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Prediction model for cardiovascular disease risk in hemodialysis patients

  • Nephrology - Original Paper
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Abstract

Purpose

To derive and validate a prediction score for cardiovascular disease (CVD) risk in hemodialysis patients in China.

Methods

Three hundred and eighty-eight patients with regular hemodialysis for more than 3 months were recruited from January 1, 2015 to September 30, 2019 and followed up till May 31, 2020. We derived a prediction score using all participants as a training data set and validated using a bootstrap validation data set. Discriminatory ability of the prediction score was assessed by the area under the receiver operating characteristic curve (AUC).

Results

Of 388 patients without CVD at baseline, 132 developed first CVD events during an average follow-up of 3.27 (inter-quartile range = 3.08) years. Of 26 clinical parameters, age, hypertension, diabetes and abnormal white blood cell (WBC) count were identified as significant predictors and included in the prediction model. Compared to those without any of these risk factors, those with one, two, and three to four points showed increased risks of CVD, with the adjusted hazards ratio and 95% confidence interval (CI) being 3.29 (1.17–9.26), 7.42 (2.68–20.51) and 15.43 (5.44–43.75), respectively. The score showed satisfactory discriminatory ability in both training and validation data set (AUC = 0.7025, 95% CI 0.6520–0.7530, and 0.6876, 95% CI 0.6553–0.7200, respectively).

Conclusion

We derived and validated a prediction score for CVD risk in hemodialysis patients in China. Given there is a rapid increase in the number of hemodialysis patients, this simple point score can be used to identify high-risk individuals in clinical practice for more precise and efficient personalized treatment.

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Data availability

The datasets generated and analysed during the current study are not publicly available due to protect patient privacy.

Code availability

Not applicable.

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Funding

This study was supported by grants from Major Science and Technology Projects in 13th 5-Year (2018ZX10715004-001-009), Natural Science Foundation of Guangdong (2018A030310272), Guangdong Medical Science and Technology Research Fund (A2018366), 3rd Affiliated Hospital of Sun Yat-Sen University, Clinical Research Program (P000-277) and Meizhou science and technology project (2019B0203003).

Author information

Author notes

  1. Xu You and Ying Yue Huang have contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. Clinical Laboratory Department, The Third Affiliated Hospital, Southern Medical University, Guangzhou, China

    Xu You

  2. School of Public Health, Sun Yat-sen University, Guangzhou, China

    Ying Yue Huang, Ying Wang & Lin Xu

  3. Department of Infectious Disease, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China

    Ming Xue Yu & Xiang Yong Li

  4. Department of Nephrology, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China

    He Qun Zou

Authors
  1. Xu You
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Contributions

XY, YYH, YW, MXY, XYL, LX and HQZ have substantial contributions to conception and design, acquisition of funding, data and interpretation of data; XY, YYH, YW and LX analysed the data, XY, YYH, XYL and LX drafted the article, MXY and HQZ revised it critically for important intellectual content. All authors read and approved the final manuscript.

Corresponding author

Correspondence to He Qun Zou.

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The authors declare that they have no conflict of interest.

Ethical approval

Ethical approval was obtained from the Ethics Committee of Third Affiliated Hospital of Southern Medical University and the Ethics Committee of Third Affiliated Hospital of Sun Yat-sen University. All participants gave informed consent before participation.

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All participants gave consent.

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Manuscript is approved by all authors for publication.

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You, X., Huang, Y.Y., Wang, Y. et al. Prediction model for cardiovascular disease risk in hemodialysis patients. Int Urol Nephrol 54, 1127–1134 (2022). https://doi.org/10.1007/s11255-021-02984-7

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  • DOI: https://doi.org/10.1007/s11255-021-02984-7

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