Prediction Model for the Result of Percutaneous Coronary Intervention in Coronary Chronic Total Occlusions

Coronary chronic total occlusions (CTOs) are very common in patients undergoing coronary angiography. There has been an increasing acceptance of the percutaneous coronary interventions (PCI) in CTOs. The success rate of PCI has been boosted over the last few years by, among else, operator experience and advances in technology, even achieving levels of approximately 90%. This study proposes a prediction model for the classification of the cases in successful and unsuccessful operations and addresses the problem of class imbalance in the response variable (operation result). It is based on the EuroCTO Registry, which is the largest database available worldwide consisting of 29,995 cases for the period 2008-2018. Binary logistic regression analysis and down-sampling were applied within a customized step-algorithm and standard statistical accuracy measures were employed for the assessment of the prediction model, such as sensitivity, specificity and the value of the area under the ROC (AUROC) curve. The analysis revealed new predictive factors, validating at the same time the impact of well-known predictors. A brief comparison has been performed with other models from the literature, which showed that the proposed model performs similarly or better than its contemporary competitors.