| Title: | Atrial fibrillation prediction by combining ECG markers and CMR radiomics |
| Journal: | Scientific Reports |
| Published: | 7 Nov 2022 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36344532/ |
| DOI: | https://doi.org/10.1038/s41598-022-21663-w |
| URL: | https://www.nature.com/articles/s41598-022-21663-w.pdf |
Publication 10671
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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 < 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.</p>
11 Authors
- 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
1 Application
| Application ID | Title |
|---|---|
| 2964 | Description of cardiovascular phenotype in the UK Biobank population based on cardiovascular magnetic resonance and carotid ultrasound |
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