Risk Analysis of Atrial Fibrillation Based on ECG Phenotypes: The RAF-ECP Study Protocol.

Abstract

Atrial fibrillation (AF) is the most common supraventricular arrhythmia in clinical practice, and many patients exhibit silent AF. Variables based on Electrocardiogram (ECG) have shown promise in assessing AF risk in the previous study. This study protocol proposes a systematic approach, named RAF-ECP, to evaluate the role of ECG phenotypes in assessing the risk of AF. The protocol aims to standardize the definition and calculation of ECG phenotypes, ensuring consistency and comparability across different research studies and healthcare settings. Data will be collected from multiple clinical laboratories, with an anticipated sample size of 10,000 cases (lead I and II, 10 s) evenly distributed between subjects with and without AF events in one-year time frame. By analyzing ECG data and baseline information, statistical tests and machine learning classifiers will be employed to identify significant risk factors and develop a comprehensive risk assessment model for AF. The anticipated outcomes include hazard ratio values, confidence intervals, p values, as well as accuracy, sensitivity, and specificity measures. The study also discusses the clinical relevance and potential benefits of standardizing ECG phenotypes, emphasizing the need for collaboration between multiple centers to obtain diverse and representative datasets. The proposed RAF-ECP study protocol offers a novel and significant approach to understanding the impact of ECG phenotypes on AF risk assessment. Its integration of statistical analysis and machine learning techniques has the potential to advance AF research and contribute to the development of improved risk prediction models and clinical decision support tools.