Table 4.
Binary logistics regression analysis of possible predictors of coronary artery ectasia. Effects of different variables on CAE were calculated using binary logistics regression
| Binary logistics regression analysis | |||
|---|---|---|---|
| Variable | OR | 95% CI | P |
| Age | 1.17 | (0.98–1.39) | 0.09 |
| Gender | 1.42 | (0.13–15.4) | 0.77 |
| Hypertension | 0.37 | (0.04–3.46) | 0.38 |
| Diabetes mellitus | 0.51 | (0.05–5.23) | 0.57 |
| Family history of CAD | 0.68 | (0.05–8.88) | 0.77 |
| Current smoking | 0.21 | (0.02–2.51) | 0.22 |
| Body mass index | 1.09 | (0.88–1.34) | 0.43 |
| SBP | 1.01 | (0.96–1.05) | 0.73 |
| DBP | 0.94 | (0.86–1.03) | 0.19 |
| LDL level | 1.02 | (1–1.04) | 0.07 |
| HDL level | 1.02 | (0.98–1.06) | 0.23 |
| NO level | 0.88 | (0.82–0.93) | 0.001 |
CAD - coronary artery disease; DBP - diastolic blood pressure; HDL - high-density lipoprotein; LDL - low-density lipoprotein; NO - nitric oxide; SBP - systolic blood pressure