Prediction of Angiography Results Using Logistic Regression and Zeroinflated Negative Binomial Models

Abstract

Background and Objectives Angiography is a common and invasive method in diagnosing cardiovascular diseases. Some patients refuse to perform angiography due to reasons such as fear, high cost, and lack of confidence in the decision of physician for angiography. This study aims to determine the factors predicting coronary artery occlusion to predict the outcome of angiography. Subjects and Methods In this cross-sectional study, participants were 1187 patients received angiography in Ghaem Hospital in Mashhad, Iran. Demographic data, lipid profile, blood sugar level, and history of underlying disorders were used in two prediction models of logistic regression and zero-inflated negative binomial (NB), fitted using R3.6.1 software. Then, their sensitivity and specificity were compared. Results Of 1187 patients, 404 (34%) had negative angiography. The results of both models showed that the risk of positive angiography was significantly higher in male and diabetic patients. The risk increased with the increase of age. The area under the ROC curve (sensitivity and specificity) for logistic regression and zero-inflated NB models were 78.4(70.4%, 70.5%) and 78.2(71.4%, 71.5%). Conclusion Age, gender, smoking, and history of diabetes are significant predictors of the angiography outcome. There is no significant difference between logistic regression and zero-inflated NB models in predicting the outcome of angiography. Due to the ease of use of logistic regression model, it can be used to predict the results of angiography.