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A nomogram for predicting mild cognitive impairment in older adults with hypertension

BACKGROUND: Hyper- and hypotension increase the risk of cognitive dysfunction. As effective control of blood pressure can reduce the risk of mild cognitive impairment (MCI), early risk assessment is necessary to identify MCI in senile hypertension as soon as possible and reduce the risk of developin...

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Autores principales: Jingyu, Lu, Wen, Ding, Liping, Zhang, Xiaoling, Liu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10561469/
https://www.ncbi.nlm.nih.gov/pubmed/37814226
http://dx.doi.org/10.1186/s12883-023-03408-y
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author Jingyu, Lu
Wen, Ding
Liping, Zhang
Xiaoling, Liu
author_facet Jingyu, Lu
Wen, Ding
Liping, Zhang
Xiaoling, Liu
author_sort Jingyu, Lu
collection PubMed
description BACKGROUND: Hyper- and hypotension increase the risk of cognitive dysfunction. As effective control of blood pressure can reduce the risk of mild cognitive impairment (MCI), early risk assessment is necessary to identify MCI in senile hypertension as soon as possible and reduce the risk of developing dementia. No perfect risk-prediction model or nomogram has been developed to evaluate the risk of MCI in older adults with hypertension. We aimed to develop a nomogram model for predicting MCI in older patients with hypertension. METHODS: We selected 345 older patients with hypertension in Xixiangtang District, Nanning City, as the modeling group and divided into the MCI (n = 197) and non-MCI groups (n = 148). Comparing the general conditions, lifestyle, disease factors, psychosocial and other indicators. Logistic regression was used to analyze risk factors for MCI in older hypertensive patients, and R Programming Language was used to draw the nomogram. We selected 146 older patients with hypertension in Qingxiu District, Nanning City, as the verification group. The effectiveness and discrimination ability of the nomogram was evaluated through internal and external verification. RESULTS: Multivariate logistic regression analysis identified 11 factors, including hypertension grade, education level, complicated diabetes, hypertension years, stress history, smoking, physical exercise, reading, social support, sleep disorders, and medication compliance, as risk factors for MCI in older patients with hypertension. To develop a nomogram model, the validity of the prediction model was evaluated by fitting the curve, which revealed a good fit for both the modeling (P = 0.98) and verification groups (P = 0.96). The discrimination of the nomogram model was evaluated in the modeling group using a receiver operating characteristic curve. The area under the curve was 0.795, and the Hosmer–Lemeshow test yielded P = 0.703. In the validation group, the area under the curve was 0.765, and the Hosmer–Lemeshow test yielded P = 0.234. CONCLUSIONS: We developed a nomogram to help clinicians identify high-risk groups for MCI among older patients with hypertension. This model demonstrated good discrimination and validity, providing a scientific basis for community medical staff to evaluate and identify the risk of MCI in these patients at an early stage.
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spelling pubmed-105614692023-10-10 A nomogram for predicting mild cognitive impairment in older adults with hypertension Jingyu, Lu Wen, Ding Liping, Zhang Xiaoling, Liu BMC Neurol Research BACKGROUND: Hyper- and hypotension increase the risk of cognitive dysfunction. As effective control of blood pressure can reduce the risk of mild cognitive impairment (MCI), early risk assessment is necessary to identify MCI in senile hypertension as soon as possible and reduce the risk of developing dementia. No perfect risk-prediction model or nomogram has been developed to evaluate the risk of MCI in older adults with hypertension. We aimed to develop a nomogram model for predicting MCI in older patients with hypertension. METHODS: We selected 345 older patients with hypertension in Xixiangtang District, Nanning City, as the modeling group and divided into the MCI (n = 197) and non-MCI groups (n = 148). Comparing the general conditions, lifestyle, disease factors, psychosocial and other indicators. Logistic regression was used to analyze risk factors for MCI in older hypertensive patients, and R Programming Language was used to draw the nomogram. We selected 146 older patients with hypertension in Qingxiu District, Nanning City, as the verification group. The effectiveness and discrimination ability of the nomogram was evaluated through internal and external verification. RESULTS: Multivariate logistic regression analysis identified 11 factors, including hypertension grade, education level, complicated diabetes, hypertension years, stress history, smoking, physical exercise, reading, social support, sleep disorders, and medication compliance, as risk factors for MCI in older patients with hypertension. To develop a nomogram model, the validity of the prediction model was evaluated by fitting the curve, which revealed a good fit for both the modeling (P = 0.98) and verification groups (P = 0.96). The discrimination of the nomogram model was evaluated in the modeling group using a receiver operating characteristic curve. The area under the curve was 0.795, and the Hosmer–Lemeshow test yielded P = 0.703. In the validation group, the area under the curve was 0.765, and the Hosmer–Lemeshow test yielded P = 0.234. CONCLUSIONS: We developed a nomogram to help clinicians identify high-risk groups for MCI among older patients with hypertension. This model demonstrated good discrimination and validity, providing a scientific basis for community medical staff to evaluate and identify the risk of MCI in these patients at an early stage. BioMed Central 2023-10-09 /pmc/articles/PMC10561469/ /pubmed/37814226 http://dx.doi.org/10.1186/s12883-023-03408-y Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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
Jingyu, Lu
Wen, Ding
Liping, Zhang
Xiaoling, Liu
A nomogram for predicting mild cognitive impairment in older adults with hypertension
title A nomogram for predicting mild cognitive impairment in older adults with hypertension
title_full A nomogram for predicting mild cognitive impairment in older adults with hypertension
title_fullStr A nomogram for predicting mild cognitive impairment in older adults with hypertension
title_full_unstemmed A nomogram for predicting mild cognitive impairment in older adults with hypertension
title_short A nomogram for predicting mild cognitive impairment in older adults with hypertension
title_sort nomogram for predicting mild cognitive impairment in older adults with hypertension
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10561469/
https://www.ncbi.nlm.nih.gov/pubmed/37814226
http://dx.doi.org/10.1186/s12883-023-03408-y
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