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Coronary risk factors used to predict coronary artery disease by logistic regression analysis
Insights
Risk factor analysis identified age, sex, diabetes mellitus, LDL-cholesterol, and HDL-cholesterol as key predictors for coronary artery disease. This analysis is valuable for screening high-risk patients but not for predicting vasospastic conditions.
Area of Science:
- Cardiology
- Preventive Medicine
- Medical Diagnostics
Background:
- Coronary artery disease (CAD) poses a significant global health burden.
- Identifying reliable risk factors is crucial for early detection and prevention strategies.
- Previous studies have explored various personal characteristics, but a comprehensive analysis is needed.
Purpose of the Study:
- To identify significant personal characteristics associated with the prevalence of coronary artery disease.
- To develop a predictive model for identifying patients with significant coronary artery stenosis.
- To evaluate the utility of risk factor analysis in screening and managing CAD.
Main Methods:
- A cohort of 303 patients undergoing coronary arteriography was analyzed.
- Thirteen potential risk factors including age, sex, obesity, smoking, alcohol intake, hypertension, diabetes mellitus, lipid profiles (total cholesterol, LDL-, HDL-cholesterol, triglyceride), and serum uric acid were assessed.
- Logistic regression analysis was employed to identify significant predictors and validate a predictive model.
Main Results:
- Significant differences in age, sex, diabetes mellitus, total cholesterol, LDL-cholesterol, HDL-cholesterol, triglyceride, and atherosclerotic indices were observed between patients with and without organic coronary artery stenosis.
- Logistic analysis identified age, sex, diabetes mellitus, LDL-cholesterol, and HDL-cholesterol as significant predictors of significant coronary artery disease.
- The validated model achieved a sensitivity of 75.8%, specificity of 68.5%, and predictive accuracy of 71.5% for coronary artery disease.
Conclusions:
- Risk factor analysis, particularly focusing on age, sex, diabetes, and lipid profiles, is valuable for screening individuals at high risk for organic coronary artery stenosis.
- The findings support the optimization of preventive and therapeutic strategies based on identified risk factors.
- Risk factor analysis demonstrated limited utility in predicting vasospastic coronary artery conditions.
Abstract:
Risk factor analysis in coronary artery disease was conducted in 303 patients who underwent coronary arteriography to identify associations between personal characteristics and the prevalence of coronary heart disease. Age, sex, obesity, smoking, alcohol intake, hypertension, diabetes mellitus, serum uric acid, total cholesterol, LDL- and HDL-cholesterol, triglyceride, and atherogenic indices were statistically analyzed. All 13 variables were first compared between patients with positive and negative ergonovine tests. Only total cholesterol was significantly different, while significant differences in age, sex, history of diabetes, total cholesterol, LDL- and HDL-cholesterol, triglyceride and atherosclerotic indices were observed between patients with and without organic coronary artery stenosis. A multivariate analysis was performed, and the resulting equation was tested using the remaining patients. Logistic analysis of all 13 variables identified 5 (age, sex, diabetes mellitus, LDL- and HDL-cholesterol) which accounted for the differences between patients with and without significant coronary artery disease and that were validated in the test group. The sensitivity for prediction of coronary artery disease was 75.8%, specificity 68.5%, and predictive accuracy 71.5% in the test group. Thus, risk factor analysis appears to be very valuable in screening subjects with high-risk organic coronary stenosis and in optimizing the preventive and therapeutic modalities, but not in predicting vasospastic subjects.