A prediction model for functional gain in stroke

VernacularTitle:脑卒中患者日常生活活动能力进展的Logistic回归分析及预测模型
Author: Yan SUN ; Jianan LI ; Hong LU ; Jiaren XU
Publication Type:Journal Article
Keywords: Prediction models; Stroke; Activities of daily living; Functional gains; Logistic regression
From: Chinese Journal of Physical Medicine and Rehabilitation 2011;33(1):35-38
CountryChina
Language:Chinese
Abstract: Objective To develop a prediction model for functional gain in the activities of daily living (ADL) after stroke rehabilitation. Methods Logistic regression was applied to 896 patient records from two hospitals. Functional gains in ADL were measured using a modified Barthel index (MBI). Results Five parameters were screened in the logistic regression model. The equation was: Logit( P/Y =1)=6.259+1.048 ( first onset to admission interval)+1.242(MBI score at admission)+0.300(number of comorbidities)+1.095(retired cadre dummy)+ 0.906(worker dummy) + 1.384 (professional dummy). This formulation accounted for about 78% of the variance in the data. Conclusions MBI score at admission, the interval between first onset and admission, comorbidities, job status and occupation are the main factors predicting functional ADL gains after stroke. The model can be used to predict outcomes for individual stroke patients at admission to rehabilitation.