Min-Su Kim1, Min Cheol Joo1, Min Kyun Sohn2, Jongmin Lee3, Deog Young Kim4, Sam-Gyu Lee5, Yong-Il Shin6, Soo-Yeon Kim6, Gyung-Jae Oh7, Yang-Soo Lee8, Eun Young Han9, Junhee Han10, Jeonghoon Ahn11, Won Hyuk Chang12, Yun-Hee Kim12, Ji Yoo Choi13, Sung Hyun Kang13, Young Taek Kim13. 1. Department of Rehabilitation Medicine, Wonkwang University School of Medicine , Iksan, Republic of Korea. 2. Department of Rehabilitation Medicine, School of Medicine, Chungnam National University , Daejeon, Republic of Korea. 3. Department of Rehabilitation Medicine, Konkuk University School of Medicine , Seoul, Republic of Korea. 4. Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine , Seoul, Republic of Korea. 5. Department of Physical and Rehabilitation Medicine, Chonnam National University Medical School , Gwangju, Republic of Korea. 6. Department of Rehabilitation Medicine, Pusan National University School of Medicine, Pusan National University Yangsan Hospital , Busan, Republic of Korea. 7. Department of Preventive Medicine, Wonkwang University, School of Medicine , Iksan, Republic of Korea. 8. Department of Rehabilitation Medicine, Kyungpook National University School of Medicine, Kyungpook National University Hospital , Daegu, Republic of Korea. 9. Department of Rehabilitation Medicine, Jeju National University Hospital, Jeju National University School of Medicine , Jeju, Republic of Korea. 10. Department of Statistics, Hallym University , Chuncheon, Republic of Korea. 11. Department of Health Convergence, Ewha Womans University , Seoul, Republic of Korea. 12. Department of Physical and Rehabilitation Medicine, Center for Prevention and Rehabilitation, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine , Seoul, Republic of Korea. 13. Korea Centers for Disease Control and Prevention, Division of Chronic Disease Prevention,Center for Disease.
Abstract
BACKGROUND: Previous studies have investigated the predictors for home discharge without considering stroke severity. OBJECTIVES: To develop a practical assessment tool that predicts home discharge for moderate stroke patients after subacute rehabilitation therapy in the tertiary hospitals. METHODS: Stroke patients with National Institutes of Health Stroke Scale scores of 6 to 13 were included in this prospective cohort study. Various demographic, clinical, and functional factors were analyzed as potential predictive factors. A weighted scoring model was developed through the following three-step process: 1) selection of the factors by logistic regression analyses, 2) development of a weighted scoring model, and 3) validation of the generalizability of the model. RESULTS: The home discharge rate was 51% (n = 372), and the overall mean length of stay of hospitalization was 32.5 days. 1) The Cognitive Functional Independence Measure, 2) the Functional Ambulation Categories, 3) the modified Charlson Comorbidity Index, and 4) marital status were independent predictors of home discharge. The coefficient value for marital status was adjusted to 1 in the scoring system, and the values of the other parameters were proportionally converted to the nearest integer. Possible total scores ranged from 0 to 13 in the model, with a higher score indicating a higher probability of home discharge. With a cutoff point of 7, this model showed 87.0% sensitivity and 86.2% specificity (area under the curve = 0.90). CONCLUSIONS: This novel assessment tool can be useful in predicting home discharge after subacute rehabilitation of moderate stroke patients.
BACKGROUND: Previous studies have investigated the predictors for home discharge without considering stroke severity. OBJECTIVES: To develop a practical assessment tool that predicts home discharge for moderate strokepatients after subacute rehabilitation therapy in the tertiary hospitals. METHODS:Strokepatients with National Institutes of Health Stroke Scale scores of 6 to 13 were included in this prospective cohort study. Various demographic, clinical, and functional factors were analyzed as potential predictive factors. A weighted scoring model was developed through the following three-step process: 1) selection of the factors by logistic regression analyses, 2) development of a weighted scoring model, and 3) validation of the generalizability of the model. RESULTS: The home discharge rate was 51% (n = 372), and the overall mean length of stay of hospitalization was 32.5 days. 1) The Cognitive Functional Independence Measure, 2) the Functional Ambulation Categories, 3) the modified Charlson Comorbidity Index, and 4) marital status were independent predictors of home discharge. The coefficient value for marital status was adjusted to 1 in the scoring system, and the values of the other parameters were proportionally converted to the nearest integer. Possible total scores ranged from 0 to 13 in the model, with a higher score indicating a higher probability of home discharge. With a cutoff point of 7, this model showed 87.0% sensitivity and 86.2% specificity (area under the curve = 0.90). CONCLUSIONS: This novel assessment tool can be useful in predicting home discharge after subacute rehabilitation of moderate strokepatients.
Entities:
Keywords:
Disability Evaluation; Patient Discharge; Recovery of Function; Rehabilitation; Stroke
Authors: Mirjam R Heldner; Caroline Chalfine; Marion Houot; Roza M Umarova; Jan Rosner; Julian Lippert; Laura Gallucci; Anne Leger; Flore Baronnet; Yves Samson; Charlotte Rosso Journal: Front Neurol Date: 2022-02-17 Impact factor: 4.003