LAUSR

BACKGROUND Assessment of collateral physiology in chronic total occlusions (CTO) currently requires dedicated devices, adds complexity, and increases the cost of the intervention. This study sought to derive collateral physiology from flow velocity changes (∆V) in donor arteries, calculated with artificial intelligence-aided angiography. METHODS Angiographies with successful percutaneous coronary intervention (PCI) in 2 centers were retrospectively analyzed. CTO collaterals were angiographically evaluated according to Rentrop and collateral connections (CC) classifications. Flow velocities in the primary and secondary collateral donor arteries (PCDA, SCDA) were automatically computed pre and post percutaneous coronary intervention (PCI), based on a novel deep-learning model to extract the length/time curve of the coronary filling in angiography. Parameters of collateral physiology, ∆collateral-flow (∆fcoll) and ∆collateral-flow-index (∆CFI), were derived from the ∆V pre-post. RESULTS The analysis was feasible in 105 out of 130 patients. Flow velocity in the PCDA significantly decreased after CTO-PCI, proportionally to the angiographic collateral grading (Rentrop 1: 0.02 ± 0.01 m/s; Rentrop 2: 0.04 ± 0.01 m/s; Rentrop 3: 0.07 ± 0.02; p < 0.001; CC0: 0.01 ± 0.01 m/s; CC1: 0.04 ± 0.02 m/s; CC2: 0.06 ± 0.02 m/s; p < 0.001). ∆fcoll and ∆CFI paralleled ∆V. SCDA also showed a greater reduction in flow velocity if its collateral channels were CC1 vs. CC0 (0.03 ± 0.01 vs. 0.01 ± 0.01 m/s; p < 0.001). For each individual patient, ∆V was more pronounced in the PCDA than in the SCDA. CONCLUSIONS Automatic assessment of collateral physiology in CTO is feasible, based on a deep-learning model analyzing the filling of the donor vessels in angiography. The changes in collateral flow with this novel method are quantitatively proportional to the angiographic grading of the collaterals.