Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry

Eur J Radiol. 2019 Oct:119:108657. doi: 10.1016/j.ejrad.2019.108657. Epub 2019 Sep 7.

Abstract

Purpose: This study investigated the impact of gender differences on the diagnostic performance of machine-learning based coronary CT angiography (cCTA)-derived fractional flow reserve (CT-FFRML) for the detection of lesion-specific ischemia.

Method: Five centers enrolled 351 patients (73.5% male) with 525 vessels in the MACHINE (Machine leArning Based CT angiograpHy derIved FFR: a Multi-ceNtEr) registry. CT-FFRML and invasive FFR ≤ 0.80 were considered hemodynamically significant, whereas cCTA luminal stenosis ≥50% was considered obstructive. The diagnostic performance to assess lesion-specific ischemia in both men and women was assessed on a per-vessel basis.

Results: In total, 398 vessels in men and 127 vessels in women were included. Compared to invasive FFR, CT-FFRML reached a sensitivity, specificity, positive predictive value, and negative predictive value of 78% (95%CI 72-84), 79% (95%CI 73-84), 75% (95%CI 69-79), and 82% (95%CI: 76-86) in men vs. 75% (95%CI 58-88), 81 (95%CI 72-89), 61% (95%CI 50-72) and 89% (95%CI 82-94) in women, respectively. CT-FFRML showed no statistically significant difference in the area under the receiver-operating characteristic curve (AUC) in men vs. women (AUC: 0.83 [95%CI 0.79-0.87] vs. 0.83 [95%CI 0.75-0.89], p = 0.89). CT-FFRML was not superior to cCTA alone [AUC: 0.83 (95%CI: 0.75-0.89) vs. 0.74 (95%CI: 0.65-0.81), p = 0.12] in women, but showed a statistically significant improvement in men [0.83 (95%CI: 0.79-0.87) vs. 0.76 (95%CI: 0.71-0.80), p = 0.007].

Conclusions: Machine-learning based CT-FFR performs equally in men and women with superior diagnostic performance over cCTA alone for the detection of lesion-specific ischemia.

Keywords: Coronary artery disease; Fractional flow reserve; Machine learning; Spiral computed tomography.

Publication types

  • Multicenter Study

MeSH terms

  • Computed Tomography Angiography / methods
  • Computed Tomography Angiography / standards*
  • Coronary Angiography / methods
  • Coronary Angiography / standards
  • Coronary Stenosis / diagnostic imaging*
  • Coronary Stenosis / physiopathology
  • Epidemiologic Methods
  • Female
  • Fractional Flow Reserve, Myocardial / physiology
  • Hemodynamics / physiology
  • Humans
  • Machine Learning
  • Male
  • Middle Aged
  • Myocardial Ischemia / diagnostic imaging*
  • Myocardial Ischemia / physiopathology
  • Sex Factors
  • Tomography, Spiral Computed / methods
  • Tomography, Spiral Computed / standards