Prognostication of Stroke Patients Using the Stroke Impairment Assessment Set and the Functional Independence Measure

  • Shigeru Sonoda
  • Eiichi Saitoh
  • Kazuhisa Domen
  • Naoichi Chino


Outcome prediction is one of the main issues in stroke rehabilitation. Although many predictors have been tested and a considerable number of statistical methods employed, none to date has resulted in a satisfactory method for prognosis. We have successfully predicted stroke outcome using regression analysis with the Stroke Impairment Assessment Set (SIAS), which we have developed, and the Functional Independence Measure (FIM). Our subjects were 192 stroke patients who had completed rehabilitation whose average number of days from stroke onset was 53.0; their mean length of hospital stay was 94.9 days. Subjects were divided into two groups according to the total FIM score on admission: less than 80 compared to 80 or more. Patterns of correlation coefficients between admission parameters and the discharge FIM score differed between the two groups. Regression equations were separately made in each group with independent variables of the SIAS, the FIM, and several other parameters (piecewise regression analysis). Adding the impairment scale, the SIAS, and related items to the disability scale as independent variables enhanced the correlation coefficient of piecewise regression from. 85 to.93. Stroke outcome can be successfully predicted using piecewise regression with the SIAS and the FIM. Stratification of stroke patients by their admission FIM scores is effective for making a good prognosis. The SIAS proved to be an adequate set to score impairment.


Stroke Patient Stroke Onset Functional Independence Measure Functional Independence Measure Score Piecewise Regression 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Tokyo 1996

Authors and Affiliations

  • Shigeru Sonoda
  • Eiichi Saitoh
  • Kazuhisa Domen
  • Naoichi Chino
    • 1
  1. 1.Department of Rehabilitation MedicineKeio University School of MedicineShinjuku-ku, Tokyo 160Japan

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