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Cardiovascular disease risk prediction models in the Chinese population- a systematic review and meta-analysis

BACKGROUND: There is an increasing prevalence of cardiovascular disease (CVD) in China, which represents the leading cause of mortality. Precise CVD risk identification is the fundamental prevention component. This study sought to systematically review the CVD risk prediction models derived and/or v...

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Autores principales: Zhiting, Guo, Jiaying, Tang, Haiying, Han, Yuping, Zhang, Qunfei, Yu, Jingfen, Jin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400257/
https://www.ncbi.nlm.nih.gov/pubmed/35999550
http://dx.doi.org/10.1186/s12889-022-13995-z
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author Zhiting, Guo
Jiaying, Tang
Haiying, Han
Yuping, Zhang
Qunfei, Yu
Jingfen, Jin
author_facet Zhiting, Guo
Jiaying, Tang
Haiying, Han
Yuping, Zhang
Qunfei, Yu
Jingfen, Jin
author_sort Zhiting, Guo
collection PubMed
description BACKGROUND: There is an increasing prevalence of cardiovascular disease (CVD) in China, which represents the leading cause of mortality. Precise CVD risk identification is the fundamental prevention component. This study sought to systematically review the CVD risk prediction models derived and/or validated in the Chinese population to promote primary CVD prevention. METHODS: Reports were included if they derived or validated one or more CVD risk prediction models in the Chinese population. PubMed, Embase, CINAHL, Web of Science, Scopus, China National Knowledge Infrastructure (CNKI), VIP database, etc., were searched. The risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed in R using the package metamisc. RESULTS: From 55,183 records, 22 studies were included. Twelve studies derived 18 CVD risk prediction models, of which seven models were derived based on a multicentre cohort including more than two provinces of mainland China, and one was a model developed based on a New Zealand cohort including Chinese individuals. The number of predictors ranged from 6 to 22. The definitions of predicted outcomes showed considerable heterogeneity. Fourteen articles described 29 validations of 8 models. The Framingham model and pooled cohort equations (PCEs) are the most frequently validated foreign tools. Discrimination was acceptable and similar for men and women among models (0.60–0.83). The calibration estimates changed substantially from one population to another. Prediction for atherosclerotic cardiovascular disease Risk in China (China-PAR) showed good calibration [observed/expected events ratio = 0.99, 95% PI (0.57,1.70)] and female sex [1.10, 95% PI (0.23,5.16)]. CONCLUSIONS: Several models have been developed or validated in the Chinese population. The usefulness of most of the models remains unclear due to incomplete external validation and head-to-head comparison. Future research should focus on externally validating or tailoring these models to local settings. TRAIL REGISTRATION: This systematic review was registered at PROSPERO (International Prospective Register of Systematic Reviews, CRD42021277453). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13995-z.
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spelling pubmed-94002572022-08-25 Cardiovascular disease risk prediction models in the Chinese population- a systematic review and meta-analysis Zhiting, Guo Jiaying, Tang Haiying, Han Yuping, Zhang Qunfei, Yu Jingfen, Jin BMC Public Health Research BACKGROUND: There is an increasing prevalence of cardiovascular disease (CVD) in China, which represents the leading cause of mortality. Precise CVD risk identification is the fundamental prevention component. This study sought to systematically review the CVD risk prediction models derived and/or validated in the Chinese population to promote primary CVD prevention. METHODS: Reports were included if they derived or validated one or more CVD risk prediction models in the Chinese population. PubMed, Embase, CINAHL, Web of Science, Scopus, China National Knowledge Infrastructure (CNKI), VIP database, etc., were searched. The risk of bias was assessed with the Prediction Model Risk of Bias Assessment Tool (PROBAST). Meta-analysis was performed in R using the package metamisc. RESULTS: From 55,183 records, 22 studies were included. Twelve studies derived 18 CVD risk prediction models, of which seven models were derived based on a multicentre cohort including more than two provinces of mainland China, and one was a model developed based on a New Zealand cohort including Chinese individuals. The number of predictors ranged from 6 to 22. The definitions of predicted outcomes showed considerable heterogeneity. Fourteen articles described 29 validations of 8 models. The Framingham model and pooled cohort equations (PCEs) are the most frequently validated foreign tools. Discrimination was acceptable and similar for men and women among models (0.60–0.83). The calibration estimates changed substantially from one population to another. Prediction for atherosclerotic cardiovascular disease Risk in China (China-PAR) showed good calibration [observed/expected events ratio = 0.99, 95% PI (0.57,1.70)] and female sex [1.10, 95% PI (0.23,5.16)]. CONCLUSIONS: Several models have been developed or validated in the Chinese population. The usefulness of most of the models remains unclear due to incomplete external validation and head-to-head comparison. Future research should focus on externally validating or tailoring these models to local settings. TRAIL REGISTRATION: This systematic review was registered at PROSPERO (International Prospective Register of Systematic Reviews, CRD42021277453). SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12889-022-13995-z. BioMed Central 2022-08-24 /pmc/articles/PMC9400257/ /pubmed/35999550 http://dx.doi.org/10.1186/s12889-022-13995-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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
Zhiting, Guo
Jiaying, Tang
Haiying, Han
Yuping, Zhang
Qunfei, Yu
Jingfen, Jin
Cardiovascular disease risk prediction models in the Chinese population- a systematic review and meta-analysis
title Cardiovascular disease risk prediction models in the Chinese population- a systematic review and meta-analysis
title_full Cardiovascular disease risk prediction models in the Chinese population- a systematic review and meta-analysis
title_fullStr Cardiovascular disease risk prediction models in the Chinese population- a systematic review and meta-analysis
title_full_unstemmed Cardiovascular disease risk prediction models in the Chinese population- a systematic review and meta-analysis
title_short Cardiovascular disease risk prediction models in the Chinese population- a systematic review and meta-analysis
title_sort cardiovascular disease risk prediction models in the chinese population- a systematic review and meta-analysis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9400257/
https://www.ncbi.nlm.nih.gov/pubmed/35999550
http://dx.doi.org/10.1186/s12889-022-13995-z
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