Application and validation of an algorithmic classification of early impairment in cognitive performance

Abstract

Objective

Due to the long prodromal period for dementia pathology, approaches are needed to detect cases before clinically recognizable symptoms are apparent, by which time it is likely too late to intervene. This study contrasted two theoretically-based algorithms for classifying early cognitive impairment (ECI) in adults aged ≥50 enrolled in the Baltimore Longitudinal Study of Aging.

Method

Two ECI algorithms were defined as poor performance (1 standard deviation [SD] below age-, sex-, race-, and education-specific means) in: (1) Card Rotations or California Verbal Learning Test (CVLT) immediate recall and (2) ≥1 (out of 2) memory or ≥3 (out of 6) non-memory tests. We evaluated concurrent criterion validity against consensus diagnoses of mild cognitive impairment (MCI) or dementia and global cognitive scores using receiver operating characteristic (ROC) curve analysis. Predictive criterion validity was evaluated using Cox proportional hazards models to examine the associations between algorithmic status and future adjudicated MCI/dementia.

Results

Among 1,851 participants (mean age = 65.2 ± 11.8 years, 50% women, 74% white), the two ECI algorithms yielded comparably moderate concurrent criterion validity with adjudicated MCI/dementia. For predictive criterion validity, the algorithm based on impairment in Card Rotations or CVLT immediate recall was the better predictor of MCI/dementia (HR = 3.53, 95%CI: 1.59–7.84) over 12.3 follow-up years.

Conclusions

Impairment in visuospatial ability or memory may be capable of detecting early cognitive changes in the preclinical phase among cognitively normal individuals.

Keywords:

• Alzheimer’s disease
• cognitive dysfunction
• neuropsychological tests
• classification
• validation study
• longitudinal studies

Disclosure statement

The authors have no conflict of interest to disclose.

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by the National Institutes of Health (NIH) grant R01AG061786. Y.C. and J.A.S. were supported by R01AG061786 and U01AG057545. A.G. was supported by the NIH K01AG050699. This study was also supported in part by the Intramural Research Program, National Institute on Aging, NIH. N.M.A., M.K-T., L.F., E.M.S., S.M.R. were all supported by the Intramural Research Program of the National Institute on Aging, NIH.