Cardiovascular revascularization medicine : including molecular interventions · 2025
Angiographic phenotypes predict FFR/iFR discordance in severe aortic stenosis: A cluster analysis.
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Dziewierz Artur, Zdzierak Barbara, Rzeszutko Łukasz, Niewiara Łukasz, Legutko Jacek, Kleczyński Paweł
Аннотация
BACKGROUND: Assessing coronary artery disease in severe aortic stenosis (AS) is challenging due to altered hemodynamics, causing discordance between fractional flow reserve (FFR) and instantaneous wave-free ratio (iFR). The role of angiographic lesion characteristics in predicting this discordance remains unclear.
OBJECTIVES: To identify angiographic predictors of FFR/iFR discordance and define distinct angiographic phenotypes using unsupervised machine learning.
METHODS: This prospective, single-center registry evaluated 401 intermediate coronary lesions from 221 patients with severe AS (aortic valve area < 1.0 cm2, mean gradient >40 mmHg) using FFR and iFR. Quantitative coronary angiography measured percent diameter stenosis (%DS), lesion length (LL), and other parameters. The primary outcome was FFR/iFR discordance. Receiver operating characteristic and decision curve analyses evaluated %DS predictive utility. K-means clustering was applied to %DS and LL to identify lesion phenotypes.
RESULTS: FFR/iFR discordance occurred in 30 lesions (7.5 %), exclusively as FFR-negative/iFR-positive pattern. Higher %DS was the only independent angiographic predictor of discordance (adjusted OR 1.35 per 10 % increase; 95 % CI 1.12-1.64; p = 0.002), with modest discriminative ability (AUC = 0.69). Cluster analysis identified three phenotypes: Cluster 0 (High %DS [median 75.0 %]/Intermediate LL [median 15.0 mm], n = 103); Cluster 1 (Low %DS [median 49.0 %]/Short-Intermediate LL [median 13.6 mm], n = 220); and Cluster 2 (Intermediate %DS [median 63.5 %]/Long LL [median 32.4 mm], n = 78). Discordance incidence was highest in Cluster 0 (16.5 %) versus Clusters 1 (2.7 %) and 2 (9.0 %) (p < 0.001).
CONCLUSIONS: In severe AS, FFR/iFR discordance manifests solely as FFR-negative/iFR-positive pattern. While %DS modestly predicts this mismatch, unsupervised clustering reveals that "High %DS / Intermediate Length" lesions primarily drive physiological discordance, identifying cases where iFR may overestimate severity due to AS-related hemodynamics.
OBJECTIVES: To identify angiographic predictors of FFR/iFR discordance and define distinct angiographic phenotypes using unsupervised machine learning.
METHODS: This prospective, single-center registry evaluated 401 intermediate coronary lesions from 221 patients with severe AS (aortic valve area < 1.0 cm2, mean gradient >40 mmHg) using FFR and iFR. Quantitative coronary angiography measured percent diameter stenosis (%DS), lesion length (LL), and other parameters. The primary outcome was FFR/iFR discordance. Receiver operating characteristic and decision curve analyses evaluated %DS predictive utility. K-means clustering was applied to %DS and LL to identify lesion phenotypes.
RESULTS: FFR/iFR discordance occurred in 30 lesions (7.5 %), exclusively as FFR-negative/iFR-positive pattern. Higher %DS was the only independent angiographic predictor of discordance (adjusted OR 1.35 per 10 % increase; 95 % CI 1.12-1.64; p = 0.002), with modest discriminative ability (AUC = 0.69). Cluster analysis identified three phenotypes: Cluster 0 (High %DS [median 75.0 %]/Intermediate LL [median 15.0 mm], n = 103); Cluster 1 (Low %DS [median 49.0 %]/Short-Intermediate LL [median 13.6 mm], n = 220); and Cluster 2 (Intermediate %DS [median 63.5 %]/Long LL [median 32.4 mm], n = 78). Discordance incidence was highest in Cluster 0 (16.5 %) versus Clusters 1 (2.7 %) and 2 (9.0 %) (p < 0.001).
CONCLUSIONS: In severe AS, FFR/iFR discordance manifests solely as FFR-negative/iFR-positive pattern. While %DS modestly predicts this mismatch, unsupervised clustering reveals that "High %DS / Intermediate Length" lesions primarily drive physiological discordance, identifying cases where iFR may overestimate severity due to AS-related hemodynamics.
Предметные рубрики MeSH
HumansFractional Flow Reserve, MyocardialPredictive Value of TestsAortic Valve StenosisCoronary AngiographyFemaleSeverity of Illness IndexPhenotypeProspective StudiesRegistriesClustering AlgorithmsMaleAgedCluster AnalysisCoronary Artery DiseaseUnsupervised Machine LearningCardiac CatheterizationReproducibility of ResultsMiddle AgedPrognosisAortic ValveAged, 80 and overHemodynamics