Predicting outcomes of patients in Japan after first acute stroke using a simple model.

OBJECTIVE

Prediction of patient outcome can be useful as an aid to clinical decision making. Many studies, including my own, have constructed predictive multivariate models for outcome following stroke rehabilitation therapy, but these have often required several minutes work with a pocket calculator. The aim is to develop a simple, easy-to-use model that has strong predictive power.

METHODS

Four hundred sixty-four consecutive patients with first stroke who were admitted to a rehabilitation hospital during a period of 19 mo were enrolled in the study. Sex, age, the stroke type, Functional Independence Measure total score on admission (X), onset to admission interval (number of days from stroke onset to rehabilitation admission), and length of rehabilitation hospital stay (number of days from hospital admission to discharge) were the independent variables. Functional Independence Measure total score at discharge (Y) was the dependent variable.

RESULTS

Stepwise multiple regression analysis resulted in the model containing age (P < 0.0001), X (P < 0.0001), and onset to admission interval (P < 0.0001). The equation was: Y = 68.6 - 0.32 (age) + 0.80X - 0.13 (onset to admission interval), a multiple correlation coefficient (R) = 0.82, and a multiple correlation coefficient squared (R2) = 0.68. Simple regression analysis revealed the relation between Xand Y: Y = 0.85X + 37.36, and R = 0.80 R2 = 0.64. In fact, plots of X vs. Ywere nonlinear, but seemed to be able to be linearized by some form of equation. It was found that there is a linear relation between logX and Y. The equation is Y = 106.88x - 95.35, where x = logX, R = 0.84, and R2 = 0.70. The correlation is improved by a regression analysis of a natural logarithmic transformation of X (R = 0.84 vs. R = 0.82).

CONCLUSION

The results in this study confirm that the simple regression model using a logarithmic transformation of X (R = 0.84) has predictive power over the simple regression model (R = 0.80). This model is well validated and clinically useful.