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ORIGINAL RESEARCH article

Front. Med.
Sec. Nephrology
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1456857

Unveiling Risk Factors: A Prognostic Model of Frequent Peritonitis in Peritoneal Dialysis Patients

Provisionally accepted
Qi-Jiang Xu Qi-Jiang Xu Zhiyun Zang Zhiyun Zang Xue-Li Zhou Xue-Li Zhou Ni-Ya Ma Ni-Ya Ma Li Pu Li Pu Zi Li Zi Li *
  • West China Hospital, Sichuan University, Chengdu, China

The final, formatted version of the article will be published soon.

    Peritoneal dialysis-associated peritonitis (PDAP) is a serious complication of peritoneal dialysis (PD) patients. The aim of this study was to construct a risk prediction model for frequent episodes in PDAP patients. This retrospective cohort study included PDAP patients in our center from January 1, 2010 to December 31, 2021. The risk prediction model for frequent episodes in PDAP patients was constructed by the binary logistic regression. We included 371 PDAP patients, of which 235 patients had single episode and 136 had frequent episodes. We randomly allocated the patients into training set (296 patients) and test set (75 patients) in the ratio of 8:2. In the training set, we found several independent risk factors significantly associated with frequent episodes in PDAP patients, including diabetes mellitus (DM), hemoglobin (HB), serum albumin (ALB), lactatic dehydrogenase (LDH), serum potassium (K), N-terminal pro-brain natriuretic peptide (NT-proBNP) and peritoneal dialysate white cell counts on day 1. And we constructed a prediction model with an area under curve (AUC) values of 0.75 in the training set and 0.76 in the test set, which showed excellent predictive performance. In conclusion, we constructed a predictive model that demonstrated excellent predictive performance for identifying high-risk frequent episodes in PDAP patients and developed a more intuitive nomogram for evaluating the risk. However, multicenter studies with a larger sample size are warranted to validate the model in the future.

    Keywords: Peritoneal Dialysis, Peritonitis, Frequent episodes, Prediction model, nomogram

    Received: 29 Jun 2024; Accepted: 13 Jan 2025.

    Copyright: © 2025 Xu, Zang, Zhou, Ma, Pu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Zi Li, West China Hospital, Sichuan University, Chengdu, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.