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Influence of Corona...
Influence of Coronary Calcium on Diagnostic Performance of Machine Learning CT-FFR Results From MACHINE Registry
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- Tesche, Christian (författare)
- 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
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- Otani, Katharina (författare)
- Siemens Healthcare KK, Adv Therapies Innovat Dept, Tokyo, Japan
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- De Cecco, Carlo N. (författare)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA
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- Coenen, Adriaan (författare)
- Erasmus MC, Dept Cardiol, Rotterdam, Netherlands; Erasmus MC, Dept Radiol, Rotterdam, Netherlands
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- De Geer, Jakob, 1970- (författare)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Röntgenkliniken i Linköping
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- Kruk, Mariusz (författare)
- Inst Cardiol, Invas Cardiol & Angiol Dept, Coronary Dis & Struct Heart Dis Dept, Warsaw, Poland
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- Kim, Young-Hak (författare)
- Univ Ulsan, Coll Med, Asan Med Ctr, Dept Cardiol,Heart Inst, Seoul, South Korea
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- Albrecht, Moritz H. (författare)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA; Univ Hosp Frankfurt, Dept Diagnost & Intervent Radiol, Frankfurt, Germany
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- Baumann, Stefan (författare)
- 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
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- Renker, Matthias (författare)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA; Kerckhoff Heart Ctr, Dept Cardiol, Bad Nauheim, Germany
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- Bayer, Richard R. (författare)
- 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
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- Duguay, Taylor M. (författare)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA
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- Litwin, Sheldon E. (författare)
- 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
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- Varga-Szemes, Akos (författare)
- Med Univ South Carolina, Dept Radiol & Radiol Sci, Div Cardiovasc Imaging, Charleston, SC 29425 USA
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- Steinberg, Daniel H. (författare)
- Med Univ South Carolina, Dept Med, Div Cardiol, Charleston, SC 29425 USA
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- Yang, Dong Hyun (författare)
- Univ Ulsan, Coll Med, Asan Med Ctr, Dept Radiol, Seoul, South Korea
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- Kepka, Cezary (författare)
- Inst Cardiol, Invas Cardiol & Angiol Dept, Coronary Dis & Struct Heart Dis Dept, Warsaw, Poland
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- Persson, Anders, 1953- (författare)
- Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Röntgenkliniken i Linköping
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- Nieman, Koen (författare)
- Erasmus MC, Dept Cardiol, Rotterdam, Netherlands; Erasmus MC, Dept Radiol, Rotterdam, Netherlands; Stanford Univ, Sch Med, Cardiovasc Inst, Stanford, CA 94305 USA
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- Schoepf, U. Joseph (författare)
- 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
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(creator_code:org_t)
- ELSEVIER SCIENCE INC, 2020
- 2020
- Engelska.
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Ingår i: JACC Cardiovascular Imaging. - : ELSEVIER SCIENCE INC. - 1936-878X .- 1876-7591. ; 13:3, s. 760-770
- Relaterad länk:
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https://doi.org/10.1...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- TEKNIK OCH TEKNOLOGIER -- Medicinteknik (hsv//swe)
- ENGINEERING AND TECHNOLOGY -- Medical Engineering (hsv//eng)
Nyckelord
- coronary artery disease
- coronary computed tomography angiography
- computational fractional flow reserve
- invasive coronary angiography
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Tesche, Christia ...
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Otani, Katharina
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De Cecco, Carlo ...
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Coenen, Adriaan
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De Geer, Jakob, ...
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Kruk, Mariusz
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Kim, Young-Hak
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Albrecht, Moritz ...
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Baumann, Stefan
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Renker, Matthias
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Bayer, Richard R ...
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Duguay, Taylor M ...
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Litwin, Sheldon ...
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Varga-Szemes, Ak ...
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Steinberg, Danie ...
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Yang, Dong Hyun
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Kepka, Cezary
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Persson, Anders, ...
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Nieman, Koen
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Schoepf, U. Jose ...
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