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Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population

PURPOSE: This study aimed to identify independent risk factors for carotid atherosclerosis (CAS) and construct and validate a CAS risk prediction model based on the Chinese population. METHODS: This retrospective study included 4,570 Chinese adults who underwent health checkups (including carotid ul...

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Autores principales: Huang, Guoqing, Jin, Qiankai, Tian, Xiaoqing, Mao, Yushan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9380015/
https://www.ncbi.nlm.nih.gov/pubmed/35983181
http://dx.doi.org/10.3389/fcvm.2022.946063
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author Huang, Guoqing
Jin, Qiankai
Tian, Xiaoqing
Mao, Yushan
author_facet Huang, Guoqing
Jin, Qiankai
Tian, Xiaoqing
Mao, Yushan
author_sort Huang, Guoqing
collection PubMed
description PURPOSE: This study aimed to identify independent risk factors for carotid atherosclerosis (CAS) and construct and validate a CAS risk prediction model based on the Chinese population. METHODS: This retrospective study included 4,570 Chinese adults who underwent health checkups (including carotid ultrasound) at the Zhenhai Lianhua Hospital, Ningbo, China, in 2020. All the participants were randomly assigned to the training and validation sets at a ratio of 7:3. Independent risk factors associated with CAS were identified using multivariate logistic regression analysis. The least absolute shrinkage and selection operator combined with 10-fold cross-validation were screened for characteristic variables, and nomograms were plotted to demonstrate the risk prediction model. C-index and receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) were used to evaluate the risk model’s discrimination, calibration, and clinical applicability. RESULTS: Age, body mass index, diastolic blood pressure, white blood cell count, mean platelet volume, alanine transaminase, aspartate transaminase, and gamma-glutamyl transferase were identified as independent risk factors for CAS. In the training, internal validation, and external validation sets, the risk model showed good discriminatory power with C-indices of 0.961 (0.953–0.969), 0.953 (0.939–0.967), and 0.930 (0.920–0.940), respectively, and excellent calibration. The results of DCA showed that the prediction model could be beneficial when the risk threshold probabilities were 1–100% in all sets. Finally, a network computer (dynamic nomogram) was developed to facilitate the physicians’ clinical operations. The website is https://nbuhgq.shinyapps.io/DynNomapp/. CONCLUSION: The development of risk models contributes to the early identification and prevention of CAS, which is important for preventing and reducing adverse cardiovascular and cerebrovascular events.
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spelling pubmed-93800152022-08-17 Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population Huang, Guoqing Jin, Qiankai Tian, Xiaoqing Mao, Yushan Front Cardiovasc Med Cardiovascular Medicine PURPOSE: This study aimed to identify independent risk factors for carotid atherosclerosis (CAS) and construct and validate a CAS risk prediction model based on the Chinese population. METHODS: This retrospective study included 4,570 Chinese adults who underwent health checkups (including carotid ultrasound) at the Zhenhai Lianhua Hospital, Ningbo, China, in 2020. All the participants were randomly assigned to the training and validation sets at a ratio of 7:3. Independent risk factors associated with CAS were identified using multivariate logistic regression analysis. The least absolute shrinkage and selection operator combined with 10-fold cross-validation were screened for characteristic variables, and nomograms were plotted to demonstrate the risk prediction model. C-index and receiver operating characteristic curves, calibration plots, and decision curve analysis (DCA) were used to evaluate the risk model’s discrimination, calibration, and clinical applicability. RESULTS: Age, body mass index, diastolic blood pressure, white blood cell count, mean platelet volume, alanine transaminase, aspartate transaminase, and gamma-glutamyl transferase were identified as independent risk factors for CAS. In the training, internal validation, and external validation sets, the risk model showed good discriminatory power with C-indices of 0.961 (0.953–0.969), 0.953 (0.939–0.967), and 0.930 (0.920–0.940), respectively, and excellent calibration. The results of DCA showed that the prediction model could be beneficial when the risk threshold probabilities were 1–100% in all sets. Finally, a network computer (dynamic nomogram) was developed to facilitate the physicians’ clinical operations. The website is https://nbuhgq.shinyapps.io/DynNomapp/. CONCLUSION: The development of risk models contributes to the early identification and prevention of CAS, which is important for preventing and reducing adverse cardiovascular and cerebrovascular events. Frontiers Media S.A. 2022-08-02 /pmc/articles/PMC9380015/ /pubmed/35983181 http://dx.doi.org/10.3389/fcvm.2022.946063 Text en Copyright © 2022 Huang, Jin, Tian and Mao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Cardiovascular Medicine
Huang, Guoqing
Jin, Qiankai
Tian, Xiaoqing
Mao, Yushan
Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population
title Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population
title_full Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population
title_fullStr Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population
title_full_unstemmed Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population
title_short Development and validation of a carotid atherosclerosis risk prediction model based on a Chinese population
title_sort development and validation of a carotid atherosclerosis risk prediction model based on a chinese population
topic Cardiovascular Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9380015/
https://www.ncbi.nlm.nih.gov/pubmed/35983181
http://dx.doi.org/10.3389/fcvm.2022.946063
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