Medicine (Baltimore). 2026 Feb 13;105(7):e47419. doi: 10.1097/MD.0000000000047419.
ABSTRACT
In Japan, the length of stay in acute care hospitals has decreased, resulting in earlier discharge of stroke patients after life-saving treatment. This trend limits opportunities for patients to practice activities of daily living and for clinicians to prepare appropriate discharge plans. Early prediction of discharge destination is therefore essential to support timely rehabilitation and discharge management. This study aimed to develop a decision tree model to predict discharge destination using data obtained within 3 days of hospitalization. A retrospective observational study was conducted on 150 acute stroke patients. Clinical and demographic characteristics, medical history, cognitive status, National Institutes of Health Stroke Scale (NIHSS), Brunnstrom recovery stage, and motor- and cognitive-functional independence measure (M-FIM, C-FIM) scores were collected. Participants were randomly divided into training (n = 106) and test (n = 44) datasets. Ninety-one patients were discharged home and 59 to other facilities. NIHSS, M-FIM, and C-FIM were identified as key predictors. Patients with NIHSS ≤ 3.5, or with NIHSS > 6.5 combined with M-FIM > 23.5 and C-FIM > 29.5, had a 100% probability of home discharge, whereas those with NIHSS > 6.5 and M-FIM ≤ 23.5 had only a 14.7% chance. The model achieved 86.4% accuracy, with sensitivity of 91.7% and specificity of 80.0%. These findings suggest that combining early neurological and functional assessments provides a reliable basis for anticipating discharge outcomes, thereby aiding rehabilitation planning and effective patient management in acute stroke care.
PMID:41686631 | DOI:10.1097/MD.0000000000047419