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Background <p>This study aimed to develop a nomogram to predict peritoneal dialysis (PD) adequacy in incident PD patients and identify those at high risk for low Kt/Vurea PD function.</p> Methods <p>We retrospectively analyzed 141 incident PD patients from January 2021 to January 2024. Baseline characteristics, including BMI, hemoglobin levels, and high transport PD membrane, were compared between patients with and without adequate PD function. Univariate logistic regression, LASSO analysis, and Random Forest (RF) algorithms were employed to identify potential biomarkers. Significant predictors were integrated into a multivariable logistic regression model to construct a predictive nomogram.</p> Results <p>The study found that 32.1% of patients had low total Kt/Vurea. Significant predictors of low Kt/Vurea included smoking (OR 2.23, CI 1.47–5.85), BMI (OR 1.35, CI 1.17–1.59), hemoglobin levels (OR 0.98, CI 0.95–0.99), and High transport (OR 0.2., CI 0.04–0.72). These factors were incorporated into a nomogram, which demonstrated strong predictive accuracy, with a C-Index of 0.802 in the main study group. The model’s AUC was 0.778 (95% CI: 0.686–0.870), and Decision Curve Analysis (DCA) confirmed its clinical utility across a wide range of threshold probabilities.</p> Conclusions <p>We developed a nomogram that accurately predicts PD total Kt/Vurea in incident PD patients. This model can be a valuable tool for identifying patients at risk of low PD total Kt/Vurea, facilitating timely interventions to improve patient outcomes.</p>

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Development and validation of a nomogram for predicting low Kt/Vurea in peritoneal dialysis patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月2日

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  • Danfeng Zhang,
  • Tian Zhao,
  • Liting Gao,
  • Huan Zhu,
  • Haowei Jin,
  • Guiling Liu,
  • Deguang Wang

摘要

Background

This study aimed to develop a nomogram to predict peritoneal dialysis (PD) adequacy in incident PD patients and identify those at high risk for low Kt/Vurea PD function.

Methods

We retrospectively analyzed 141 incident PD patients from January 2021 to January 2024. Baseline characteristics, including BMI, hemoglobin levels, and high transport PD membrane, were compared between patients with and without adequate PD function. Univariate logistic regression, LASSO analysis, and Random Forest (RF) algorithms were employed to identify potential biomarkers. Significant predictors were integrated into a multivariable logistic regression model to construct a predictive nomogram.

Results

The study found that 32.1% of patients had low total Kt/Vurea. Significant predictors of low Kt/Vurea included smoking (OR 2.23, CI 1.47–5.85), BMI (OR 1.35, CI 1.17–1.59), hemoglobin levels (OR 0.98, CI 0.95–0.99), and High transport (OR 0.2., CI 0.04–0.72). These factors were incorporated into a nomogram, which demonstrated strong predictive accuracy, with a C-Index of 0.802 in the main study group. The model’s AUC was 0.778 (95% CI: 0.686–0.870), and Decision Curve Analysis (DCA) confirmed its clinical utility across a wide range of threshold probabilities.

Conclusions

We developed a nomogram that accurately predicts PD total Kt/Vurea in incident PD patients. This model can be a valuable tool for identifying patients at risk of low PD total Kt/Vurea, facilitating timely interventions to improve patient outcomes.