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Individualized, cross-validated prediction of future dementia using cognitive assessments in people with mild cognitive symptoms

Borland, Emma LU orcid ; Mattson-Carlgren, Niklas LU orcid ; Tideman, Pontus LU ; Stomrud, Erik LU orcid ; Hansson, Oskar LU orcid and Palmqvist, Sebastian LU orcid (2024) In Alzheimer's and Dementia
Abstract

INTRODUCTION: We aimed to develop an algorithm to predict the individualized risk of future dementia using brief cognitive tests suitable for primary care. METHODS: We included 612 participants with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, assessed for at least 4 years or until progression to dementia. A logistic regression model, using cognitive tests as predictors and dementia progression as an outcome, stratified participants into low, intermediate, or high risk. A second model, including 1-year cognitive test changes, was applied to the intermediate group. The models were replicated in 392 SCD/MCI participants from the BioFINDER-1 study.... (More)

INTRODUCTION: We aimed to develop an algorithm to predict the individualized risk of future dementia using brief cognitive tests suitable for primary care. METHODS: We included 612 participants with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, assessed for at least 4 years or until progression to dementia. A logistic regression model, using cognitive tests as predictors and dementia progression as an outcome, stratified participants into low, intermediate, or high risk. A second model, including 1-year cognitive test changes, was applied to the intermediate group. The models were replicated in 392 SCD/MCI participants from the BioFINDER-1 study. RESULTS: The best two-step model for predicting dementia incorporated Trail Making Test B (attention/executive function), Animal Fluency (verbal fluency), Mini-Mental State Examination (global cognition), and 10-word list recall (memory). The model's positive predictive value in ADNI was 85.8% and negative predictive value was 92.2% versus 62.5% and 95.6%, respectively, in BioFINDER-1. DISCUSSION: This two-step model accurately predicts individualized dementia risk. Highlights: To our knowledge, this is the first algorithm for predicting all-cause dementia using a novel two-step model utilizing brief cognitive tests. Applying a validated model including the Trail Making Test B, Animal Fluency, MMSE, Alzheimer's Disease Assessment Scale delayed, and immediate recall can robustly and accurately categorize individuals into low, intermediate, or high risk of dementia progression and can facilitate clinical decision-making and personalized patient care. We created an app that is available for research and educational purposes at https://brainapps.shinyapps.io/PredictAllCauseDementia to provide an individualized risk score for dementia progression.

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author
; ; ; ; and
author collaboration
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
cognitive impairment, dementia, mild cognitive impairment, prediction dementia, subjective cognitive decline, two-step model
in
Alzheimer's and Dementia
publisher
Wiley
external identifiers
  • pmid:39417379
  • scopus:85206492381
ISSN
1552-5260
DOI
10.1002/alz.14305
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2024 The Author(s). Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
id
f3b4e9f9-77e3-4651-84c9-672d530336bc
date added to LUP
2024-12-18 14:14:32
date last changed
2025-07-03 06:35:50
@article{f3b4e9f9-77e3-4651-84c9-672d530336bc,
  abstract     = {{<p>INTRODUCTION: We aimed to develop an algorithm to predict the individualized risk of future dementia using brief cognitive tests suitable for primary care. METHODS: We included 612 participants with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study, assessed for at least 4 years or until progression to dementia. A logistic regression model, using cognitive tests as predictors and dementia progression as an outcome, stratified participants into low, intermediate, or high risk. A second model, including 1-year cognitive test changes, was applied to the intermediate group. The models were replicated in 392 SCD/MCI participants from the BioFINDER-1 study. RESULTS: The best two-step model for predicting dementia incorporated Trail Making Test B (attention/executive function), Animal Fluency (verbal fluency), Mini-Mental State Examination (global cognition), and 10-word list recall (memory). The model's positive predictive value in ADNI was 85.8% and negative predictive value was 92.2% versus 62.5% and 95.6%, respectively, in BioFINDER-1. DISCUSSION: This two-step model accurately predicts individualized dementia risk. Highlights: To our knowledge, this is the first algorithm for predicting all-cause dementia using a novel two-step model utilizing brief cognitive tests. Applying a validated model including the Trail Making Test B, Animal Fluency, MMSE, Alzheimer's Disease Assessment Scale delayed, and immediate recall can robustly and accurately categorize individuals into low, intermediate, or high risk of dementia progression and can facilitate clinical decision-making and personalized patient care. We created an app that is available for research and educational purposes at https://brainapps.shinyapps.io/PredictAllCauseDementia to provide an individualized risk score for dementia progression.</p>}},
  author       = {{Borland, Emma and Mattson-Carlgren, Niklas and Tideman, Pontus and Stomrud, Erik and Hansson, Oskar and Palmqvist, Sebastian}},
  issn         = {{1552-5260}},
  keywords     = {{cognitive impairment; dementia; mild cognitive impairment; prediction dementia; subjective cognitive decline; two-step model}},
  language     = {{eng}},
  publisher    = {{Wiley}},
  series       = {{Alzheimer's and Dementia}},
  title        = {{Individualized, cross-validated prediction of future dementia using cognitive assessments in people with mild cognitive symptoms}},
  url          = {{http://dx.doi.org/10.1002/alz.14305}},
  doi          = {{10.1002/alz.14305}},
  year         = {{2024}},
}