ArticleInternational journal of general medicine2024
Exploring Predictors of Long-Term Care Facility Admissions in Stroke Survivors: Insights from a Taiwanese Hospital-Based Study.
Article in International journal of general medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: Acute stroke significantly increases the risk of long-term care facility (LTCF) admission, due to sudden functional impairments. This study aims to identify risk factors associated with LTCF admission among stroke patients, specifically targeting those who transitioned from independence to disability after stroke. Patients and Methods: We retrospectively enrolled 2027 stroke patients admitted between 2017 and 2022 from the Chi Mei Medical Center's stroke registry in Southern Taiwan, focusing on those with pre-stroke modified Rankin Scale (mRS) scores ≤ 2 and post-stroke mRS scores ≥ 3. Patients were categorized into LTCF and non-LTCF groups. Stroke severity, comorbidities, and discharge outcomes were evaluated, using logistic regression analyses to identify LTCF admission risk factors. Results: Of the 2027 patients, 343 (16.9%) were admitted to LTCFs post-discharge. The LTCF group exhibited higher discharge mRS and National Institute of Health Stroke Scale scores, and lower Barthel Index scores. Factors linked to LTCF admission included higher discharge mRS scores, lower Barthel Index scores, nasogastric tube placement at discharge, and longer hospital stays. Barthel Index scores showed no significant change from admission to discharge in the LTCF group. Conclusion: Stroke severity, post-stroke functional status and nasogastric tube placement are significant predictors of LTCF admission in stroke patients. Early recognition of these factors is crucial for effective discharge planning and reducing the need for institutionalization. The study emphasizes the need for personalized interventions targeting these risk factors to improve patient outcomes and optimize medical resource utilization.
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