Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry / Stefan Baumann, Matthias Renker, U. Joseph Schoepf, Carlo N. De Cecco, Adriaan Coenen, Jakob De Geer, Mariusz Kruk, Young-Hak Kim, Moritz H. Albrecht, Taylor M. Duguay, Brian E. Jacobs, Richard R. Bayer, Sheldon E. Litwin, Christel Weiss, Ibrahim Akin, Martin Borggrefe, Dong Hyun Yang, Cezary Kepka, Anders Persson, Koen Nieman, Christian Tesche

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..

Medienart:

E-Artikel

Erscheinungsjahr:

2019

Erschienen:

2019

Enthalten in:

Zur Gesamtaufnahme - volume:119

Enthalten in:

European journal of radiology - 119(2019) Artikel-Nummer 108657, 6 Seiten

Sprache:

Englisch

Beteiligte Personen:

Baumann, Stefan, 1983- [VerfasserIn]
Renker, Matthias [VerfasserIn]
Schoepf, U. Joseph [VerfasserIn]
De Cecco, Carlo N. [VerfasserIn]
Coenen, Adriaan [VerfasserIn]
De Geer, Jakob [VerfasserIn]
Kruk, Mariusz [VerfasserIn]
Kim, Young-Hak [VerfasserIn]
Albrecht, Moritz H. [VerfasserIn]
Duguay, Taylor M. [VerfasserIn]
Jacobs, Brian E. [VerfasserIn]
Bayer, Richard R. [VerfasserIn]
Litwin, Sheldon E. [VerfasserIn]
Weiß, Christel, 1958- [VerfasserIn]
Akın, Ibrahim, 1978- [VerfasserIn]
Borggrefe, Martin [VerfasserIn]
Yang, Dong Hyun [VerfasserIn]
Kepka, Cezary [VerfasserIn]
Persson, Anders [VerfasserIn]
Nieman, Koen [VerfasserIn]
Tesche, Christian [VerfasserIn]

Links:

Volltext

Themen:

Computed Tomography Angiography
Coronary Angiography
Coronary Stenosis
Coronary artery disease
Epidemiologic Methods
Female
Fractional Flow Reserve, Myocardial
Fractional flow reserve
Hemodynamics
Humans
Machine Learning
Machine learning
Male
Middle Aged
Myocardial Ischemia
Sex Factors
Spiral computed tomography
Tomography, Spiral Computed

Anmerkungen:

Gesehen am 03.01.2020

Umfang:

6

doi:

10.1016/j.ejrad.2019.108657

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

1686400802

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