Comparing clinical outcomes in patients with diabetes undergoing coronary artery bypass graft and percutaneous coronary intervention in real world practice in Iranian population

Predictors of Premature Mortality Following Coronary Artery Bypass Grafting: An Iranian Single-Centre Study

Modifiable risk factors play an important role in the premature mortality among patients undergoing coronary artery bypass grafting (CABG). The aim of this study was to examine the factors that influence the early death of patients who had CABG. We conducted a prospective cohort study and followed 2863 patients after their CABG, and collected data on their characteristics and blood tests. We used the Cox proportional hazards regression model in Stata, version 16, to identify the predictors of early mortality. Out of 2863 patients, 162 died during the follow-up period. The survival rate was 99.2% within the first three days after the surgery, 96.2% from the fourth day to the end of the first year, 94.9% at the end of the second year, and 93.6% at the end of the third year. After adjusting for confounding factors, we found that older age (hazard ratio [HR] 1.05, 95% CI 1.02, 1.08 for one year increase in age), obesity (HR 2.16, 95% CI 1.25, 3.72), ejection fraction < 50% (HR 1.61, 95% CI 1.06, 2.44), number of rehospitalizations (HR 2.63, 95% CI 1.35, 5.12 for two or more readmissions), history of stroke (HR 2.91, 95% CI 1.63, 5.21), living in rural areas (HR 1.58, 95% CI 1.06, 2.34), opium use (HR 2.08, 95% CI 1.40, 3.09), and impaired glomerular filtration rate increased the risk of early death after CABG, while taking a beta-blocker (HR 0.59, 95% CI 0.38, 0.91) reduced the risk. We conclude that modifiable risk factors such as excess body mass, high blood glucose, opium use, and kidney dysfunction should be monitored and managed in patients who had CABG to improve their survival outcomes.

Intelligent prediction of major adverse cardiovascular events (MACCE) following percutaneous coronary intervention using ANFIS-PSO model

Background

This study aimed to use the hybrid method based on an adaptive neuro-fuzzy inference system (ANFIS) and particle swarm optimization (PSO) to predict the long term occurrence of major adverse cardiac and cerebrovascular events (MACCE) of patients underwent percutaneous coronary intervention (PCI) with stent implantation.

Method

This retrospective cohort study included a total of 220 patients (69 women and 151 men) who underwent PCI in Ekbatan medical center in Hamadan city, Iran, from March 2009 to March 2012. The occurrence and non-occurrence of MACCE, (including death, CABG, stroke, repeat revascularization) were considered as a binary outcome. The predictive performance of ANFIS model for predicting MACCE was compared with ANFIS-PSO and logistic regression.

Results

During ten years of follow-up, ninety-six patients (43.6%) experienced the MACCE event. By applying multivariate logistic regression, the traditional predictors such as age (OR = 1.05, 95%CI: 1.02–1.09), smoking (OR = 3.53, 95%CI: 1.61–7.75), diabetes (OR = 2.17, 95%CI: 2.05–16.20) and stent length (OR = 3.12, 95%CI: 1.48–6.57) was significantly predicable to MACCE. The ANFIS-PSO model had higher accuracy (89%) compared to the ANFIS (81%) and logistic regression (72%) in the prediction of MACCE.

Conclusion

The predictive performance of ANFIS-PSO is more efficient than the other models in the prediction of MACCE. It is recommended to use this model for intelligent monitoring, classification of high-risk patients and allocation of necessary medical and health resources based on the needs of these patients. However, the clinical value of these findings should be tested in a larger dataset.