Stroke outcome prediction using reciprocal number of initial activities of daily living status

Shigeru Sonoda, Eiichi Saito, Shota Nagai, Yuko Okuyama, Toru Suzuki, Miho Suzuki

Research output: Contribution to journalArticle

19 Citations (Scopus)

Abstract

Multiple regression analysis was performed in 87 stroke patients who were admitted to a rehabilitation hospital to predict the total motor subscore of the Functional Independence Measure (FIM) at discharge. In addition to the total cognitive subscore of the FIM at admission, age, and days from stroke onset to admission, the total motor subscore of the FIM at admission or its reciprocal number was added to independent variables. The correlation coefficients between the predicted and actual values were. 88 (ordinary regression) and. 93 (reciprocal regression) in the validation group (44 stroke patients). The median of the residuals (i.e, absolute values of subtraction of predicted motor-FIM from actual motor-FIM at discharge) of the reciprocal prediction (4.57) was significantly smaller than that of the ordinary prediction (6.26). In conclusion, the reciprocal prediction of regression analysis provided a more precise prediction without additional complex calculations.

Original languageEnglish
Pages (from-to)8-11
Number of pages4
JournalJournal of Stroke and Cerebrovascular Diseases
Volume14
Issue number1
DOIs
Publication statusPublished - 01-01-2005

All Science Journal Classification (ASJC) codes

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

Fingerprint Dive into the research topics of 'Stroke outcome prediction using reciprocal number of initial activities of daily living status'. Together they form a unique fingerprint.

  • Cite this