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

Coronary computed tomography angiography (CCTA) derived machine learning fractional flow reserve (ML-FFR) can assess the hemodynamic significance of coronary artery stenoses. We aimed to assess sex differences in the association of ML-FFR 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 was computed using a machine learning algorithm with significant stenosis defined as ML-FFR < 0.8. The primary outcome was a composite of death or non-fatal myocardial infarction (D/MI). Our study population consisted of 471 patients with mean (SD) age 65 (13) years, 53% men, and multiple comorbidities (78% hypertension, 66% diabetes, 81% dyslipidemia). Compared to men, women were less likely to have obstructive stenosis by CCTA (9% vs. 18%; p = 0.006), less multivessel CAD (4% vs. 6%; p = 0.25), lower prevalence of ML-FFR < 0.8 (39% vs. 44%; p = 0.23) and higher median (IQR) ML-FFR (0.76 (0.53-0.86) vs. 0.71 (0.47-0.84); p = 0.047). In multivariable adjusted models, there was no significant association between ML-FFR < 0.8 and D/MI [Hazard Ratio 0.82, 95% confidence interval (0.30, 2.20); p = 0.25 for interaction with sex.]. In a high-risk cohort of symptomatic patients who underwent CCTA and SPECT testing, ML-FFR was higher in women than men. There was no significant association between ML-FFR and incident mortality or MI and no evidence that the prognostic value of ML-FFR differs by sex.