Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study

Marie Bruun (Corresponding Author), Kristian S. Frederiksen, Hanneke F.M. Rhodius-Meester, Marta Baroni, Le Gjerum, Juha Koikkalainen, Timo Urhemaa, Antti Tolonen, Mark van Gils, Tong Tong, Ricardo Guerrero, Daniel Rueckert, Nadia Dyremose, Birgitte Bo Andersen, Anja H. Simonsen, Afina W. Lemstra, Merja Hallikainen, Sudhir Kurl, Sanna-Kaisa Herukka, Anne M. RemesGunhild Waldemar, Hilkka Soininen, Patrizia Mecocci, Wiesje M. van der Flier, Jyrki Lötjönen, Steen G. Hasselbalch

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    25 Citations (Scopus)

    Abstract

    Background: Determining the underlying etiology of dementia can be challenging. Computer- based Clinical Decision Support Systems (CDSS) have the potential to provide an objective comparison of data and assist clinicians. Objectives: To assess the diagnostic impact of a CDSS, the PredictND tool, for differential diagnosis of dementia in memory clinics. Methods: In this prospective multicenter study, we recruited 779 patients with either subjective cognitive decline (n=252), mild cognitive impairment (n=219) or any type of dementia (n=274) and followed them for minimum 12 months. Based on all available patient baseline data (demographics, neuropsychological tests, cerebrospinal fluid biomarkers, and MRI visual and computed ratings), the PredictND tool provides a comprehensive overview and analysis of the data with a likelihood index for five diagnostic groups; Alzheimer´s disease, vascular dementia, dementia with Lewy bodies, frontotemporal dementia and subjective cognitive decline. At baseline, a clinician defined an etiological diagnosis and confidence in the diagnosis, first without and subsequently with the PredictND tool. The follow-up diagnosis was used as the reference diagnosis. Results: In total, 747 patients completed the follow-up visits (53% female, 69±10 years). The etiological diagnosis changed in 13% of all cases when using the PredictND tool, but the diagnostic accuracy did not change significantly. Confidence in the diagnosis, measured by a visual analogue scale (VAS, 0-100%) increased (ΔVAS=3.0%, p<0.0001), especially in correctly changed diagnoses (ΔVAS=7.2%, p=0.0011). Conclusion: Adding the PredictND tool to the diagnostic evaluation affected the diagnosis and increased clinicians’ confidence in the diagnosis indicating that CDSSs could aid clinicians in the differential diagnosis of dementia.
    Original languageEnglish
    Pages (from-to)91-101
    JournalCurrent Alzheimer Research
    Volume16
    Issue number2
    DOIs
    Publication statusPublished - Feb 2019
    MoE publication typeA1 Journal article-refereed

    Funding

    This work was co-funded by the European Commission under grant agreement 611005 (PredictND). For development of the PredictND tool, VTT Technical Research Center of Finland Ltd has received funding from European Union’s Seventh Framework Programme for research, technological development and demonstration under grant agreements 601055 (VPH-DARE@IT), 224328 and 611005. The PredictND consortium consisted of collaborates from VTT Technical Research Centre of Finland, GE Healthcare Ltd, Imperial College London, Alzheimer Europe, Alzheimer Center - VU University Medical Center, Amsterdam, the Netherlands, the Danish Dementia Research Centre, Copenhagen University Hospital, Denmark, the department of Gerontology and Geriatrics of the University of Perugia, ‘S. Maria della Misericordia’ Hospital of Peru-gia, Italy, the department of Neurology from the University of Eastern Finland.

    Keywords

    • Computer-assisted diagnosis
    • neurodegenerative disease
    • CDSS
    • differential diagnosis
    • Alzheimer´s disease
    • Frontotemporal disease
    • Dementia with Lewy body
    • Vascular dementia
    • Neurodegenerative disease
    • Differential diagnosis
    • Dementia with lewy body

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