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Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study
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 cogniti...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6425602/ https://www.ncbi.nlm.nih.gov/pubmed/30894218 http://dx.doi.org/10.1186/s13195-019-0482-3 |
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author | Bruun, Marie Frederiksen, Kristian S. Rhodius-Meester, Hanneke F. M. Baroni, Marta Gjerum, Le Koikkalainen, Juha Urhemaa, Timo Tolonen, Antti van Gils, Mark Rueckert, Daniel Dyremose, Nadia Andersen, Birgitte B. Lemstra, Afina W. Hallikainen, Merja Kurl, Sudhir Herukka, Sanna-Kaisa Remes, Anne M. Waldemar, Gunhild Soininen, Hilkka Mecocci, Patrizia van der Flier, Wiesje M. Lötjönen, Jyrki Hasselbalch, Steen G. |
author_facet | Bruun, Marie Frederiksen, Kristian S. Rhodius-Meester, Hanneke F. M. Baroni, Marta Gjerum, Le Koikkalainen, Juha Urhemaa, Timo Tolonen, Antti van Gils, Mark Rueckert, Daniel Dyremose, Nadia Andersen, Birgitte B. Lemstra, Afina W. Hallikainen, Merja Kurl, Sudhir Herukka, Sanna-Kaisa Remes, Anne M. Waldemar, Gunhild Soininen, Hilkka Mecocci, Patrizia van der Flier, Wiesje M. Lötjönen, Jyrki Hasselbalch, Steen G. |
author_sort | Bruun, Marie |
collection | PubMed |
description | 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13195-019-0482-3) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6425602 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64256022019-03-29 Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study Bruun, Marie Frederiksen, Kristian S. Rhodius-Meester, Hanneke F. M. Baroni, Marta Gjerum, Le Koikkalainen, Juha Urhemaa, Timo Tolonen, Antti van Gils, Mark Rueckert, Daniel Dyremose, Nadia Andersen, Birgitte B. Lemstra, Afina W. Hallikainen, Merja Kurl, Sudhir Herukka, Sanna-Kaisa Remes, Anne M. Waldemar, Gunhild Soininen, Hilkka Mecocci, Patrizia van der Flier, Wiesje M. Lötjönen, Jyrki Hasselbalch, Steen G. Alzheimers Res Ther Research 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13195-019-0482-3) contains supplementary material, which is available to authorized users. BioMed Central 2019-03-20 /pmc/articles/PMC6425602/ /pubmed/30894218 http://dx.doi.org/10.1186/s13195-019-0482-3 Text en © The Author(s). 2019 Open AccessThis 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. |
spellingShingle | Research Bruun, Marie Frederiksen, Kristian S. Rhodius-Meester, Hanneke F. M. Baroni, Marta Gjerum, Le Koikkalainen, Juha Urhemaa, Timo Tolonen, Antti van Gils, Mark Rueckert, Daniel Dyremose, Nadia Andersen, Birgitte B. Lemstra, Afina W. Hallikainen, Merja Kurl, Sudhir Herukka, Sanna-Kaisa Remes, Anne M. Waldemar, Gunhild Soininen, Hilkka Mecocci, Patrizia van der Flier, Wiesje M. Lötjönen, Jyrki Hasselbalch, Steen G. 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 |
title_full | Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study |
title_fullStr | Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study |
title_full_unstemmed | Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study |
title_short | Impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study |
title_sort | impact of a clinical decision support tool on prediction of progression in early-stage dementia: a prospective validation study |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6425602/ https://www.ncbi.nlm.nih.gov/pubmed/30894218 http://dx.doi.org/10.1186/s13195-019-0482-3 |
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