心房颤动
医学
无线电技术
内科学
心脏病学
心电图
心律失常
放射科
作者
Esmeralda Ruiz Pujadas,Zahra Raisi‐Estabragh,Liliána Szabó,Cristian Izquierdo Morcillo,Víctor M. Campello,Carlos Martín-Isla,Hajnalka Vágó,Béla Merkely,Nicholas C. Harvey,Steffen E. Petersen,Karim Lekadir
标识
DOI:10.1038/s41598-022-21663-w
摘要
Atrial fibrillation (AF) is the most common cardiac arrhythmia. It is associated with a higher risk of important adverse health outcomes such as stroke and death. AF is linked to distinct electro-anatomic alterations. The main tool for AF diagnosis is the Electrocardiogram (ECG). However, an ECG recorded at a single time point may not detect individuals with paroxysmal AF. In this study, we developed machine learning models for discrimination of prevalent AF using a combination of image-derived radiomics phenotypes and ECG features. Thus, we characterize the phenotypes of prevalent AF in terms of ECG and imaging alterations. Moreover, we explore sex-differential remodelling by building sex-specific models. Our integrative model including radiomics and ECG together resulted in a better performance than ECG alone, particularly in women. ECG had a lower performance in women than men (AUC: 0.77 vs 0.88, p < 0.05) but adding radiomics features, the accuracy of the model was able to improve significantly. The sensitivity also increased considerably in women by adding the radiomics (0.68 vs 0.79, p < 0.05) having a higher detection of AF events. Our findings provide novel insights into AF-related electro-anatomic remodelling and its variations by sex. The integrative radiomics-ECG model also presents a potential novel approach for earlier detection of AF.
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