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Status: Bibliographieeintrag

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Verfasst von:Baumann, Stefan [VerfasserIn]   i
 Renker, Matthias [VerfasserIn]   i
 Schoepf, U. Joseph [VerfasserIn]   i
 De Cecco, Carlo N. [VerfasserIn]   i
 Coenen, Adriaan [VerfasserIn]   i
 De Geer, Jakob [VerfasserIn]   i
 Kruk, Mariusz [VerfasserIn]   i
 Kim, Young-Hak [VerfasserIn]   i
 Albrecht, Moritz H. [VerfasserIn]   i
 Duguay, Taylor M. [VerfasserIn]   i
 Jacobs, Brian E. [VerfasserIn]   i
 Bayer, Richard R. [VerfasserIn]   i
 Litwin, Sheldon E. [VerfasserIn]   i
 Weiß, Christel [VerfasserIn]   i
 Akın, Ibrahim [VerfasserIn]   i
 Borggrefe, Martin [VerfasserIn]   i
 Yang, Dong Hyun [VerfasserIn]   i
 Kepka, Cezary [VerfasserIn]   i
 Persson, Anders [VerfasserIn]   i
 Nieman, Koen [VerfasserIn]   i
 Tesche, Christian [VerfasserIn]   i
Titel:Gender differences in the diagnostic performance of machine learning coronary CT angiography-derived fractional flow reserve -results from the MACHINE registry
Verf.angabe: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
Jahr:2019
Umfang:6 S.
Fussnoten:Gesehen am 03.01.2020
Titel Quelle:Enthalten in: European journal of radiology
Ort Quelle:Amsterdam [u.a.] : Elsevier Science, 1990
Jahr Quelle:2019
Band/Heft Quelle:119(2019) Artikel-Nummer 108657, 6 Seiten
ISSN Quelle:1872-7727
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.
DOI:doi:10.1016/j.ejrad.2019.108657
URL:Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.

Volltext: https://doi.org/10.1016/j.ejrad.2019.108657
 DOI: https://doi.org/10.1016/j.ejrad.2019.108657
Datenträger:Online-Ressource
Sprache:eng
Sach-SW:Computed Tomography Angiography
 Coronary Angiography
 Coronary artery disease
 Coronary Stenosis
 Epidemiologic Methods
 Female
 Fractional flow reserve
 Fractional Flow Reserve, Myocardial
 Hemodynamics
 Humans
 Machine learning
 Machine Learning
 Male
 Middle Aged
 Myocardial Ischemia
 Sex Factors
 Spiral computed tomography
 Tomography, Spiral Computed
K10plus-PPN:1686400802
Verknüpfungen:→ Zeitschrift

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