Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study

Title: Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study
Authors: Bruun, M
Frederiksen, KS
Rhodius-Meester, HFM
Baroni, M
Gjerum, L
Koikkalainen, J
Urhemaa, T
Tolonen, A
Van Gils, M
Rueckert, D
Dyremose, N
Andersen, BB
Lemstra, AW
Hallikainen, M
Kurl, S
Herukka, S-K
Remes, AM
Waldemar, G
Soininen, H
Mecocci, P
Van der Flier, WM
Lotjonen, J
Hasselbalch, SG
Item Type: Journal Article
Abstract: Background In 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. Methods In 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. Results After 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 tool (6.4%, 95%CI 2.1%; 10.7%; p = 0.004). Specifically, the negative predictive value was high. Moreover, confidence in the prediction increased significantly (∆VAS = 4%, p < .0001). Conclusions Adding the PredictND tool to the clinical evaluation increased clinicians’ confidence. Furthermore, the results indicate that the tool has the potential to improve prediction of progression for patients with more certain classifications.
Issue Date: 20-Mar-2019
Date of Acceptance: 11-Mar-2019
URI: http://hdl.handle.net/10044/1/70616
DOI: https://doi.org/10.1186/s13195-019-0482-3
ISSN: 1758-9193
Publisher: BMC
Journal / Book Title: Alzheimers Research & Therapy
Volume: 11
Issue: 1
Copyright Statement: © 2019 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Sponsor/Funder: Commission of the European Communities
Commission of the European Communities
Funder's Grant Number: No. 224328 - FP7
611005
Keywords: Science & Technology
Life Sciences & Biomedicine
Clinical Neurology
Neurosciences
Neurosciences & Neurology
Dementia
Alzheimer's disease
Conversion
Progression
Mild cognitive impairment
Subjective cognitive decline
CDSS
Computer-assisted
MILD COGNITIVE IMPAIRMENT
ALZHEIMERS ASSOCIATION WORKGROUPS
DIFFERENTIAL-DIAGNOSIS
NATIONAL INSTITUTE
STRUCTURAL MRI
DISEASE
RECOMMENDATIONS
BIOMARKERS
GUIDELINES
ATROPHY
Alzheimer’s disease
CDSS
Computer-assisted
Conversion
Dementia
Mild cognitive impairment
Progression
Subjective cognitive decline
Science & Technology
Life Sciences & Biomedicine
Clinical Neurology
Neurosciences
Neurosciences & Neurology
Dementia
Alzheimer's disease
Conversion
Progression
Mild cognitive impairment
Subjective cognitive decline
CDSS
Computer-assisted
MILD COGNITIVE IMPAIRMENT
ALZHEIMERS ASSOCIATION WORKGROUPS
DIFFERENTIAL-DIAGNOSIS
NATIONAL INSTITUTE
STRUCTURAL MRI
DISEASE
RECOMMENDATIONS
BIOMARKERS
GUIDELINES
ATROPHY
11 Medical and Health Sciences
Publication Status: Published
Article Number: 25
Online Publication Date: 2019-03-20
Appears in Collections:Computing



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