Assessment Model to Identify Patients With Stroke With a High Possibility of Discharge to Home: A Retrospective Cohort Study. Issue 10 (October 2017)

• Record Type: Journal Article
• Authors: Itaya, Takahiro; Murakami, Yusuke; Ota, Akiko; Nomura, Eiichi; Fukushima, Tomoko; Nishigaki, Masakazu
• Is Part Of: Stroke. Volume 48:Issue 10(2017)
• Publication Date: 2017-10
• Subjects: hospitalization -- inpatients -- patient discharge -- stroke -- stroke rehabilitation; Cerebrovascular disease -- Periodicals; Cerebral circulation -- Periodicals; 616.81
• DOI: 10.1161/STROKEAHA.117.018075
• Languages: English
• ISSNs: 0039-2499

Abstract

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.