We collected and analyzed large-scale data concerning the fall of stroke inpatients in convalescent rehabilitation wards. Three hundred seventy-four of 1,107 inpatients experienced one fall or more, and 16 factors associated with falls were clarified by the chi-square test. To extract the significant item from a multifactor, the logistic regression analysis of 16 factors was carried out, and we developed an assessment sheet for the risk degree of first fall prediction in stroke inpatients. We selected eight variables as the items on the assessment sheet : history of previous falls, central paralysis, visual impairment, sensory disturbance, urinary incontinence, use of psychotropic medicines, mode of locomotion, and cognitive impairment. The total score of the assessment sheet was ranged from 0 to 10 and the mean score of fallers (6.4±1.5) among subjects was significantly higher than that of non-fallers (5.1±1.9) (p <0.001). When the subjects were classified into three groups, a significant difference (p<0.001) in the tendency of fall incidence in term of days after admission was found among the three groups on the basis of the Kaplan-Meier survival curve.

OBJECTIVEWe 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.

METHODSThe 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.

CONCLUSIONWe 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.