Abstract

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 cm3, 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.
Original languageEnglish
Pages (from-to)529-537
JournalAlzheimer's and Dementia: Diagnosis, Assessment and Disease Monitoring
Volume11
DOIs
Publication statusPublished - 2019

Cite this

@article{a21274f1b78e44a4b40b98afcb64f27e,
title = "Added value of amyloid PET in individualized risk predictions for MCI patients",
abstract = "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 cm3, 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.",
author = "{van Maurik}, {Ingrid S.} and {van der Kall}, {Laura M.} and {de Wilde}, Arno and Bouwman, {Femke H.} and Philip Scheltens and {van Berckel}, {Bart N. M.} and Johannes Berkhof and {van der Flier}, {Wiesje M.}",
year = "2019",
doi = "10.1016/j.dadm.2019.04.011",
language = "English",
volume = "11",
pages = "529--537",
journal = "Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring",
issn = "2352-8729",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Added value of amyloid PET in individualized risk predictions for MCI patients

AU - van Maurik, Ingrid S.

AU - van der Kall, Laura M.

AU - de Wilde, Arno

AU - Bouwman, Femke H.

AU - Scheltens, Philip

AU - van Berckel, Bart N. M.

AU - Berkhof, Johannes

AU - van der Flier, Wiesje M.

PY - 2019

Y1 - 2019

N2 - 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 cm3, 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.

AB - 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 cm3, 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.

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U2 - 10.1016/j.dadm.2019.04.011

DO - 10.1016/j.dadm.2019.04.011

M3 - Article

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EP - 537

JO - Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

JF - Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring

SN - 2352-8729

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