NIH Toolbox Picture Sequence Memory Test for Assessing Clinical Memory Function: Diagnostic Relationship to the Rey Auditory Verbal Learning Test.

ARCHIVES OF CLINICAL NEUROPSYCHOLOGY(2019)

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摘要
BACKGROUND:The NIH Cognitive Toolbox Picture Sequence Memory Test (PSMT) was developed as a measure of learning ability. PSMT use in clinical populations is only beginning to be investigated. METHOD:PSMT performance was analyzed in a retrospective series of 221 patients referred to either the Deep Brain Stimulation Clinic (n = 128) for presurgical evaluation, or to the Cognitive Screening Clinic (n = 93). Patients were also administered the Rey Verbal Auditory Verbal Learning test (AVLT). In addition to correlation between measures, classification agreement was examined based upon performance ratings of normal (>16th percentile), borderline (5-16th percentile), or impaired (<5th percentile). RESULTS:Correlation between measures was significant (r = 0.48, p < .0001), with classification agreement of 62% (weighted Kappa = 0.43). For patients with valid PVT scores (n = 147), correlation between tests was 0.67 (p < .0001) with a classification agreement of 72% (weighted Kappa = 0.44). Multiple level likelihood ratios (LRs) relating PSMT to various dichotomous AVLT learning classifications were modest, with the largest group LR obtained for impaired PSMT increasing the likelihood of obtaining impaired AVLT by 7.62 (95% CI = 3.54-16.42). CONCLUSION:Despite significant correlations between measures, the NIH Toolbox PSMT and AVLT learning score often generate different interpretive results. Impaired PSMT appears better at predicting impaired AVLT performance rather than predicting combined borderline/impaired AVLT performance. Ultimately, individual clinicians will need to determine whether the PSMT can be used independently without other memory tests in the clinical environment in which they practice while further validation studies are performed.
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关键词
NIH Cognitive Toolbox, Picture Sequence Memory Test, Rey Auditory Verbal Learning Test, Diagnostic accuracy, Logistic models
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