Titre
Machine-learning-based prediction of fractional flow reserve after percutaneous coronary intervention.
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Hamaya, R.
Auteure/Auteur
Goto, S.
Auteure/Auteur
Hwang, D.
Auteure/Auteur
Zhang, J.
Auteure/Auteur
Yang, S.
Auteure/Auteur
Lee, J.M.
Auteure/Auteur
Hoshino, M.
Auteure/Auteur
Nam, C.W.
Auteure/Auteur
Shin, E.S.
Auteure/Auteur
Doh, J.H.
Auteure/Auteur
Chen, S.L.
Auteure/Auteur
Toth, G.G.
Auteure/Auteur
Piroth, Z.
Auteure/Auteur
Hakeem, A.
Auteure/Auteur
Uretsky, B.F.
Auteure/Auteur
Hokama, Y.
Auteure/Auteur
Tanaka, N.
Auteure/Auteur
Lim, H.S.
Auteure/Auteur
Ito, T.
Auteure/Auteur
Matsuo, A.
Auteure/Auteur
Azzalini, L.
Auteure/Auteur
Leesar, M.A.
Auteure/Auteur
Collet, C.
Auteure/Auteur
Koo, B.K.
Auteure/Auteur
De Bruyne, B.
Auteure/Auteur
Kakuta, T.
Auteure/Auteur
Liens vers les personnes
Liens vers les unités
ISSN
1879-1484
Statut éditorial
Publié
Date de publication
2023-10
Volume
383
Première page
117310
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Multicenter Study ; Journal Article
Publication Status: ppublish
Publication Status: ppublish
Résumé
Post-percutaneous coronary intervention (PCI) fractional flow reserve (FFR) reflects residual atherosclerotic burden and is associated with future events. How much post-PCI FFR can be predicted based on baseline basic information and the clinical relevance have not been investigated.
We compiled a multicenter registry of patients undergoing pre- and post-PCI FFR. Machine-learning (ML) algorithms were designed to predict post-PCI FFR levels from baseline demographics, quantitative coronary angiography, and pre-PCI FFR. FFR deviation was defined as actual minus ML-predicted post-PCI FFR levels, and its association with incident target vessel failure (TVF) was evaluated.
Median (IQR) pre- and post-PCI FFR values were 0.71 (0.61, 0.77) and 0.88 (0.84, 0.93), respectively. The Spearman correlation coefficient of the actual and predicted post-PCI FFR was 0.54 (95% CI: 0.52, 0.57). FFR deviation was non-linearly associated with incident TVF (HR [95% CI] with Q3 as reference: 1.65 [1.14, 2.39] in Q1, 1.42 [0.98, 2.08] in Q2, 0.81 [0.53, 1.26] in Q4, and 1.04 [0.69, 1.56] in Q5). A model with polynomial function of continuous FFR deviation indicated increasing TVF risk for FFR deviation ≤0 but plateau risk with FFR deviation >0.
An ML-based algorithm using baseline data moderately predicted post-PCI FFR. The deviation of post-PCI FFR from the predicted value was associated with higher vessel-oriented event.
We compiled a multicenter registry of patients undergoing pre- and post-PCI FFR. Machine-learning (ML) algorithms were designed to predict post-PCI FFR levels from baseline demographics, quantitative coronary angiography, and pre-PCI FFR. FFR deviation was defined as actual minus ML-predicted post-PCI FFR levels, and its association with incident target vessel failure (TVF) was evaluated.
Median (IQR) pre- and post-PCI FFR values were 0.71 (0.61, 0.77) and 0.88 (0.84, 0.93), respectively. The Spearman correlation coefficient of the actual and predicted post-PCI FFR was 0.54 (95% CI: 0.52, 0.57). FFR deviation was non-linearly associated with incident TVF (HR [95% CI] with Q3 as reference: 1.65 [1.14, 2.39] in Q1, 1.42 [0.98, 2.08] in Q2, 0.81 [0.53, 1.26] in Q4, and 1.04 [0.69, 1.56] in Q5). A model with polynomial function of continuous FFR deviation indicated increasing TVF risk for FFR deviation ≤0 but plateau risk with FFR deviation >0.
An ML-based algorithm using baseline data moderately predicted post-PCI FFR. The deviation of post-PCI FFR from the predicted value was associated with higher vessel-oriented event.
PID Serval
serval:BIB_D5749FCE06CA
PMID
Date de création
2023-10-09T11:41:22.270Z
Date de création dans IRIS
2025-05-21T04:02:43Z