Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome

Knebel Döberitz, Philipp L. von; Cecco, Carlo N. de; Schöpf, U. Joseph; Albrecht, Moritz H.; Assen, Marly van; Santis, Domenico de; Gaskins, Jeffrey; Martin, Simon; Bauer, Maximilian J.; Ebersberger, Ullrich; Giovagnoli, Dante A.; Varga-Szemes, Akos; Bayer, Richard R.; Schönberg, Stefan O. und Tesche, Christian (2019): Impact of Coronary Computerized Tomography Angiography-Derived Plaque Quantification and Machine-Learning Computerized Tomography Fractional Flow Reserve on Adverse Cardiac Outcome. In: American Journal of Cardiology, Bd. 124, Nr. 9: S. 1340-1348

Abstract

This study investigated the impact of coronary CT angiography (cCTA)-derived plaque markers and machine-learning-based CT-derived fractional flow reserve (CT-FFR) to identify adverse cardiac outcome. Data of 82 patients (60 +/- 11 years, 62% men) who underwent cCTA and invasive coronary angiography (ICA) were analyzed in this single-center retrospective, institutional review board-approved, HIPAA-compliant study. Follow-up was performed to record major adverse cardiac events (MACE). Plaque quantification of lesions responsible for MACE and control lesions was retrospectively performed semiautomatically from cCTA together with machine-learning based CT-FFR. The discriminatory value of plaque markers and CT-FFR to predict MACE was evaluated. After a median follow-up of 18.5 months (interquartile range 11.5 to 26.6 months), MACE was observed in 18 patients (21 %). In a multivariate analysis the following markers were predictors of MACE (odds ratio [OR]): lesion length (OR 1.16, p = 0.018), low-attenuation plaque (<30 HU) (OR 4.59, p = 0.003), Napkin ring sign (OR 2.71, p = 0.034), stenosis >= 50% (OR 3.83, p 0.042), and CT-FFR <= 0.80 (OR 7.78, p = 0.001). Receiver operating characteristics analysis including stenosis >= 50%, plaque markers and CT-FFR <= 0.80 (Area under the curve 0.94) showed incremental discriminatory power over stenosis >= 50% alone (Area under the curve 0.60, p <0.0001) for the prediction of MACE. cCTA-derived plaque markers and machine-learning CT-FFR demonstrate predictive value to identify MACE. In conclusion, combining plaque markers with machine-learning CT-FFR shows incremental discriminatory power over cCTA stenosis grading alone. (C) 2019 Elsevier Inc. All rights reserved.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 0002-9149
• Sprache: Englisch