Allen W Brown1, Melody Lee2, Ryan J Lennon3, Paulette M Niewczyk4. 1. Department of Physical Medicine & Rehabilitation, Mayo Clinic, Rochester, Minnesota. Electronic address: brown.allen@mayo.edu. 2. Department of Physical Medicine & Rehabilitation, Mayo Clinic, Rochester, Minnesota. 3. Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, Minnesota. 4. Uniform Data System for Medical Rehabilitation, University at Buffalo, Department of Health Promotion, Daemen College, Amherst, New York.
Abstract
BACKGROUND: Some clinical features of patients after stroke may be modifiable and used to predict outcomes. Identifying these features may allow for refining plans of care and informing estimates of posthospital service needs. The purpose of this study was to identify key factors that predict functional independence and living setting 3 months after rehabilitation hospital discharge by using a large comprehensive national data set of patients with stroke. METHODS: The Uniform Data System for Medical Rehabilitation was queried for the records of patients with a diagnosis of stroke who were hospitalized for inpatient rehabilitation from 2005 through 2007. The system includes demographic, administrative, and clinical variables collected at rehabilitation admission, discharge, and 3-month follow-up. Primary outcome measures were the Functional Independence Measure score and living setting 3 months after rehabilitation hospital discharge. RESULTS: The sample included 16,346 patients (80% white; 50% women; mean [SD] age, 70.3 [13.1] years; 97% ischemic stroke). The strongest predictors of Functional Independence Measure score and living setting at 3 months were those same factors at rehabilitation discharge, despite considering multiple other predictor variables including age, lesion laterality, initial neurologic impairment, and stroke-related comorbid conditions. CONCLUSIONS: These data can inform clinicians, patients with stroke, and their families about what to expect in the months after hospital discharge. The predictive power of these factors, however, was modest, indicating that other factors may influence postacute outcomes. Future predictive modeling may benefit from the inclusion of educational status, socioeconomic factors, and brain imaging to improve predictive power.
BACKGROUND: Some clinical features of patients after stroke may be modifiable and used to predict outcomes. Identifying these features may allow for refining plans of care and informing estimates of posthospital service needs. The purpose of this study was to identify key factors that predict functional independence and living setting 3 months after rehabilitation hospital discharge by using a large comprehensive national data set of patients with stroke. METHODS: The Uniform Data System for Medical Rehabilitation was queried for the records of patients with a diagnosis of stroke who were hospitalized for inpatient rehabilitation from 2005 through 2007. The system includes demographic, administrative, and clinical variables collected at rehabilitation admission, discharge, and 3-month follow-up. Primary outcome measures were the Functional Independence Measure score and living setting 3 months after rehabilitation hospital discharge. RESULTS: The sample included 16,346 patients (80% white; 50% women; mean [SD] age, 70.3 [13.1] years; 97% ischemic stroke). The strongest predictors of Functional Independence Measure score and living setting at 3 months were those same factors at rehabilitation discharge, despite considering multiple other predictor variables including age, lesion laterality, initial neurologic impairment, and stroke-related comorbid conditions. CONCLUSIONS: These data can inform clinicians, patients with stroke, and their families about what to expect in the months after hospital discharge. The predictive power of these factors, however, was modest, indicating that other factors may influence postacute outcomes. Future predictive modeling may benefit from the inclusion of educational status, socioeconomic factors, and brain imaging to improve predictive power.
Authors: Brian J Douthit; Rachel L Walden; Kenrick Cato; Cynthia P Coviak; Christopher Cruz; Fabio D'Agostino; Thompson Forbes; Grace Gao; Theresa A Kapetanovic; Mikyoung A Lee; Lisiane Pruinelli; Mary A Schultz; Ann Wieben; Alvin D Jeffery Journal: Appl Clin Inform Date: 2022-02-09 Impact factor: 2.342