Bruun, M., Frederiksen, K.S., Rhodius-Meester, H.F.M. et al. Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study. Alz Res Therapy 11, 25 (2019). https://doi.org/10.1186/s13195-019-0482-3
Impact of a clinical decision support tool on prediction of progression in early-stage dementia : a prospective validation study
|Author:||Bruun, Marie1; Frederiksen, Kristian S.1; Rhodius-Meester, Hanneke F. M.2;|
1Danish Dementia Research Centre, Neuroscience Centre, Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen University Hospital, Blegdamsvej 9, 2100, Copenhagen, Denmark
2Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
3Institute of Gerontology and Geriatrics, University of Perugia, Perugia, Italy
4Combinostics Ltd., Tampere, Finland
5VTT Technical Research Centre of Finland Ltd, Tampere, Finland
6Department of Computing, Imperial College London, London, UK
7Neurology, Institute of Clinical Medicine, University of Eastern Finland, Kuopio, Finland
8Medical Research Center, Oulu University Hospital, Oulu, Finland
9Neurology, Neuro Center, Kuopio University Hospital, Kuopio, Finland
10Neurology, Unit of Clinical Neuroscience, University of Oulu, Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.1 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2020042722648
|Publish Date:|| 2020-04-27
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.
Alzheimers research & therapy
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
3124 Neurology and psychiatry
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. Furthermore, the work was supported by the Ellen Mørch´s foundation and the family Hede Nielsen´s foundation.
© The Author(s). 2019. 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.