Key result
High CRP and smoking strongly predict CAD in obese and nonobese patients respectively.
Why the study?
The best regression model for identifying conventional and novel risk factors in obese and nonobese patients with CAD was unclear.
Observational (n=398)
Multivariate forward logistic regression analysis may be a more useful statistical model for identifying risk factors in patients with coronary artery disease due to its high predictable ratio.
This study was designed to assess conventional and novel risk factors in obese and nonobese patients with coronary artery disease (CAD) by using multivariate forward and univariate logistic regression analysis and to find the best model of analysis for identifying these risk factors. The study group consisted of 398 patients who consecutively underwent coronary angiography for the investigation of chest pain, except overweight patients. In univariate logistic regression analysis, high C-reactive protein and cigarette smoking were found to be the strongest variables in obese and nonobese patients with CAD, respectively. In multivariate forward logistic regression analysis, some risk factors were not found as predictors of CAD. Multivariate forward logistic regression analysis with the advantage of a high predictable ratio may be more useful for the analysis of risk factors in patients with CAD.
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Yoloğlu et al. (2007) conducted an observational in Coronary artery disease (n=398). Conventional and novel risk factors (e.g., high C-reactive protein, cigarette smoking) was evaluated on Predictors of coronary artery disease. High C-reactive protein and cigarette smoking were identified as the strongest risk factors for coronary artery disease in obese and nonobese patients, respectively.
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