トモリ　コウノスケ   友利　幸之介    所属   医療保健学部 リハビリテーション学科　作業療法学専攻    職種   教授
言語種別	英語
発行・発表の年月	2025/08
形態種別	学術論文
査読	査読あり
標題	Classifying Patient Characteristics and Determining a Predictor in Acute Stroke Patients: Application of Latent Class Analysis in Rehabilitation Practice
執筆形態	共著
掲載誌名	J Clin Med
掲載区分	国外
巻・号・頁	4(14),pp.5466
著者・共著者	Uchida J, Yamada M, Nagayama H, Tomori K, Ikeda K, Yamauchi K.
概要	Background/Objectives: Predicting comprehensive patient characteristics is essential for optimal individualized rehabilitation plans for acute stroke patients. However, current models primarily predict single outcomes. This study aimed to assess the applicability of latent class analysis (LCA) in rehabilitation practice by identifying comprehensive characteristics and associated predictors in acute stroke patients. Methods: We conducted a retrospective observational study using the Japan Association of Rehabilitation Database, including 10,270 stroke patients admitted to 37 acute-care hospitals between January 2005 and March 2016. Patients were classified using LCA based on outcomes at discharge, including Functional Independence Measure (FIM), National Institutes of Health Stroke Scale (NIHSS) subscales for upper-extremity function, length of hospitalization, and discharge destination. Predictor variables at admission included age, FIM scores, NIHSS subscales for upper-extremity function, stroke type, and daily rehabilitation volume. Results: 6881 patients were classified into nine distinct classes (class size: 4-29%). Class 1, representing the mildest cases, was noted for independent ambulation and good upper limb function. Class 2 comprised those with the most severe clinical outcome. Other classes exhibited a gradient of severity, commonly encountered in clinical practice. For instance, Class 7 included right-sided paralysis with preserved motor activities of daily living (ADLs) and modified dependence in cognitive functions, such as communication. All predictors at admission were significantly associated with class membership at discharge (p < 0.001). Conclusions: LCA effectively identified unique clinical subgroups among acute stroke patients and demonstrated that key admission variables could predict class membership. This approach offers a promising insight into targeted, personalized rehabilitation practice for acute stroke patients.
外部リンクURL	https://www.mdpi.com/2077-0383/14/15/5466