National Institutes of Health Stroke Scale Item Profiles as Predictor of Patient Outcome

Abstract

Background and Purpose— National Institutes of Health Stroke Scale (NIHSS) item profiles that were recently proposed may prove useful both clinically and for research studies. We aimed to validate the NIHSS item profiles in an acute cohort. Methods— We conducted a retrospective analysis on pooled data from randomized clinical trials. We applied the latent class analysis probabilities of profile membership developed from the derivation study to obtain symptom grouping, a -NIHSS item profiles. We implemented an independent latent class analysis to derive secondary symptom grouping, b -NIHSS item profiles. Validation was performed by assessing the associations with outcomes and evaluating both sets of NIHSS item profiles’ discrimination and calibration to the data. The outcomes evaluated included modified Rankin Scale (mRS; using the full distribution and dichotomized, mRS, 0–1) at day 90 and mortality by 90 days. Results— We identified 10 271 patients. Ordinal analysis of mRS confirmed increased odds of better outcome across the profiles in a stepwise manner, adjusted for age and thrombolysis treatment, for each set of NIHSS item profiles. Similar patterns were observed for mRS 0 to 1, and inverse patterns were seen for mortality. The c -statistics of a -NIHSS and b -NIHSS item profiles for mRS 0 to 1 were similar at 0.71 (95% confidence interval, 0.70–0.72) and for mortality, 0.74 (0.73–0.75) and 0.75 (0.73–0.76), respectively. Calibration was good. Conclusions— These NIHSS item profiles identified using latent class analysis offer a reliable approach to capture the true response patterns that are associated with functional and outcome and mortality post stroke. This approach has the potential to enhance the clinical value of the overall NIHSS score.