Optimizing Cognitive Assessment Outcome Measures for Alzheimer's Disease by Matching Wordlist Memory Test Features to Scoring Methodology.

Cognitive assessment with wordlist memory tests is a cost-effective and non-invasive method of identifying cognitive changes due to Alzheimer's disease and measuring clinical outcomes. However, with a rising need for more precise and granular measures of cognitive changes, especially in earlier or preclinical stages of Alzheimer's disease, traditional scoring methods have failed to provide adequate accuracy and information. Well-validated and widely adopted wordlist memory tests vary in many ways, including list length, number of learning trials, order of word presentation across trials, and inclusion of semantic categories, and these differences meaningfully impact cognition. While many simple scoring methods fail to account for the information that these features provide, extensive effort has been made to develop scoring methodologies, including the use of latent models that enable capture of this information for preclinical differentiation and prediction of cognitive changes. In this perspective article, we discuss prominent wordlist memory tests in use, their features, how different scoring methods fail or successfully capture the information these features provide, and recommendations for emerging cognitive models that optimally account for wordlist memory test features. Matching the use of such scoring methods to wordlist memory tests with appropriate features is key to obtaining precise measurement of subtle cognitive changes.

Methods to improve the detection of mild cognitive impairment.

We examined whether the performance of the National Institute of Aging's Consortium to Establish a Registry for Alzheimer's Disease's 10-word list (CWL), part of the consortium's neuropsychological battery, can be improved for detecting Alzheimer's disease and related disorders early. We focused on mild cognitive impairment (MCI) and mild dementia because these stages often go undetected, and their detection is important for treatment. Using standardized diagnostic criteria combined with history, physical examination, and cognitive, laboratory, and neuroimaging studies, we staged 471 community-dwelling subjects for dementia severity by using the Clinical Dementia Rating Scale. We then used correspondence analysis (CA) to derive a weighted score for each subject from their item responses over the three immediate- and one delayed-recall trials of the CWL. These CA-weighted scores were used with logistic regression to predict each subject's probability of impairment, and receiver operating characteristic analysis was used to measure accuracy. For MCI vs. normal, accuracy was 97% [confidence interval (C.I.) 97-98%], sensitivity was 94% (C.I. 93-95%), and specificity was 89% (C.I. 88-91%). For MCI/mild dementia vs. normal, accuracy was 98% (C.I. 98-99%), sensitivity was 96% (C.I. 95-97%), and specificity was 91% (C.I. 89-93%). MCI sensitivity was 12% higher (without lowering specificity) than that obtained with the delayed-recall total score (the standard method for CWL interpretation). Optimal positive and negative predictive values were 100% and at least 96.6%. These results show that CA-weighted scores can significantly improve early detection of Alzheimer's disease and related disorders.