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Added value of amyloid PET in individualized risk predictions for MCI patients
INTRODUCTION: To construct a prognostic model based on amyloid positron emission tomography (PET) to predict clinical progression in individual patients with mild cognitive impairment (MCI). METHODS: We included 411 MCI patients from the Alzheimer's Disease Neuroimaging Initiative. Prognostic m...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6667768/ https://www.ncbi.nlm.nih.gov/pubmed/31388557 http://dx.doi.org/10.1016/j.dadm.2019.04.011 |
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author | van Maurik, Ingrid S. van der Kall, Laura M. de Wilde, Arno Bouwman, Femke H. Scheltens, Philip van Berckel, Bart N.M. Berkhof, Johannes van der Flier, Wiesje M. |
author_facet | van Maurik, Ingrid S. van der Kall, Laura M. de Wilde, Arno Bouwman, Femke H. Scheltens, Philip van Berckel, Bart N.M. Berkhof, Johannes van der Flier, Wiesje M. |
author_sort | van Maurik, Ingrid S. |
collection | PubMed |
description | INTRODUCTION: To construct a prognostic model based on amyloid positron emission tomography (PET) to predict clinical progression in individual patients with mild cognitive impairment (MCI). METHODS: We included 411 MCI patients from the Alzheimer's Disease Neuroimaging Initiative. Prognostic models were constructed with Cox regression with demographics, magnetic resonance imaging, and/or amyloid PET to predict progression to Alzheimer's disease dementia. The models were validated in the Amsterdam Dementia Cohort. RESULTS: The combined model (Harrell's C = 0.82 [0.78–0.86]) was significantly superior to demographics (β = 0.100, P < .001), magnetic resonance imaging (β = 0.037, P = .011), and PET only models (β = 0.053, P = .003).The models can be used to calculate individualized risk, for example, a female MCI patient (age = 60, APOE ε4 positive, Mini-Mental State Examination = 25, hippocampal volume = 5.8 cm(3), amyloid PET positive) has 35% (19–57) risk in one year and 85% (64–97) risk in three years. Model performances in the Amsterdam Dementia Cohort were reasonable. DISCUSSION: The present study facilitates the interpretation of an amyloid PET result in the context of a patient's own characteristics and clinical assessment. |
format | Online Article Text |
id | pubmed-6667768 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-66677682019-08-06 Added value of amyloid PET in individualized risk predictions for MCI patients van Maurik, Ingrid S. van der Kall, Laura M. de Wilde, Arno Bouwman, Femke H. Scheltens, Philip van Berckel, Bart N.M. Berkhof, Johannes van der Flier, Wiesje M. Alzheimers Dement (Amst) Neuroimaging INTRODUCTION: To construct a prognostic model based on amyloid positron emission tomography (PET) to predict clinical progression in individual patients with mild cognitive impairment (MCI). METHODS: We included 411 MCI patients from the Alzheimer's Disease Neuroimaging Initiative. Prognostic models were constructed with Cox regression with demographics, magnetic resonance imaging, and/or amyloid PET to predict progression to Alzheimer's disease dementia. The models were validated in the Amsterdam Dementia Cohort. RESULTS: The combined model (Harrell's C = 0.82 [0.78–0.86]) was significantly superior to demographics (β = 0.100, P < .001), magnetic resonance imaging (β = 0.037, P = .011), and PET only models (β = 0.053, P = .003).The models can be used to calculate individualized risk, for example, a female MCI patient (age = 60, APOE ε4 positive, Mini-Mental State Examination = 25, hippocampal volume = 5.8 cm(3), amyloid PET positive) has 35% (19–57) risk in one year and 85% (64–97) risk in three years. Model performances in the Amsterdam Dementia Cohort were reasonable. DISCUSSION: The present study facilitates the interpretation of an amyloid PET result in the context of a patient's own characteristics and clinical assessment. Elsevier 2019-07-29 /pmc/articles/PMC6667768/ /pubmed/31388557 http://dx.doi.org/10.1016/j.dadm.2019.04.011 Text en © 2019 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Neuroimaging van Maurik, Ingrid S. van der Kall, Laura M. de Wilde, Arno Bouwman, Femke H. Scheltens, Philip van Berckel, Bart N.M. Berkhof, Johannes van der Flier, Wiesje M. Added value of amyloid PET in individualized risk predictions for MCI patients |
title | Added value of amyloid PET in individualized risk predictions for MCI patients |
title_full | Added value of amyloid PET in individualized risk predictions for MCI patients |
title_fullStr | Added value of amyloid PET in individualized risk predictions for MCI patients |
title_full_unstemmed | Added value of amyloid PET in individualized risk predictions for MCI patients |
title_short | Added value of amyloid PET in individualized risk predictions for MCI patients |
title_sort | added value of amyloid pet in individualized risk predictions for mci patients |
topic | Neuroimaging |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6667768/ https://www.ncbi.nlm.nih.gov/pubmed/31388557 http://dx.doi.org/10.1016/j.dadm.2019.04.011 |
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