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

Autor:	Christel Weiss, Stefan Baumann, Tobias Becher, Marlon Rutsch, Stefan Pfleger, Daniel Overhoff, Ibrahim Akin, Dirk Lossnitzer, Sonja Janssen, Martin Borggrefe, Leonard Chandra
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	coronary physiology medicine.medical_treatment lcsh:Medicine Hemodynamics Fractional flow reserve 030204 cardiovascular system & hematology Revascularization coronary CT angiography Article Coronary artery disease 03 medical and health sciences 0302 clinical medicine medicine non-invasive test 030212 general & internal medicine fractional flow reserve business.industry lcsh:R General Medicine medicine.disease Computed tomographic angiography Pre- and post-test probability Coronary arteries Stenosis medicine.anatomical_structure CT derived fractional flow reserve revascularization atherosclerosis business Nuclear medicine coronary artery disease
Zdroj:	Journal of Clinical Medicine Journal of Clinical Medicine, Vol 9, Iss 3, p 676 (2020) Volume 9 Issue 3
ISSN:	2077-0383
Popis:	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 &le 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 &le 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.
Databáze:	OpenAIRE
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