Risk Prediction of Coronary Artery Stenosis in Patients with Coronary Heart Disease Based on Logistic Regression and Artificial Neural Network.

Objective: Coronary heart disease (CHD) is considered an inflammatory relative disease. This study is aimed at analyzing the health information of serum interferon in CHD based on logistic regression and artificial neural network (ANN) model. Method: A total of 155 CHD patients diagnosed by coronary angiography in our department from January 2017 to March 2020 were included. All patients were randomly divided into a training set (n = 108) and a test set (n = 47). Logistic regression and ANN models were constructed using the training set data. The predictive factors of coronary artery stenosis were screened, and the predictive effect of the model was evaluated by using the test set data. All the health information of participants was collected. Expressions of serum IFN-γ, MIG, and IP-10 were detected by double antibody sandwich ELISA. Spearman linear correlation analysis determined the relationship between the interferon and degree of stenosis. The logistic regression model was used to evaluate independent risk factors of CHD. Result: The Spearman correlation analysis showed that the degree of stenosis was positively correlated with serum IFN-γ, MIG, and IP-10 levels. The logistic regression analysis and ANN model showed that the MIG and IP-10 were independent predictors of Gensini score: MIG (95% CI: 0.876~0.934, P < 0.001) and IP-10 (95% CI: 1.009~1.039, P < 0.001). There was no statistically significant difference between the logistic regression and the ANN model (P > 0.05). Conclusion: The logistic regression model and ANN model have similar predictive performance for coronary artery stenosis risk factors in patients with CHD. In patients with CHD, the expression levels of IFN-γ, IP-10, and MIG are positively correlated with the degree of stenosis. The IP-10 and MIG are independent risk factors for coronary artery stenosis.

Serum Monokine Induced by Gamma Interferon Is Associated With Severity of Coronary Artery Disease.

The immune system may play important roles in the pathogenesis of cardiovascular disease. T-cell mediated immune responses in human progression of atherosclerotic disease and hypertension have recently been revealed, but the significance of T-cell specific chemokines in coronary artery heart disease has not been confirmed. In our study, we sought to examine the association between serum levels of the monokine induced by gamma interferon (MIG)/CXCL9 and the severity of coronary artery disease. We studied 117 patients with coronary heart disease and 80 patients with no coronary heart disease. The severity of coronary artery disease was assessed via coronary artery angiography and the Gensini score was calculated. Clinical and biochemical indices, including serum levels of MIG, CD40L, and IFN-γ were analyzed in all subjects. Finally, we found there was a significant correlation between serum MIG levels and the severity of coronary artery disease, quantified by the Gensini score (r = 0.122, P = 0.009). Furthermore, multivariate regression analysis revealed that serum MIG levels were independently associated with the severity of coronary artery disease, quantified by the Gensini score (ß = 0.100, P = 0.021). Our findings could indicate the potential clinical implication of MIG with respect to early coronary artery atherosclerosis in humans.