Affiliations: [a]
ARC Training Centre in Cognitive Computing for Medical Technologies, University of Melbourne, Carlton, VIC, Australia
| [b]
Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, VIC, Australia
| [c] CogState Ltd, Melbourne, VIC, Australia
Correspondence:
[*]
Correspondence to: Benjamin Goudey, PhD, 700 Swanston St, University of Melbourne, Carlton, VIC, 3053, Australia. E-mail: [email protected].
Abstract: Background: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. Objective: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. Methods: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). Result: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. Conclusions: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.
