Objectives The aims of this study were to describe the 10-year experience of a single operator dedicated to chronic total occlusion (CTO) and to establish a model for predicting technical failure. Background During the last decade, the interest in percutaneous coronary interventions (PCIs) of chronic total occlusions (CTOs) has increased, allowing the improvement of success rate. Methods One thousand nineteen patients with CTO underwent 1,073 CTO procedures performed by a single CTO-dedicated operator. The study population was subdivided into 2 groups by time period: period 1 (January 2005 to December 2009, n = 378) and period 2 (January 2010 to December 2014, n = 641). Observations were randomly assigned to a derivation set and a validation set (in a 2:1 ratio). A prediction score was established by assigning points for each independent predictor of technical failure in the derivation set according to the beta coefficient and summing all points accrued. Results Lesions attempted in period 2 were more complex in comparison with those in period 1. Compared with period 1, both technical and clinical success rates significantly improved (from 87.8% to 94.4% [p = 0.001] and from 77.6% to 89.9% [p < 0.001], respectively). A prediction score for technical failure including age ≥75 years (1 point), ostial location (1 point), and collateral filling Rentrop grade <2 (2 points) was established, stratifying procedures into 4 difficulty groups: easy (0), intermediate (1), difficult (2), and very difficult (3 or 4), with decreasing technical success rates. In derivation and validation sets, areas under the curve were comparable (0.728 and 0.772, respectively). Conclusions With growing expertise, the success rate has increased despite increasing complexity of attempted lesions. The established model predicted the probability of technical failure and thus might be applied to grading the difficulty of CTO procedures.
|Numero di pagine||12|
|Rivista||JACC: Cardiovascular Interventions|
|Stato di pubblicazione||Published - 2016|
All Science Journal Classification (ASJC) codes