| Autor: |
Esmeralda Ruiz Pujadas, Zahra Raisi-Estabragh, Liliana Szabo, Cristian Izquierdo Morcillo, Víctor M. Campello, Carlos Martin-Isla, Hajnalka Vago, Bela Merkely, Nicholas C. Harvey, Steffen E. Petersen, Karim Lekadir |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Scientific Reports, Vol 12, Iss 1, Pp 1-15 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2045-2322 |
| DOI: |
10.1038/s41598-022-21663-w |
| Popis: |
Abstract 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 |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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