Objective: To clarify the relationship between cognitive Functional Independence Measure (FIM) and motor FIM gain. Methods: We examined 1,137 patients with stroke in a Kaifukuki rehabilitation ward. Both motor and cognitive FIM scores at admission were divided into six separate groups (three groups per parameter), and we then compared these groups with motor FIM gain as the objective variable. We also performed a multiple regression analysis using motor FIM gain as the objective variable. Results: In the groups where motor FIM scores at admission were 13-38 points and 39-64 points, motor FIM gain was significantly higher in individuals that had high cognitive FIM scores at admission. In the multiple regression analysis, we found that motor FIM gain increased by 0.889 points when cognitive FIM scores at admission increased by 1 point in patients whose motor FIM score at admission was between 13 and 34 points and whose cognitive FIM score at admission was between 5 and 14 points. Conclusion:This study clarified the relationship between cognitive FIM scores at admission and motor FIM gain in individuals with stroke.

Comparison between convalescent rehabilitation hospitals participating in the stroke liaison critical pathway with respect to the gain of Nichijo-seikatsu-kino-hyokahyo score. Jpn J Compr Rehabil Sci 2012; 3: 11-17. Purpose: To clarify the difference in mean gain of the Nichijo-seikatsu-kino-hyokahyo (NSKH; English translation: Functional Assessment of Daily Living Table ) scores between the convalescent rehabilitation hospitals (CRHs) participating in the stroke liaison critical pathway. Methods: The mean gain of NSKH score differs depending on patient type. Therefore, stroke patients were stratifi ed according to their total NSKH scores on admission to CRHs and the gains were calculated.Then adjusted mean gain was calculated for each hospital by correcting the mean gain assuming that the severity distribution in each CRH is the same as the severity distribution in all CRHs. Results: The patients were stratifi ed into 10 groups based on the total NSKH scores on admission divided into intervals of two points. The number of patients in the group with 0-1 point was the largest, while the gain was generally large in the groups with 6 to 13 points and was the largest in the group with 8-9 point. The adjusted mean gain exceeded the mean gain in Hospital B that had more mildly impaired patients, while the adjusted mean gain was below the mean gain in the remaining hospitals that had many critically ill patients. Conclusion: It is possible to make comparisons between hospitals, regions or years using the adjusted mean gain of NSKH score. Thus, this parameter seems to be useful in the assessment of outcome in CRHs participating in the stroke liaison critical pathway.

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Objective We conducted a study to develop an assessment sheet for fall prediction in stroke inpatients that is handy and reliable to help ward staff to devise a fall prevention strategy for each inpatient immediately upon admission. Methods The study consisted of three steps: (1) developing a data sampling form to record variables related to risk of falls in stroke inpatients and conducting a follow-up survey for stroke inpatients from their admission to discharge by using the form; (2) carrying out analyses of characteristics of the present subjects and selecting variables showing a high hazard ratio (HR) for falls using the Cox regression analysis; (3) developing an assessment sheet for fall prediction involving variables giving the integral coefficient for each variable in accordance with the HR determined in the second step. Results and discussion (1) Subjects of the present survey were 704 inpatients from 17 hospitals including 270 fallers.(2) We selected seven variables as predictors of the first fall: central paralysis, history of previous falls, use of psychotropic medicines, visual impairment, urinary incontinence, mode of locomotion and cognitive impairment. (3) We made 960 trial models in combination with possible coefficients for each variable, and among them we finally selected the most suitable model giving coefficient number 1 to each variable except mode of locomotion, which was given 1 or 2. The area under the ROC curve of the selected model was 0.73, and sensitivity and specificity were 0.70 and 0.69, respectively (4/5 at the cut-off point). Scores calculated from the assessment sheets of the present subjects by adding coefficients of each variable showed normal distribution and a significantly higher mean score in fallers (4.94 ± 1.29) than in non-fallers (3.65 ± 1.58) (P = 0.001). The value of the Barthel Index as the index of ADL of each subject was indicated to be in proportion to the assessment score of each subject. Conclusion We developed an assessment sheet for fall prediction in stroke inpatients that was shown to be available and valid to screen inpatients with risk of falls immediately upon admission.