A systematic review of studies reporting multivariable models to predict functional outcomes after post-stroke inpatient rehabilitation

Abstract

Only a selected group of variables have repeatedly proven to be significant predictors of functional ability after post-stroke inpatient rehabilitation. [Box: see text].

Comparison of Functional Independence Measure (FIM) Gain between Groups with Extreme Differences in FIM Score at Admission

“…First is that it is not known whether the independent variables used in the present study was appropriate or not. In their review of multiple regression analysis performed on acute phase stroke patients, Meyer et al [15] found that in 27 studies, 63 out of 126 factors used as independent variables were significant. However, the number of significant independent variables actually included in a single predictive formula was an average of no more than 4.1 (standard deviation of 2.5) [15].…”

Methods for Improving the Predictive Accuracy Using Multiple Linear Regression Analysis to Predict the Improvement Degree of Functional Independence Measure for Stroke Patients

“…In the review of Meyer et al [4], R 2 was on average 0.65 (minimum 0.35 to the maximum 0.82) when the objective variable was FIM at discharge, 0.22 (0.08 to 0.4) when the objective variable was FIM gain (FIM at discharge minus FIM at admission), and 0.08 (0.03 to 0.14) when the objective variable was FIM efficiency (FIM gain divided by the number of days in hospital). For accurate prediction, it is necessary that R 2 is 0.5 or more (desirably 0.7 or more).…”

“…Factors that were used in more than five prediction formulas and were significant in more than half of them were FIM at admission (significant in 46 formulas of 51 formulas, 46/51), age (30/45), previous stroke (5/10), Barthel Index at admission (6/6), neglect (4/6), dysphasia (4/6), impulsivity (4/6), and National Institute of Health Stroke Scale (5/5) [4]. However, the number of significant explanatory variables incorporated into one prediction formula was only 4.1 on average [4]. Furthermore, the Japanese Guidelines for the Management of Stroke 2015 [5] says that "even if the variables used for prediction are simply increased, the prediction accuracy does not necessarily rise [6,7], and the advantages of using the simplest prediction method are also shown [8]".…”

“…In a review of reports that used multiple regression analysis in acute stroke patients by Meyer et al [4], 126 factors were used as explanatory variables, and 63 of which were significant. Factors that were used in more than five prediction formulas and were significant in more than half of them were FIM at admission (significant in 46 formulas of 51 formulas, 46/51), age (30/45), previous stroke (5/10), Barthel Index at admission (6/6), neglect (4/6), dysphasia (4/6), impulsivity (4/6), and National Institute of Health Stroke Scale (5/5) [4]. However, the number of significant explanatory variables incorporated into one prediction formula was only 4.1 on average [4].…”

“…So, there are many reports that used FIM at discharge as the objective variable. Specifically, in Meyer's et al review [4], the objective value was FIM at discharge in 33 formulas, FIM gain in 20 formulas, and FIM efficiency in 3 formulas. But is it really the best way to choose FIM at discharge as the objective variable?…”