Takahiro Itaya1, Yusuke Murakami1, Akiko Ota1, Eiichi Nomura1, Tomoko Fukushima1, Masakazu Nishigaki2. 1. From the Department of Nursing, Human Health Sciences, Kyoto University, Japan (T.I., M.N.); and Department of Rehabilitation (Y.M.), Department of Neurology (E.N.), and Department of Cerebrovascular Research (A.O.,T.F., M.N.) Brain Attack Center Ota Memorial Hospital, Fukuyama, Japan. 2. From the Department of Nursing, Human Health Sciences, Kyoto University, Japan (T.I., M.N.); and Department of Rehabilitation (Y.M.), Department of Neurology (E.N.), and Department of Cerebrovascular Research (A.O.,T.F., M.N.) Brain Attack Center Ota Memorial Hospital, Fukuyama, Japan. nishigaki.masakazu.6e@kyoto-u.ac.jp.
Abstract
BACKGROUND AND PURPOSE:Discharge planning for inpatients with acute stroke can enhance reasonable use of healthcare resources, as well as improve clinical outcomes and decrease financial burden of patients. Especially, prediction for discharge destination is crucial for discharge planning. This study aimed to develop an assessment model to identify patients with a high possibility of discharge to home after an acute stroke. METHODS: We reviewed the electronic medical records of 3200 patients with acute stroke who were admitted to a stroke center in Japan between January 1, 2011, and December 31, 2015. The outcome variable was the discharge destination of postacute stroke patients. The predictive variables were identified through logistic regression analysis. Data were divided into 2 data sets: the learning data set (n=2240) for developing the instrument and the test data set (n=960) for evaluating the predictive capability of the model. RESULTS: In all, 1548 (48%) patients were discharged to their homes. Multiple logistic regression analysis identified 5 predictive variables for discharge to home: living situation, type of stroke, functional independence measure motor score on admission, functional independence measure cognitive score on admission, and paresis. The assessment model showed a sensitivity of 85.0% and a specificity of 75.3% with an area under the curve equal to 0.88 (95% confidence interval, 0.86-0.89) when the cutoff point was 10. On evaluating the predictive capabilities, the model showed a sensitivity of 88.0% and a specificity of 68.7% with an area under the curve equal to 0.87 (95% confidence interval, 0.85-0.89). CONCLUSIONS: We have developed an assessment model for identifying patients with a high possibility of being discharged to their homes after an acute stroke. This model would be useful for health professionals to adequately plan patients' discharge soon after their admission.
RCT Entities:
BACKGROUND AND PURPOSE: Discharge planning for inpatients with acute stroke can enhance reasonable use of healthcare resources, as well as improve clinical outcomes and decrease financial burden of patients. Especially, prediction for discharge destination is crucial for discharge planning. This study aimed to develop an assessment model to identify patients with a high possibility of discharge to home after an acute stroke. METHODS: We reviewed the electronic medical records of 3200 patients with acute stroke who were admitted to a stroke center in Japan between January 1, 2011, and December 31, 2015. The outcome variable was the discharge destination of postacute stroke patients. The predictive variables were identified through logistic regression analysis. Data were divided into 2 data sets: the learning data set (n=2240) for developing the instrument and the test data set (n=960) for evaluating the predictive capability of the model. RESULTS: In all, 1548 (48%) patients were discharged to their homes. Multiple logistic regression analysis identified 5 predictive variables for discharge to home: living situation, type of stroke, functional independence measure motor score on admission, functional independence measure cognitive score on admission, and paresis. The assessment model showed a sensitivity of 85.0% and a specificity of 75.3% with an area under the curve equal to 0.88 (95% confidence interval, 0.86-0.89) when the cutoff point was 10. On evaluating the predictive capabilities, the model showed a sensitivity of 88.0% and a specificity of 68.7% with an area under the curve equal to 0.87 (95% confidence interval, 0.85-0.89). CONCLUSIONS: We have developed an assessment model for identifying patients with a high possibility of being discharged to their homes after an acute stroke. This model would be useful for health professionals to adequately plan patients' discharge soon after their admission.
Authors: Steffi Jírů-Hillmann; Katharina M A Gabriel; Michael Schuler; Silke Wiedmann; Johannes Mühler; Klaus Dötter; Hassan Soda; Alexandra Rascher; Sonka Benesch; Peter Kraft; Mathias Pfau; Joachim Stenzel; Karin von Nippold; Mohamed Benghebrid; Kerstin Schulte; Ralf Meinck; Jens Volkmann; Karl Georg Haeusler; Peter U Heuschmann Journal: BMC Geriatr Date: 2022-03-19 Impact factor: 3.921