Search: WFRF:(Duguay Taylor M.) >
Influence of Corona...
Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR Results From MACHINE Registry
-
- Tesche, Christian (author)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA; Heart Ctr Munich Bogenhausen, Dept Cardiol & Intens Care Med, Munich, Germany; Ludwig Maximilians Univ Munchen, Munich Univ Clin, Dept Cardiol, Munich, Germany
-
- Otani, Katharina (author)
- Siemens Healthcare KK, Adv Therapies Innovat Dept, Tokyo, Japan
-
- De Cecco, Carlo N. (author)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA
-
show more...
-
- Coenen, Adriaan (author)
- Erasmus MC, Dept Cardiol, Rotterdam, Netherlands; Erasmus MC, Dept Radiol, Rotterdam, Netherlands
-
- De Geer, Jakob, 1970- (author)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Röntgenkliniken i Linköping
-
- Kruk, Mariusz (author)
- Inst Cardiol, Invas Cardiol & Angiol Dept, Coronary Dis & Struct Heart Dis Dept, Warsaw, Poland
-
- Kim, Young-Hak (author)
- Univ Ulsan, Coll Med, Asan Med Ctr, Dept Cardiol,Heart Inst, Seoul, South Korea
-
- Albrecht, Moritz H. (author)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA; Univ Hosp Frankfurt, Dept Diagnost & Intervent Radiol, Frankfurt, Germany
-
- Baumann, Stefan (author)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA; Heidelberg Univ, Univ Med Ctr Mannheim UMM, Fac Med Mannheim, Dept Med 1, Mannheim, Germany
-
- Renker, Matthias (author)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA; Kerckhoff Heart Ctr, Dept Cardiol, Bad Nauheim, Germany
-
- Bayer, Richard R. (author)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA; Med Univ South Carolina, Dept Med, Div Cardiol, Charleston, SC 29425 USA
-
- Duguay, Taylor M. (author)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA
-
- Litwin, Sheldon E. (author)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA; Med Univ South Carolina, Dept Med, Div Cardiol, Charleston, SC 29425 USA
-
- Varga-Szemes, Akos (author)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA
-
- Steinberg, Daniel H. (author)
- Med Univ South Carolina, Dept Med, Div Cardiol, Charleston, SC 29425 USA
-
- Yang, Dong Hyun (author)
- Univ Ulsan, Coll Med, Asan Med Ctr, Dept Radiol, Seoul, South Korea
-
- Kepka, Cezary (author)
- Inst Cardiol, Invas Cardiol & Angiol Dept, Coronary Dis & Struct Heart Dis Dept, Warsaw, Poland
-
- Persson, Anders, 1953- (author)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Röntgenkliniken i Linköping
-
- Nieman, Koen (author)
- Erasmus MC, Dept Cardiol, Rotterdam, Netherlands; Erasmus MC, Dept Radiol, Rotterdam, Netherlands; Stanford Univ, Sch Med, Cardiovasc Inst, Stanford, CA 94305 USA
-
- Schoepf, U. Joseph (author)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA; Med Univ South Carolina, Dept Med, Div Cardiol, Charleston, SC 29425 USA
-
show less...
-
(creator_code:org_t)
- ELSEVIER SCIENCE INC, 2020
- 2020
- English.
-
In: JACC Cardiovascular Imaging. - : ELSEVIER SCIENCE INC. - 1936-878X .- 1876-7591. ; 13:3, s. 760-770
- Related links:
-
https://doi.org/10.1...
-
show more...
-
https://doi.org/10.1...
-
https://urn.kb.se/re...
-
https://doi.org/10.1...
-
show less...
Abstract
Subject headings
Close
- OBJECTIVESThis study was conducted to investigate the influence of coronary artery calcium (CAC) score on the diagnostic performance of machine-learning-based coronary computed tomography (CT) angiography (cCTA)-derived fractional flow reserve (CT-FFR).BACKGROUNDCT-FFR is used reliably to detect lesion-specific ischemia. Novel CT-FFR algorithms using machine-learning artificial intelligence techniques perform fast and require less complex computational fluid dynamics. Yet, influence of CAC score on diagnostic performance of the machine-learning approach has not been investigated.METHODSA total of 482 vessels from 314 patients (age 62.3 +/- 9.3 years, 77% male) who underwent cCTA followed by invasive FFR were investigated from the MACHINE (Machine Learning based CT Angiography derived FFR: a Multi-center Registry) registry data. CAC scores were quantified using the Agatston convention. The diagnostic performance of CT-FFR to detect lesion-specific ischemia was assessed across all Agatston score categories (CAC 0, >0 to <100, 100 to <400, and >=$400) on a per-vessel level with invasive FFR as the reference standard.RESULTSThe diagnostic accuracy of CT-FFR versus invasive FFR was superior to cCTA alone on a per-vessel level (78% vs. 60%) and per patient level (83% vs. 73%) across all Agatston score categories. No statistically significant differences in the diagnostic accuracy, sensitivity, or specificity of CT-FFR were observed across the categories. CT-FFR showed good discriminatory power in vessels with high Agatston scores (CAC >= 400) and high performance in low-to-intermediate Agatston scores (CAC >0 to <400) with a statistically significant difference in the area under the receiver-operating characteristic curve (AUC) (AUC: 0.71 [95% confidence interval (CI): 0.57 to 0.85] vs. 0.85 [95% CI: 0.82 to 0.89], p = 0.04). CT-FFR showed superior diagnostic value over cCTA in vessels with high Agatston scores (CAC >= 400: AUC 0.71 vs. 0.55, p = 0.04) and low-to-intermediate Agatston scores (CAC >0 to <400: AUC 0.86 vs. 0.63, p < 0.001).CONCLUSIONSMachine-learning-based CT-FFR showed superior diagnostic performance over cCTA alone in CAC with a significant difference in the performance of CT-FFR as calcium burden/Agatston calcium score increased. (Machine Learning Based CT Angiography Derived FFR: a Multicenter, Registry [MACHINE] NCT02805621). (C) 2020 by the American College of Cardiology Foundation.
Subject headings
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering (hsv//eng)
Keyword
- coronary artery disease
- coronary computed tomography angiography
- computational fractional flow reserve
- invasive coronary angiography
Publication and Content Type
- ref (subject category)
- art (subject category)
Find in a library
To the university's database
- By the author/editor
-
Tesche, Christia ...
-
Otani, Katharina
-
De Cecco, Carlo ...
-
Coenen, Adriaan
-
De Geer, Jakob, ...
-
Kruk, Mariusz
-
show more...
-
Kim, Young-Hak
-
Albrecht, Moritz ...
-
Baumann, Stefan
-
Renker, Matthias
-
Bayer, Richard R ...
-
Duguay, Taylor M ...
-
Litwin, Sheldon ...
-
Varga-Szemes, Ak ...
-
Steinberg, Danie ...
-
Yang, Dong Hyun
-
Kepka, Cezary
-
Persson, Anders, ...
-
Nieman, Koen
-
Schoepf, U. Jose ...
-
show less...
- About the subject
-
- ENGINEERING AND TECHNOLOGY
-
ENGINEERING AND ...
-
and Medical Engineer ...
- Articles in the publication
-
JACC Cardiovascu ...
- By the university
-
Linköping University