Duff Kevin, Schoenberg Mike R, Patton Doyle E, Mold James W, Scott James G, Adams Russell L
Department of Psychiatry, University of Iowa, Iowa City, IA 52242-1000, USA.
Clin Neuropsychol. 2008 Jul;22(4):651-61. doi: 10.1080/13854040701448785.
The determination of clinically significant cognitive change across time is an important issue in neuropsychology, and repeated assessments are common with older adults. Regression-based prediction formulas, which use initial test performance and demographic variables to predict follow-up test performance, have been utilized with patient and healthy control samples. Comparisons between predicted and observed follow-up performances can assist clinicians in determining the significance of change in the individual patient. In the current study, multiple regression-based prediction equations for the five Indexes and Total Score of the RBANS were developed for a sample of 146 community-dwelling older adults across a 2-year interval. These algorithms were then validated on a separate elderly sample (n = 145). Minimal differences were present between Observed and Predicted follow-up scores in the validation sample, suggesting that the prediction formulas are clinically useful for practitioners who assess older adults. A case example is presented that illustrates how the algorithms can be used clinically.
确定随时间推移具有临床意义的认知变化是神经心理学中的一个重要问题，对老年人进行重复评估很常见。基于回归的预测公式利用初始测试表现和人口统计学变量来预测后续测试表现，已应用于患者样本和健康对照样本。预测的和观察到的后续表现之间的比较可以帮助临床医生确定个体患者变化的显著性。在本研究中，针对146名社区居住的老年人样本，在两年时间间隔内，为RBANS的五个指数和总分开发了基于多元回归的预测方程。然后在另一个老年样本（n = 145）上对这些算法进行了验证。验证样本中观察到的和预测的后续分数之间存在最小差异，这表明预测公式对评估老年人的从业者具有临床实用性。文中给出了一个案例，说明这些算法如何在临床上使用。
