Formula for predicting FIM for stroke patients at discharge from an acute ward or convalescent rehabilitation ward

Abstract

Objective: To develop formulas for predicting Functional Independence Measure (FIM) at the time of discharge from an acute or convalescent hospital ward using multicenter data. Methods: Data from 4,311 acute patients (22 hospitals) and 1,941 convalescent patients (24 hospitals) were divided into two groups (calculation group and verification group). Multiple regression analysis was performed to develop formulas for predicting discharge FIM and test their validity with data from the verification group. Results: The formula derived for predicting discharge FIM for acute patients was 85.04 + (-0.53 × age) + (12.06 × subarachnoid hemorrhage) + (-7.90 × complication present) + (-0.70 × number of days from onset of stroke until admission) + (1.24 × admission GCS) + (-1.08 × admission NIHSS) + (-4.15 × modified Rankin Scale score before stroke) + (0.30 × admission motor FIM) + (1.03 × admission cognitive FIM), with R 2 = 0.78. The formula derived for predicting discharge FIM for convalescent patients was 33.04 + (-0.34 × age) + (-3.88 × complication present) + (-0.11 × number of days from onset of stroke until admission) + (2.44 × admission GCS) + (-1.68 × modified Rankin Scale score before stroke) + (0.53 × admission motor FIM) + (1.25 × admission cognitive FIM) (R 2 = 0.66). Conclusion: Using a large multicenter database, we developed separate formulas for predicting FIM at discharge from an acute ward and from a convalescent ward with proven external validity.

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

“…Therefore, a comparison of outcome prediction methods using the same patient population for constructing prediction formulas and for confirming the accuracy of outcome prediction is useful, and is even better if the patient population contains a large number of homogeneous subjects. Although outcome comparison studies using the data of a large number of cases from the Japan Association of Rehabilitation Database have been conducted, there is a limitation that the data are submitted voluntarily from diverse rehabilitation environments [8,[13][14][15][16][17]. In the present study, all the subjects participated in the full-time integrated treatment program incorporating motor learning.…”

The external validity of multiple regression analyses predicting discharge FIM score in patients with stroke hospitalized in <i>Kaifukuki</i> rehabilitation wards—An analysis of the Japan Rehabilitation Database—

“…Five studies [8][9][10][11][12] did not describe the prediction equations and were also excluded. The remaining four studies: Jeong et al [2], Sonoda et al [3], Iwai et al [4], and Inouye et al [5], were analyzed in this report. The 2014 version of the Japan Rehabilitation Database (stroke, Kaifukuki rehabilitation wards) [14] had 4,949 stroke patient cases registered.…”

“…Moreover, the days from onset to Kaifukuki rehabilitation ward admission and the length of hospital stay were restricted, and cases of subarachnoid hemorrhage were excluded, to remove the effects of exceptional cases. The subject patient data was inputted into the six prediction equations (Table 2) reported in the four studies [2][3][4][5], and predicted values were obtained. A correlation analysis between the measured values and the predicted values of the discharge FIM score was performed by Pearson's correlation coefficient test (significance level < 5%).…”

“…However, it has been pointed out that the accuracy of prediction is not very high, and simply increasing the variables used for the prediction does not necessarily increase the prediction accuracy [1]. By searching for reports in Japan which used multiple regression analysis to predict the Functional Independence Measure (FIM) in Kaifukuki rehabilitation ward stroke patients at the time of discharge, 11 studies were identified [2][3][4][5][6][7][8][9][10][11][12]. Of these, only three studies [2,3,9] grouped the patients into two groups (a calculation group and a validation group), and performed an internal validity analysis of the prediction equation using the data of the validation group.…”

“…By searching for reports in Japan which used multiple regression analysis to predict the Functional Independence Measure (FIM) in Kaifukuki rehabilitation ward stroke patients at the time of discharge, 11 studies were identified [2][3][4][5][6][7][8][9][10][11][12]. Of these, only three studies [2,3,9] grouped the patients into two groups (a calculation group and a validation group), and performed an internal validity analysis of the prediction equation using the data of the validation group. Furthermore, although an external validity analysis using patients from a different institution would be ideal [13], no such studies were identified.…”