Diagnostic accuracy of coronary computed tomography angiography-derived fractional flow reserve (CT-FFR) in patients before liver transplantation using CT-FFR machine learning algorithm.
Humans
Fractional Flow Reserve, Myocardial
Computed Tomography Angiography
/ methods
Coronary Angiography
/ methods
Liver Transplantation
Constriction, Pathologic
ROC Curve
Coronary Stenosis
/ diagnostic imaging
Coronary Artery Disease
/ diagnostic imaging
Tomography, X-Ray Computed
Machine Learning
Predictive Value of Tests
Computed tomography
Coronary angiography
Fractional flow reserve
Liver transplantation
Machine learning
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
Dec 2022
Dec 2022
Historique:
received:
13
02
2022
accepted:
30
05
2022
revised:
19
05
2022
pubmed:
22
6
2022
medline:
1
12
2022
entrez:
21
6
2022
Statut:
ppublish
Résumé
Liver transplantation (LT) is associated with high stress on the cardiovascular system. Ruling out coronary artery disease (CAD) is an important part of evaluation for LT. The aim of our study was to assess whether CT-derived fractional flow reserve (CT-FFR) allows for differentiation of hemodynamically significant and non-significant coronary stenosis in patients evaluated for LT. In total, 201 patients undergoing LT evaluation were included in the study. The patients received coronary computed tomography angiography (CCTA) to rule out CAD and invasive coronary angiography (ICA) to further evaluate coronary lesions found in CCTA if a significant (≥ 50 % on CCTA) stenosis was suspected. CT-FFR was computed from CCTA datasets using a machine learning-based algorithm and compared to ICA as a standard of reference. Coronary lesions with CT-FFR ≤ 0.80 were defined as hemodynamically significant. In 127 of 201 patients (63%), an obstructive CAD was ruled out by CCTA. In the remaining 74 patients (37%), at least one significant stenosis was suspected in CCTA. Compared to ICA, sensitivity, specificity, PPV, and NPV of the CT-FFR measurements were 71% (49-92%), 90% (82-98%), 67% (45-88%), and 91% (84-99%), respectively. The diagnostic accuracy was 85% (85-86%). In 69% of cases (52 of 75 lesions), additional analysis by CT-FFR correctly excluded the hemodynamic significance of the stenosis. Machine learning-based CT-FFR seems to be a very promising noninvasive approach for exclusion of hemodynamic significant coronary stenoses in patients undergoing evaluation for LT and could help to reduce the rate of invasive coronary angiography in this high-risk population. • Machine learning-based computed tomography-derived fractional flow reserve (CT-FFR) seems to be a very promising noninvasive approach for exclusion of hemodynamic significance of coronary stenoses in patients undergoing evaluation for liver transplantation and could help to reduce the rate of invasive coronary angiography in this high-risk population.
Identifiants
pubmed: 35729425
doi: 10.1007/s00330-022-08921-1
pii: 10.1007/s00330-022-08921-1
pmc: PMC9705488
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
8761-8768Informations de copyright
© 2022. The Author(s).
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