Imperial College London


Faculty of EngineeringDepartment of Computing

Head of Department of Computing



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BibTex format

author = {Bruun, M and Frederiksen, KS and Rhodius-Meester, HFM and Baroni, M and Gjerum, L and Koikkalainen, J and Urhemaa, T and Tolonen, A and van, Gils M and Tong, T and Guerrero, R and Rueckert, D and Dyremose, N and Andersen, BB and Simonsen, AH and Lemstra, A and Hallikainen, M and Kurl, S and Herukka, S-K and Remes, AM and Waldemar, G and Soininen, H and Mecocci, P and van, der Flier WM and Lötjönen, J and Hasselbalch, SG},
doi = {10.2174/1567205016666190103152425},
journal = {Curr Alzheimer Res},
pages = {91--101},
title = {Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study.},
url = {},
volume = {16},
year = {2019}

RIS format (EndNote, RefMan)

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.
AU - Bruun,M
AU - Frederiksen,KS
AU - Rhodius-Meester,HFM
AU - Baroni,M
AU - Gjerum,L
AU - Koikkalainen,J
AU - Urhemaa,T
AU - Tolonen,A
AU - van,Gils M
AU - Tong,T
AU - Guerrero,R
AU - Rueckert,D
AU - Dyremose,N
AU - Andersen,BB
AU - Simonsen,AH
AU - Lemstra,A
AU - Hallikainen,M
AU - Kurl,S
AU - Herukka,S-K
AU - Remes,AM
AU - Waldemar,G
AU - Soininen,H
AU - Mecocci,P
AU - van,der Flier WM
AU - Lötjönen,J
AU - Hasselbalch,SG
DO - 10.2174/1567205016666190103152425
EP - 101
PY - 2019///
SP - 91
TI - Impact of a Clinical Decision Support Tool on Dementia Diagnostics in Memory Clinics: The PredictND Validation Study.
T2 - Curr Alzheimer Res
UR -
UR -
VL - 16
ER -