1. Sex differences in machine learning computed tomography-derived fractional flow reserve
- Author
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Mahmoud Al Rifai, Ahmed Ibrahim Ahmed, Yushui Han, Jean Michel Saad, Talal Alnabelsi, Faisal Nabi, Su Min Chang, Myra Cocker, Chris Schwemmer, Juan C. Ramirez-Giraldo, William A. Zoghbi, John J. Mahmarian, and Mouaz H. Al-Mallah
- Subjects
Medicine ,Science - Abstract
Abstract Coronary computed tomography angiography (CCTA) derived machine learning fractional flow reserve (ML-FFRCT) can assess the hemodynamic significance of coronary artery stenoses. We aimed to assess sex differences in the association of ML-FFRCT and incident cardiovascular outcomes. We studied a retrospective cohort of consecutive patients who underwent clinically indicated CCTA and single photon emission computed tomography (SPECT). Obstructive stenosis was defined as ≥ 70% stenosis severity in non-left main vessels or ≥ 50% in the left main coronary. ML-FFRCT was computed using a machine learning algorithm with significant stenosis defined as ML-FFRCT
- Published
- 2022
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