Objectives: The aim of this study was to develop a hybrid approach-specific model to predict chronic total coronary artery occlusion (CTO) percutaneous coronary intervention success, useful for experienced but not ultra-high-volume operators.
Background: CTO percutaneous coronary intervention success rates vary widely and have improved with the "hybrid approach," but current predictive models for success have major limitations.
Methods: Data were obtained from consecutively attempted patients from 7 clinical sites (9 operators, mean annual CTO volume 61 ± 17 cases). Angiographic analysis of 21 lesion variables was performed centrally. Statistical modeling was performed on a randomly designated training group and tested in a separate validation cohort. The primary outcome of interest was technical success.
Results: A total of 436 patients (456 lesions) met entry criteria. Twenty-five percent of lesions had prior failed percutaneous coronary interventions at the site. The right coronary artery was the most common location (56.4%), and mean occlusion length was 24 ± 20 mm. The initial approach was most often antegrade wire escalation (70%), followed by retrograde (22%). Success was achieved in 79.4%. Failure was most closely correlated with presence of an ambiguous proximal cap, and in the presence of an ambiguous proximal cap, specifically defined collateral score (combination of Werner and tortuosity scores) and retrograde tortuosity. Without an ambiguous proximal cap, poor distal target, occlusion length >10 mm, ostial location, and 1 operator variable contributed. Prior failure, and Werner and tortuosity scores alone, were only weakly correlated with outcomes. The basic 7-item model predicted success, with C statistics of 0.753 in the training cohort and 0.738 in the validation cohort, the later superior (p < 0.05) to that of the J-CTO (Multicenter CTO Registry of Japan) (0.55) and PROGRESS CTO (Prospective Global Registry for the Study of Chronic Total Occlusion Intervention) (0.61) scores.
Conclusions: Success can be reasonably well predicted, but that prediction requires modification and combination of angiographic variables. Differences in operator skill sets may make it challenging to create a powerful, generalizable, predictive tool.
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http://dx.doi.org/10.1016/j.jcin.2017.03.016 | DOI Listing |
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