Literature DB >> 31305321

Identifying an Optimal Cutoff of the Montreal Cognitive Assessment to Predict Amyloid-PET Positivity in a Referral Memory Clinic.

Anil K Nair1, Srinath Ramaswamy, Krystal Kan, Shreya Nair.   

Abstract

BACKGROUND: Brain amyloid- positron emission tomography (PET) imaging is highly sensitive for identifying Alzheimer disease. Currently, there is a lack of insight on the association between amyloid-PET status and the widely used Montreal cognitive assessment (MoCA). Studying this relationship may optimize the clinical use of amyloid-PET imaging.
OBJECTIVES: To evaluate the relationship between amyloid-PET status and MoCA scores and to identify a MoCA score cutoff that translates to amyloid-PET positivity.
METHODS: Using retrospective chart review, patients from 2010 to 2017 with amyloid-PET scans (positive or negative) and MoCA test scores were included. We studied the relationship between amyloid-PET status and MoCA scores and the influence of age, sex, education, and race. A MoCA score cutoff for amyloid-PET positivity was estimated.
RESULTS: Among the 684 clinic patients with dementia, 99 fulfilled inclusion criteria. Amyloid-PET positivity was associated significantly with lower MoCA scores (median=19, U=847, P=0.01). The MoCA score cutoff (25) used for minimal cognitive impairment (MCI) predicted amyloid-PET positivity suboptimally (sensitivity=94.6%, specificity=13.9%). A MoCA score cutoff of 20 patients had optimal sensitivity (64.2%) and specificity (67.4%).
CONCLUSIONS: Amyloid-PET positivity is associated with lower MoCA scores. Clinical utility of amyloid-PET scan is likely to be suboptimal at the MoCA score cutoff for minimal cognitive impairment.

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Year:  2019        PMID: 31305321     DOI: 10.1097/WAD.0000000000000330

Source DB:  PubMed          Journal:  Alzheimer Dis Assoc Disord        ISSN: 0893-0341            Impact factor:   2.703


  1 in total

1.  Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction.

Authors:  Bingyu Ren; Yingtong Wu; Liumei Huang; Zhiguo Zhang; Bingsheng Huang; Huajie Zhang; Jinting Ma; Bing Li; Xukun Liu; Guangyao Wu; Jian Zhang; Liming Shen; Qiong Liu; Jiazuan Ni
Journal:  Hum Brain Mapp       Date:  2021-12-16       Impact factor: 5.038

  1 in total

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