A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy

Tokunaga, Makoto; Watanabe, Susumu; Sonoda, Shigeru

doi:10.1016/j.jstrokecerebrovasdis.2017.06.028

Cited by 12 publications

(12 citation statements)

References 13 publications

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“…Based on the report by Tokunaga et al [6], the independent variables used in this study were: age, duration from stroke onset to admission, mFIM on admission, total score of FIM cognitive items (cFIM) on admission, gender (male/female), and type of stroke (hemorrhage/cerebral infarction). Dummy variables were used for gender and type of stroke (male 0/female 1, cerebral hemorrhage 0/cerebral infarction 1).…”

Section: Three Methods Of Multiple Regression Analysismentioning

confidence: 99%

“…Prediction formula E: First, according to the report by Tokunaga et al [6], multiple regression analysis was performed using mFIM effectiveness as the dependent variable and the above-mentioned items as independent variables. Using the predicted mFIM effectiveness obtained, predicted mFIM at discharge was calculated using the equation: mFIM on admission + predicted mFIM effectiveness × (91 − mFIM on admission).…”

Section: Three Methods Of Multiple Regression Analysismentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Wada

Sonoda

Okamoto

et al. 2019

JJCRS

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Objective: We compared the accuracy of formulas for predicting ADL outcome constructed by multiple regression analysis in post-stroke patients admitted to a Kaifukuki rehabilitation ward. Methods: We divided 1,502 post-stroke patients into a construction group used to generate prediction formulas, and a validation group used to confirm the prediction accuracy. Prediction formula S was constructed by conventional multiple regression analysis using Functional Independence Measuremotor score (mFIM) at discharge as the dependent variable. Prediction formula R was constructed by reciprocal multiple regression analysis. Prediction equation E was constructed by calculating mFIM at discharge via mFIM effectiveness. In the validation group, predicted mFIM at discharge was calculated, and intraclass correlation coefficient and absolute value of residual were compared. Results: Intraclass correlation coefficients were 0.86 using prediction formula S, 0.90 using prediction formula R, and 0.89 using prediction formula E. Absolute values of residual were 9.38±6.62 using prediction formula S, 7.30±6.56 using prediction formula R, and 7.56±6.45 using prediction formula E. The Steel-Dwass test detected a significant difference between prediction formulas S and R, and between prediction formulas S and E (both p<0.05). Conclusion:The prediction accuracy of formulas for predicting ADL outcome constructed by multiple regression analysis is improved by adding a transformation that brings the model toward linearity.

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Section: Three Methods Of Multiple Regression Analysismentioning

confidence: 99%

Section: Three Methods Of Multiple Regression Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wada

Sonoda

Okamoto

et al. 2019

JJCRS

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“…By adding mFIM at admission to this predicted mFIM gain, we then obtained the predicted mFIM at discharge. The mFIM at discharge that we obtained in this way had a higher prediction accuracy than that directly predicted by multiple regression analysis (the correlation coefficient between measured and predicted values of mFIM at discharge was 0.916 for the former and 0.878 for the later) [24]. We believe that this new method is effective in the accurate prediction of FIM at discharge.…”

Section: Relationship Between Fim At Admission and Fim Effectivenessmentioning

confidence: 55%

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

Tokunaga¹,

Hashimoto²,

Watanabe³

et al. 2017

Int J Phys Med Rehabil

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Multiple linear regression analysis is frequently used in studies investigating the degree of Functional Independence Measure (FIM) improvement in stroke patients. However, the coefficient of determination R 2 is about 0.46 to 0.73, meaning that the prediction accuracy is not necessarily high. In order to improve the prediction accuracy, the following methods are used; using appropriate explanatory variables, using FIM effectiveness which corrected the ceiling effect as the objective variable, creating multiple prediction formulas, converting numerical variable of explanatory variables into dummy variable, adding FIM improvement for one month to the explanatory variables. Even so, it is difficult to predict patients whose FIM gain is extremely large or small. It is desirable to combine these methods or develop new methods to achieve the accurate prediction.

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“…Therefore, therapists are required to confirm the improvement in ADL of approximately 1 point every 5 days. Research related to the recovery phase rehabilitation wards in Japan has reported that FIM-M score at discharge can be predicted with high degree of precision with multivariate analysis using FIM motor effectiveness 14) . In the previous study 13) , the cut-off FIM-T score of 63/126 points is useful to discriminate patients with adverse events among those with acute stroke.…”

Section: Discussionmentioning

confidence: 99%

The role of the functional independence measure score in predicting the home discharge of inpatients with cerebrovascular diseases in convalescent rehabilitation wards

Shirahama

Fudano²,

Imai

et al. 2020

J Phys Ther Sci

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To clarify the cutoff point of the Functional Independence Measure tool for predicting home discharge of patients in convalescent rehabilitation wards. [Participants and Methods] This retrospective observational study analyzed the data of 91 inpatients with cerebrovascular disease who received rehabilitation treatment at a convalescent rehabilitation ward. We categorized the participants into two groups: the home-discharged group and the non-home discharged group. We divided the outcome parameters in the two groups into different categories and compared them using the Mann-Whitney U test. To identify the relevant cutoff points in a clinical setting, we applied the receiver operating characteristic curve. [Results] In patients with cerebrovascular disease, the Functional Independence Measure motor, cognitive, and total scores were significantly higher in the home-discharged group than in the non-home discharged group. We statistically calculated the Functional Independence Measure motor score cutoff point that can predict home discharge, and the predicted Functional Independence Measure motor score at the time of hospitalization was 53.5-60.0 points. [Conclusion] It is important to start rehabilitation at an early stage and increase the amount of training to increase the likelihood of returning home.

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A Method of Calculating Functional Independence Measure at Discharge from Functional Independence Measure Effectiveness Predicted by Multiple Regression Analysis Has a High Degree of Predictive Accuracy

Cited by 12 publications

References 13 publications

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

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

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

The role of the functional independence measure score in predicting the home discharge of inpatients with cerebrovascular diseases in convalescent rehabilitation wards

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