Machine-learning-based prediction of fractional flow reserve after percutaneous coronary intervention.
Détails
ID Serval
serval:BIB_D5749FCE06CA
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Institution
Titre
Machine-learning-based prediction of fractional flow reserve after percutaneous coronary intervention.
Périodique
Atherosclerosis
ISSN
1879-1484 (Electronic)
ISSN-L
0021-9150
Statut éditorial
Publié
Date de publication
10/2023
Peer-reviewed
Oui
Volume
383
Pages
117310
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.
Mots-clé
Humans, Coronary Artery Disease/diagnosis, Coronary Artery Disease/therapy, Fractional Flow Reserve, Myocardial, Treatment Outcome, Percutaneous Coronary Intervention, Coronary Angiography, Predictive Value of Tests, Fractional flow reserve, Machine-learning, Percutaneous coronary intervention
Pubmed
Web of science
Création de la notice
09/10/2023 13:41
Dernière modification de la notice
19/12/2023 8:14