BACKGROUND AND PURPOSE: Early identification of stroke patients in need of rehabilitation or long-term nursing facility (NF) care may promote more efficient use of health care resources and lead to better outcomes. The NIH Stroke Scale (NIHSS) is an attractive candidate predictor of disposition because it is widely used, is easily learned, and can be performed rapidly on admission. METHODS: We present a retrospective study of stroke patients admitted within 24 hours of symptom onset to a university hospital from March through June 2000. Medical records were reviewed for demographic information, stroke type, prestroke living arrangement and independence, initial NIHSS, and medical complications during hospitalization. RESULTS: Among 94 patients evaluated during the study period, 59% were discharged home, 30% to rehabilitation, and 11% to NF. In multivariate analyses, disposition was associated only with initial NIHSS. For each 1-point increase in NIHSS, the likelihood of going home was significantly reduced (odds ratio, 0.79; 95% CI, 0.70 to 0.89, P<0.001). Categorization of NIHSS was also predictive of disposition, with NIHSS < or =5 being most strongly associated with discharge home, NIHSS 6 to 13 with rehabilitation, and NIHSS >13 with NF (P<0.001). Although no other baseline characteristics predicted disposition, major medical complications during hospitalization tended to reduce the odds of going home (odds ratio, 0.30; 95% CI, 0.08 to 1.0, P=0.07). CONCLUSION: The NIHSS predicts postacute care disposition among stroke patients. Predicting disposition on the first day of admission may facilitate the time-consuming and costly process of securing a bed at rehabilitation or NF, and perhaps decrease unnecessary length of stay in acute care settings.
BACKGROUND AND PURPOSE: Early identification of strokepatients in need of rehabilitation or long-term nursing facility (NF) care may promote more efficient use of health care resources and lead to better outcomes. The NIH Stroke Scale (NIHSS) is an attractive candidate predictor of disposition because it is widely used, is easily learned, and can be performed rapidly on admission. METHODS: We present a retrospective study of strokepatients admitted within 24 hours of symptom onset to a university hospital from March through June 2000. Medical records were reviewed for demographic information, stroke type, prestroke living arrangement and independence, initial NIHSS, and medical complications during hospitalization. RESULTS: Among 94 patients evaluated during the study period, 59% were discharged home, 30% to rehabilitation, and 11% to NF. In multivariate analyses, disposition was associated only with initial NIHSS. For each 1-point increase in NIHSS, the likelihood of going home was significantly reduced (odds ratio, 0.79; 95% CI, 0.70 to 0.89, P<0.001). Categorization of NIHSS was also predictive of disposition, with NIHSS < or =5 being most strongly associated with discharge home, NIHSS 6 to 13 with rehabilitation, and NIHSS >13 with NF (P<0.001). Although no other baseline characteristics predicted disposition, major medical complications during hospitalization tended to reduce the odds of going home (odds ratio, 0.30; 95% CI, 0.08 to 1.0, P=0.07). CONCLUSION: The NIHSS predicts postacute care disposition among strokepatients. Predicting disposition on the first day of admission may facilitate the time-consuming and costly process of securing a bed at rehabilitation or NF, and perhaps decrease unnecessary length of stay in acute care settings.
Authors: Stephen R McCauley; Elisabeth A Wilde; Tara M Kelly; Annie M Weyand; Ragini Yallampalli; Eric J Waldron; Claudia Pedroza; Kathleen P Schnelle; Corwin Boake; Harvey S Levin; Paolo Moretti Journal: J Neurotrauma Date: 2010-06 Impact factor: 5.269
Authors: Elisabeth A Wilde; Stephen R McCauley; Tara M Kelly; Harvey S Levin; Claudia Pedroza; Guy L Clifton; Claudia S Robertson; Alex B Valadka; Paolo Moretti Journal: J Neurotrauma Date: 2010-06 Impact factor: 5.269
Authors: M Elizabeth Sandel; Alan M Jette; Jed Appelman; Joseph Terdiman; Marian TeSelle; Richard L Delmonico; Hua Wang; Michelle Camicia; Elizabeth K Rasch; Diane E Brandt; Leighton Chan Journal: PM R Date: 2012-11-14 Impact factor: 2.298