Format

Send to:

Choose Destination
See comment in PubMed Commons below
Arch Phys Med Rehabil. 2000 Jul;81(7):863-8.

The stroke impairment assessment set: its internal consistency and predictive validity.

Author information

  • 1Department of Rehabilitation Medicine, Keio University School of Medicine, Tokyo, Japan.

Abstract

OBJECTIVE:

To study the scale quality and predictive validity of the Stroke Impairment Assessment Set (SIAS) developed for stroke outcome research.

DESIGN:

Rasch analysis of the SIAS; stepwise multiple regression analysis to predict discharge functional independence measure (FIM) raw scores from demographic data, the SIAS scores, and the admission FIM scores; cross-validation of the prediction rule.

SETTING:

Tertiary rehabilitation center in Japan.

PATIENTS:

One hundred ninety stroke inpatients for the study of the scale quality and the predictive validity; a second sample of 116 stroke inpatients for the cross-validation study.

MAIN OUTCOME MEASURES:

Mean square fit statistics to study the degree of fit to the unidimensional model; logits to express item difficulties; discharge FIM scores for the study of predictive validity.

RESULTS:

The degree of misfit was acceptable except for the shoulder range of motion (ROM), pain, visuospatial function, and speech items; and the SIAS items could be arranged on a common unidimensional scale. The difficulty patterns were identical at admission and at discharge except for the deep tendon reflexes, ROM, and pain items. They were also similar for the right- and left-sided brain lesion groups except for the speech and visuospatial items. For the prediction of the discharge FIM scores, the independent variables selected were age, the SIAS total scores, and the admission FIM scores; and the adjusted R2 was .64 (p < .0001). Stability of the predictive equation was confirmed in the cross-validation sample (R2 = .68, p < .001).

CONCLUSIONS:

The unidimensionality of the SIAS was confirmed, and the SIAS total scores proved useful for stroke outcome prediction.

PMID:
10895996
[PubMed - indexed for MEDLINE]
PubMed Commons home

PubMed Commons

0 comments
How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for Elsevier Science
    Loading ...
    Write to the Help Desk