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

Author(s): Marie Bruun*, 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 Lemstra, Merja Hallikainen, Sudhir Kurl, Sanna-Kaisa Herukka, Anne M. Remes, Gunhild Waldemar, Hilkka Soininen, Patrizia Mecocci, Wiesje M. van der Flier, Jyrki Lötjönen, Steen G. Hasselbalch.

Journal Name: Current Alzheimer Research

Volume 16 , Issue 2 , 2019

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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.

Keywords: Computer-assisted diagnosis, neurodegenerative disease, CDSS, differential diagnosis, Alzheimer´s disease, Frontotemporal disease, Dementia with Lewy body, Vascular dementia.

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Article Details

VOLUME: 16
ISSUE: 2
Year: 2019
Page: [91 - 101]
Pages: 11
DOI: 10.2174/1567205016666190103152425

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