Evidence map›Paper›PMID 34327424›Full record

ArticleThe Lancet regional health. Western Pacific2021

Validating World Health Organization cardiovascular disease risk charts and optimizing risk assessment in China.

Jianxin Li, Fangchao Liu, Xueli Yang, Jie Cao, Shufeng Chen, Jichun Chen, Keyong Huang, Chong Shen, Xiaoqing Liu, Ling Yu and 8 more

Abstract read
In one paragraph

Article in The Lancet regional health. Western Pacific, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  16. Cardiovascular risk assessment: The key path toward precision prevention.Chronic diseases and translational medicine · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

18 authors.

Jianxin LiDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Fangchao LiuDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Xueli YangDepartment of Occupational and Environmental Health, School of Public Health, Tianjin Medical University, Tianjin 300070, China.
Jie CaoDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Shufeng ChenDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Jichun ChenDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Keyong HuangDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Chong ShenDepartment of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing 211166, China.
Xiaoqing LiuDivision of Epidemiology, Guangdong Provincial People's Hospital and Cardiovascular Institute, Guangzhou 510080, China.
Ling YuDepartment of Cardiology, Fujian Provincial Hospital, Fuzhou 350014, China.
Yingxin ZhaoCardio-Cerebrovascular Control and Research Center, Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan 250062, China.
Xianping WuCenter for Chronic and Non-communicable Disease Control and Prevention, Sichuan Center for Disease Control and Prevention, Chengdu 610041, China.
Liancheng ZhaoDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Xigui WuDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Ying LiDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Dongsheng HuDepartment of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen 518071, China.
Jianfeng HuangDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
Xiangfeng LuDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWorld Health Organization (WHO) released region-specific cardiovascular disease (CVD) risk prediction charts recently, but the extent to which the charts can apply to Chinese population is unknown. We aimed to validate the WHO CVD risk charts for East Asia, and evaluate their practicability combining with China-PAR (Prediction for Atherosclerotic Cardiovascular Disease Risk in China) equations among Chinese adults.

methodsThe China-PAR cohort with 93,234 participants aged 40-80 years was followed up during 1992-2015, including 29,337 participants from three sub-cohorts with follow-up period of over 10 years. We validated the WHO CVD risk charts using the China-PAR cohort by assessment of the predicted number of events, C index, calibration χ², and calibration plots, further elaborated the concordance between the China-PAR equations and the WHO risk charts.

findingsDuring an average follow-up of 13•64 years, 1849 incident CVD cases were identified from 29,337 participants. Both the laboratory-based and non-laboratory-based charts overestimated CVD events by 59% and 58% in men, and by 72% and 85% in women, respectively. However, 92% of participants identified as high risk by the China-PAR equations could be successfully detected by the laboratory-based charts at the cut-off point of 10%. We also observed that the non-laboratory-based charts demonstrated the poor performance for diabetic population, with high proportion of high-risk individuals (17% for men, 31% for women) would be missed.

interpretationAlthough the WHO CVD risk charts for East Asia apparently overestimated CVD risk among Chinese population, they could be pragmatic pre-selection tools, as potential supplement to the China-PAR equations. The widespread use of the WHO risk charts along with the China-PAR equations might facilitate the implementation of the risk-based CVD prevention in China.

fundingFull funding sources are listed at the end of the paper (see Acknowledgments).

Identifiers

PMID34327424
PMCPMC8315380

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