Combined cCTA and TAVR Planning for Ruling Out Significant CAD: Added Value of ML-Based CT-FFR.

Aged Aged, 80 and over Computed Tomography Angiography / methods Coronary Angiography / methods Coronary Artery Disease / diagnostic imaging Coronary Stenosis Fractional Flow Reserve, Myocardial Humans Machine Learning Predictive Value of Tests Tomography, X-Ray Computed Transcatheter Aortic Valve Replacement / adverse effects

aortic stenosis computed tomography coronary angiography coronary angiography coronary artery disease diagnostic accuracy machine learning transcatheter aortic valve implantation

Journal

JACC. Cardiovascular imaging

ISSN: 1876-7591

Titre abrégé: JACC Cardiovasc Imaging

Pays: United States

ID NLM: 101467978

Informations de publication

Date de publication:
03 2022

Historique:

received: 26 03 2021

revised: 07 09 2021

accepted: 10 09 2021

pubmed: 22 11 2021

medline: 28 4 2022

entrez: 21 11 2021

Statut: ppublish

Résumé

The purpose of this study was to analyze the ability of machine-learning (ML)-based computed tomography (CT)-derived fractional flow reserve (CT-FFR) to further improve the diagnostic performance of coronary CT angiography (cCTA) for ruling out significant coronary artery disease (CAD) during pre-transcatheter aortic valve replacement (TAVR) evaluation in patients with a high pre-test probability for CAD. CAD is a frequent comorbidity in patients undergoing TAVR. Current guidelines recommend its assessment before TAVR. If significant CAD can be excluded on cCTA, invasive coronary angiography (ICA) may be avoided. Although cCTA is a very sensitive test, it is limited by relatively low specificity and positive predictive value, particularly in high-risk patients. Overall, 460 patients (age 79.6 ± 7.4 years) undergoing pre-TAVR CT were included and examined with an electrocardiogram-gated CT scan of the heart and high-pitch scan of the vascular access route. Images were evaluated for significant CAD. Patients routinely underwent ICA (388/460), which was omitted at the discretion of the local Heart Team if CAD could be effectively ruled out on cCTA (72/460). CT examinations in which CAD could not be ruled out (CAD ML-based CT-FFR was successfully performed in 79.4% (216/272) of all CAD ML-based CT-FFR may further improve the diagnostic performance of cCTA by correctly reclassifying a considerable proportion of patients with morphological signs of obstructive CAD on cCTA during pre-TAVR evaluation. Thereby, CT-FFR has the potential to further reduce the need for ICA in this challenging elderly group of patients before TAVR.

Sections du résumé

OBJECTIVES

BACKGROUND

CAD is a frequent comorbidity in patients undergoing TAVR. Current guidelines recommend its assessment before TAVR. If significant CAD can be excluded on cCTA, invasive coronary angiography (ICA) may be avoided. Although cCTA is a very sensitive test, it is limited by relatively low specificity and positive predictive value, particularly in high-risk patients.

METHODS

Overall, 460 patients (age 79.6 ± 7.4 years) undergoing pre-TAVR CT were included and examined with an electrocardiogram-gated CT scan of the heart and high-pitch scan of the vascular access route. Images were evaluated for significant CAD. Patients routinely underwent ICA (388/460), which was omitted at the discretion of the local Heart Team if CAD could be effectively ruled out on cCTA (72/460). CT examinations in which CAD could not be ruled out (CAD

RESULTS

ML-based CT-FFR was successfully performed in 79.4% (216/272) of all CAD

CONCLUSIONS

ML-based CT-FFR may further improve the diagnostic performance of cCTA by correctly reclassifying a considerable proportion of patients with morphological signs of obstructive CAD on cCTA during pre-TAVR evaluation. Thereby, CT-FFR has the potential to further reduce the need for ICA in this challenging elderly group of patients before TAVR.

Identifiants

DOI: 10.1016/j.jcmg.2021.09.013 PMID: 34801449

pubmed: 34801449

pii: S1936-878X(21)00697-5

doi: 10.1016/j.jcmg.2021.09.013

pii:

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Pagination

476-486

Commentaires et corrections

Type : CommentIn

Informations de copyright

Déclaration de conflit d'intérêts

Funding Support and Author Disclosures Mr Panknin is an employee of Siemens Healthcare. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.