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

Makoto Tokunaga, Susumu Watanabe, Shigeru Sonoda

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Background 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. Methods 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. Result The correlation coefficients were.916 for A and.878 for B. Conclusion 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.

Original languageEnglish
Pages (from-to)1923-1928
Number of pages6
JournalJournal of Stroke and Cerebrovascular Diseases
Volume26
Issue number9
DOIs
Publication statusPublished - 09-2017
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Surgery
  • Rehabilitation
  • Clinical Neurology
  • Cardiology and Cardiovascular Medicine

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