Title:Auditory Verbal Learning Test is Superior to Rey-Osterrieth Complex Figure Memory for Predicting Mild Cognitive Impairment to Alzheimer’s Disease
Volume: 12
Issue: 6
Author(s): Qianhua Zhao, Qihao Guo, Xiaoniu Liang, Meirong Chen, Yan Zhou, Ding Ding and Zhen Hong
Affiliation:
Keywords:
Alzheimer’s disease, auditory verbal learning test, mild cognitive impairment, operational criteria, Rey-Osterrieth
complex figure test, subjective cognitive decline.
Abstract: Objective: To carry out meaningful comparisons on results of different research studies
on mild cognitive impairment (MCI), it is critical to select an appropriate objective memory test to
examine memory deficit. We aim to refine the operational criteria of amnestic MCI (aMCI) on neuropsychological
tests that optimally balance the sensitivity and specificity. Methods: We focused on
206 non-demented subjects from memory clinic. We then classified each individual as having MCI
or subjective cognitive decline (SCD) according to different neuropsychological criteria. By following them longitudinally,
clinical outcomes were compared to evaluate the stability of MCI diagnoses and prediction of progression.
Results: The delayed recall of auditory verbal learning test (AVLT_DR) identified 116 subjects as MCI, resulted in the
conversion rate as 44% over the roughly 30-month time interval, missed 7.8% incipient Alzheimer’s disease (AD) patients
in SCD group who eventually converted to dementia. The delayed recall of complex figure test (CFT_DR) identified
fewer MCI patients (n=95) and misdiagnosed more preclinical AD patients (15.3%), in comparison with AVLT
criterion. Criterion requiring deficits in both tests produced higher conversion rate (54.3%), but resulted in higher misdiagnosis
rate (14.7%) simultaneously. The AVLT criterion had the largest area under the curve (0.7248, p<0.05).
Conclusion: AVLT is superior to CFT in the stability of diagnoses and prediction of progression. In the clinical setting,
the “one test” criterion AVLT has similar sensitivity to both-deficits methods, and is optimal in balancing sensitivity
and specificity.