Table 6.

Binary logistic regression analysis—predictors for good outcome in PCS and ACS.

	Odds ratio	95% CI	P value
Stepwise multivariate binary logistic regression analysis
PCS
Admission NIHSS	0.864	0.790–0.944	0.001
ACS
Age	0.952	0.935–0.970	< 0.001
Admission NIHSS	0.840	0.806–0.876	< 0.001
Known symptom onset	1.869	1.111–3.144	0.018

Multivariate stepwise backward binary logistic regression analysis for outcome in posterior circulation stroke (PCS) and anterior cerebral circulation stroke (ACS), separately. Age, sex, medical history of diabetes, atrial fibrillation, hypertension, dyslipidaemia, smoking, previous stroke or TIA, the admission NIHSS, knowledge of symptom onset and the onset-to-treatment time were examined for their predictive impact on good functional outcome defined as ≤ 2 on the mRS at 90 days. Data are expressed as odds ratios and the 95% CI of the odds ratios. 95% CI indicated 95% confidence interval; NIHSS, National Institutes of Health Stroke Scale. P values < 0.05 are shown in bold.