Statistical Approach

PCA is a factor analysis approach that summarizes a large number of variables in terms of fewer underlying principal components (PCs) [13]. A variable correlating well with the PC is said to be loading onto that PC, and the weights of the loading variables represent strength of correlation with the PC. PCA was performed with the 15-item 24-h NIHSS using Varimax rotation with the Kaiser Normalization method in SPSS (version 23.0). Scree plots showing the proportion of variance in the 24-h NIHSS explained by individual PCs were plotted, and within these PCs, key variables that were most highly loaded were identified. The top 3 highly loaded variables within each PC were considered for developing abbreviated iterations of the 24-h NIHSS. Receiver-operating characteristic curve analyses were performed to determine prognostic accuracies (area under the curve [AUC]) in predicting 3-month good outcome (mRS 0–2), poor outcome (mRS 5–6), and functional independence (Barthel Index ≥95). AUCs of these iterations were compared to the total 24-h NIHSS and the baseline NIHSS and also assessed separately in right and left hemispheric stroke patients. The prognostic accuracy of the abbreviated form was validated in the NINDS-TPA trial [12]. Pair-wise comparisons of AUC were done using the χ² test. Correlation between observed probabilities for good outcome in IMS-3 and NINDS-TPA was assessed.

To determine the interrater reliability of the aNIHSS, we used previously published interrater reliability data for individual components of the 15-item NIHSS derived from 6 studies [5]. The mean weighted kappa statistic was calculated for the total 24-h NIHSS and the aNIHSS using the weighted kappa for each NIHSS item [14]. Paired analysis (paired two-tailed t test) was performed to determine whether the aNIHSS improved interrater reliability as compared to the total 24-h NIHSS. For all statistical comparisons, p < 0.05 was considered statistically significant.