Prediction of Angiography Results Using Logistic Regression and ZeroinflatedNegative Binomial Models

Angiography is a common and invasive method in diagnosing cardiovasculardiseases. Some patients refuse to perform angiography due to reasons such as fear, high cost, and lackof confidence in the decision of physician for angiography. This study aims to determine the factorspredicting coronary artery occlusion to predict the outcome of angiography.

In this cross-sectional study, participants were 1187 patients received angiographyin Ghaem Hospital in Mashhad, Iran. Demographic data, lipid profile, blood sugar level, and history ofunderlying disorders were used in two prediction models of logistic regression and zero-inflated negativebinomial (NB), fitted using R3.6.1 software. Then, their sensitivity and specificity were compared.

Of 1187 patients, 404 (34%) had negative angiography. The results of both models showed thatthe risk of positive angiography was significantly higher in male and diabetic patients. The risk increasedwith the increase of age. The area under the ROC curve (sensitivity and specificity) for logistic regressionand zero-inflated NB models were 78.4(70.4%, 70.5%) and 78.2(71.4%, 71.5%).

Age, gender, smoking, and history of diabetes are significant predictors of the angiographyoutcome. There is no significant difference between logistic regression and zero-inflated NB models inpredicting the outcome of angiography. Due to the ease of use of logistic regression model, it can beused to predict the results of angiography.