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

Autoři: Hanneke F. M. Rhodius-Meester aff001;  Ingrid S. van Maurik aff001;  Juha Koikkalainen aff004;  Antti Tolonen aff005;  Kristian S. Frederiksen aff006;  Steen G. Hasselbalch aff006;  Hilkka Soininen aff007;  Sanna-Kaisa Herukka aff007;  Anne M. Remes aff007;  Charlotte E. Teunissen aff010;  Frederik Barkhof aff011;  Yolande A. L. Pijnenburg aff001;  Philip Scheltens aff001;  Jyrki Lötjönen aff004;  Wiesje M. van der Flier aff001
Působiště autorů: Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands aff001;  Department of Internal Medicine, Geriatric Medicine section, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands aff002;  Department of Epidemiology and Biostatistics, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, the Netherlands aff003;  Combinostics Ltd., Tampere, Finland aff004;  VTT Technical Research Centre of Finland Ltd., Tampere, Finland aff005;  Department of Neurology, Danish Dementia Research Centre, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark aff006;  Department of Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland aff007;  Department of Research Neurology, Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland aff008;  MRC Oulu, Oulu University Hospital, Oulu, Finland aff009;  Neurochemistry Lab and Biobank, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands aff010;  Department of Radiology and Nuclear Medicine, Amsterdam Neuroscience, Amsterdam UMC, Amsterdam, the Netherlands aff011;  Institutes of Neurology and Healthcare Engineering, UCL, London, England, United Kingdom aff012
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226784



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.


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.


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).


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.

Klíčová slova:

Alzheimer's disease – Biomarkers – Cerebrospinal fluid – Diagnostic medicine – Magnetic resonance imaging – Neuropsychology – Vascular dementia – Frontotemporal dementia


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