Assessment of the effects of factors in stroke rehabilitation using eight multiple regression analyses—An analysis of the Japan Rehabilitation Database

Tokunaga M, Taniguchi M, Nakakado K, Mihono T, Okido A, Ushijima T, Eguchi G, Watanabe S, Nakanishi R, Yamanaga H. Assessment of the effects of factors in stroke rehabilitation using eight multiple regression analyses—An analysis of the Japan Rehabilitation Database—. Jpn J Compr Rehabil Sci 2015; 6...

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Veröffentlicht in:Japanese Journal of Comprehensive Rehabilitation Science 2015, Vol.6, pp.78-85
Hauptverfasser: Tokunaga, Makoto, Taniguchi, Mayu, Nakakado, Keiichi, Mihono, Taiki, Okido, Asami, Ushijima, Takeshi, Eguchi, Gihachiro, Watanabe, Susumu, Nakanishi, Ryoji, Yamanaga, Hiroaki
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Sprache:eng
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Zusammenfassung:Tokunaga M, Taniguchi M, Nakakado K, Mihono T, Okido A, Ushijima T, Eguchi G, Watanabe S, Nakanishi R, Yamanaga H. Assessment of the effects of factors in stroke rehabilitation using eight multiple regression analyses—An analysis of the Japan Rehabilitation Database—. Jpn J Compr Rehabil Sci 2015; 6: 78-85. Objective: The objective of the present study was to determine via multiple regression analysis what types of patient groups demonstrate large effects for factors in stroke rehabilitation. Methods: The subjects were 1,465 stroke patients in Kaifukuki rehabilitation wards who were registered in the 2014 Japan Rehabilitation Database. The subjects were stratified into eight groups based on age, motor functional independence measure (FIM) score at hospital admission, and cognitive FIM score at admission; multiple regression analysis was then performed with motor FIM score at discharge as the dependent variable. Results: Among the eight groups, the following independent variables were significant: motor FIM score at admission in seven groups, Nichijo-seikatsu-kino-hyokahyo at admission in five groups, age and post-onset duration of hospitalization in four groups, cognitive FIM score at admission in three groups, and pre-onset modified Rankin Scale in one group. Conclusion: The creation of multiple predictive formulas in multiple regression analysis enables identification of the types of patient groups which demonstrate large effects for factors in stroke rehabilitation.
ISSN:2185-5323
2185-5323
DOI:10.11336/jjcrs.6.78