Literature DB >> 32968637

Machine learning-based CT fractional flow reserve assessment in acute chest pain: first experience.

Matthias Eberhard1, Tin Nadarevic2, Andrej Cousin1, Jochen von Spiczak1,3, Ricarda Hinzpeter1, Andre Euler1, Fabian Morsbach1, Robert Manka1,3,4, Dagmar I Keller5, Hatem Alkadhi1.   

Abstract

BACKGROUND: Computed tomography (CT)-derived fractional flow reserve (FFRCT) enables the non-invasive functional assessment of coronary artery stenosis. We evaluated the feasibility and potential clinical role of FFRCT in patients presenting to the emergency department with acute chest pain who underwent chest-pain CT (CPCT).
METHODS: For this retrospective IRB-approved study, we included 56 patients (median age: 62 years, 14 females) with acute chest pain who underwent CPCT and who had at least a mild (≥25% diameter) coronary artery stenosis. CPCT was evaluated for the presence of acute plaque rupture and vulnerable plaque features. FFRCT measurements were performed using a machine learning-based software. We assessed the agreement between the results from FFRCT and patient outcome (including results from invasive catheter angiography and from any non-invasive cardiac imaging test, final clinical diagnosis and revascularization) for a follow-up of 3 months.
RESULTS: FFRCT was technically feasible in 38/56 patients (68%). Eleven of the 38 patients (29%) showed acute plaque rupture in CPCT; all of them underwent immediate coronary revascularization. Of the remaining 27 patients (71%), 16 patients showed vulnerable plaque features (59%), of whom 11 (69%) were diagnosed with acute coronary syndrome (ACS) and 10 (63%) underwent coronary revascularization. In patients with vulnerable plaque features in CPCT, FFRCT had an agreement with outcome in 12/16 patients (75%). In patients without vulnerable plaque features (n=11), one patient showed myocardial ischemia (9%). In these patients, FFRCT and patient outcome showed an agreement in 10/11 patients (91%).
CONCLUSIONS: Our preliminary data show that FFRCT is feasible in patients with acute chest pain who undergo CPCT provided that image quality is sufficient. FFRCT has the potential to improve patient triage by reducing further downstream testing but appears of limited value in patients with CT signs of acute plaque rupture. 2020 Cardiovascular Diagnosis and Therapy. All rights reserved.

Entities:  

Keywords:  Acute coronary syndrome (ACS); computed tomography angiography; fractional flow reserve; machine learning; myocardial

Year:  2020        PMID: 32968637      PMCID: PMC7487397          DOI: 10.21037/cdt-20-381

Source DB:  PubMed          Journal:  Cardiovasc Diagn Ther        ISSN: 2223-3652


  35 in total

1.  High-risk atherosclerotic plaque features for cardiovascular risk assessment in the Prospective Multicenter Imaging Study for Evaluation of Chest Pain trial.

Authors:  Subhi J Al'Aref; Jessica M Peña; James K Min
Journal:  Cardiovasc Diagn Ther       Date:  2019-02

Review 2.  Triple rule-out computed tomographic angiography for chest pain: a diagnostic systematic review and meta-analysis.

Authors:  David Ayaram; M Fernanda Bellolio; M Hassan Murad; Torrey A Laack; Annie T Sadosty; Patricia J Erwin; Judd E Hollander; Victor M Montori; Ian G Stiell; Erik P Hess
Journal:  Acad Emerg Med       Date:  2013-09       Impact factor: 3.451

3.  2019 ESC Guidelines for the diagnosis and management of chronic coronary syndromes.

Authors:  Juhani Knuuti; William Wijns; Antti Saraste; Davide Capodanno; Emanuele Barbato; Christian Funck-Brentano; Eva Prescott; Robert F Storey; Christi Deaton; Thomas Cuisset; Stefan Agewall; Kenneth Dickstein; Thor Edvardsen; Javier Escaned; Bernard J Gersh; Pavel Svitil; Martine Gilard; David Hasdai; Robert Hatala; Felix Mahfoud; Josep Masip; Claudio Muneretto; Marco Valgimigli; Stephan Achenbach; Jeroen J Bax
Journal:  Eur Heart J       Date:  2020-01-14       Impact factor: 29.983

4.  Clinical Use of Coronary CTA-Derived FFR for Decision-Making in Stable CAD.

Authors:  Bjarne L Nørgaard; Jakob Hjort; Sara Gaur; Nicolaj Hansson; Hans Erik Bøtker; Jonathon Leipsic; Ole N Mathiassen; Erik L Grove; Kamilla Pedersen; Evald H Christiansen; Anne Kaltoft; Lars C Gormsen; Michael Mæng; Christian J Terkelsen; Steen D Kristensen; Lars R Krusell; Jesper M Jensen
Journal:  JACC Cardiovasc Imaging       Date:  2016-04-13

5.  Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography.

