Prabhakaran Shyam, Zarahn Eric, Riley Claire, Speizer Allison, Chong Ji Y, Lazar Ronald M, Marshall Randolph S, Krakauer John W
Neurological Institute, Columbia University, Stroke and Critical Care Division, New York, New York 10032, USA.
Neurorehabil Neural Repair. 2008 Jan-Feb;22(1):64-71. doi: 10.1177/1545968307305302. Epub 2007 Aug 8.
Motor recovery after stroke is predicted only moderately by clinical variables, implying that there is still a substantial amount of unexplained, biologically meaningful variability in recovery. Regression diagnostics can indicate whether this is associated simply with Gaussian error or instead with multiple subpopulations that vary in their relationships to the clinical variables.
To perform regression diagnostics on a linear model for recovery versus clinical predictors.
Forty-one patients with ischemic stroke were studied. Impairment was assessed using the upper extremity Fugl-Meyer Motor Score. Motor recovery was defined as the change in the upper extremity Fugl-Meyer Motor Score from 24 to 72 hours after stroke to 3 or 6 months later. The clinical predictors in the model were age, gender, infarct location (subcortical vs cortical), diffusion weighted imaging infarct volume, time to reassessment, and acute upper extremity Fugl-Meyer Motor Score. Regression diagnostics included a Kolmogorov-Smirnov test for Gaussian errors and a test for outliers using Studentized deleted residuals.
In the random sample, clinical variables explained only 47% of the variance in recovery. Among the patients with the most severe initial impairment, there was a set of regression outliers who recovered very poorly. With the outliers removed, explained variance in recovery increased to 89%, and recovery was well approximated by a proportional relationship with initial impairment (recovery congruent with 0.70 x initial impairment).
Clinical variables only moderately predict motor recovery. Regression diagnostics demonstrated the existence of a subpopulation of outliers with severe initial impairment who show little recovery. When these outliers were removed, clinical variables were good predictors of recovery among the remaining patients, showing a tight proportional relationship to initial impairment.
中风后的运动恢复仅能通过临床变量得到一定程度的预测,这意味着在恢复过程中仍存在大量无法解释的、具有生物学意义的变异性。回归诊断可以表明这仅仅是与高斯误差相关,还是与多个在与临床变量关系上有所不同的亚组相关。
对恢复与临床预测指标的线性模型进行回归诊断。
对41例缺血性中风患者进行研究。使用上肢Fugl-Meyer运动评分评估损伤情况。运动恢复定义为中风后24至72小时至上3或6个月后上肢Fugl-Meyer运动评分的变化。模型中的临床预测指标包括年龄、性别、梗死部位(皮质下与皮质)、弥散加权成像梗死体积、重新评估时间以及急性上肢Fugl-Meyer运动评分。回归诊断包括对高斯误差的Kolmogorov-Smirnov检验以及使用学生化删除残差的异常值检验。
在随机样本中,临床变量仅解释了恢复中47%的方差。在初始损伤最严重的患者中,有一组回归异常值患者恢复情况很差。去除这些异常值后,恢复的解释方差增加到89%,并且恢复情况可以通过与初始损伤的比例关系很好地近似(恢复与0.70×初始损伤一致)。
临床变量只能适度预测运动恢复。回归诊断表明存在一组初始损伤严重且恢复很少的异常值亚组。去除这些异常值后,临床变量是其余患者恢复的良好预测指标,与初始损伤呈现紧密的比例关系。