Diagnostic accuracy of the Cogstate Brief Battery for prevalent MCI and prodromal AD (MCI A⁺T⁺) in a population‐based sample

Abstract

Introduction This study evaluated the diagnostic accuracy of the Cogstate Brief Battery (CBB) for mild cognitive impairment (MCI) and prodromal Alzheimer's disease (AD) in a population‐based sample. Methods Participants included adults ages 50+ classified as cognitively unimpaired (CU, n = 2866) or MCI (n = 226), and a subset with amyloid (A) and tau (T) positron emission tomography who were AD biomarker negative (A–T–) or had prodromal AD (A+T+). Results Diagnostic accuracy of the Learning/Working Memory Composite (Lrn/WM) for discriminating all CU and MCI was moderate (area under the curve [AUC] = 0.75), but improved when discriminating CU A–T– and MCI A+T+ (AUC = 0.93) and when differentiating MCI participants without AD biomarkers from those with prodromal AD (AUC = 0.86). Conventional cut‐offs yielded lower than expected sensitivity for both MCI (38%) and prodromal AD (73%). Discussion Clinical utility of the CBB for detecting MCI in a population‐based sample is lower than expected. Caution is needed when using currently available CBB normative data for clinical interpretation.

A Remote Digital Memory Composite to Detect Cognitive Impairment in Memory Clinic Samples in Unsupervised Settings using Mobile Devices

“…The Remote Digital Memory Composite allowed to differentiate individuals with and without PACC5-based MCI-grade impairment with high diagnostic accuracy. This is higher or comparable to several other recently reported unsupervised (Mackin et al, 2018) or in-clinic and supervised digital cognitive assessments (Alden et al, 2021;Groppell et al, 2019;Kalafatis et al, 2021;Ye et al, 2020). Importantly, however, several earlier approaches reported outcomes by comparing MCI patients against samples that exclusively consisted of healthy asymptomatic older adults (Alden et al, 2021;Groppell et al, 2019;Kalafatis et al, 2021;Mackin et al, 2018;Maruff et al, 2013;Ye et al, 2020).…”

“…This is higher or comparable to several other recently reported unsupervised (Mackin et al, 2018) or in-clinic and supervised digital cognitive assessments (Alden et al, 2021;Groppell et al, 2019;Kalafatis et al, 2021;Ye et al, 2020). Importantly, however, several earlier approaches reported outcomes by comparing MCI patients against samples that exclusively consisted of healthy asymptomatic older adults (Alden et al, 2021;Groppell et al, 2019;Kalafatis et al, 2021;Mackin et al, 2018;Maruff et al, 2013;Ye et al, 2020). In health care settings, the main challenge is to identify significant impairment within memory complainers.…”

A paradox in digital memory assessment: Increased sensitivity with reduced difficulty

“…While the standard OCL80 had been shown in many settings to be sensitive to changes in memory, recent studies applying this to characterize memory impairment in individuals classified clinically with mild cognitive impairment (MCI) or with biomarker positive status, showed lower than expected binary classification accuracy (though equivalent to delayed auditory verbal learning), did not discriminate between biomarker subgroups within cognitively unimpaired individuals and showed poor sensitivity using conventional cut-offs (22,23). Analysis of the outcomes from these studies suggested that the lower-than-expected sensitivity arose from a restriction in the range of the performance scores.…”

“…One important difference between decisions about cognitive change and cognitive impairment is that decisions about change are based on comparison of test performance withinindividuals over time, while decisions about cognitive impairment are based on comparison of an individual's performance to normative data ranges. Some studies using the OCL to classify impairment in AD show that its sensitivity is lower than expected for some classifications (22,23).…”

“…Consideration of data from cross-sectional studies applying the OCL in symptomatic AD indicates that performance in this group was often close to chance (i.e., 50%), suggesting there is a floor effect in test performance in these groups. This floor effect can truncate the numerical difference between mean OCL performance in controls (i.e., in the normative data) and in symptomatic AD groups so that, even at their worse, OCL performance scores in symptomatic AD can only be 1.5 standard deviations below that of controls (22,23). Thus, the sensitivity of OCL to abnormal memory in symptomatic AD would also improve if average performance in normative groups was also better.…”