BACKGROUND AND PURPOSE: Prehospital stroke scales should identify stroke patients and measure stroke severity. The goal of this study was to identify a subset of the 15 items in the National Institutes of Health Stroke Scale (NIHSS-15) that measures stroke severity and predicts outcomes. METHODS: Using 2 distinct data sets from acute stroke clinical trials, we derived and validated shortened versions of the NIHSS (sNIHSS). Stepwise logistic regression and bootstrap techniques were used in selection of NIHSS-15 items. Areas under the receiver operator characteristic curve (C statistics) were used to compare predictive performance of logistic models incorporating differing versions of the NIHSS. RESULTS: The derivation analyses suggested the 8 NIHSS-15 items that were most predictive of "good outcome" 3 months after stroke, in order of decreasing importance: right leg item, left leg, gaze, visual fields, language, level of consciousness, facial palsy, and dysarthria. The sNIHSS-8 comprises all 8 and the sNIHSS-5, the first 5. In the validation models, C statistics were NIHSS-15=0.80, sNIHSS-8=0.77, and sNIHSS-5=0.76. Statistical comparisons suggested that the NIHSS-15 had better predictive performance than the sNIHSS-8 or the sNIHSS-5; the absolute difference in C statistics was small. There was no significant difference between the sNIHSS-8 and the sNIHSS-5. CONCLUSIONS: Much of the predictive performance of the full NIHSS-15 was retained with a shortened scale, the sNIHSS-5. Shortening the NIHSS-15 will facilitate its use during prehospital evaluations. The sNIHSS severity information may be useful to triage acute stroke patients in communities and to provide a baseline stroke severity for prehospital acute stroke trials.
BACKGROUND AND PURPOSE: Prehospital stroke scales should identify strokepatients and measure stroke severity. The goal of this study was to identify a subset of the 15 items in the National Institutes of Health Stroke Scale (NIHSS-15) that measures stroke severity and predicts outcomes. METHODS: Using 2 distinct data sets from acute stroke clinical trials, we derived and validated shortened versions of the NIHSS (sNIHSS). Stepwise logistic regression and bootstrap techniques were used in selection of NIHSS-15 items. Areas under the receiver operator characteristic curve (C statistics) were used to compare predictive performance of logistic models incorporating differing versions of the NIHSS. RESULTS: The derivation analyses suggested the 8 NIHSS-15 items that were most predictive of "good outcome" 3 months after stroke, in order of decreasing importance: right leg item, left leg, gaze, visual fields, language, level of consciousness, facial palsy, and dysarthria. The sNIHSS-8 comprises all 8 and the sNIHSS-5, the first 5. In the validation models, C statistics were NIHSS-15=0.80, sNIHSS-8=0.77, and sNIHSS-5=0.76. Statistical comparisons suggested that the NIHSS-15 had better predictive performance than the sNIHSS-8 or the sNIHSS-5; the absolute difference in C statistics was small. There was no significant difference between the sNIHSS-8 and the sNIHSS-5. CONCLUSIONS: Much of the predictive performance of the full NIHSS-15 was retained with a shortened scale, the sNIHSS-5. Shortening the NIHSS-15 will facilitate its use during prehospital evaluations. The sNIHSS severity information may be useful to triage acute strokepatients in communities and to provide a baseline stroke severity for prehospital acute stroke trials.
Authors: David M Kent; Robin Ruthazer; Carole Decker; Philip G Jones; Jeffrey L Saver; Erich Bluhmki; John A Spertus Journal: Neurology Date: 2015-08-19 Impact factor: 9.910
Authors: Brandon R Nye; Christina E Hyde; Georgios Tsivgoulis; Karen C Albright; Andrei V Alexandrov; Anne W Alexandrov Journal: Am J Crit Care Date: 2012-11 Impact factor: 2.228
Authors: Bijen Nazliel; Sidney Starkman; David S Liebeskind; Bruce Ovbiagele; Doojin Kim; Nerses Sanossian; Latisha Ali; Brian Buck; Pablo Villablanca; Fernando Vinuela; Gary Duckwiler; Reza Jahan; Jeffrey L Saver Journal: Stroke Date: 2008-06-12 Impact factor: 7.914