Relationship between cognitive FIM score and motor FIM gain in patients with stroke in a <i>Kaifukuki</i> rehabilitation ward

Abstract

Objective: To clarify the relationship between cognitive Functional Independence Measure (FIM) and motor FIM gain. Methods: We examined 1,137 patients with stroke in a Kaifukuki rehabilitation ward. Both motor and cognitive FIM scores at admission were divided into six separate groups (three groups per parameter), and we then compared these groups with motor FIM gain as the objective variable. We also performed a multiple regression analysis using motor FIM gain as the objective variable. Results: In the groups where motor FIM scores at admission were 13-38 points and 39-64 points, motor FIM gain was significantly higher in individuals that had high cognitive FIM scores at admission. In the multiple regression analysis, we found that motor FIM gain increased by 0.889 points when cognitive FIM scores at admission increased by 1 point in patients whose motor FIM score at admission was between 13 and 34 points and whose cognitive FIM score at admission was between 5 and 14 points. Conclusion:This study clarified the relationship between cognitive FIM scores at admission and motor FIM gain in individuals with stroke.

Corrected FIM effectiveness as an index independent of FIM score on admission

“…We found similar associations of motor FIM score on admission with motor FIM gain (Figure 1a) and with motor FIM effectiveness (Figure 1b) as previously reported [3,4].…”

“…Even if the mean FIM score on admission for a particular hospital is known, FIM gain cannot be corrected in the absence of information on the proportions of patients with mild, moderate, and severe disability. Instead of FIM gain with the above-mentioned issues, other methods have been used in outcome studies, including (1) FIM effectiveness, (2) multiple regression analysis for severe patients (those with motor FIM scores on admission of 13-34 points) [3], (3) multiple regression…”

The stratification of motor FIM and cognitive FIM and the creation of four prediction formulas to enable higher prediction accuracy of multiple linear regression analysis with motor FIM gain as the objective variable&horbar;An analysis of the Japan Rehabilitation Database

“…Tokunaga et al [5] stratified the three factors of motor FIM score at admission, cognitive FIM score at admission and age, and created eight prediction formulas. Regarding multiple linear regression analysis predicting FIM gain, Wang et al [6] and Tokunaga et al [7] stratified FIM score at admission into two groups and created two prediction formulas, and Imada et al [8] stratified motor FIM score and cognitive FIM at admission, creating three prediction formulas. Nevertheless, in these reports [2][3][4][5][6][7][8] it was not clear to what degree the prediction accuracy increased with multiple prediction formulas as opposed to a single prediction formula.…”

Comparison of prediction accuracy of the total score of FIM motor items at discharge in post-stroke patients in a Kaifukuki rehabilitation ward

“…To improve prediction accuracy, in addition to the selection of variables to be included in regression models [3], various methods have been proposed. These methods include prior transformation of the variables used for prediction [4], using predicted FIM effectiveness, which is the ratio of the actual amount of improvement achieved to the maximum amount that can be improved [5,6], and construction of multiple prediction formulas within the same study population [7][8][9]. Although previous studies constructed prediction formulas by multiple regression analysis and compared their accuracy, the dependent variables and other conditions of analysis differed among the studies.…”