Inouye M
Department of Internal Medicine, Hyogo Rehabilitation Center Hospital, Kobe, Japan.
Am J Phys Med Rehabil. 2001 Sep;80(9):645-9. doi: 10.1097/00002060-200109000-00003.
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.
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.
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).
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.
预测患者的预后情况有助于临床决策。包括我自己的研究在内，许多研究都构建了中风康复治疗后预后的多变量预测模型，但这些模型通常需要用袖珍计算器花费几分钟时间来计算。目的是开发一种简单易用且具有强大预测能力的模型。
本研究纳入了在19个月期间连续入住一家康复医院的464例首次中风患者。性别、年龄、中风类型、入院时功能独立性测量总分（X）、发病至入院间隔时间（从中风发病到康复入院的天数）以及康复住院时间（从入院到出院的天数）为自变量。出院时功能独立性测量总分（Y）为因变量。
逐步多元回归分析得出的模型包含年龄（P < 0.0001）、X（P < 0.0001）和发病至入院间隔时间（P < 0.0001）。方程为：Y = 68.6 - 0.32（年龄）+ 0.80X - 0.13（发病至入院间隔时间），复相关系数（R）= 0.82，复相关系数平方（R2）= 0.68。简单回归分析揭示了X与Y之间的关系：Y = 0.85X + 37.36，R = 0.80，R2 = 0.64。实际上，X与Y的散点图呈非线性，但似乎可以通过某种形式的方程线性化。发现logX与Y之间存在线性关系。方程为Y = 106.88x - 95.35，其中x = logX，R = 0.84，R2 = 0.70。通过对X进行自然对数变换的回归分析，相关性得到了提高（R = 0.84对比R = 0.82）。
本研究结果证实了对X进行对数变换的简单回归模型（R = 0.84）比简单回归模型（R = 0.80）具有更强的预测能力。该模型经过了充分验证且在临床上有用。
