Sex differences in machine learning computed tomography-derived fractional flow reserve.

ABSTRACT: Coronary computed tomography angiography (CCTA) derived machine learning fractional flow reserve (ML-FFR_CT) can assess the hemodynamic significance of coronary artery stenoses. We aimed to assess sex differences in the association of ML-FFR_CT 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-FFR_CT was computed using a machine learning algorithm with significant stenosis defined as ML-FFR_CT < 0.8. The primary outcome was a composite of death or non-fatal myocardial infarction (D/MI). Ou

SUBMITTER: Al Rifai M 

PROVIDER: S-EPMC9381799 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature