Imperial College London

ProfessorDanielRueckert

Faculty of EngineeringDepartment of Computing

Head of Department of Computing
 
 
 
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Contact

 

+44 (0)20 7594 8333d.rueckert Website

 
 
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Location

 

568Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Bruun:2019:10.1186/s13195-019-0482-3,
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 Rueckert, D and Dyremose, N and Andersen, BB and Lemstra, AW 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 Lotjonen, J and Hasselbalch, SG},
doi = {10.1186/s13195-019-0482-3},
journal = {Alzheimers Research & Therapy},
title = {Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study},
url = {http://dx.doi.org/10.1186/s13195-019-0482-3},
volume = {11},
year = {2019}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - BackgroundIn clinical practice, it is often difficult to predict which patients with cognitive complaints or impairment will progress or remain stable. We assessed the impact of using a clinical decision support system, the PredictND tool, to predict progression in patients with subjective cognitive decline (SCD) and mild cognitive impairment (MCI) in memory clinics.MethodsIn this prospective multicenter study, we included 429 patients with SCD (n = 230) and MCI (n = 199) (female 54%, age 67 ± 9, MMSE 28 ± 2) and followed them for at least 12 months. Based on all available patient baseline data (demographics, cognitive tests, cerebrospinal fluid biomarkers, and MRI), the PredictND tool provides a comprehensive overview of the data and a classification defining the likelihood of progression. At baseline, a clinician defined an expected follow-up diagnosis and estimated the level of confidence in their prediction using a visual analogue scale (VAS, 0–100%), first without and subsequently with the PredictND tool. As outcome measure, we defined clinical progression as progression from SCD to MCI or dementia, and from MCI to dementia. Correspondence between the expected and the actual clinical progression at follow-up defined the prognostic accuracy.ResultsAfter a mean follow-up time of 1.7 ± 0.4 years, 21 (9%) SCD and 63 (32%) MCI had progressed. When using the PredictND tool, the overall prognostic accuracy was unaffected (0.4%, 95%CI − 3.0%; + 3.9%; p = 0.79). However, restricting the analysis to patients with more certain classifications (n = 203), we found an increase of 3% in the accuracy (95%CI − 0.6%; + 6.5%; p = 0.11). Furthermore, for this subgroup, the tool alone showed a statistically significant increase in the prognostic accuracy compared to the evaluation without too
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 - Rueckert,D
AU - Dyremose,N
AU - Andersen,BB
AU - Lemstra,AW
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 - Lotjonen,J
AU - Hasselbalch,SG
DO - 10.1186/s13195-019-0482-3
PY - 2019///
SN - 1758-9193
TI - Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study
T2 - Alzheimers Research & Therapy
UR - http://dx.doi.org/10.1186/s13195-019-0482-3
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000462299000001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/70616
VL - 11
ER -