Purpose：We aimed to build a predictive model for ADL, applicable to all patients admitted to a convalescent rehabilitation ward, that uses a simple index as explanatory variables, which is accessible early during hospitalization.Methods：We included 1153 patients admitted to our convalescent rehabilitation ward. Stepwise multiple regression analysis was conducted using 18 Functional Independence Measure (FIM) sub-items at admission, with sex, age, and time since onset as explanatory variables. The total FIM motor score was calculated by summing 13 motor sub-items at discharge. Cross-validation was performed on 85 participants, independent of the analysis group, to confirm the model's clinical applicability.Results：The multiple regression analysis results for the analysis group showed a significant equation with an R2 of 0.712. In the validation group with 85 patients, difference between the predicted and actual FIM scores at discharge was compared between the two groups, and a strong correlation of r=0.888 (p＜0.019 was observed, without any significant difference between the groups.Conclusion：This model is less labor intensive when constructing a prediction equation because the objective variable is easily obtained. Our findings indicate that other convalescent rehabilitation wards can develop prognostic models using their data and implement them clinically.

Purpose：We aimed to build a predictive model for ADL, applicable to all patients admitted to a convalescent rehabilitation ward, that uses a simple index as explanatory variables, which is accessible early during hospitalization.Methods：We included 1153 patients admitted to our convalescent rehabilitation ward. Stepwise multiple regression analysis was conducted using 18 Functional Independence Measure (FIM) sub-items at admission, with sex, age, and time since onset as explanatory variables. The total FIM motor score was calculated by summing 13 motor sub-items at discharge. Cross-validation was performed on 85 participants, independent of the analysis group, to confirm the model's clinical applicability.Results：The multiple regression analysis results for the analysis group showed a significant equation with an R2 of 0.712. In the validation group with 85 patients, difference between the predicted and actual FIM scores at discharge was compared between the two groups, and a strong correlation of r=0.888 (p＜0.01) was observed, without any significant difference between the groups.Conclusion：This model is less labor intensive when constructing a prediction equation because the objective variable is easily obtained. Our findings indicate that other convalescent rehabilitation wards can develop prognostic models using their data and implement them clinically.