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Evaluating combinations of diagnostic tests to discriminate different dementia types

Authors :
Marie Bruun
Hanneke F.M. Rhodius‐Meester
Juha Koikkalainen
Marta Baroni
Le Gjerum
Afina W. Lemstra
Frederik Barkhof
Anne M. Remes
Timo Urhemaa
Antti Tolonen
Daniel Rueckert
Mark vanGils
Kristian S. Frederiksen
Gunhild Waldemar
Philip Scheltens
Patrizia Mecocci
Hilkka Soininen
Jyrki Lötjönen
Steen G. Hasselbalch
Wiesje M. van derFlier
Source :
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring, Vol 10, Iss 1, Pp 509-518 (2018)
Publication Year :
2018
Publisher :
Wiley, 2018.

Abstract

Abstract Introduction We studied, using a data‐driven approach, how different combinations of diagnostic tests contribute to the differential diagnosis of dementia. Methods In this multicenter study, we included 356 patients with Alzheimer's disease, 87 frontotemporal dementia, 61 dementia with Lewy bodies, 38 vascular dementia, and 302 controls. We used a classifier to assess accuracy for individual performance and combinations of cognitive tests, cerebrospinal fluid biomarkers, and automated magnetic resonance imaging features for pairwise differentiation between dementia types. Results Cognitive tests had good performance in separating any type of dementia from controls. Cerebrospinal fluid optimally contributed to identifying Alzheimer's disease, whereas magnetic resonance imaging features aided in separating vascular dementia, dementia with Lewy bodies, and frontotemporal dementia. Combining diagnostic tests increased the accuracy, with balanced accuracies ranging from 78% to 97%. Discussion Different diagnostic tests have their distinct roles in differential diagnostics of dementias. Our results indicate that combining different diagnostic tests may increase the accuracy further.

Details

Language :
English
ISSN :
23528729
Volume :
10
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring
Publication Type :
Academic Journal
Accession number :
edsdoj.2375510fcb748fdb2c24700152bc3c9
Document Type :
article
Full Text :
https://doi.org/10.1016/j.dadm.2018.07.003