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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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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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cited_by	cdi_FETCH-LOGICAL-c470t-9ce83028da205009350baf9eeb47cce87e31c2ec45d91af976cf4f2b8de185e03
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container_end_page	1928
container_issue	9
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container_title	Journal of stroke and cerebrovascular diseases
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creator	Tokunaga, Makoto Watanabe, Susumu Sonoda, Shigeru
description	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.
doi_str_mv	10.1016/j.jstrokecerebrovasdis.2017.06.028
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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. 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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. 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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. 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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
title	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
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