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―An analysis of the Japan Rehabilitation Database

Abstract

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 -An analysis of the Japan Rehabilitation Database.

Comparison of the accuracy of multiple regression analysis using four methods to predict the functional independence measure at discharge

“…Since mFIM at discharge = mFIM at admission + mFIM gain, mFIM at discharge with high R 2 was predicted by multiple regression analysis, and the mFIM at admission was subtracted from the predicted value of mFIM at discharge to obtain the predicted value of mFIM gain. The correlation between the measured and the predicted value of mFIM gain obtained by this method was the same as that of when the mFIM gain was predicted directly by multiple regression analysis [15]. That is, when mFIM at discharge is the objective variable, R 2 is large only in appearance, and the prediction accuracy of mFIM at discharge and the prediction accuracy of mFIM gain are the same.…”

“…Previous studies have shown that S2 prediction has higher prediction accuracy than that of the standard S prediction [8,9,15]. However, the previous studies did not compare the accuracy of S2 prediction, R prediction, or E prediction.…”

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.…”

Explanatory variables to use in a multiple regression analysis to predict stroke patients' motor FIM score at discharge from convalescent rehabilitation wards: an investigation of patients with a motor FIM score of less than 40 points at admission

“…Therefore, it is necessary to stratify patients. Although methods have been reported to improve prediction accuracy in the high mFIM group [4][5][6], these methods were not effective in the low mFIM group [8]. In this study, we aimed to improve the prediction accuracy of the low mFIM group by using appropriate explanatory variables.…”

“…To improve the accuracy of predicting motor FIM (mFIM) improvement through a multiple regression analysis, three methods were reported: 1) conversion of mFIM at admission to a reciprocal number (1/mFIM at admission) and using it as one of the explanatory variables [4], 2) predicting mFIM improvement rate (mFIM effectiveness) through a multiple regression analysis and converting it to mFIM at discharge [5], and 3) creating two prediction formulas using the data of patients with low and high mFIM at admission [6]. However, these methods deal with the problem of the ceiling effect of mFIM gain (small mFIM gain in patients with high mFIM score at admission) [7,8].…”