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open access

Ahead of print
Original Article
Submitted: 2022-05-20
Accepted: 2022-08-26
Published online: 2022-09-16
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Automatic assessment of collateral physiology in chronic total occlusions by means of artificial intelligence

Lili Liu1, Fenghua Ding1, Ying Shen1, Shengxian Tu2, Junqing Yang3, Qiuyang Zhao2, Miao Chu2, Weifeng Shen1, Ruiyan Zhang1, Marco Zimarino4, Gerald S. Werner5, Juan Luis Gutiérrez-Chico16
DOI: 10.5603/CJ.a2022.0089
·
Pubmed: 36117292
Affiliations
  1. Department of Cardiovascular Medicine, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
  2. Biomedical Instrument Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
  3. Department of Cardiology, Guangdong Provincial People’s Hospital, Guangdong, China
  4. Institute of Cardiology, G. D'Annunzio University, Chieti-Pescara, Italy
  5. Klinikum Darmstadt GmbH, Medizinische Klinik I, Darmstadt, Germany
  6. Bundeswehrzentralkrankenhaus (Federal Armed Forces Central Hospital), Koblenz, Germany

open access

Ahead of print
Original articles
Submitted: 2022-05-20
Accepted: 2022-08-26
Published online: 2022-09-16

Abstract

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.

Abstract

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.

Full Text:

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Keywords

chronic total occlusion, coronary collateral circulation, deep learning, collateral donor artery, intracoronary physiology

Supp./Additional Files (3)
Supplementary Table 1. Clinical and lesion characteristics of the patients, distributed according to the Rentrop classification of the CTO artery and CC grading of the PCDA
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Supplementary Figure 1. Paired changes in flow velocity at the primary (left) and secondary (right) collaterals donor arteries of the same patient after CTO revascularisation
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Supplementary material. Computational method of coronary blood flow velocity
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About this article
Title

Automatic assessment of collateral physiology in chronic total occlusions by means of artificial intelligence

Journal

Cardiology Journal

Issue

Ahead of print

Article type

Original Article

Published online

2022-09-16

Page views

869

Article views/downloads

327

DOI

10.5603/CJ.a2022.0089

Pubmed

36117292

Keywords

chronic total occlusion
coronary collateral circulation
deep learning
collateral donor artery
intracoronary physiology

Authors

Lili Liu
Fenghua Ding
Ying Shen
Shengxian Tu
Junqing Yang
Qiuyang Zhao
Miao Chu
Weifeng Shen
Ruiyan Zhang
Marco Zimarino
Gerald S. Werner
Juan Luis Gutiérrez-Chico

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