Defining heterogeneity of epicardial functional stenosis with low coronary flow reserve by unsupervised machine learning.

Low CFR is associated with poor prognosis, whereas it is a heterogeneous condition according to the actual coronary flow, such as high resting or low hyperemic coronary flow, which should have different physiological traits and clinical implications. This study aimed to detect and define the sub-phenotypes of vessels with low coronary flow reserve (CFR) epicardial disease by unsupervised machine-learning methods. Hierarchical clustering was applied to 376 vessels from 364 patients with CFR less than the median and fractional flow reserve ≤ 0.8 from a global, multicenter registry. Detailed features of coronary flow physiology and survivals from vessel-oriented composite outcomes (VOCO) were assessed according to the clusters. Clustering defined three distinct physiological subgroups (PS). PS1 (n = 151) were characterized by high resting coronary flow, dominantly left anterior descending artery (LAD) lesions. PS2 (n = 131) were, in contrast, low hyperemic coronary flow, mainly LAD lesions. PS3 (n = 82) mostly consisted of non-LAD lesions with similar flow status to PS1 except for the low hyperemic Pd. Survivals from VOCO were significantly different according to the clusters (p = 0.005) and PS3 had the highest rate of VOCO. In a COX proportional model predicting VOCO, there was a significant interaction between PCI and PSs, suggesting potentially different effects of PCI on outcome between PS1 and PS2. The unsupervised machine-learning approaches provided unique insights into low CFR condition. Among low CFR epicardial lesions, high resting flow with low hyperemic Pd might be related to poor prognosis, and low hyperemic flow in LAD could benefit from elective PCI. CLINICAL TRIAL REGISTRATION INFORMATION: https://clinicaltrials.gov/ct2/show/NCT03690713 , NCT03690713.