Relationship between Functional Independence Measure (FIM) score on admission and influence of inhibitive factors in a comprehensive inpatient stroke rehabilitation ward

Abstract

Objective: Investigate how the influence of inhibitive factors was changed by the status of activities of daily living (ADL). Methods: Subjects were 2,650 stroke hemiplegic patients admitted to our comprehensive inpatient rehabilitation wards. Decision tree analysis was performed in which motor subscore of the Functional Independence Measure (FIM-M) at discharge was set as the target variable. Distribution of the verticality item of the Stroke Impairment Assessment Set, age, and the cognitive subscore of the FIM were calculated for every score of the FIM-M on admission. The FIM-M gain was compared by stratifying trunk function, age, and FIM cognitive subscore with certain ranges of the FIM-M on admission. Results:The FIM-M at discharge and the FIM-M gain were significantly low (p<0.05) in patients with decreased trunk function and low ADL score on admission. Both were significantly low (p<0.05) in patients with relatively wide-ranged ADL scores in elderly patients whose age was 68 years or older, and also significantly low (p<0.05) in low cognitive function patients who needed moderate or severe assistance. Conclusion:These results demonstrated that the influence of inhibitive factors is not uniform but instead differs according to degree of ADL level on admission.

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

“…However, there is a concern regarding mixing methods, with the result that one may get closer to the goal of rehabilitation by waiting to collect temporal data; it is important to consider the trade-off between improvement of prediction accuracy and delay of prediction time. Furthermore, when adding an inhibition factor to the variables, it is important to consider which patient group is adversely affected by the inhibition factor [25]. In the future, neural networks [26] and AI may be applied to outcome prediction.…”

Relationship between change in ADL during two weeks from admission and ADL at discharge in post-stroke patients admitted to Kaifukuki rehabilitation wards

“…As reported by Okamoto et al [14], the influence of inhibiting factors is not uniform but differs depending on the ADL level of the patients. In addition, as demonstrated in our present study, if the inhibiting factors improve during hospitalization, a better outcome may be achieved.…”

“…ADL outcome is influenced by various inhibiting factors such as cognitive function decline and sensory impairment [1,2,[11][12][13]. These factors exert diverse effects on outcome depending on various patient attributes [14]. However, except the article by Niki [10], previous studies have not presumed that the inhibiting factors themselves may change.…”

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

“…These factors were sex, stroke type, laterality of the lesion, whether the lesion was above or below the cerebellum tent, hemispatial neglect, and aphasia [11]. Furthermore, it was reported that trunk function [12] and the Japan Coma Scale (JCS) [13] are useful for predicting outcomes in the low mFIM group. Therefore, the abovementioned 17 factors are promising candidates for the explanatory variables that should be used in a multiple regression analysis to predict mFIM improvement in patients with stroke.…”

“…The magnitude of the effect of factors on mFIM improvement varies depending on the patient population. In other words, the influence of inhibiting factors is not uniform, but instead differs according to the degree of activities of daily living (ADL) at admission [12,20]. Therefore, it is necessary to stratify patients.…”