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Visit-to-visit blood pressure variability and clinical outcomes in peritoneal dialysis – based on machine learning algorithms

Hypertension Research volume 48, pages 1702–1715 (2025)Cite this article

A Comment to this article was published on 04 July 2025

Abstract

This study aims to investigate the association between visit-to-visit blood pressure variability (VVV) in early stage of continuous ambulatory peritoneal dialysis (CAPD) and long-term clinical outcomes, utilizing machine learning algorithms. Patients who initiated CAPD therapy between January 1, 2006, and December 31, 2009 were enrolled. VVV parameters were collected during the first six months of CAPD therapy. Patient follow-up extended to December 31, 2021, for up to 15.8 years. The primary outcome was the occurrence of a three-point major adverse cardiovascular event (MACE). Four machine learning algorithms and competing risk regression analysis were applied to construct predictive models. A total of 666 participants were included in the analysis with a mean age of 47.9 years. One of the six VVV parameters, standard deviation of diastolic blood pressure (SDDBP), was finally enrolled into the MACE predicting model and mortality predicting model. In the MACE predicting model, higher SDDBP was associated with 99% higher MACE risk. The association between SDDBP and MACE risk was attenuated by better residual renal function (p for interaction <0.001). In the mortality predicting model, higher SDDBP was associated with 46% higher mortality risk. This cohort study discerned that high SDDBP in early stage of CAPD indicated increased long-term MACE and mortality risks.

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The original contributions presented in the study are included in the article and supplementary material, further inquiries can be directed to the corresponding authors.

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Acknowledgements

This study was granted by Guangdong Basic and Applied Basic Research Foundation (grant number 2021A1515111114 and 2022A1515010433), Guangdong Provincial Key Laboratory of Nephrology (grant number 2020B1212060028) and Innovative Program in Higher Education of Guangdong (grant number 2024KTSCX138). We would like to thank the patients and personnel involved in the study.

Funding

Guangdong Basic and Applied Basic Research Foundation (grant number 2021A1515111114 and 2022A1515010433), Guangdong Provincial Key Laboratory of Nephrology (grant number 2020B1212060028) and Innovative Program in Higher Education of Guangdong (grant number 2024KTSCX138).

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Author notes

  1. These authors contributed equally: Yan Lin, Chunyan Yi.

Authors and Affiliations

  1. Department of Nephrology, The First Affiliated Hospital of Sun Yat-sen University and Key Laboratory of Nephrology, National Health Commission and Guangdong Province, Guangzhou, China

    Yan Lin, Chunyan Yi, Peiyi Cao, Jianxiong Lin, Wei Chen, Haiping Mao, Xiao Yang & Qunying Guo

  2. Yunkang School of Medicine and Health, Nanfang College, Guangzhou, China

    Yan Lin

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Contributions

LY: conceptualization, investigation, funding acquisition and writing-original draft; YCY: data curation, investigation; CPY and LJX: data curation; CW and MHP: supervision; YX: project administration; GQY: funding acquisition, methodology. All authors have reviewed the manuscript.

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Correspondence to Xiao Yang or Qunying Guo.

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Lin, Y., Yi, C., Cao, P. et al. Visit-to-visit blood pressure variability and clinical outcomes in peritoneal dialysis – based on machine learning algorithms. Hypertens Res 48, 1702–1715 (2025). https://doi.org/10.1038/s41440-025-02142-x

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