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

Abstract

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