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

Mahmoud Al Rifai1, Ahmed Ibrahim Ahmed1, Yushui Han1

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Area of Science:

  • Cardiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Coronary computed tomography angiography (CCTA) is crucial for diagnosing coronary artery disease.
  • Machine learning fractional flow reserve (ML-FFRCT) offers a non-invasive method to assess the hemodynamic significance of coronary stenoses.
  • Understanding sex-based differences in cardiovascular risk assessment is vital for personalized medicine.

Purpose of the Study:

  • To investigate potential sex differences in the association between ML-FFRCT and incident cardiovascular outcomes.
  • To compare ML-FFRCT values and their prognostic implications in men and women with suspected coronary artery disease.

Main Methods:

  • A retrospective cohort study included 471 patients undergoing CCTA and single photon emission computed tomography (SPECT).
  • ML-FFRCT was calculated using a machine learning algorithm, with ML-FFRCT < 0.8 defining significant stenosis.
  • The primary outcome was a composite of death or non-fatal myocardial infarction (D/MI), analyzed using multivariable adjusted models.

Main Results:

  • Women had less obstructive stenosis by CCTA (9% vs. 18%) and a higher median ML-FFRCT (0.76 vs. 0.71) compared to men.
  • The prevalence of significant stenosis (ML-FFRCT < 0.8) was similar between sexes (39% vs. 44%).
  • Multivariable analysis revealed no significant association between ML-FFRCT < 0.8 and D/MI, with no significant interaction detected for sex.

Conclusions:

  • In symptomatic patients undergoing CCTA and SPECT, ML-FFRCT values were higher in women than men.
  • ML-FFRCT did not show a significant association with incident death or myocardial infarction in this cohort.
  • The prognostic value of ML-FFRCT for cardiovascular events does not appear to differ significantly between men and women.