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

2020 ◽  
Vol 10 (4) ◽  
pp. 820-830
Author(s):  
Matthias Eberhard ◽  
Tin Nadarevic ◽  
Andrej Cousin ◽  
Jochen von Spiczak ◽  
Ricarda Hinzpeter ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chest Pain ◽  
Fractional Flow Reserve ◽  
Acute Chest Pain ◽  
Fractional Flow ◽  
Flow Reserve

scholarly journals Value of Machine Learning–based Coronary CT Fractional Flow Reserve Applied to Triple-Rule-Out CT Angiography in Acute Chest Pain

2020 ◽  
Vol 2 (3) ◽  
pp. e190137 ◽  
Author(s):  
Simon S. Martin ◽  
Domenico Mastrodicasa ◽  
Marly van Assen ◽  
Carlo N. De Cecco ◽  
Richard R. Bayer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Chest Pain ◽  
Ct Angiography ◽  
Fractional Flow Reserve ◽  
Acute Chest Pain ◽  
Fractional Flow ◽  
Coronary Ct ◽  
Flow Reserve ◽  
Triple Rule Out ◽  
Rule Out

scholarly journals Non-invasive fractional flow reserve (FFRCT) in the evaluation of acute chest pain – Concepts and first experiences

2021 ◽  
Vol 138 ◽  
pp. 109633
Author(s):  
Andreas M. Fischer ◽  
Marly van Assen ◽  
U. Joseph Schoepf ◽  
Andrew J. Matuskowitz ◽  
Akos Varga-Szemes ◽  
...  
Keyword(s):  
Chest Pain ◽  
Fractional Flow Reserve ◽  
Acute Chest Pain ◽  
Fractional Flow ◽  
Non Invasive ◽  
Flow Reserve

P6175Prediction of coronary revascularization by coronary computed tomography angiography derived fractional flow reserve - different algorithms for interpretation

2019 ◽  
Vol 40 (Supplement_1) ◽  
Author(s):  
K T Madsen ◽  
K T Veien ◽  
B L Noergaard ◽  
P Larsen ◽  
L Deibjerg ◽  
...  
Keyword(s):  
Chest Pain ◽  
Fractional Flow Reserve ◽  
Predictive Value ◽  
Diagnostic Performance ◽  
Coronary Revascularization ◽  
Standard Of Care ◽  
Fractional Flow ◽  
Coronary Cta ◽  
Flow Reserve ◽  
Stable Chest Pain

Abstract Introduction Coronary CT angiography (CTA) derived fractional flow reserve (FFRct) is increasingly used for guiding referral to invasive procedures in patients with stable chest pain. However, optimal interpretation of FFRct-analysis in terms of location and threshold of applied FFRct-values is unclear. Purpose To evaluate the clinical performance of various vessel-specific physiological FFRct derived measures of ischemia for prediction of standard of care guided coronary revascularization in patients with stable chest pain and coronary artery disease as determined by coronary CTA. Methods Retrospective study in patients with stable chest pain referred for coronary angiography based on coronary CTA. Standard acquired coronary CTA data sets were transmitted for core-laboratory analysis at HeartFlow. Any FFRct value in the major coronary arteries ≥1.8 mm in diameter, including side branches, were registered. Lesions were categorized as positive for ischemia using 6 different algorithms: Lowest in vessel FFRct-value (1) ≤0.75 or (2) ≤0.80; 2 cm distal-to-lesion FFRct-value (3) ≤0.75 or (4) ≤0.80; ΔFFRct (5) ≥0.06 or a combination of 2 and 5. The personnel responsible for downstream patient management had no information regarding FFRct test results. Results A total of 172 patients were included. Revascularization was performed in 62 (35%) patients. The diagnostic performance of different FFRct algorithms for predicting standard of care guided coronary revascularization is shown in the Table. Revascularization Predictions by FFRct N=172 Diagnostic performance FFRCT false negative FFRCT false positive Values given as (%) No. of revasc vessels No. of abnormal vessels FFRCT Algorithm Sens Spec PPV NPV Acc 1 2 3 1 2 3 Distal FFRCT ≤0.75 77 68 58 84 72 12 2 0 29 5 1 Distal FFRCT ≤0.80 92 43 48 90 61 5 0 0 40 20 3 Lesion-specific FFRCT ≤0.75 68 86 74 83 80 17 3 0 12 3 0 Lesion-specific FFRCT ≤0.80 82 78 68 89 80 10 2 0 21 3 1 ΔFFRCT ≥0.06 98 36 47 98 59 1 0 0 51 19 0 Combinationa 92 54 53 92 67 5 0 0 39 12 0 aDistal FFRCT ≤0.80 and ΔFFRCT ≥0.06. Sens = sensitivity; Spec = specificity; PPV = positive predictive value; NPV = negative predictive value; Acc = accuracy; FFRCT = fractional flow reserve derived from coronary CTA; ΔFFRCT = difference between FFRCT-value immediately proximal and distal to lesion; Revasc = revascularized. Conclusion The diagnostic performance of FFRct in terms of predicting standard of care guided coronary revascularization is dependent on the applied algorithm for interpretation of the FFRct-analysis.

scholarly journals Latest Advances in Cardiac CT

2020 ◽  
Vol 15 ◽  
Author(s):  
Thomas D Heseltine ◽  
Scott W Murray ◽  
Balazs Ruzsics ◽  
Michael Fisher
Keyword(s):  
Machine Learning ◽  
Fractional Flow Reserve ◽  
Cardiac Ct ◽  
Disease Risk ◽  
Clinical Care ◽  
Plaque Burden ◽  
Extensive Literature ◽  
Fractional Flow ◽  
Flow Reserve ◽  
Recent Advances

Recent rapid technological advancements in cardiac CT have improved image quality and reduced radiation exposure to patients. Furthermore, key insights from large cohort trials have helped delineate cardiovascular disease risk as a function of overall coronary plaque burden and the morphological appearance of individual plaques. The advent of CT-derived fractional flow reserve promises to establish an anatomical and functional test within one modality. Recent data examining the short-term impact of CT-derived fractional flow reserve on downstream care and clinical outcomes have been published. In addition, machine learning is a concept that is being increasingly applied to diagnostic medicine. Over the coming decade, machine learning will begin to be integrated into cardiac CT, and will potentially make a tangible difference to how this modality evolves. The authors have performed an extensive literature review and comprehensive analysis of the recent advances in cardiac CT. They review how recent advances currently impact on clinical care and potential future directions for this imaging modality.

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