Authors:  Majd Zreik; Robbert W van Hamersvelt; Nadieh Khalili; Jelmer M Wolterink; Michiel Voskuil; Max A Viergever; Tim Leiner; Ivana Isgum
Journal:  IEEE Trans Med Imaging       Date:  2019-11-12       Impact factor: 10.048

6.  Experience With an On-Site Coronary Computed Tomography-Derived Fractional Flow Reserve Algorithm for the Assessment of Intermediate Coronary Stenoses.

Authors:  Patrick M Donnelly; Márton Kolossváry; Júlia Karády; Peter A Ball; Stephanie Kelly; Donna Fitzsimons; Mark S Spence; Csilla Celeng; Tamás Horváth; Bálint Szilveszter; Hendrik W van Es; Martin J Swaans; Béla Merkely; Pál Maurovich-Horvat
Journal:  Am J Cardiol       Date:  2017-10-10       Impact factor: 2.778

7.  Coronary CT Angiography-derived Fractional Flow Reserve: Machine Learning Algorithm versus Computational Fluid Dynamics Modeling.

Authors:  Christian Tesche; Carlo N De Cecco; Stefan Baumann; Matthias Renker; Tindal W McLaurin; Taylor M Duguay; Richard R Bayer; Daniel H Steinberg; Katharine L Grant; Christian Canstein; Chris Schwemmer; Max Schoebinger; Lucian M Itu; Saikiran Rapaka; Puneet Sharma; U Joseph Schoepf
Journal:  Radiology       Date:  2018-04-10       Impact factor: 11.105

8.  CAD-RADS(TM) Coronary Artery Disease - Reporting and Data System. An expert consensus document of the Society of Cardiovascular Computed Tomography (SCCT), the American College of Radiology (ACR) and the North American Society for Cardiovascular Imaging (NASCI). Endorsed by the American College of Cardiology.

Authors:  Ricardo C Cury; Suhny Abbara; Stephan Achenbach; Arthur Agatston; Daniel S Berman; Matthew J Budoff; Karin E Dill; Jill E Jacobs; Christopher D Maroules; Geoffrey D Rubin; Frank J Rybicki; U Joseph Schoepf; Leslee J Shaw; Arthur E Stillman; Charles S White; Pamela K Woodard; Jonathon A Leipsic
Journal:  J Cardiovasc Comput Tomogr       Date:  2016-06-15

9.  Computed tomographic angiography characteristics of atherosclerotic plaques subsequently resulting in acute coronary syndrome.

Authors:  Sadako Motoyama; Masayoshi Sarai; Hiroto Harigaya; Hirofumi Anno; Kaori Inoue; Tomonori Hara; Hiroyuki Naruse; Junichi Ishii; Hitoshi Hishida; Nathan D Wong; Renu Virmani; Takeshi Kondo; Yukio Ozaki; Jagat Narula
Journal:  J Am Coll Cardiol       Date:  2009-06-30       Impact factor: 24.094

10.  Effects of complete revascularization on long-term treatment outcomes in patients with multivessel coronary artery disease over 80 years of age admitted for acute coronary syndrome.

Authors:  Kirill Berezhnoi; Leonid Kokov; Alexandr Vanyukov
Journal:  Cardiovasc Diagn Ther       Date:  2019-08
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  3 in total

1.  Long-term prognostic value of the serial changes of CT-derived fractional flow reserve and perivascular fat attenuation index.

Authors:  Xu Dai; Yang Hou; Chunxiang Tang; Zhigang Lu; Chengxing Shen; Longjiang Zhang; Jiayin Zhang
Journal:  Quant Imaging Med Surg       Date:  2022-01

Review 2.  Artificial Intelligence-A Good Assistant to Multi-Modality Imaging in Managing Acute Coronary Syndrome.

Authors:  Ming-Hao Liu; Chen Zhao; Shengfang Wang; Haibo Jia; Bo Yu
Journal:  Front Cardiovasc Med       Date:  2022-02-16

Review 3.  Clinical Applications of Artificial Intelligence-An Updated Overview.

Authors:  Ștefan Busnatu; Adelina-Gabriela Niculescu; Alexandra Bolocan; George E D Petrescu; Dan Nicolae Păduraru; Iulian Năstasă; Mircea Lupușoru; Marius Geantă; Octavian Andronic; Alexandru Mihai Grumezescu; Henrique Martins
Journal:  J Clin Med       Date:  2022-04-18       Impact factor: 4.964

  3 in total

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