All patients gave written consent prior to coronary intervention. Coronary angiography was reviewed by two interventional cardiologists. All frames were calibrated with the tip of the catheter as a reference guide before contrast injection. Two orthogonal

projections were used before and after stent implantation. Whenever a patient had two or more atrial branches arising from the same coronary artery, we selected for this study the largest branch. In each coronary segment, we measured the luminal diameters and the Alisertib percentage of stenosis using the QCA. The coronary artery flow was qualitatively evaluated using the TIMI score [15]. Patients were divided into two groups according to the loss or preservation of the AB flow at the end of angioplasty. ABO group were those patients in whom the AB flow fell from TIMI grades 2–3 to 0–1 after the procedure. Non-ABO group were those patients in whom the baseline TIMI was normal and did not change after PTCA. We also evaluated the length of the coronary lesion and the plaque composition characteristics according to the American College of Cardiology/American Heart Association (ACC/AHA) classification [16]. In each

AB, we specifically analyzed the presence of atherosclerotic plaques, maximal luminal diameter, and TIMI flow before and after the PTCA. To assess the spatial relationship between the location selleck chemicals of the target atherosclerotic plaques for PTCA and the output of the AB, we followed the Medina’s classification [17]. Due to the variety of stent models implanted in this series of patients, the influence of a given model on ABO could not be specifically analyzed and therefore we created the variable “Bare-metal nearly stent (BMS) versus drug-eluting stent (DES)” to asses statistical differences.

Descriptive analyses were performed at the first step. Categorical variables were described by frequencies and percentages and statistical differences were analyzed using a 2 × 2 table test and the χ2 test. Continuous variables were described by the mean ± standard deviation and statistical differences were analyzed using the Student’s t test in the case of a normal distribution. A multivariable logistic regression model was performed, adjusting for the covariates statistically significant at the univariable analysis (p value less than 0.20 as a criterion of entry into multivariate analysis), to identify independent predictors of ABO. A forward step method was used to define the final model and the independent predictors of ABO. Additionally, the final model was adjusted for those variables categorized as clinically relevant. Significant predictors of ABO were expressed in terms of odds ratio and 95% confidence intervals (CIs). To assess the model’s predictive ability of our data, we calculated the area under the receiver operating characteristics following a nonparametric distribution assumption. A p value less than 0.