial and early economic evaluation.

INTRODUCTION: Achieving optimal placement of the left ventricular (LV) lead in cardiac resynchronisation therapy (CRT) is a prerequisite in order to achieve maximum clinical benefit, and is likely to help avoid non-response. Pacing outside scar tissue and targeting late activated segments may improve outcome. The present study will be the first randomised controlled trial to compare the efficacy of real-time image-guided LV lead delivery to conventional CRT implantation. In addition, to estimate the cost-effectiveness of targeted lead implantation, an early decision analytic model was developed, and described here. METHODS AND ANALYSIS: A multicentre, interventional, randomised, controlled trial will be conducted in a total of 130 patients with a class I or IIa indication for CRT implantation. Patients will be stratified to ischaemic heart failure aetiology and 1:1 randomised to either empirical lead placement or live image-guided lead placement. Ultimate lead location and echocardiographic assessment will be performed by core laboratories, blinded to treatment allocation and patient information. Late gadolinium enhancement cardiac magnetic resonance imaging (CMR) and CINE-CMR with feature-tracking postprocessing software will be used to semi-automatically determine myocardial scar and late mechanical activation. The subsequent treatment file with optimal LV-lead positions will be fused with the fluoroscopy, resulting in live target-visualisation during the procedure. The primary endpoint is the difference in percentage of successfully targeted LV-lead location. Secondary endpoints are relative percentage reduction in indexed LV end-systolic volume, a hierarchical clinical endpoint, and quality of life. The early analytic model was developed using a Markov-model, consisting of seven mutually exclusive health states. ETHICS AND DISSEMINATION: The protocol was approved by the Medical Research Ethics Committee Utrecht (NL73416.041.20). All participants are required to provide written informed consent. Results will be submitted to peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT05053568; Trial NL8666.

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology.

The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.