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

Autor: Dante A. Giovagnoli, Philipp L. von Knebel Doeberitz, Stefan O. Schönberg, Maximilian J. Bauer, Ullrich Ebersberger, Moritz H. Albrecht, Akos Varga-Szemes, Simon S. Martin, Jeffrey Gaskins, U. Joseph Schoepf, Richard R. Bayer nd, Carlo N. De Cecco, Christian Tesche, Marly van Assen, Domenico De Santis
Rok vydání: 2019
Předmět:
Male
medicine.medical_specialty
Multivariate analysis
Computed Tomography Angiography
coronary computerized tomography angiography
plaque quantification
machine-learning
computerized tomography fractional flow reserve
SOCIETY
Fractional flow reserve
030204 cardiovascular system & hematology
AMERICAN-COLLEGE
GUIDELINES
Coronary Angiography
Severity of Illness Index
030218 nuclear medicine & medical imaging
Machine Learning
03 medical and health sciences
0302 clinical medicine
ATHEROSCLEROTIC LESIONS
Interquartile range
Internal medicine
Severity of illness
Multidetector Computed Tomography
medicine
ARTERY-DISEASE
Humans
cardiovascular diseases
Retrospective Studies
medicine.diagnostic_test
business.industry
Coronary Stenosis
Retrospective cohort study
Odds ratio
Middle Aged
CT ANGIOGRAPHY
Prognosis
Coronary Vessels
Plaque
Atherosclerotic
United States
Fractional Flow Reserve
Myocardial
Survival Rate
ROC Curve
Angiography
Cardiology
Female
Cardiology and Cardiovascular Medicine
business
Mace
Follow-Up Studies
Zdroj: American Journal of Cardiology, 124(9), 1340-1348. EXCERPTA MEDICA INC-ELSEVIER SCIENCE INC
ISSN: 1879-1913
0002-9149
Popis: 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, p0.0001) 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.
Databáze: OpenAIRE
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