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Risk Prediction Performance of the Thai Cardiovascular Risk Score for Mild Cognitive Impairment in Adults with Metabolic Risk Factors in Thailand

Individuals with metabolic risks are at high risk of cognitive impairment. We aimed to investigate whether the Thai Cardiovascular Risk (TCVR) score can be used to predict mild cognitive impairment (MCI) in Thai adults with metabolic risks. The study was conducted using secondary data of patients wi...

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Autores principales: Buawangpong, Nida, Aramrat, Chanchanok, Pinyopornpanish, Kanokporn, Phrommintikul, Arintaya, Soontornpun, Atiwat, Jiraporncharoen, Wichuda, Pliannuom, Suphawita, Angkurawaranon, Chaisiri
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9602158/
https://www.ncbi.nlm.nih.gov/pubmed/36292406
http://dx.doi.org/10.3390/healthcare10101959
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author Buawangpong, Nida
Aramrat, Chanchanok
Pinyopornpanish, Kanokporn
Phrommintikul, Arintaya
Soontornpun, Atiwat
Jiraporncharoen, Wichuda
Pliannuom, Suphawita
Angkurawaranon, Chaisiri
author_facet Buawangpong, Nida
Aramrat, Chanchanok
Pinyopornpanish, Kanokporn
Phrommintikul, Arintaya
Soontornpun, Atiwat
Jiraporncharoen, Wichuda
Pliannuom, Suphawita
Angkurawaranon, Chaisiri
author_sort Buawangpong, Nida
collection PubMed
description Individuals with metabolic risks are at high risk of cognitive impairment. We aimed to investigate whether the Thai Cardiovascular Risk (TCVR) score can be used to predict mild cognitive impairment (MCI) in Thai adults with metabolic risks. The study was conducted using secondary data of patients with metabolic risks from Maharaj Nakorn Chiang Mai Hospital. MCI was indicated by an MoCA score of less than 25. Six different TCVR models were used with various combinations of ten different variables for predicting the risk of MCI. The area under the receiver operator characteristic curve (AuROC) and Hosmer–Lemeshow goodness of fit tests were used for determining discriminative performance and model calibration. The sensitivity of the discriminative performance was further evaluated by stratifying by age and gender. From a total of 421 participants, 348 participants had MCI. All six TCVR models showed a similar AuROC, varying between 0.58 and 0.61. The anthropometric-based model showed the best risk prediction performance in the older age group (AuROC 0.69). The laboratory-based model provided the highest discriminative performance for the younger age group (AuROC 0.60). There is potential for the development of an MCI risk model based on values from routine cardiovascular risk assessments among patients with metabolic risks.
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spelling pubmed-96021582022-10-27 Risk Prediction Performance of the Thai Cardiovascular Risk Score for Mild Cognitive Impairment in Adults with Metabolic Risk Factors in Thailand Buawangpong, Nida Aramrat, Chanchanok Pinyopornpanish, Kanokporn Phrommintikul, Arintaya Soontornpun, Atiwat Jiraporncharoen, Wichuda Pliannuom, Suphawita Angkurawaranon, Chaisiri Healthcare (Basel) Article Individuals with metabolic risks are at high risk of cognitive impairment. We aimed to investigate whether the Thai Cardiovascular Risk (TCVR) score can be used to predict mild cognitive impairment (MCI) in Thai adults with metabolic risks. The study was conducted using secondary data of patients with metabolic risks from Maharaj Nakorn Chiang Mai Hospital. MCI was indicated by an MoCA score of less than 25. Six different TCVR models were used with various combinations of ten different variables for predicting the risk of MCI. The area under the receiver operator characteristic curve (AuROC) and Hosmer–Lemeshow goodness of fit tests were used for determining discriminative performance and model calibration. The sensitivity of the discriminative performance was further evaluated by stratifying by age and gender. From a total of 421 participants, 348 participants had MCI. All six TCVR models showed a similar AuROC, varying between 0.58 and 0.61. The anthropometric-based model showed the best risk prediction performance in the older age group (AuROC 0.69). The laboratory-based model provided the highest discriminative performance for the younger age group (AuROC 0.60). There is potential for the development of an MCI risk model based on values from routine cardiovascular risk assessments among patients with metabolic risks. MDPI 2022-10-07 /pmc/articles/PMC9602158/ /pubmed/36292406 http://dx.doi.org/10.3390/healthcare10101959 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Buawangpong, Nida
Aramrat, Chanchanok
Pinyopornpanish, Kanokporn
Phrommintikul, Arintaya
Soontornpun, Atiwat
Jiraporncharoen, Wichuda
Pliannuom, Suphawita
Angkurawaranon, Chaisiri
Risk Prediction Performance of the Thai Cardiovascular Risk Score for Mild Cognitive Impairment in Adults with Metabolic Risk Factors in Thailand
title Risk Prediction Performance of the Thai Cardiovascular Risk Score for Mild Cognitive Impairment in Adults with Metabolic Risk Factors in Thailand
title_full Risk Prediction Performance of the Thai Cardiovascular Risk Score for Mild Cognitive Impairment in Adults with Metabolic Risk Factors in Thailand
title_fullStr Risk Prediction Performance of the Thai Cardiovascular Risk Score for Mild Cognitive Impairment in Adults with Metabolic Risk Factors in Thailand
title_full_unstemmed Risk Prediction Performance of the Thai Cardiovascular Risk Score for Mild Cognitive Impairment in Adults with Metabolic Risk Factors in Thailand
title_short Risk Prediction Performance of the Thai Cardiovascular Risk Score for Mild Cognitive Impairment in Adults with Metabolic Risk Factors in Thailand
title_sort risk prediction performance of the thai cardiovascular risk score for mild cognitive impairment in adults with metabolic risk factors in thailand
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9602158/
https://www.ncbi.nlm.nih.gov/pubmed/36292406
http://dx.doi.org/10.3390/healthcare10101959
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