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Dementia risk in the general population: large-scale external validation of prediction models in the AGES-Reykjavik study
We aimed to evaluate the external performance of prediction models for all-cause dementia or AD in the general population, which can aid selection of high-risk individuals for clinical trials and prevention. We identified 17 out of 36 eligible published prognostic models for external validation in t...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8542560/ https://www.ncbi.nlm.nih.gov/pubmed/34308533 http://dx.doi.org/10.1007/s10654-021-00785-x |
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author | Vonk, Jet M. J. Greving, Jacoba P. Gudnason, Vilmundur Launer, Lenore J. Geerlings, Mirjam I. |
author_facet | Vonk, Jet M. J. Greving, Jacoba P. Gudnason, Vilmundur Launer, Lenore J. Geerlings, Mirjam I. |
author_sort | Vonk, Jet M. J. |
collection | PubMed |
description | We aimed to evaluate the external performance of prediction models for all-cause dementia or AD in the general population, which can aid selection of high-risk individuals for clinical trials and prevention. We identified 17 out of 36 eligible published prognostic models for external validation in the population-based AGES-Reykjavik Study. Predictive performance was assessed with c statistics and calibration plots. All five models with a c statistic > .75 (.76–.81) contained cognitive testing as a predictor, while all models with lower c statistics (.67–.75) did not. Calibration ranged from good to poor across all models, including systematic risk overestimation or overestimation for particularly the highest risk group. Models that overestimate risk may be acceptable for exclusion purposes, but lack the ability to accurately identify individuals at higher dementia risk. Both updating existing models or developing new models aimed at identifying high-risk individuals, as well as more external validation studies of dementia prediction models are warranted. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10654-021-00785-x. |
format | Online Article Text |
id | pubmed-8542560 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-85425602021-11-08 Dementia risk in the general population: large-scale external validation of prediction models in the AGES-Reykjavik study Vonk, Jet M. J. Greving, Jacoba P. Gudnason, Vilmundur Launer, Lenore J. Geerlings, Mirjam I. Eur J Epidemiol Neuro-Epidemiology We aimed to evaluate the external performance of prediction models for all-cause dementia or AD in the general population, which can aid selection of high-risk individuals for clinical trials and prevention. We identified 17 out of 36 eligible published prognostic models for external validation in the population-based AGES-Reykjavik Study. Predictive performance was assessed with c statistics and calibration plots. All five models with a c statistic > .75 (.76–.81) contained cognitive testing as a predictor, while all models with lower c statistics (.67–.75) did not. Calibration ranged from good to poor across all models, including systematic risk overestimation or overestimation for particularly the highest risk group. Models that overestimate risk may be acceptable for exclusion purposes, but lack the ability to accurately identify individuals at higher dementia risk. Both updating existing models or developing new models aimed at identifying high-risk individuals, as well as more external validation studies of dementia prediction models are warranted. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10654-021-00785-x. Springer Netherlands 2021-07-25 2021 /pmc/articles/PMC8542560/ /pubmed/34308533 http://dx.doi.org/10.1007/s10654-021-00785-x Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Neuro-Epidemiology Vonk, Jet M. J. Greving, Jacoba P. Gudnason, Vilmundur Launer, Lenore J. Geerlings, Mirjam I. Dementia risk in the general population: large-scale external validation of prediction models in the AGES-Reykjavik study |
title | Dementia risk in the general population: large-scale external validation of prediction models in the AGES-Reykjavik study |
title_full | Dementia risk in the general population: large-scale external validation of prediction models in the AGES-Reykjavik study |
title_fullStr | Dementia risk in the general population: large-scale external validation of prediction models in the AGES-Reykjavik study |
title_full_unstemmed | Dementia risk in the general population: large-scale external validation of prediction models in the AGES-Reykjavik study |
title_short | Dementia risk in the general population: large-scale external validation of prediction models in the AGES-Reykjavik study |
title_sort | dementia risk in the general population: large-scale external validation of prediction models in the ages-reykjavik study |
topic | Neuro-Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8542560/ https://www.ncbi.nlm.nih.gov/pubmed/34308533 http://dx.doi.org/10.1007/s10654-021-00785-x |
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