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Predicting risk on cardiovascular or cerebrovascular disease based on a physical activity cohort: Results from APAC study

Commonly used prediction models have been primarily constructed without taking physical activity into account. Using the Kailuan physical activity cohorts from Asymptomatic Polyvascular Abnormalities in Community (APAC) study, we developed a 9‐year cardiovascular or cerebrovascular disease (CVD) ris...

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Autores principales: Zhao, Juan, Yu, Ye, Zhu, Xiaolan, Xie, Yuling, Ai, Songwei, Lehmann, H. Immo, Deng, Xuan, Hu, Feifei, Li, Guoping, Zhou, Yong, Xiao, Junjie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999708/
https://www.ncbi.nlm.nih.gov/pubmed/36911159
http://dx.doi.org/10.1002/mco2.220
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author Zhao, Juan
Yu, Ye
Zhu, Xiaolan
Xie, Yuling
Ai, Songwei
Lehmann, H. Immo
Deng, Xuan
Hu, Feifei
Li, Guoping
Zhou, Yong
Xiao, Junjie
author_facet Zhao, Juan
Yu, Ye
Zhu, Xiaolan
Xie, Yuling
Ai, Songwei
Lehmann, H. Immo
Deng, Xuan
Hu, Feifei
Li, Guoping
Zhou, Yong
Xiao, Junjie
author_sort Zhao, Juan
collection PubMed
description Commonly used prediction models have been primarily constructed without taking physical activity into account. Using the Kailuan physical activity cohorts from Asymptomatic Polyvascular Abnormalities in Community (APAC) study, we developed a 9‐year cardiovascular or cerebrovascular disease (CVD) risk prediction equation. Participants in this study were included from APAC cohort, which included 5440 participants from the Kailuan cohort in China. Cox proportional hazard regression model was applied to construct sex‐specific risk prediction equations for the physical activity cohort (PA equation). Proposed equations were compared with the 10‐year risk prediction model, which is developed for atherosclerotic cardiovascular disease risk in Chinese cohorts (China‐PAR equation). C statistics of PA equations were 0.755 (95% confidence interval, 0.750–0.758) for men and 0.801 (95% confidence interval, 0.790–0.813) for women. The estimated area under the receiver operating characteristic curves in the validation set shows that the PA equations perform as good as the China‐PAR. From calibration among four categories of predicted risks, the predicted risk rates by PA equations were almost identical to the Kaplan–Meier observed rates. Therefore, our developed sex‐specific PA equations have effective performance for predicting CVD for physically active cohorts in the physical activity cohort in Kailuan.
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spelling pubmed-99997082023-03-11 Predicting risk on cardiovascular or cerebrovascular disease based on a physical activity cohort: Results from APAC study Zhao, Juan Yu, Ye Zhu, Xiaolan Xie, Yuling Ai, Songwei Lehmann, H. Immo Deng, Xuan Hu, Feifei Li, Guoping Zhou, Yong Xiao, Junjie MedComm (2020) Original Articles Commonly used prediction models have been primarily constructed without taking physical activity into account. Using the Kailuan physical activity cohorts from Asymptomatic Polyvascular Abnormalities in Community (APAC) study, we developed a 9‐year cardiovascular or cerebrovascular disease (CVD) risk prediction equation. Participants in this study were included from APAC cohort, which included 5440 participants from the Kailuan cohort in China. Cox proportional hazard regression model was applied to construct sex‐specific risk prediction equations for the physical activity cohort (PA equation). Proposed equations were compared with the 10‐year risk prediction model, which is developed for atherosclerotic cardiovascular disease risk in Chinese cohorts (China‐PAR equation). C statistics of PA equations were 0.755 (95% confidence interval, 0.750–0.758) for men and 0.801 (95% confidence interval, 0.790–0.813) for women. The estimated area under the receiver operating characteristic curves in the validation set shows that the PA equations perform as good as the China‐PAR. From calibration among four categories of predicted risks, the predicted risk rates by PA equations were almost identical to the Kaplan–Meier observed rates. Therefore, our developed sex‐specific PA equations have effective performance for predicting CVD for physically active cohorts in the physical activity cohort in Kailuan. John Wiley and Sons Inc. 2023-03-10 /pmc/articles/PMC9999708/ /pubmed/36911159 http://dx.doi.org/10.1002/mco2.220 Text en © 2023 The Authors. MedComm published by Sichuan International Medical Exchange & Promotion Association (SCIMEA) and John Wiley & Sons Australia, Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Zhao, Juan
Yu, Ye
Zhu, Xiaolan
Xie, Yuling
Ai, Songwei
Lehmann, H. Immo
Deng, Xuan
Hu, Feifei
Li, Guoping
Zhou, Yong
Xiao, Junjie
Predicting risk on cardiovascular or cerebrovascular disease based on a physical activity cohort: Results from APAC study
title Predicting risk on cardiovascular or cerebrovascular disease based on a physical activity cohort: Results from APAC study
title_full Predicting risk on cardiovascular or cerebrovascular disease based on a physical activity cohort: Results from APAC study
title_fullStr Predicting risk on cardiovascular or cerebrovascular disease based on a physical activity cohort: Results from APAC study
title_full_unstemmed Predicting risk on cardiovascular or cerebrovascular disease based on a physical activity cohort: Results from APAC study
title_short Predicting risk on cardiovascular or cerebrovascular disease based on a physical activity cohort: Results from APAC study
title_sort predicting risk on cardiovascular or cerebrovascular disease based on a physical activity cohort: results from apac study
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9999708/
https://www.ncbi.nlm.nih.gov/pubmed/36911159
http://dx.doi.org/10.1002/mco2.220
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