Department of Psychology and Logopedics, University of Helsinki, Helsinki, Finland.
Population Health Unit, Finnish Institute for Health and Welfare, Helsinki, Finland.
Dement Geriatr Cogn Disord. 2022;51(1):42-55. doi: 10.1159/000521982. Epub 2022 Feb 23.
The educational background and size of the elderly population are undergoing significant changes in Finland during the 2020s. A similar process is likely to occur also in several European countries. For cognitive screening of early Alzheimer's disease (AD), using outdated norms and cutoff scores may negatively affect clinical accuracy. The aim of the present study was to examine the effects of education, age, and gender on the Consortium to Establish a Registry for Alzheimer's Disease neuropsychological battery (CERAD-nb) in a large register-based, clinical sample of patients with mild AD and nondemented at-risk persons from the general population (controls) and to examine whether corrected cutoff scores would increase the accuracy of differentiation between the 2 groups.
CERAD-nb scores were obtained from AD patients (n = 389, 58% women, mean age 74.0 years) and from controls (n = 1,980, 52% women, mean age 68.5 years). The differences in CERAD-nb performance were evaluated by univariate GLM. Differentiation between the 2 groups was evaluated using a receiver operating characteristic (ROC) curve, where a larger area under the ROC curve represents better discrimination. Youden's J was calculated for the overall performance and accuracy of each of the measures.
Of the demographic factors, education was the strongest predictor of CERAD-nb performance, explaining more variation than age or gender in both the AD patients and the controls. Education corrected cutoff scores had better diagnostic accuracy in discriminating between the AD patients and controls than existing uncorrected scores. The highest level of discrimination between the 2 groups overall was found for two CERAD-nb total scores.
Education-corrected cutoff scores were superior to uncorrected scores in differentiating between controls and AD patients especially for the highest level of education and should therefore be used in clinical cognitive screening, also as the proportion of the educated elderly is increasing substantially during the 2020s. Our results also indicate that total scores of the CERAD-nb are better at discriminating AD patients from controls than any single subtest score. A digital tool for calculating the total scores and comparing education-based cutoffs would increase the efficiency and usability of the test.
在 2020 年代，芬兰的教育背景和老年人口规模正在发生重大变化。类似的情况也可能发生在几个欧洲国家。对于早期阿尔茨海默病（AD）的认知筛查，使用过时的常模和截断分数可能会对临床准确性产生负面影响。本研究的目的是在一个大型的基于登记的、临床样本中，检查教育、年龄和性别对认知障碍评估联盟神经心理学测试包（CERAD-nb）的影响，该样本由轻度 AD 患者和来自普通人群的无痴呆高危人群（对照组）组成，并检查校正后的截断分数是否会提高区分这两组的准确性。
从 AD 患者（n=389，58%为女性，平均年龄 74.0 岁）和对照组（n=1980，52%为女性，平均年龄 68.5 岁）中获得 CERAD-nb 分数。通过单变量 GLM 评估 CERAD-nb 表现的差异。使用接收者操作特征（ROC）曲线评估两组之间的差异，ROC 曲线下的面积越大表示区分度越好。计算了每个指标的整体表现和准确性的 Youden's J。
在人口统计学因素中，教育是 CERAD-nb 表现的最强预测因素，在 AD 患者和对照组中，教育解释的变异量均大于年龄或性别。与现有的未校正分数相比，教育校正的截断分数在区分 AD 患者和对照组方面具有更好的诊断准确性。在两组之间的整体区分度最高的是两个 CERAD-nb 总分。
在区分对照组和 AD 患者方面，教育校正的截断分数优于未校正的分数，尤其是对于最高教育水平的患者，因此应在临床认知筛查中使用，也因为在 2020 年代，受过教育的老年人口比例大幅增加。我们的结果还表明，CERAD-nb 的总分数比任何单一的子测试分数更能区分 AD 患者和对照组。用于计算总分和比较基于教育的截断值的数字工具将提高测试的效率和可用性。
