Coronary disease & ACSDiagnostic or prognostic study
Predicting Successful Chronic Total Occlusion Crossing With Primary Antegrade Wiring Using Machine Learning
- Prognostic model development and validation study (registry)
- machine learning
- multicentre
- 48 centres
Authors' conclusion
We developed an ML model with 14 features and high predictive capacity for successful primary AW in chronic total occlusion percutaneous coronary intervention.
Study context
- Population
- Primary antegrade wiring cases in CTO PCI, PROGRESS CTO registry, 2012–2023
- Participants
- 12,136 primary antegrade wiring (AW) cases
- Outcome
- Successful CTO crossing with primary antegrade wiring
Performance
As reported in the abstract
Prediction of successful primary AW crossing
Primary AW successful
6,965 cases (57.4%)
Extreme gradient boosting best of 5 machine learning (ML) models, average AUC
0.775 (± 0.010)
After hyperparameter tuning, average AUC, training set
0.782
After hyperparameter tuning, average AUC, testing set
0.780
Further findings
Feature importance (SHAP)
- Occlusion length had the most significant impact, followed by blunt/no stump, interventional collaterals, vessel diameter and proximal cap ambiguity
- Aorto-ostial lesion location had the least impact
Original publication
Predicting Successful Chronic Total Occlusion Crossing With Primary Antegrade Wiring Using Machine Learning. JACC Cardiovasc Interv. Published 3 July 2024. doi:10.1016/j.jcin.2024.04.043
Summarised from the published abstract; every number is checked against it