Liu Shu, Maruff Paul, Fedyashov Victor, Masters Colin L, Goudey Benjamin
ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia.
Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC, Australia.
J Alzheimers Dis. 2024;101(3):889-899. doi: 10.3233/JAD-231319.
Integrating scores from multiple cognitive tests into a single cognitive composite has been shown to improve sensitivity to detect AD-related cognitive impairment. However, existing composites have little sensitivity to amyloid-β status (Aβ +/-) in preclinical AD.
Evaluate whether a data-driven approach for deriving cognitive composites can improve the sensitivity to detect Aβ status among cognitively unimpaired (CU) individuals compared to existing cognitive composites.
Based on the data from the Anti-Amyloid Treatment in the Asymptomatic Alzheimer's Disease (A4) study, a novel composite, the Data-driven Preclinical Alzheimer's Cognitive Composite (D-PACC), was developed based on test scores and response durations selected using a machine learning algorithm from the Cogstate Brief Battery (CBB). The D-PACC was then compared with conventional composites in the follow-up A4 visits and in individuals from the Alzheimer's Disease Neuroimaging Initiative (ADNI).
The D-PACC showed a comparable or significantly higher ability to discriminate Aβ status [median Cohen's d = 0.172] than existing composites at the A4 baseline visit, with similar results at the second visit. The D-PACC demonstrated the most consistent sensitivity to Aβ status in both A4 and ADNI datasets.
The D-PACC showed similar or improved sensitivity when screening for Aβ+ in CU populations compared to existing composites but with higher consistency across studies.
将多个认知测试的分数整合为一个单一的认知综合指标已被证明可提高检测与阿尔茨海默病（AD）相关的认知障碍的敏感性。然而，现有的综合指标对临床前AD中的淀粉样蛋白-β状态（Aβ +/-）几乎没有敏感性。
评估与现有的认知综合指标相比，一种数据驱动的推导认知综合指标的方法是否能提高在认知未受损（CU）个体中检测Aβ状态的敏感性。
基于无症状阿尔茨海默病抗淀粉样蛋白治疗（A4）研究的数据，一种新的综合指标，即数据驱动的临床前阿尔茨海默病认知综合指标（D-PACC），是根据使用机器学习算法从Cogstate简短电池测试（CBB）中选择的测试分数和反应持续时间开发的。然后在后续的A4访视以及来自阿尔茨海默病神经影像倡议（ADNI）的个体中，将D-PACC与传统综合指标进行比较。
在A4基线访视时，D-PACC显示出与现有综合指标相当或显著更高的区分Aβ状态的能力[中位数科恩d值=0.172]，第二次访视时结果相似。D-PACC在A4和ADNI数据集中对Aβ状态表现出最一致的敏感性。
与现有综合指标相比，在CU人群中筛查Aβ+时，D-PACC显示出相似或更高的敏感性，但在各研究中具有更高的一致性。
