A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy

Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at...

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Bibliographic Details
Published in:Journal of stroke and cerebrovascular diseases 2017-09, Vol.26 (9), p.1923-1928
Main Authors: Tokunaga, Makoto, Watanabe, Susumu, Sonoda, Shigeru
Format: Article
Language:English
Subjects:
Activities of Daily Living
Age Factors
Aged
Aged, 80 and over
Cognition
Disability Evaluation
Female
FIM at discharge
FIM effectiveness
FIM gain
Health Status
Humans
Linear Models
Male
Middle Aged
Motor Activity
Multiple linear regression analysis
Patient Admission
Patient Discharge
predictive accuracy
Predictive Value of Tests
Recovery of Function
Sex Factors
Stroke - diagnosis
Stroke - physiopathology
Stroke - psychology
Stroke - therapy
Stroke Rehabilitation
Time Factors
Treatment Outcome
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Summary:Multiple linear regression analysis is often used to predict the outcome of stroke rehabilitation. However, the predictive accuracy may not be satisfactory. The objective of this study was to elucidate the predictive accuracy of a method of calculating motor Functional Independence Measure (mFIM) at discharge from mFIM effectiveness predicted by multiple regression analysis. The subjects were 505 patients with stroke who were hospitalized in a convalescent rehabilitation hospital. The formula “mFIM at discharge = mFIM effectiveness × (91 points − mFIM at admission) + mFIM at admission” was used. By including the predicted mFIM effectiveness obtained through multiple regression analysis in this formula, we obtained the predicted mFIM at discharge (A). We also used multiple regression analysis to directly predict mFIM at discharge (B). The correlation between the predicted and the measured values of mFIM at discharge was compared between A and B. The correlation coefficients were .916 for A and .878 for B. Calculating mFIM at discharge from mFIM effectiveness predicted by multiple regression analysis had a higher degree of predictive accuracy of mFIM at discharge than that directly predicted.
ISSN:1052-3057
1532-8511
DOI:10.1016/j.jstrokecerebrovasdis.2017.06.028