Predicting Successful Chronic Total Occlusion Crossing With Primary Antegrade Wiring Using Machine Learning
Recommended Citation
Rempakos A, Alexandrou M, Mutlu D, Kalyanasundaram A, Ybarra LF, Bagur R, Choi JW, Poommipanit P, Khatri JJ, Young L, Davies R, Benton S, Gorgulu S, Jaffer FA, Chandwaney R, Jaber W, Rinfret S, Nicholson W, Azzalini L, Kearney KE, Alaswad K, Basir MB, Krestyaninov O, Khelimskii D, Abi-Rafeh N, Elguindy A, Goktekin O, Aygul N, Rangan BV, Mastrodemos OC, Al-Ogaili A, Sandoval Y, Burke MN, and Brilakis ES. Predicting Successful Chronic Total Occlusion Crossing With Primary Antegrade Wiring Using Machine Learning. JACC Cardiovasc Interv 2024; 17(14):1707-1716.
Document Type
Article
Publication Date
7-22-2024
Publication Title
JACC Cardiovasc Interv
Abstract
BACKGROUND: There is limited data on predicting successful chronic total occlusion crossing using primary antegrade wiring (AW).
OBJECTIVES: The aim of this study was to develop and validate a machine learning (ML) prognostic model for successful chronic total occlusion crossing using primary AW.
METHODS: We used data from 12,136 primary AW cases performed between 2012 and 2023 at 48 centers in the PROGRESS CTO registry (Prospective Global Registry for the Study of Chronic Total Occlusion Intervention; NCT02061436) to develop 5 ML models. Hyperparameter tuning was performed for the model with the best performance, and the SHAP (SHapley Additive exPlanations) explainer was implemented to estimate feature importance.
RESULTS: Primary AW was successful in 6,965 cases (57.4%). Extreme gradient boosting was the best performing ML model with an average area under the receiver-operating characteristic curve of 0.775 (± 0.010). After hyperparameter tuning, the average area under the receiver-operating characteristic curve of the extreme gradient boosting model was 0.782 in the training set and 0.780 in the testing set. Among the factors examined, occlusion length had the most significant impact on predicting successful primary AW crossing followed by blunt/no stump, presence of interventional collaterals, vessel diameter, and proximal cap ambiguity. In contrast, aorto-ostial lesion location had the least impact on the outcome. A web-based application for predicting successful primary AW wiring crossing is available online (PROGRESS-CTO website) (https://www.progresscto.org/predict-aw-success).
CONCLUSIONS: We developed an ML model with 14 features and high predictive capacity for successful primary AW in chronic total occlusion percutaneous coronary intervention.
Medical Subject Headings
Humans; Coronary Occlusion; Machine Learning; Registries; Male; Female; Treatment Outcome; Chronic Disease; Aged; Middle Aged; Percutaneous Coronary Intervention; Predictive Value of Tests; Reproducibility of Results; Risk Factors; Decision Support Techniques; Time Factors
PubMed ID
38970585
Volume
17
Issue
14
First Page
1707
Last Page
1716
