The external validity of multiple regression analyses predicting discharge FIM score in patients with stroke hospitalized in Kaifukuki rehabilitation wards—An analysis of the Japan Rehabilitation Database

• Published in: Japanese Journal of Comprehensive Rehabilitation Science Vol. 6; pp. 14 - 20
• Main Authors: Yonemitsu, Hiroyuki, Sannomiya, Katsuhiko, Tokunaga, Makoto, Nakanishi, Ryoji
• Format: Journal Article
• Language: English
• Published: Kaifukuki Rehabilitation Ward Association 2015
• Subjects: compariso; external validity; FIM at discharge; multiple regression analysis; stroke
• ISSN: 2185-5323; 2185-5323
• DOI: 10.11336/jjcrs.6.14

Tokunaga M, Sannomiya K, Nakanishi R, Yonemitsu H. The external validity of multiple regression analyses predicting discharge FIM score in patients with stroke hospitalized in Kaifukuki rehabilitation wards—An analysis of the Japan Rehabilitation Database—. Jpn J Compr Rehabil Sci 2015; 6: 14-20. Objective: To compare the prediction accuracy of multiple regression analyses for predicting the discharge FIM score in stroke patients reported to date in Japan. Methods: The subjects were 1,229 stroke cases in Kaifukuki rehabilitation wards registered in the Japan Rehabilitation Database (2014). The subject patient data was inputted into the six types of prediction formulas described in four reports, and prediction values were obtained. The residuals between the measured values and predicted values were then analyzed. Results: The residuals were the smallest in the prediction equation of Jeong et al. (mean 0.44 ± 15.60; median - 0.16), Sonoda et al. (mean 0.26 ± 13.49; median 1.22), and Iwai et al. (mean - 0.92 ± 15.85; median - 2.09). Further, the residuals of the prediction equation of Sonoda et al. which uses the reciprocal of motor FIM at admission as the explanatory variable, and those of the two equations of Inouye, were larger compared to the three prediction equations above. Conclusion: It is necessary to assess the external validity of the reported multiple regression analyses, and to compare them against other prediction equations.