Diagnostic performance of angiography-derived fractional flow reserve compared to pressure wire-derived fractional flow reserve: Rationale and design of MPFFR pivotal trial.

Analytical equation-based model Angiography-derived fractional flow rate Artificial intelligence Fractional flow rate

Journal

Cardiovascular revascularization medicine : including molecular interventions
ISSN: 1878-0938
Titre abrégé: Cardiovasc Revasc Med
Pays: United States
ID NLM: 101238551

Informations de publication

Date de publication:
24 Sep 2024
Historique:
received: 08 07 2024
revised: 16 09 2024
accepted: 23 09 2024
medline: 3 10 2024
pubmed: 3 10 2024
entrez: 1 10 2024
Statut: aheadofprint

Résumé

Cardiovascular disease remains the leading cause of death and the use of percutaneous coronary intervention (PCI) is steadily increasing. Current guidelines advocate the use of the fractional flow reserve (FFR) to assess coronary stenosis and treatment strategies; however, invasive FFR has some limitations. Angiography-derived FFR is a potential alternative for calculating FFR from two-dimensional (2D) angiographic images, thereby reducing invasiveness and complications. A novel artificial intelligence (AI)-based angiography-derived FFR, named "MPFFR," offers automated operator-independent hemodynamic calculations; this phase 3 trial aims to validate its diagnostic performance against 2D-quantitative coronary angiography (QCA). This pivotal MPFFR trial is a prospective, multicenter, single-blind study. This trial involves patients with coronary artery disease (CAD) from eight cardiovascular centers. Invasive FFR will be performed according to standard guidelines and defined as the reference standard. Angiography-derived FFR will be computed using a proprietary method and 2D-QCA will be performed using validated software. The primary endpoint is the area under the curve for identifying physiologically significant coronary stenosis (FFR ≤0.80), with secondary endpoints including diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and correlations between angiography-derived and invasive FFR. This study is designed to demonstrate the superiority of angiography-derived FFR over 2D-QCA and is powered to achieve this with a sample size of 240 patients. Medipixel Inc. supports the trial and is not involved in the data analysis or management.

Sections du résumé

BACKGROUND BACKGROUND
Cardiovascular disease remains the leading cause of death and the use of percutaneous coronary intervention (PCI) is steadily increasing. Current guidelines advocate the use of the fractional flow reserve (FFR) to assess coronary stenosis and treatment strategies; however, invasive FFR has some limitations. Angiography-derived FFR is a potential alternative for calculating FFR from two-dimensional (2D) angiographic images, thereby reducing invasiveness and complications. A novel artificial intelligence (AI)-based angiography-derived FFR, named "MPFFR," offers automated operator-independent hemodynamic calculations; this phase 3 trial aims to validate its diagnostic performance against 2D-quantitative coronary angiography (QCA).
METHODS AND ANALYSIS METHODS
This pivotal MPFFR trial is a prospective, multicenter, single-blind study. This trial involves patients with coronary artery disease (CAD) from eight cardiovascular centers. Invasive FFR will be performed according to standard guidelines and defined as the reference standard. Angiography-derived FFR will be computed using a proprietary method and 2D-QCA will be performed using validated software. The primary endpoint is the area under the curve for identifying physiologically significant coronary stenosis (FFR ≤0.80), with secondary endpoints including diagnostic accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and correlations between angiography-derived and invasive FFR. This study is designed to demonstrate the superiority of angiography-derived FFR over 2D-QCA and is powered to achieve this with a sample size of 240 patients. Medipixel Inc. supports the trial and is not involved in the data analysis or management.

Identifiants

pubmed: 39353758
pii: S1553-8389(24)00677-8
doi: 10.1016/j.carrev.2024.09.015
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Hyun-Wook Chu (HW)

Department of Internal Medicine, Seoul National University College of Medicine and Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, South Korea; Department of Internal Medicine, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, South Korea.

Chang-Hwan Yoon (CH)

Department of Internal Medicine, Seoul National University College of Medicine and Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, South Korea.

Donghoon Han (D)

Department of Internal Medicine, Hallym University Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea.

Won-Woo Seo (WW)

Department of Internal Medicine, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, South Korea.

Sang-Don Park (SD)

Department of Internal Medicine, Inha University Hospital, Inha University College of Medicine, Incheon, South Korea.

Joon Hyung Doh (JH)

Department of Internal Medicine, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, South Korea.

Chang-Wook Nam (CW)

Department of Internal Medicine, Keimyung University School of Medicine, Keimyung University Dongsan Hospital, Daegu, South Korea.

Eun-Seok Shin (ES)

Department of Internal Medicine, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, South Korea.

Bon-Kwon Koo (BK)

Department of Internal Medicine, Seoul National University College of Medicine and Cardiovascular Center, Seoul National University Hospital, Seoul, South Korea.

In-Ho Chae (IH)

Department of Internal Medicine, Seoul National University College of Medicine and Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, South Korea.

Tae-Jin Youn (TJ)

Department of Internal Medicine, Seoul National University College of Medicine and Cardiovascular Center, Seoul National University Bundang Hospital, Seongnam, South Korea. Electronic address: ytjmd@snubh.org.

Classifications MeSH