Considering the base rates of low performance in cognitively healthy older adults improves the accuracy to identify neurocognitive impairment with the Consortium to Establish a Registry for Alzheimer's Disease-Neuropsychological Assessment Battery (CERAD-NAB).

It is common for some healthy older adults to obtain low test scores when a battery of neuropsychological tests is administered, which increases the risk of the clinician misdiagnosing cognitive impairment. Thus, base rates of healthy individuals' low scores are required to more accurately interpret neuropsychological results. At present, this information is not available for the German version of the Consortium to Establish a Registry for Alzheimer's Disease-Neuropsychological Assessment Battery (CERAD-NAB), a frequently used battery in the USA and in German-speaking Europe. This study aimed to determine the base rates of low scores for the CERAD-NAB and to tabulate a summary figure of cut-off scores and numbers of low scores to aid in clinical decision making. The base rates of low scores on the ten German CERAD-NAB subscores were calculated from the German CERAD-NAB normative sample (N = 1,081) using six different cut-off scores (i.e., 1st, 2.5th, 7th, 10th, 16th, and 25th percentile). Results indicate that high percentages of one or more "abnormal" scores were obtained, irrespective of the cut-off criterion. For example, 60.6% of the normative sample obtained one or more scores at or below the 10th percentile. These findings illustrate the importance of considering the prevalence of low scores in healthy individuals. The summary figure of CERAD-NAB base rates is an important supplement for test interpretation and can be used to improve the diagnostic accuracy of neurocognitive disorders.

Serial position effects are sensitive predictors of conversion from MCI to Alzheimer's disease dementia.

BACKGROUND: It is unclear whether the predictive strength of established cognitive variables for progression to Alzheimer's disease (AD) dementia from mild cognitive impairment (MCI) varies depending on time to conversion. We investigated which cognitive variables were best predictors, and which of these variables remained predictive for patients with longer times to conversion. METHODS: Seventy-five participants with MCI were assessed on measures of learning, memory, language, and executive function. Relative predictive strengths of these measures were analyzed using Cox regression models. RESULTS: Measures of word-list position-namely, serial position scores-together with Short Delay Free Recall of word-list learning best predicted conversion to AD dementia. However, only serial position scores predicted those participants with longer time to conversion. CONCLUSIONS: Results emphasize that the predictive strength of cognitive variables varies depending on time to conversion to dementia. Moreover, finer measures of learning captured by serial position scores were the most sensitive predictors of AD dementia.

Varying strength of cognitive markers and biomarkers to predict conversion and cognitive decline in an early-stage-enriched mild cognitive impairment sample.

BACKGROUND: Several cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers predict conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) dementia. However, predictors might be more or less powerful depending on the characteristics of the MCI sample. OBJECTIVE: To investigate which cognitive markers and biomarkers predict conversion to AD dementia and course of cognitive functioning in a MCI sample with a high proportion of early-stage MCI patients. METHODS: Variables known to predict progression in MCI patients and hypothesized to predict progression in early-stage MCI patients were selected. Cognitive (long-delay free recall, regional primacy score), imaging (hippocampal and entorhinal cortex volumes, fornix fractional anisotropy), and CSF (Aß1-42/t-tau, Aß1-42) variables from 36 MCI patients were analyzed with Cox regression and mixed-effect models to determine their individual and combined abilities to predict time to conversion to AD dementia and course of global cognitive functioning, respectively. RESULTS: Those variables hypothesized to predict the course of early-stage MCI patients were most predictive for MCI progression. Specifically, regional primacy score (a measure of word-list position learning) most consistently predicted conversion to AD dementia and course of cognitive functioning. Both the prediction of conversion and course of cognitive functioning were maximized by including CSF Aß1-42 and fornix integrity biomarkers, respectively, indicating the complementary information carried by cognitive variables and biomarkers. CONCLUSION: Predictors of MCI progression need to be interpreted in light of the characteristics of the respective MCI sample. Future studies should aim to compare predictive strengths of markers between early-stage and late-stage MCI patients.