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Machine-learning-based prediction of fractional flow reserve after percutaneous coronary intervention

Hamaya, R | Goto, S | Hwang, D | Zhang, J | Yang, S | Lee, JM | Hoshino, M | Nam, CW | Shin, ES | Doh, JH | Chen, SL | Toth, GG | Piroth, Z | Hakeem, A | Uretsky, BF | Hokama, Y | Tanaka, N | Lim, HS  | Ito, T | Matsuo, A | Azzalini, L | Leesar, MA | Collet, C | Koo, BK | De Bruyne, B | Kakuta, T
Atherosclerosis, 383. : 117310-117310, 2023
Journal Title
Background and aims: 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. Methods: 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. Results: 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. Conclusions: 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.


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Journal Papers > School of Medicine / Graduate School of Medicine > Cardiology
Ajou Authors
임, 홍석
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