Table 3.
Binary Multivariate Logistic Regression Analysis for ICU Hospitalization and Death
| Dependent Variables | Models | Independent Variables | B | OR | P-value |
|---|---|---|---|---|---|
| ICU | Model 1 | NO | 0.216 | 1.241 | <0.0001 |
| Model 2 | Ferritin | 0.010 | 1.010 | 0.016 | |
| NO | 0.148 | 1.159 | 0.007 | ||
| Death | Model 1 | D-dimer | 0.099 | 1.104 | <0.0001 |
| Model 2 | D-dimer | 0.124 | 1.132 | 0.004 | |
| MDA | 0.311 | 1.365 | 0.010 | ||
| Model 3 | NLR | 1.162 | 3.196 | 0.497 | |
| D-dimer | 0.125 | 1.133 | 0.296 | ||
| MDA | 1.211 | 3.356 | 0.481 |
Notes: Binary multivariate logistic regression was used for utilizing those variables (Lymphocyte, neutrophil, Ferritin, NLR, SOD, zinc, copper, CRP, D-dimer, GPX, Catalase, NO, MDA, TAC, and Vitamin C) as predictors for ICU hospitalization and Death (non-survival) in COVID-19 patients. The forward conditional method was applied to exclude variables that were no associated with Dependent Variables. A p-value of less than 0.05 was considered statistically significant.
Abbreviations: B, regression coefficient; D-dimer, fibrin degradation product; ICU, intensive care unit; MDA, malondialdehyde; NO, nitric oxide; NLR, neutrophil/lymphocyte ratio; OR, odds ratio.