The correlation of everyday cognition test scores and the progression of Alzheimer's disease: a data analytics study.
ADNI
Alzheimer’s disease
Cognitive tests
Data analytics
Dementia
Everyday cognition
Longitudinal study
Machine learning
Journal
Health information science and systems
ISSN: 2047-2501
Titre abrégé: Health Inf Sci Syst
Pays: England
ID NLM: 101638060
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
25
03
2020
accepted:
14
07
2020
entrez:
9
8
2020
pubmed:
9
8
2020
medline:
9
8
2020
Statut:
epublish
Résumé
The process of diagnosing dementia conditions, especially Alzheimer's disease, and the cognitive tests that are involved in this process, are important areas of study. Everyday Cognition (ECog) is one test that can be used as part of Alzheimer's disease diagnosis to measure cognitive decline in different areas. In this study, we investigate two versions of the ECog test: the study partner reported version (ECogSP), and the patient reported version (ECogPT). We compare these, using statistical analysis and machine learning techniques, to create classification models to demonstrate the progression in ECog scores over time by using the Alzheimer's Disease Neuroimaging Initiative longitudinal data repository (ADNI); participants are classed with having normal cognition, mild cognitive impairment, or Alzheimer's disease. We found that participants who are diagnosed with Alzheimer's disease at baseline, or during a subsequent visit, tend to self-report consistent ECogPT scores over time indicating no change in cognitive ability. However, study partners tend to report higher and increasing ECogSP scores on behalf of participants in the same diagnosis category; this would indicate a degradation in the participant's cognitive ability over time, consistent with the progress of Alzheimer's disease.
Identifiants
pubmed: 32765845
doi: 10.1007/s13755-020-00114-8
pii: 114
pmc: PMC7378134
doi:
Types de publication
Journal Article
Langues
eng
Pagination
24Informations de copyright
© Springer Nature Switzerland AG 2020.
Références
Neuroimage Clin. 2016 Dec 18;13:415-427
pubmed: 28116234
Brain. 2009 Apr;132(Pt 4):1067-77
pubmed: 19251758
J Am Geriatr Soc. 2012 Jun;60(6):1128-34
pubmed: 22690986
J Pharmacokinet Pharmacodyn. 2012 Dec;39(6):601-18
pubmed: 22990808
Neurology. 2002 Apr 23;58(8):1188-96
pubmed: 11971085
Neurobiol Aging. 2010 Aug;31(8):1364-74
pubmed: 20570399
Am J Psychiatry. 1984 Nov;141(11):1356-64
pubmed: 6496779
Neurology. 2000 Dec 12;55(11):1724-6
pubmed: 11113230
Brain. 2009 May;132(Pt 5):1355-65
pubmed: 19339253
Lancet Neurol. 2010 Jan;9(1):119-28
pubmed: 20083042
Neurobiol Aging. 2011 Dec;32(12):2322.e19-27
pubmed: 20594615
J Am Geriatr Soc. 2017 May;65(5):1067-1072
pubmed: 28205215
J Psychiatr Res. 1975 Nov;12(3):189-98
pubmed: 1202204
J Gerontol. 1982 May;37(3):323-9
pubmed: 7069156
Neurology. 2010 Jul 13;75(2):143-51
pubmed: 20625167
Clin Neuropsychol. 2015;29(7):1002-9
pubmed: 26617181
Mol Psychiatry. 2005 Feb;10(2):160-84
pubmed: 15356639
Neurology. 2009 Mar 24;72(12):1048-55
pubmed: 19109536
Int J Geriatr Psychiatry. 2015 Feb;30(2):192-200
pubmed: 24816477
Brain. 2009 Aug;132(Pt 8):2026-35
pubmed: 19416949
Eur Radiol. 2010 Mar;20(3):674-82
pubmed: 19760240
Neuroimaging Clin N Am. 2005 Nov;15(4):869-77, xi-xii
pubmed: 16443497
Neuropsychology. 2008 Jul;22(4):531-44
pubmed: 18590364
Alzheimers Dement. 2012 Feb;8(1 Suppl):S1-68
pubmed: 22047634