TY - JOUR
T1 - Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics
T2 - The PredictND Validation Study
AU - Bruun, Marie
AU - Frederiksen, Kristian S.
AU - Rhodius-Meester, Hanneke F.M.
AU - Baroni, Marta
AU - Gjerum, Le
AU - Koikkalainen, Juha
AU - Urhemaa, Timo
AU - Tolonen, Antti
AU - van Gils, Mark
AU - Tong, Tong
AU - Guerrero, Ricardo
AU - Rueckert, Daniel
AU - Dyremose, Nadia
AU - Bo Andersen, Birgitte
AU - Simonsen, Anja H.
AU - Lemstra, Afina W.
AU - Hallikainen, Merja
AU - Kurl, Sudhir
AU - Herukka, Sanna-Kaisa
AU - Remes, Anne M.
AU - Waldemar, Gunhild
AU - Soininen, Hilkka
AU - Mecocci, Patrizia
AU - van der Flier, Wiesje M.
AU - Lötjönen, Jyrki
AU - Hasselbalch, Steen G.
N1 - Funding Information:
RG worked part-time at IXICO ltd. as a Senior Scientist. HS has served in advisory boards for ACImmune, MSD, and Orion Pharma. WMvdF performs contract research for and Biogen. Research programs of WMvdF have been funded by ZonMW, NWO, EU-FP7, Alzheimer Nederland, CardioVascular Onderzoek Nederland, stichting Dioraphte, Gieskes-Strijbis fonds, Boehringer Ingelheim, Piramal Neuroimaging, Roche BV, Janssen Stellar, Combinostics. All funding is paid to her institution. JL and JK are shareholders in Combi-nostics Oy that owns the following IPR related to the patent: 1. J. Koikkalainen and J. Lotjonen. A method for inferring the state of a system, US7,840,510 B2, PCT/FI2007/050277. 2. J. Lotjonen, J. Koikkalainen and J. Mattila. State Inference in a heterogeneous system, PCT/FI2010/050545. FI20125177. All other authors have no conflicts of interest to declare.
Funding Information:
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.
Publisher Copyright:
© 2019 Bentham Science Publishers.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2019/2
Y1 - 2019/2
N2 - 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.
AB - 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.
KW - Computer-assisted diagnosis
KW - neurodegenerative disease
KW - CDSS
KW - differential diagnosis
KW - Alzheimer´s disease
KW - Frontotemporal disease
KW - Dementia with Lewy body
KW - Vascular dementia
UR - http://www.scopus.com/inward/record.url?scp=85061191635&partnerID=8YFLogxK
U2 - 10.2174/1567205016666190103152425
DO - 10.2174/1567205016666190103152425
M3 - Article
SN - 1567-2050
VL - 16
SP - 91
EP - 101
JO - Current Alzheimer Research
JF - Current Alzheimer Research
IS - 2
ER -