Self- and Informant-Report Cognitive Decline Discordance and Mild Cognitive Impairment Diagnosis

Subjective report of cognitive and functional decline from participant-study partner dyads can efficiently assess risk of cognitive impairment and clinical progression. Accuracy of self-report subjective cognitive decline may be limited by lack of awareness about one's own cognitive abilities in adults with MCI and dementia, and the extent to which discordance between self- and study partner-report is associated with diagnosis of cognitive impairment is unknown.

To investigate the association between discordance between self- and study partner-reported cognitive and/or functional decline and MCI diagnosis.

This multisite, cross-sectional study used baseline data from 2 longitudinal, observational studies. A total of 921 participant-study partner dyads enrolled in the Alzheimer Disease Neuroimaging Initiative (ADNI) from December 2016 to July 2022, and 279 dyads enrolled in the Brain Health Registry Electronic Validation of Online Methods Study (eVAL) from January 2020 to July 2023 were included.

Participants and study partners completed the Everyday Cognition Scale (ECog). Participants completed a demographics survey and the Geriatric Depression Scale-Short Form (GDS).

The model selection procedure in ADNI identified variables, which were included in a model that was externally validated in the eVAL cohort. The primary outcome was MCI vs cognitively unimpaired (CU) among participants.

ADNI participants (921 dyads) had a mean (SD) age of 71 (7) years and mean (SD) of 17 (3) years of education; 485 (53%) were female, 30 (3%) were Asian, 105 (11%) were Black, and 756 (82%) were White. eVAL participants (279 dyads) had a mean (SD) age of 71 (8) years and mean (SD) of 17 (2) years of education; 151 (54%) were female, 17 (6%) were Asian, 12 (4%) were Black, and 245 (88%) were White. The model distinguished CU vs MCI in the validation cohort with an area under the curve of 0.87 (95% CI, 0.88-0.96), sensitivity of 0.50 (95% CI, 0.49-0.80), and specificity of 0.97 (95% CI, 0.95-0.99) based on a regression model. The model included 4 discordance metrics, participant demographics (gender, age, and education), study partner demographics (gender and cohabitation), and depressive symptoms (GDS score).

In this cross-sectional study of 1200 dyads, measures of ECog score discordance helped distinguish CU from MCI individuals with high specificity. Participant and study partner agreement on lack of observed changes in the participant was associated with lower likelihood of MCI, highlighting the value of dyadic discordance metrics for ruling out MCI in diverse settings.