Table 2.
Binary logistic regression analysis of factors related to covid-19 prognosis.
| Variable | Model | ||||
|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | ||
| Demographic factors | Age | 1.072 (1.000–1.149) | 1.062 (1.007–1.121) | 1.104 (1.025–1.188) | 1.057 (1.002–1.115) |
| Sex | 0.178 (0.029–1.113) | 0.255 (0.048–1.347) | 0.128 (0.018–0.918) | 0.249 (0.048–1.284) | |
| Smoker | 0.777 (0.146–4.125) | 0.358 (0.070–1.838) | 0.522 (0.088–3.100) | 0.335 (0.066–1.706) | |
| Inflammatory factors | CRP | 1.023 (1.012–1.034) | 1.024 (1.013–1.034) | - | - |
| Ferritin | - | - | 1.002 (1.001–1.003) | 1.003 (1.002–1.004) | |
| Tumor markers | CEA | 1.311 (1.096–1.568) | - | 1.317 (1.099–1.579) | - |
| AFP | - | 0.489 (0.272–0.879) | - | 0.503 (0.281–0.899) | |
| Model evaluation | Nagelkerke R2 | 0.724 | 0.620 | 0.729 | 0.655 |
| AUC | 0.936 | 0.914 | 0.940 | 0.924 | |
Variables in the model are showed as OR (95% CI).