Literature DB >> 24308978

Predictive value for the rural Chinese population of the Framingham hypertension risk model: results from Liaoning Province.

Liqiang Zheng1, Zhaoqing Sun, Xingang Zhang, Jue Li, Dayi Hu, Jie Chen, Yingxian Sun.   

Abstract

BACKGROUND: A prediction model from the US Framingham Heart Study (FHS) population has been established to estimate an individual's risk of developing hypertension. However, this model has not been widely tested in other cohorts. In this study, we examined the predictive capability of the FHS prediction model in a rural Chinese population.
METHODS: A total of 24,434 rural Chinese adults aged ≥35 years, without prevalent hypertension, diabetes mellitus, stroke, and coronary heart disease at baseline, were followed for the incidence of hypertension. Standard clinical examinations of blood pressure, weight and height, smoking status, and parental history of hypertension were observed biennially.
RESULTS: The mean age was 47.9 (SD = 10.2) years, and 49.5% of subjects were women. During a median 4.8 years of follow-up, we recorded a total of 8,675 incident hypertension cases. The cumulative 2-year and 4-year hypertension incidence rates were 7.7% and 25.6%, respectively. The C statistics for the 2-year and 4-year incidences of hypertension were 0.537 (95% confidence interval (CI) = 0.524-0.550) and 0.610 (95% CI = 0.602-0.618) for the FHS model, respectively. The Hosmer-Lemeshow χ(2) test results for 2-year and 4-year incidence of hypertension were 2,287.7 (P < 0.0001) and 8,227.1 (P < 0.0001), respectively. Sensitivity analysis indicates that the FHS prediction model still has a poor performance, although the predictive ability was better than for the overall population.
CONCLUSIONS: The FHS hypertension prediction model is not a valid tool with which to estimate the risk of incidence of hypertension among the rural Chinese population. A new hypertension risk equation for the rural Chinese population is needed.

Entities:  

Keywords:  Framingham Heart Study; blood pressure; epidemiology; hypertension; risk prediction; rural area.

Mesh:

Year:  2013        PMID: 24308978     DOI: 10.1093/ajh/hpt229

Source DB:  PubMed          Journal:  Am J Hypertens        ISSN: 0895-7061            Impact factor:   2.689


  16 in total

1.  Prediction model and assessment of probability of incident hypertension: the Rural Chinese Cohort Study.

Authors:  Bingyuan Wang; Yu Liu; Xizhuo Sun; Zhaoxia Yin; Honghui Li; Yongcheng Ren; Yang Zhao; Ruiyuan Zhang; Ming Zhang; Dongsheng Hu
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Review 2.  Future Direction for Using Artificial Intelligence to Predict and Manage Hypertension.

Authors:  Chayakrit Krittanawong; Andrew S Bomback; Usman Baber; Sripal Bangalore; Franz H Messerli; W H Wilson Tang
Journal:  Curr Hypertens Rep       Date:  2018-07-06       Impact factor: 5.369

3.  Development and validation of hypertension prediction models: The Korean Genome and Epidemiology Study_Cardiovascular Disease Association Study (KoGES_CAVAS).

Authors:  Hyun Kyung Namgung; Hye Won Woo; Jinho Shin; Min-Ho Shin; Sang Baek Koh; Hyeon Chang Kim; Yu-Mi Kim; Mi Kyung Kim
Journal:  J Hum Hypertens       Date:  2022-02-18       Impact factor: 3.012

4.  Incident hypertension and its prediction model in a prospective northern urban Han Chinese cohort study.

Authors:  Y Chen; C Wang; Y Liu; Z Yuan; W Zhang; X Li; Y Yang; X Sun; F Xue; C Zhang
Journal:  J Hum Hypertens       Date:  2016-06-02       Impact factor: 3.012

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Journal:  J Clin Hypertens (Greenwich)       Date:  2018-03-31       Impact factor: 3.738

Review 6.  Recent development of risk-prediction models for incident hypertension: An updated systematic review.

Authors:  Dongdong Sun; Jielin Liu; Lei Xiao; Ya Liu; Zuoguang Wang; Chuang Li; Yongxin Jin; Qiong Zhao; Shaojun Wen
Journal:  PLoS One       Date:  2017-10-30       Impact factor: 3.240

7.  Short-Term and Long-Term Blood Pressure Changes and the Risk of All-Cause and Cardiovascular Mortality.

Authors:  Yue Dai; Yali Wang; Yanxia Xie; Jia Zheng; Rongrong Guo; Zhaoqing Sun; Liying Xing; Xingang Zhang; Yingxian Sun; Liqiang Zheng
Journal:  Biomed Res Int       Date:  2019-08-06       Impact factor: 3.411

8.  Short-term blood pressure changes have a more strong impact on stroke and its subtypes than long-term blood pressure changes.

Authors:  Rongrong Guo; Yanxia Xie; Jia Zheng; Yali Wang; Yue Dai; Zhaoqing Sun; Liying Xing; Xingang Zhang; Yingxian Sun; Liqiang Zheng
Journal:  Clin Cardiol       Date:  2019-07-30       Impact factor: 2.882

9.  Relationship of Blood Pressure With Mortality and Cardiovascular Events Among Hypertensive Patients aged ≥ 60 years in Rural Areas of China: A Strobe-Compliant Study.

Authors:  Liqiang Zheng; Jue Li; Zhaoqing Sun; Xingang Zhang; Dayi Hu; Yingxian Sun
Journal:  Medicine (Baltimore)       Date:  2015-09       Impact factor: 1.817

10.  The Association of Stage 1 Hypertension Defined by the 2017 ACC/AHA Guideline with Stroke and Its Subtypes among Elderly Chinese.

Authors:  Jinyue Gao; Yue Dai; Yanxia Xie; Jia Zheng; Yali Wang; Rongrong Guo; Zhaoqing Sun; Liying Xing; Xingang Zhang; Yingxian Sun; Liqiang Zheng
Journal:  Biomed Res Int       Date:  2020-02-07       Impact factor: 3.411

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