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  • 1.
    Coenen, Adriaan
    et al.
    Erasmus Univ, Netherlands.
    Kim, Young-Hak
    Univ Ulsan, South Korea.
    Kruk, Mariusz
    Inst Cardiol, Poland.
    Tesche, Christian
    Med Univ South Carolina, SC 29425 USA.
    De Geer, Jakob
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV). Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping.
    Kurata, Akira
    Ehime Univ, Japan.
    Lubbers, Marisa L.
    Erasmus Univ, Netherlands.
    Daemen, Joost
    Erasmus Univ, Netherlands.
    Itu, Lucian
    Siemens SRL, Romania.
    Rapaka, Saikiran
    Siemens Healthcare, NJ USA.
    Sharma, Puneet
    Siemens Healthcare, NJ USA.
    Schwemmer, Chris
    Siemens Healthcare GmbH, Germany.
    Persson, Anders
    Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Center for Diagnostics, Department of Radiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Schoepf, U. Joseph
    Med Univ South Carolina, SC 29425 USA.
    Kepka, Cezary
    Inst Cardiol, Poland.
    Yang, Dong Hyun
    Univ Ulsan, South Korea.
    Nieman, Koen
    Erasmus Univ, Netherlands; Stanford Univ, CA 94305 USA.
    Diagnostic Accuracy of a Machine-Learning Approach to Coronary Computed Tomographic Angiography-Based Fractional Flow Reserve Result From the MACHINE Consortium2018In: Circulation Cardiovascular Imaging, ISSN 1941-9651, E-ISSN 1942-0080, Vol. 11, no 6, article id e007217Article in journal (Refereed)
    Abstract [en]

    Background: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease. Methods and Results: At 5 centers in Europe, Asia, and the United States, 351 patients, including 525 vessels with invasive FFR comparison, were included. ML-based and CFD-based CT-FFR were performed on the CTA data, and diagnostic performance was evaluated using invasive FFR as reference. Correlation between ML-based and CFD-based CT-FFR was excellent (R=0.997). ML-based (area under curve, 0.84) and CFD-based CT-FFR (0.84) outperformed visual CTA (0.69; Pamp;lt;0.0001). On a per-vessel basis, diagnostic accuracy improved from 58% (95% confidence interval, 54%-63%) by CTA to 78% (75%-82%) by ML-based CT-FFR. The per-patient accuracy improved from 71% (66%-76%) by CTA to 85% (81%-89%) by adding ML-based CT-FFR as 62 of 85 (73%) false-positive CTA results could be correctly reclassified by adding ML-based CT-FFR. Conclusions: On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR.

  • 2.
    Stoll, Victoria M.
    et al.
    Univ Oxford, England.
    Hess, Aaron T.
    Univ Oxford, England.
    Rodgers, Christopher T.
    Univ Oxford, England; Univ Cambridge, England.
    Bissell, Malenka M.
    Univ Oxford, England.
    Dyverfeldt, Petter
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Ebbers, Tino
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Primary Care Center, Primary Health Care Center Ödeshög. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Myerson, Saul G.
    Univ Oxford, England.
    Carlhäll, Carljohan
    Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Clinical Physiology in Linköping. Linköping University, Center for Medical Image Science and Visualization (CMIV).
    Neubauer, Stefan
    Univ Oxford, England.
    Left Ventricular Flow Analysis Novel Imaging Biomarkers and Predictors of Exercise Capacity in Heart Failure2019In: Circulation Cardiovascular Imaging, ISSN 1941-9651, E-ISSN 1942-0080, Vol. 12, no 5Article in journal (Refereed)
    Abstract [en]

    Background: Cardiac remodeling, after a myocardial insult, often causes progression to heart failure. The relationship between alterations in left ventricular blood flow, including kinetic energy (KE), and remodeling is uncertain. We hypothesized that increasing derangements in left ventricular blood flow would relate to (1) conventional cardiac remodeling markers, (2) increased levels of biochemical remodeling markers, (3) altered cardiac energetics, and (4) worsening patient symptoms and functional capacity. Methods: Thirty-four dilated cardiomyopathy patients, 30 ischemic cardiomyopathy patients, and 36 controls underwent magnetic resonance including 4-dimensional flow, BNP (brain-type natriuretic peptide) measurement, functional capacity assessment (6-minute walk test), and symptom quantification. A subgroup of dilated cardiomyopathy and control subjects underwent cardiac energetic assessment. Left ventricular flow was separated into 4 components: direct flow, retained inflow, delayed ejection flow, and residual volume. Average KE throughout the cardiac cycle was calculated. Results: Patients had reduced direct flow proportion and direct-flow average KE compared with controls (Pamp;lt;0.0001). The residual volume proportion and residual volume average KE were increased in patients (Pamp;lt;0.0001). Importantly, in a multiple linear regression model to predict the patients 6-minute walk test, the independent predictors were age (beta=-0.3015; P=0.019) and direct-flow average KE (beta=0.280, P=0.035; R-2 model, 0.466, P=0.002). In contrast, neither ejection fraction nor left ventricular volumes were independently predictive. Conclusions: This study demonstrates an independent predictive relationship between the direct-flow average KE and a prognostic measure of functional capacity. Intracardiac 4-dimensional flow parameters are novel biomarkers in heart failure and may provide additive value in monitoring new therapies and predicting prognosis.

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