Table 2.
Nonintensive care admission estimation by binary logistic regression
| Logistic regression model | ||||
|---|---|---|---|---|
| χ2-test | Degrees of freedom | p‑value | ||
| Omnibus test | – | 27.61 | 8 | < 0.01 |
| Hosmer-Lemeshow test | – | 5.74 | 8 | 0.68 |
| % correctly estimated | 61.20% | – | – | – |
| Independent variables | Odds ratio | 95% CI for odds ratio | p‑value | |
| Medical condition | ||||
| COPD | – | 5.37 | 0.65–44.69 | 0.12 |
| Pulmonary edema | – | 9.41 | 1.10–80.53 | 0.04 |
| Pneumonia | – | 3.07 | 0.35–26.80 | 0.31 |
| Asthma (ref.) | – | – | – | 0.01 |
| Year of intervention | ||||
| 2015 | – | 0.85 | 0.54–1.34 | 0.48 |
| 2016 | – | 0.40 | 0.20–0.80 | 0.01 |
| 2017 | – | 0.57 | 0.15–2.12 | 0.40 |
| 2018 (ref.) | – | – | – | 0.07 |
| Sex (ref. = male) | ||||
| Female | – | 0.76 | 0.50–1.17 | 0.21 |
| Age (years) | – | 1.03 | 1.00–1.05 | 0.03 |
The dependent variable of this logistic regression model was the primary composite outcome. The odds ratio gives the ratio of the odds for the primary composite outcome being 1 (=non-intensive care admission) in a certain category compared to the reference category in the variable while “controlling” for all other variables (while assuming all other variables stay the same). Values are shown to two decimal points
CI confidence interval