Short-term delayed recall of auditory verbal learning test provides equivalent value to long-term delayed recall in predicting MCI clinical outcomes: A longitudinal follow-up study

Abstract

This study was conducted to compare predictive power of Auditory verbal learning test (AVLT) recall measures for Alzheimer’s disease (AD) conversion and normal cognition (NC) reversion from mild cognitive impairment (MCI). A total of 262 amnestic MCI patients were followed up longitudinally, who were then classified into different groups based on clinical outcomes. Demographic information and AVLT recall scores at baseline were compared among these groups. Receiver operating characteristic (ROC) curves were constructed to evaluate differentiating value of AVLT recall measures for MCI outcomes. Binary stepwise logistic regression analysis was performed to identify predictive AVLT measures. After average 30.8 ± 11.6 months follow-up, 89 patients converted to AD and 88 participants reverted to NC. At baseline, AD converters scored significantly lower in AVLT than nonconverters, while NC reverters performed much better than nonreverters (p < .01). AVLT-SR and AVLT-LR had larger areas under curve than AVLT-IR and AVLT-REC in distinguishing patients who progressed to AD or not (p < .05). Both AVLT-SR and AVLT-LR were significant predictors of MCI-to-AD conversion and MCI-to-NC reversion. Among AVLT recall measures, AVLT-SR and AVLT-LR provided the best and equivalent values in predicting MCI outcomes. Patients with lower AVLT-SR and AVLT-LR scores are more likely to develop AD. Consequently, AVLR-SR is a valuable and time-saving memory measure that deserves further application in memory clinical practice.

Keywords:

• Alzheimer’s disease
• delayed recall
• mild cognitive impairment
• prediction

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by a grant from the National Key R&D Program of China under Grant 2016YFC1306305; a grant to Q.-H. Guo from the National Natural Science Foundation under Grant 81171019; and a grant to F. Li from the Beijing Municipal Commission of Science and Technology under Grant Z141107002514177.