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Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model

BACKGROUND: Donepezil, galantamine, rivastigmine and memantine are potentially effective interventions for cognitive impairment in dementia, but the use of these drugs has not been personalised to individual patients yet. We examined whether artificial intelligence-based recommendations can identify...

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Autores principales: Liu, Qiang, Vaci, Nemanja, Koychev, Ivan, Kormilitzin, Andrey, Li, Zhenpeng, Cipriani, Andrea, Nevado-Holgado, Alejo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805393/
https://www.ncbi.nlm.nih.gov/pubmed/35101059
http://dx.doi.org/10.1186/s12916-022-02250-2
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author Liu, Qiang
Vaci, Nemanja
Koychev, Ivan
Kormilitzin, Andrey
Li, Zhenpeng
Cipriani, Andrea
Nevado-Holgado, Alejo
author_facet Liu, Qiang
Vaci, Nemanja
Koychev, Ivan
Kormilitzin, Andrey
Li, Zhenpeng
Cipriani, Andrea
Nevado-Holgado, Alejo
author_sort Liu, Qiang
collection PubMed
description BACKGROUND: Donepezil, galantamine, rivastigmine and memantine are potentially effective interventions for cognitive impairment in dementia, but the use of these drugs has not been personalised to individual patients yet. We examined whether artificial intelligence-based recommendations can identify the best treatment using routinely collected patient-level information. METHODS: Six thousand eight hundred four patients aged 59–102 years with a diagnosis of dementia from two National Health Service (NHS) Foundation Trusts in the UK were used for model training/internal validation and external validation, respectively. A personalised prescription model based on the Recurrent Neural Network machine learning architecture was developed to predict the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores post-drug initiation. The drug that resulted in the smallest decline in cognitive scores between prescription and the next visit was selected as the treatment of choice. Change of cognitive scores up to 2 years after treatment initiation was compared for model evaluation. RESULTS: Overall, 1343 patients with MMSE scores were identified for internal validation and 285 [21.22%] took the drug recommended. After 2 years, the reduction of mean [standard deviation] MMSE score in this group was significantly smaller than the remaining 1058 [78.78%] patients (0.60 [0.26] vs 2.80 [0.28]; P = 0.02). In the external validation cohort (N = 1772), 222 [12.53%] patients took the drug recommended and reported a smaller MMSE reduction compared to the 1550 [87.47%] patients who did not (1.01 [0.49] vs 4.23 [0.60]; P = 0.01). A similar performance gap was seen when testing the model on patients prescribed with AChEIs only. CONCLUSIONS: It was possible to identify the most effective drug for the real-world treatment of cognitive impairment in dementia at an individual patient level. Routine care patients whose prescribed medications were the best fit according to the model had better cognitive performance after 2 years. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02250-2.
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spelling pubmed-88053932022-02-03 Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model Liu, Qiang Vaci, Nemanja Koychev, Ivan Kormilitzin, Andrey Li, Zhenpeng Cipriani, Andrea Nevado-Holgado, Alejo BMC Med Research Article BACKGROUND: Donepezil, galantamine, rivastigmine and memantine are potentially effective interventions for cognitive impairment in dementia, but the use of these drugs has not been personalised to individual patients yet. We examined whether artificial intelligence-based recommendations can identify the best treatment using routinely collected patient-level information. METHODS: Six thousand eight hundred four patients aged 59–102 years with a diagnosis of dementia from two National Health Service (NHS) Foundation Trusts in the UK were used for model training/internal validation and external validation, respectively. A personalised prescription model based on the Recurrent Neural Network machine learning architecture was developed to predict the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores post-drug initiation. The drug that resulted in the smallest decline in cognitive scores between prescription and the next visit was selected as the treatment of choice. Change of cognitive scores up to 2 years after treatment initiation was compared for model evaluation. RESULTS: Overall, 1343 patients with MMSE scores were identified for internal validation and 285 [21.22%] took the drug recommended. After 2 years, the reduction of mean [standard deviation] MMSE score in this group was significantly smaller than the remaining 1058 [78.78%] patients (0.60 [0.26] vs 2.80 [0.28]; P = 0.02). In the external validation cohort (N = 1772), 222 [12.53%] patients took the drug recommended and reported a smaller MMSE reduction compared to the 1550 [87.47%] patients who did not (1.01 [0.49] vs 4.23 [0.60]; P = 0.01). A similar performance gap was seen when testing the model on patients prescribed with AChEIs only. CONCLUSIONS: It was possible to identify the most effective drug for the real-world treatment of cognitive impairment in dementia at an individual patient level. Routine care patients whose prescribed medications were the best fit according to the model had better cognitive performance after 2 years. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12916-022-02250-2. BioMed Central 2022-02-01 /pmc/articles/PMC8805393/ /pubmed/35101059 http://dx.doi.org/10.1186/s12916-022-02250-2 Text en © The Author(s) 2022 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Liu, Qiang
Vaci, Nemanja
Koychev, Ivan
Kormilitzin, Andrey
Li, Zhenpeng
Cipriani, Andrea
Nevado-Holgado, Alejo
Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model
title Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model
title_full Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model
title_fullStr Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model
title_full_unstemmed Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model
title_short Personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model
title_sort personalised treatment for cognitive impairment in dementia: development and validation of an artificial intelligence model
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8805393/
https://www.ncbi.nlm.nih.gov/pubmed/35101059
http://dx.doi.org/10.1186/s12916-022-02250-2
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