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Original research
Epidemiological study of calcified aortic valve stenosis in a Chinese community population
  1. Jun Chen1,2,
  2. Lingchun Lyu1,
  3. Jiayi Shen1,
  4. Yuesong Pan3,
  5. Jing Jing3,
  6. Yong-Jun Wang3,
  7. Tiemin Wei1
  1. 1Department of Cardiology, Lishui Central Hospital and Fifth Affiliated Hospital of Wenzhou Medical College, Lishui, Zhejiang, China
  2. 2Department of Cardiology, First Clinical Medical College, Zhejiang Chinese Medical University, Hangzhou, Zhejiang, China
  3. 3Department of Neurology, Beijing Tiantan Hospital, Beijing, China
  1. Correspondence to Dr Tiemin Wei, Lishui Central Hospital and Fifth Affiliated Hospital of Wenzhou Medical College, Lishui, Zhejiang, China; lswtm{at}sina.com

Abstract

Background and aims Due to the ageing global population, calcified aortic valve disease is currently the most common cardiac valve disorder. This study aimed to investigate the prevalence and the risk factors for calcified aortic valve stenosis (CAVS), and develop a prediction model for predicting CAVS risk.

Methods and results This study was derived from the cross-sectional baseline survey of the PRECISE study (NCT03178448). The demographic, clinical and laboratory information of each participant was obtained. Univariable and multivariable logistic regression models were used to determine CAVS risk factors. A prediction model for predicting CAVS risk based on risk factors was developed and the result was performed by nomogram. The discrimination of the prediction model was assessed by receiver operating characteristic curve analysis. The degree of fitting for the prediction model was assessed by calibration curve analysis. A total of 3067 participants (1427 men and 1640 women) were included. The prevalence of CAVS among those aged below 60 years old, 60–70 years old and over 70 years old was 4.1%, 10.3% and 21.9%, respectively. Multivariable regression analysis revealed that age (OR: 1.099; 95% CI: 1.076 to 1.123, p<0.001), pulse pressure (OR: 1.020; 95% CI: 1.009 to 1.031, p<0.001), uric acid (OR: 1.003; 95% CI: 1.001 to 1.004, p<0.001), glycosylated haemoglobin (HbA1c) (OR: 1.152; 95% CI: 1.028 to 1.292, p=0.015) and lipoprotein(a) (OR: 1.002; 95% CI: 1.001 to 1.002, p<0.001) were independent risk factors for CAVS. High-density lipoprotein cholesterol (HDL-C) was a protective factor for CAVS (OR: 0.539; 95% CI: 0.349 to 0.831, p=0.005). The prediction model including the above risk factors showed a risk prediction of CAVS with good discrimination. The area under the curve value was found to be 0.743 (95% CI: 0.711 to 0.775).

Conclusion CAVS is currently prevalent in the elderly Chinese population. Age, pulse pressure, HbA1c, lower-level HDL-C, lipoprotein(a) and uric acid are the independent risk factors for CAVS.

  • cardiac epidemiology
  • valvular heart disease
  • risk management
  • epidemiology
  • cardiology

Data availability statement

Data are available upon reasonable request.

http://dx.doi.org/10.1136/pmj-2022-141721

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    Data availability statement

    Data are available upon reasonable request.

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    Footnotes

    • Contributors TW, LL and Y-JW conceived and designed research. YP, JS, JC and JJ analysed and interpreted data. JC wrote the initial paper. TW approved the final version to be submitted. JC had primary responsibility for final content. All authors read and approved the final manuscript. TW as the guarantor of this study.

    • Competing interests None declared.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.