Cardiac Arrhythmias: Physiological Mechanisms, Clinical Insights, and Epidemiological Research
A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".
Deadline for manuscript submissions: 28 February 2025 | Viewed by 99
Special Issue Editor
Special Issue Information
Dear Colleagues,
Cardiac arrhythmias encompass several acquired heart diseases, such as atrial fibrilation, and inherited conditions, such as inherited channelopathies and arrhythmogenic cardiomyopathies. They are a significant cause of mortality and morbidity, representing a major burden to the individual, healthcare system, and society. A body of clinical and epidemiological studies over the past decades has identified several clinical, environmental, social, and genetic factors contributing to the development of arrhythmias. However, there is still a paucity of data on the granular dissection of the mechanisms contributing to arrhythmogenesis. Moreover, current clinical prediction models underperform when deployed in real-world settings.
This Special Issue will focus on the latest developments in pro-arrhythmic mechanisms and how they translate to improved risk stratification of patients and treatment options. The goal is to gather insights from molecular pathways, meta-genomics, single-cell technologies, multimodal approaches, and cellular and in vivo genome editing to refine our understanding of arrhythmogenesis. This Special Issue will also explore novel methods in the molecular epidemiology of cardiac arrhythmias, ranging from propensity score matching, genome-wide association studies, mendelian randomisation through phenome-wide association studies, polygenic prediction models, machine learning models, and natural language models. This Special Issue will span preclinical through clinical research and state-of-the-art reviews.
Dr. Thomas A. Agbaedeng
Guest Editor
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Keywords
- cardiac arrhythmias
- atrial fibrillation
- sudden cardiac death
- ventricular arrhythmias
- electrophysiological remodelling
- structural remodelling
- omics
- risk factors
- (epi)genetics
- GWAS
- PheWas
- polygenic risk
- machine learning models
- natural language models
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