Supplementary Material for: Education-Based Cutoffs for Cognitive Screening of Alzheimer’s Disease

<b><i>Introduction:</i></b> 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 For cognitive screening of early Alzheimer’s disease (AD), using outdated norms and cutoff scores may negatively affect clinical accuracy.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. <b><i>Methods:</i></b> CERAD-nb scores were obtained from AD patients (<i>n</i> = 389, 58% women, mean age 74.0 years) and from controls (<i>n</i> = 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. <b><i>Results:</i></b> 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. <b><i>Conclusions:</i></b> 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.

<b><i>引言：</i></b> 21世纪20年代，芬兰老年人口的受教育水平与人口规模正经历显著变化，这一趋势亦可能在多个欧洲国家上演。在早期阿尔茨海默病（Alzheimer’s Disease, AD）的认知筛查中，使用过时的常模与分界值可能会对临床诊断准确性产生负面影响。本研究旨在探讨受教育程度、年龄与性别对阿尔茨海默病登记联盟神经心理学成套测验（Consortium to Establish a Registry for Alzheimer’s Disease neuropsychological battery, CERAD-nb）得分的影响，研究样本为基于大型登记库的临床队列，包括轻度AD患者以及来自普通人群的非痴呆高危人群（对照组）；同时检验校正后的分界值是否能提升两组间的区分准确性。<b><i>研究方法：</i></b> 本研究收集了AD患者（<i>n</i> = 389，女性占比58%，平均年龄74.0岁）与对照组（<i>n</i> = 1,980，女性占比52%，平均年龄68.5岁）的CERAD-nb得分。采用单变量广义线性模型（General Linear Model, GLM）评估两组的CERAD-nb表现差异。通过受试者工作特征（Receiver Operating Characteristic, ROC）曲线评估两组间的区分效能，ROC曲线下面积越大，代表区分能力越强。同时计算各指标整体表现与诊断准确性的约登指数（Youden’s J）。<b><i>研究结果：</i></b> 在人口统计学因素中，受教育程度是CERAD-nb表现最强的预测因子，在AD患者与对照组中，其解释的变异量均高于年龄与性别。经受教育程度校正的分界值在区分AD患者与对照组时，相较现有未校正的分界值具有更优的诊断准确性。整体而言，两项CERAD-nb总分对两组的区分效能最高。<b><i>研究结论：</i></b> 经受教育程度校正的分界值在区分对照组与AD患者时优于未校正的分界值，尤其在高受教育程度人群中表现更突出；鉴于21世纪20年代受过高等教育的老年人口占比正大幅提升，此类校正分界值应应用于临床认知筛查工作。本研究结果同时显示，CERAD-nb总分相较于任意单项分测验得分，更能有效区分AD患者与对照组。开发一款可计算总分并对比基于受教育程度的分界值的数字化工具，将提升该测验的使用效率与易用性。