University of Oulu

Rhodius-Meester HFM, van Maurik IS, Koikkalainen J, Tolonen A, Frederiksen KS, Hasselbalch SG, et al. (2020) Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy. PLoS ONE 15(1): e0226784. https://doi.org/10.1371/journal.pone.0226784

Selection of memory clinic patients for CSF biomarker assessment can be restricted to a quarter of cases by using computerized decision support, without compromising diagnostic accuracy

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Author: Rhodius-Meester, Hanneke F. M.1,2; van Maurik, Ingrid S.1,3; Koikkalainen, Juha4;
Organizations: 1Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
2Department of Internal Medicine, Geriatric Medicine section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
3Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands
4Combinostics Ltd., Tampere, Finland
5VTT Technical Research Centre of Finland Ltd., Tampere, Finland
6Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark
7Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
8Department of Research Neurology, Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland
9MRC Oulu, Oulu University Hospital, Oulu, Finland
10Neurochemistry Lab and Biobank, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands
11Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands
12Institutes of Neurology and Healthcare Engineering, UCL, London, England, United Kingdom
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 1.1 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2020050825700
Language: English
Published: Public Library of Science, 2020
Publish Date: 2020-05-08
Description:

Abstract

Introduction: An accurate and timely diagnosis for Alzheimer’s disease (AD) is important, both for care and research. The current diagnostic criteria allow the use of CSF biomarkers to provide pathophysiological support for the diagnosis of AD. How these criteria should be operationalized by clinicians is unclear. Tools that guide in selecting patients in which CSF biomarkers have clinical utility are needed. We evaluated computerized decision support to select patients for CSF biomarker determination.

Methods: We included 535 subjects (139 controls, 286 Alzheimer’s disease dementia, 82 frontotemporal dementia and 28 vascular dementia) from three clinical cohorts. Positive (AD like) and negative (normal) CSF biomarker profiles were simulated to estimate whether knowledge of CSF biomarkers would impact (confidence in) diagnosis. We applied these simulated CSF values and combined them with demographic, neuropsychology and MRI data to initiate CSF testing (computerized decision support approach). We compared proportion of CSF measurements and patients diagnosed with sufficient confidence (probability of correct class ≥0.80) based on an algorithm with scenarios without CSF (only neuropsychology, MRI and APOE), CSF according to the appropriate use criteria (AUC) and CSF for all patients.

Results: The computerized decision support approach recommended CSF testing in 140 (26%) patients, which yielded a diagnosis with sufficient confidence in 379 (71%) of all patients. This approach was more efficient than CSF in none (0% CSF, 308 (58%) diagnosed), CSF selected based on AUC (295 (55%) CSF, 350 (65%) diagnosed) or CSF in all (100% CSF, 348 (65%) diagnosed).

Conclusions: We used a computerized decision support with simulated CSF results in controls and patients with different types of dementia. This approach can support clinicians in making a balanced decision in ordering additional biomarker testing. Computer-supported prediction restricts CSF testing to only 26% of cases, without compromising diagnostic accuracy.

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Series: PLoS one
ISSN: 1932-6203
ISSN-E: 1932-6203
ISSN-L: 1932-6203
Volume: 15
Issue: 1
Article number: e0226784
DOI: 10.1371/journal.pone.0226784
OADOI: https://oadoi.org/10.1371/journal.pone.0226784
Type of Publication: A1 Journal article – refereed
Field of Science: 3124 Neurology and psychiatry
3112 Neurosciences
Subjects:
Funding: This study is partly funded by Combinostics. The funder provided support in the form of salaries for authors [JK and JL], and had an additional role in the study as Juha Koikkalainen and Jyrki Lötjönen developed the method and quantitative raw data were generated using Combinostics’ tools. They also reviewed the manuscript. The specific roles of these authors are articulated in the ‘author contributions’ section. For development of the PredictAD tool, VTT Technical Research Centre of Finland has received funding from European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreements 601055 (VPH-DARE@IT), 224328 (PredictAD), and 611005 (PredictND). The latter had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.'
Copyright information: © 2020 Rhodius-Meester et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
  https://creativecommons.org/licenses/by/4.0/