Literature DB >> 31664509

Correlation of machine learning computed tomography-based fractional flow reserve with instantaneous wave free ratio to detect hemodynamically significant coronary stenosis.

Stefan Baumann1,2,3, Markus Hirt4,5, U Joseph Schoepf6, Marlon Rutsch4,5, Christian Tesche7, Matthias Renker8, Joseph W Golden6, Sebastian J Buss9, Tobias Becher4,5,10, Waldemar Bojara11, Christel Weiss12, Theano Papavassiliu4,5, Ibrahim Akin4,5, Martin Borggrefe4,5, Stefan O Schoenberg13, Holger Haubenreisser13, Daniel Overhoff13, Dirk Lossnitzer4,5.   

Abstract

BACKGROUND: Fractional flow reserve based on coronary CT angiography (CT-FFR) is gaining importance for non-invasive hemodynamic assessment of coronary artery disease (CAD). We evaluated the on-site CT-FFR with a machine learning algorithm (CT-FFRML) for the detection of hemodynamically significant coronary artery stenosis in comparison to the invasive reference standard of instantaneous wave free ratio (iFR®).
METHODS: This study evaluated patients with CAD who had a clinically indicated coronary computed tomography angiography (cCTA) and underwent invasive coronary angiography (ICA) with iFR®-measurements. Standard cCTA studies were acquired with third-generation dual-source computed tomography and analyzed with on-site prototype CT-FFRML software.
RESULTS: We enrolled 40 patients (73% males, mean age 67 ± 12 years) who had iFR®-measurement and CT-FFRML calculation. The mean calculation time of CT-FFRML values was 11 ± 2 min. The CT-FFRML algorithm showed, on per-patient and per-lesion level, respectively, a sensitivity of 92% (95% CI 64-99%) and 87% (95% CI 59-98%), a specificity of 96% (95% CI 81-99%) and 95% (95% CI 84-99%), a positive predictive value of 92% (95% CI 64-99%), and 87% (95% CI 59-98%), and a negative predictive value of 96% (95% CI 81-99%) and 95% (95% CI 84-99%). The area under the receiver operating characteristic curve for CT-FFRML on per-lesion level was 0.97 (95% CI 0.91-1.00). Per lesion, the Pearson's correlation between the CT-FFRML and iFR® showed a strong correlation of r = 0.82 (p < 0.0001; 95% CI 0.715-0.920).
CONCLUSION: On-site CT-FFRML correlated well with the invasive reference standard of iFR® and allowed for the non-invasive detection of hemodynamically significant coronary stenosis.

Entities:  

Keywords:  Coronary CT angiography; Coronary artery disease; Fractional flow reserve derived from coronary computed tomography angiography; Instantaneous wave-free ratio; Invasive coronary angiography; Myocardial ischemia

Year:  2019        PMID: 31664509     DOI: 10.1007/s00392-019-01562-3

Source DB:  PubMed          Journal:  Clin Res Cardiol        ISSN: 1861-0684            Impact factor:   5.460


  8 in total

Review 1.  [Morphological and functional diagnostics of coronary artery disease by computed tomography].

Authors:  S Baumann; D Overhoff; C Tesche; G Korosoglou; S Kelle; M Nassar; S J Buss; F Andre; M Renker; U J Schoepf; I Akin; S Waldeck; S O Schoenberg; D Lossnitzer
Journal:  Herz       Date:  2022-03-04       Impact factor: 1.443

Review 2.  Machine learning applications in cardiac computed tomography: a composite systematic review.

Authors:  Jonathan James Hyett Bray; Moghees Ahmad Hanif; Mohammad Alradhawi; Jacob Ibbetson; Surinder Singh Dosanjh; Sabrina Lucy Smith; Mahmood Ahmad; Dominic Pimenta
Journal:  Eur Heart J Open       Date:  2022-03-17

3.  Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials.

Authors:  Fay M A Nous; Ricardo P J Budde; Marisa M Lubbers; Yuzo Yamasaki; Isabella Kardys; Tobias A Bruning; Jurgen M Akkerhuis; Marcel J M Kofflard; Bas Kietselaer; Tjebbe W Galema; Koen Nieman
Journal:  Eur Radiol       Date:  2020-03-12       Impact factor: 5.315

4.  Feasibility and Comparison of Resting Full-Cycle Ratio and Computed Tomography Fractional Flow Reserve in Patients with Severe Aortic Valve Stenosis.

Authors:  Hendrik Wienemann; Marcel C Langenbach; Victor Mauri; Maryam Banazadeh; Konstantin Klein; Christopher Hohmann; Samuel Lee; Isabel Breidert; Alexander Hof; Kaveh Eghbalzadeh; Elmar Kuhn; Marcel Halbach; David Maintz; Stephan Baldus; Alexander Bunck; Matti Adam
Journal:  J Cardiovasc Dev Dis       Date:  2022-04-14

5.  Diagnostic performance of deep learning and computational fluid dynamics-based instantaneous wave-free ratio derived from computed tomography angiography.

Authors:  Jingyuan Zhang; Kun Xu; Yumeng Hu; Lin Yang; Xiaochang Leng; Hongfeng Jin; Yiming Tang; Xiaowei Liu; Chen Ye; Yitao Guo; Lei Wang; Jianjun Zhang; Yue Feng; Caiyun Mou; Lijiang Tang; Jianping Xiang; Changqing Du
Journal:  BMC Cardiovasc Disord       Date:  2022-02-05       Impact factor: 2.298

6.  Stable patients with suspected myocardial ischemia: comparison of machine-learning computed tomography-based fractional flow reserve and stress perfusion cardiovascular magnetic resonance imaging to detect myocardial ischemia.

Authors:  Dirk Lossnitzer; Selina Klenantz; Florian Andre; Johannes Goerich; U Joseph Schoepf; Kyle L Pazzo; Andre Sommer; Matthias Brado; Friedemann Gückel; Roman Sokiranski; Tobias Becher; Ibrahim Akin; Sebastian J Buss; Stefan Baumann
Journal:  BMC Cardiovasc Disord       Date:  2022-02-05       Impact factor: 2.298

7.  Case Report: Invasive and Non-invasive Hemodynamic Assessment of Coronary Artery Disease: Strengths and Weaknesses.

Authors:  Ganesh Gajanan; Saurabhi Samant; Chad Hovseth; Yiannis S Chatzizisis
Journal:  Front Cardiovasc Med       Date:  2022-04-25

8.  Comparison of Machine Learning Computed Tomography-Based Fractional Flow Reserve and Coronary CT Angiography-Derived Plaque Characteristics with Invasive Resting Full-Cycle Ratio.

Authors:  Stefan Baumann; Markus Hirt; Christina Rott; Gökce H Özdemir; Christian Tesche; Tobias Becher; Christel Weiss; Svetlana Hetjens; Ibrahim Akin; Stefan O Schoenberg; Martin Borggrefe; Sonja Janssen; Daniel Overhoff; Dirk Lossnitzer
Journal:  J Clin Med       Date:  2020-03-06       Impact factor: 4.241

  8 in total

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