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Risk Factors and Prediction Nomogram of Cognitive Frailty with Diabetes in the Elderly

PURPOSE: Diabetes is a well-recognized risk factor for cognitive frailty. This study aimed to investigate the influencing factors of cognitive frailty in elderly patients with diabetes and develop a nomogram for its assessment. METHODS: We collected the clinical data of diabetic patients aged 60 yea...

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Autores principales: Deng, Yinhui, Li, Na, Wang, Yaru, Xiong, Chen, Zou, Xiaofang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10588717/
https://www.ncbi.nlm.nih.gov/pubmed/37867632
http://dx.doi.org/10.2147/DMSO.S426315
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author Deng, Yinhui
Li, Na
Wang, Yaru
Xiong, Chen
Zou, Xiaofang
author_facet Deng, Yinhui
Li, Na
Wang, Yaru
Xiong, Chen
Zou, Xiaofang
author_sort Deng, Yinhui
collection PubMed
description PURPOSE: Diabetes is a well-recognized risk factor for cognitive frailty. This study aimed to investigate the influencing factors of cognitive frailty in elderly patients with diabetes and develop a nomogram for its assessment. METHODS: We collected the clinical data of diabetic patients aged 60 years or older and the patients were divided into training and validation cohorts at a ratio of 7:3. In the training cohort, logistic regression was used to screen out the influencing factors of cognitive frailty in elderly diabetic patients, and a risk prediction model and nomogram were constructed and verified in the validation cohort. The performance of the model was evaluated using various measures, including the area under the receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test and decision curve analysis. RESULTS: A total of 315 elderly diabetic patients were included, of which 87 (27.6%) patients had cognitive frailty. Age, albumin levels, calf circumference, duration of diabetes, intellectual activity, and depressive state were identified as independent risk factors for cognitive frailty in older patients with diabetes (P < 0.05). The training cohort and validation cohort demonstrated area under curve (AUC) values of 0.866 and 0.821, respectively. CONCLUSION: Older patients with diabetes have a higher prevalence of cognitive frailty. The nomogram model exhibited satisfactory calibration and identification, providing a reliable tool for assessing the risk of cognitive frailty in individuals with diabetes.
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spelling pubmed-105887172023-10-21 Risk Factors and Prediction Nomogram of Cognitive Frailty with Diabetes in the Elderly Deng, Yinhui Li, Na Wang, Yaru Xiong, Chen Zou, Xiaofang Diabetes Metab Syndr Obes Original Research PURPOSE: Diabetes is a well-recognized risk factor for cognitive frailty. This study aimed to investigate the influencing factors of cognitive frailty in elderly patients with diabetes and develop a nomogram for its assessment. METHODS: We collected the clinical data of diabetic patients aged 60 years or older and the patients were divided into training and validation cohorts at a ratio of 7:3. In the training cohort, logistic regression was used to screen out the influencing factors of cognitive frailty in elderly diabetic patients, and a risk prediction model and nomogram were constructed and verified in the validation cohort. The performance of the model was evaluated using various measures, including the area under the receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test and decision curve analysis. RESULTS: A total of 315 elderly diabetic patients were included, of which 87 (27.6%) patients had cognitive frailty. Age, albumin levels, calf circumference, duration of diabetes, intellectual activity, and depressive state were identified as independent risk factors for cognitive frailty in older patients with diabetes (P < 0.05). The training cohort and validation cohort demonstrated area under curve (AUC) values of 0.866 and 0.821, respectively. CONCLUSION: Older patients with diabetes have a higher prevalence of cognitive frailty. The nomogram model exhibited satisfactory calibration and identification, providing a reliable tool for assessing the risk of cognitive frailty in individuals with diabetes. Dove 2023-10-16 /pmc/articles/PMC10588717/ /pubmed/37867632 http://dx.doi.org/10.2147/DMSO.S426315 Text en © 2023 Deng et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Deng, Yinhui
Li, Na
Wang, Yaru
Xiong, Chen
Zou, Xiaofang
Risk Factors and Prediction Nomogram of Cognitive Frailty with Diabetes in the Elderly
title Risk Factors and Prediction Nomogram of Cognitive Frailty with Diabetes in the Elderly
title_full Risk Factors and Prediction Nomogram of Cognitive Frailty with Diabetes in the Elderly
title_fullStr Risk Factors and Prediction Nomogram of Cognitive Frailty with Diabetes in the Elderly
title_full_unstemmed Risk Factors and Prediction Nomogram of Cognitive Frailty with Diabetes in the Elderly
title_short Risk Factors and Prediction Nomogram of Cognitive Frailty with Diabetes in the Elderly
title_sort risk factors and prediction nomogram of cognitive frailty with diabetes in the elderly
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10588717/
https://www.ncbi.nlm.nih.gov/pubmed/37867632
http://dx.doi.org/10.2147/DMSO.S426315
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