عنوان انگلیسی مقاله:
Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry
ترجمه فارسی عنوان مقاله:
تفاوت های جنسیتی در عملکرد تشخیصی یادگیری دستگاه یادگیری عروق کرونر CT-نتیجه حاصل از کسری جریان کسری ناشی از آنژیوگرافی از رجیستری ماشین
Sciencedirect - Elsevier - European Journal of Radiology, 119 (2019) 108657: doi:10:1016/j:ejrad:2019:108657
Stefan Baumanna,b, Matthias Renkera,c, U. Joseph Schoepfa,d,⁎, Carlo N. De Ceccoa, Adriaan Coenene,f, Jakob De Geerg, Mariusz Krukh, Young-Hak Kimi, Moritz H. Albrechta,j, Taylor M. Duguaya, Brian E. Jacobsa, Richard R. Bayera,d, Sheldon E. Litwina,d, Christel Weissk, Ibrahim Akinb, Martin Borggrefeb, Dong Hyun Yangl, Cezary Kepkah, Anders Perssong, Koen Niemane,f,m, Christian Teschea
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
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 | Machine learning | Spiral computed tomography | Fractional flow reserve