Coronary Computed Tomographic Angiography-Derived Fractional Flow Reserve Based on Machine Learning for Risk Stratification of Non-Culprit Coronary Narrowings in Patients with Acute Coronary Syndrome

2017 ◽  
Vol 120 (8) ◽  
pp. 1260-1266 ◽  
Author(s):  
Taylor M. Duguay ◽  
Christian Tesche ◽  
Rozemarijn Vliegenthart ◽  
Carlo N. De Cecco ◽  
Han Lin ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acute Coronary Syndrome ◽  
Risk Stratification ◽  
Computed Tomographic Angiography ◽  
Fractional Flow ◽  
Coronary Computed Tomographic Angiography ◽  
Computed Tomographic ◽  
Coronary Syndrome ◽  
Flow Reserve ◽  
Tomographic Angiography

scholarly journals Additional Value of Machine-Learning Computed Tomographic Angiography-Based Fractional Flow Reserve Compared to Standard Computed Tomographic Angiography

2020 ◽  
Vol 9 (3) ◽  
pp. 676
Author(s):  
Dirk Lossnitzer ◽  
Leonard Chandra ◽  
Marlon Rutsch ◽  
Tobias Becher ◽  
Daniel Overhoff ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Fractional Flow Reserve ◽  
Predictive Value ◽  
Computed Tomographic Angiography ◽  
Fractional Flow ◽  
Computed Tomographic ◽  
Coronary Syndrome ◽  
Flow Reserve ◽  
Tomographic Angiography

Background: Machine-learning-based computed-tomography-derived fractional flow reserve (CT-FFRML) obtains a hemodynamic index in coronary arteries. We examined whether it could reduce the number of invasive coronary angiographies (ICA) showing no obstructive lesions. We further compared CT-FFRML-derived measurements to clinical and CT-derived scores. Methods: We retrospectively selected 88 patients (63 ± 11years, 74% male) with chronic coronary syndrome (CCS) who underwent clinically indicated coronary computed tomography angiography (cCTA) and ICA. cCTA image data were processed with an on-site prototype CT-FFRML software. Results: CT-FFRML revealed an index of >0.80 in coronary vessels of 48 (55%) patients. This finding was corroborated in 45 (94%) patients by ICA, yet three (6%) received revascularization. In patients with an index ≤ 0.80, three (8%) of 40 were identified as false positive. A total of 48 (55%) patients could have been retained from ICA. CT-FFRML (AUC = 0.96, p ≤ 0.0001) demonstrated a higher diagnostic accuracy compared to the pretest probability or CT-derived scores and showed an excellent sensitivity (93%), specificity (94%), positive predictive value (PPV; 93%) and negative predictive value (NPV; 94%). Conclusion: CT-FFRML could be beneficial for clinical practice, as it may identify patients with CAD without hemodynamical significant stenosis, and may thus reduce the rate of ICA without necessity for coronary intervention.

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