Predictors of Well Developed Coronary Collateral Circulation in Patients with Stable Angina Pectoris

Abstract

Objective: We aimed to demonstrate the predictors of Coronary Collateral Development (CCD) in patients with Stable Angina Pectoris (SAP). Methods: We prospectively enrolled 52 patients with at least 90% coronary stenosis. Their demographic, anthropometric and clinical data as well as hematological and biochemical parameters, medications, Ejection Fraction (EF) and perfusion index values and angiographic findings were used for determining the predictors for CCD. The Rentrop score (between 0 and 3) was used for the angiographic categorization and patients in Rentrop grades 0 and 1 were classified as poor CCD, and patients in Rentrop grades 2 and 3 were classified as well CCD. Results: We found moderate negative correlation between the Rentrop score and ejection fraction (r=-0,469, p<0.001). There was also positive correlation between the Gensini score (GS) and the Rentrop score (r=0.627, p<0.001). We made Classification and Regression Tree Model (C&RT) to define best predictors of CCD and found that EF (cut off 55%) and GS (cut off 41%) together constitute a useful prediction algorithm. Furthermore an advanced research was conducted to develop another algorithm to forecast coronary collateral development prior to angiography. The analysis was repeated after the extraction of angiographic data from the first C&RT model. It was concluded that EF (cut off 55%) and mean platelet volume (MPV) (cut off 9 fl) can be used in the second algorithm. Conclusions: CCD is negatively related to EF and positively related to GS. EF and MPV together constitute a simple and cost effective algorithm to predict the CCD before the angiography. However, GS and EF seem to be the best predictors of CCD among the whole variables.