评估基于网络和临床主观认知衰退评估在检测认知障碍方面的准确性：多队列研究。 - Suppr | 超能文献

Abstract

BACKGROUND

Scalable tools to efficiently identify individuals likely to have cognitive impairment (CI) are critical in the Alzheimer disease and related dementias field. The Everyday Cognition scale (ECog) and its short form (ECog12) assess subjective cognitive and functional changes and are useful in predicting CI. Recent advances in online technology have enabled the use of web-based cognitive tests and questionnaires to identify CI with greater convenience and scalability. While the effectiveness of the ECog has been demonstrated in clinical settings, its potential to detect CI in remote, unsupervised formats remains underexplored.

OBJECTIVE

This study aimed to compare the ability of the web-based ECog and the in-clinic ECog in distinguishing between individuals with CI and those who are cognitively unimpaired (CU), and to evaluate the effectiveness of the ECog12-the short version of the ECog-compared to the full-length ECog in a web-based setting.

METHODS

Participants were recruited from the Brain Health Registry (BHR; web-based) and Alzheimer's Disease Neuroimaging Initiative (ADNI; in-clinic) settings with available clinical diagnoses. The ability of the self-reported ECog (Self-ECog), study partner-reported ECog (SP-ECog), Self-ECog12, and SP-ECog12 to discriminate individuals with CI from CU was assessed using receiver operating characteristic (ROC) curves. Area under the ROC curves (AUCs) between BHR and ADNI were compared using the DeLong test, as were AUCs between ECog12 and ECog in BHR.

RESULTS

Web-based Self-ECog and SP-ECog scores effectively discriminated CI from CU with AUCs of 0.722 and 0.818, respectively. Similarly, the abbreviated web-based versions, Self-ECog12 and SP-ECog12, also demonstrated discriminative ability (AUC=0.709 and 0.777, respectively). When compared to in-clinic ECog scores, there were no significant differences in the ability to distinguish CI from CU between web-based and in-clinic versions (BHR Self-ECog AUC=0.722 vs ADNI Self-ECog AUC=0.769, DeLong P=.06; BHR SP-ECog AUC=0.818 vs ADNI SP-ECog AUC=0.840, DeLong P=.50). Additionally, the comparison between web-based ECog and ECog12 showed no significant difference in AUCs (BHR Self-ECog AUC=0.722 vs BHR Self-ECog12 AUC=0.709, DeLong P=.18).

CONCLUSIONS

Web-based ECog scores, both the full-length and short-form, were as valid as in-clinic ECog scores for identifying clinically diagnosed CI. In addition, Self-ECog12 was as effective as full-length Self-ECog to identify CI in a web-based setting, offering a cost-effective and accessible screening tool for large-scale studies. These results highlight the value of the web-based ECog as a valid tool for identifying older adults with CI in a remote clinical study, facilitating early detection and referral for comprehensive evaluations for identifying potential candidates for disease-modifying therapy.

摘要

背景

在阿尔茨海默病及相关痴呆症领域，拥有可扩展的工具来有效识别可能患有认知障碍（CI）的个体至关重要。日常认知量表（ECog）及其简版（ECog12）可评估主观认知和功能变化，有助于预测CI。在线技术的最新进展使得基于网络的认知测试和问卷得以应用，从而能更便捷且可扩展地识别CI。虽然ECog在临床环境中的有效性已得到证实，但其在远程、无监督模式下检测CI的潜力仍未得到充分探索。

目的

本研究旨在比较基于网络的ECog与临床使用的ECog在区分CI个体与认知未受损（CU）个体方面的能力，并评估在基于网络的环境中，与完整版ECog相比，ECog简版ECog12的有效性。

方法

从脑健康登记处（BHR；基于网络）和阿尔茨海默病神经影像倡议组织（ADNI；临床环境）招募有可用临床诊断的参与者。使用受试者工作特征（ROC）曲线评估自我报告的ECog（Self-ECog）、研究伙伴报告的ECog（SP-ECog）、Self-ECog12和SP-ECog12区分CI个体与CU个体的能力。使用DeLong检验比较BHR和ADNI之间的ROC曲线下面积（AUC），以及BHR中ECog12和ECog之间的AUC。

结果

基于网络的Self-ECog和SP-ECog分数能有效区分CI与CU，AUC分别为0.722和0.818。同样，基于网络的简版Self-ECog12和SP-ECog12也具有判别能力（AUC分别为0.709和0.777）。与临床ECog分数相比，基于网络的版本和临床版本在区分CI与CU的能力上无显著差异（BHR Self-ECog AUC = 0.722 对 ADNI Self-ECog AUC = 0.769，DeLong P = 0.06；BHR SP-ECog AUC = 0.818 对 ADNI SP-ECog AUC = 0.840，DeLong P = 0.50）。此外，基于网络的ECog与ECog12之间的AUC比较无显著差异（BHR Self-ECog AUC = 0.722 对 BHR Self-ECog12 AUC = 0.709，DeLong P = 0.18）。

结论

基于网络的完整版和简版ECog分数在识别临床诊断的CI方面与临床ECog分数同样有效。此外，Self-ECog12在基于网络的环境中识别CI与完整版Self-ECog同样有效，为大规模研究提供了一种经济高效且可及的筛查工具。这些结果凸显了基于网络的ECog作为在远程临床研究中识别患有CI的老年人的有效工具的价值，有助于早期检测并转诊进行全面评估，以确定疾病修饰治疗的潜在候选者。