Assessing risk factors of bypass graft surgery through the implementation of Bayesian and non-Bayesian methodologies - Dler Hussein Kadir; Azhin Mohammed Khudhur (Results in Engineering, 2024)
The aim of this research is to determine the key risk factors that influence the need for Bypass Graft surgery (BGS). A cohort of one hundred adult patients underwent coronary angiography operations at the Erbil Cardiac Center from January 2016 to December 2020. Multiple logistic regression (MLR) analysis was used to identify the determinants of BGS. Upon detailed statistical analysis, a significant association with BGS was found for several variables, including Age, BMI, Eosinophil, WBC, DBP, HbA1c, MCH, Blood Sugar, PLT, and T3. It was found that the significant variables contributing the most to predicting the likelihood of Bypass Graft surgery through Bayesian logistic regression analysis were Age, WBC, and MCH. These variables were identified as the pivotal risk factors associated with the probability of undergoing BGS. Age, DBP, and Blood Sugar had minimal influence on the likelihood of Bypass surgery. However, for each unit increase in WBC, the odds of surgery reduced by 38 %, and for MCH, the odds reduced by 19 %. DBP and Blood Sugar had little effect on Bypass surgery (Bayesian odds ratio close to 1). This study revealed that Bayesian logistic models offer superiority over classical logistic models. The Bayesian-based models provide more accurate predictions by incorporating informative prior distributions. To improve the accuracy and generalizability of the findings, future research should consider a larger sample size and incorporate informative prior distributions within the Bayesian framework. Also, machine learning algorithms can offer new insights into BGS outcomes by considering various variables and assessing model sensitivity.