Table 2:
Comparison of the ability of imaging paradigms in discriminating clinical outcomes using logistic regression modelinga
| Comparison | ||||||
|---|---|---|---|---|---|---|
| Imaging Paradigm/Criteria | No. | Odds Ratio (95% CI) | P Value | C-Statistic | AIC/BIC | AIC/BIC (o) |
| 90-day mRS | ||||||
| mCTA (>3 vs ≤3) | 82 | 9.6 (1.9–48.8) | .001 | 0.86 | 95.7/114.9 | 300.6/331.9 |
| DEFUSE-3 criteria | 82 | 5.5 (1.2–25.3) | .028 | 0.84 | 99.0/118.3 | |
| DAWN criteria | 82 | 9.3 (0.9–98.8) | .065 | 0.83 | 99.3/118.6 | 303.1/334.4 |
| Early neurologic improvement (≥50% drop in 24-hr NIHSS score from baseline) | ||||||
| mCTA (>3 vs ≤3) | 82 | 13.3 (2.9–61) | .001 | 0.80 | 98.2 | 117.5 |
| DEFUSE-3 criteria | 82 | 8.5 (1.9–37.5) | .005 | 0.74 | 105.9 | 125.1 |
| DAWN criteria | 82 | 5.6 (0.6–56.1) | .141 | 0.71 | 109.6 | |
a
Variables age, sex, baseline NIHSS score, baseline NCCT ASPECTS, onset/last known well to imaging time, EVT, and the interaction term imaging paradigm × EVT (yes versus no) were included in all models. C-statistic represents the area under a receiver operating characteristic curve. AIC and BIC are Bayesian information criteria methods to assess model fit in which the model with the lowest AIC or BIC is preferred. AIC/BIC (o) denotes the AIC and BIC for the ordinal regression models.