Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry. Issue 119 (October 2019) - Baumann, Stefan; Renker, Matthias; Schoepf, U. Joseph; De Cecco, Carlo N.; Coenen, Adriaan; De Geer, Jakob; Kruk, Mariusz; Kim, Young-Hak; Albrecht, Moritz H.; Duguay, Taylor M.; Jacobs, Brian E.; Bayer, Richard R.; et al.
Highlights: CT-FFRML demonstrates high diagnostic accuracy both in men and women. CT-FFRML shows no significant difference in accuracy between men and women. CT-FFRML shows superior performance over cCTA alone in men, but not in women. 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.