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Updated: May 9, 2025
Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
Development and validation of a nomogram for predicting low Kt/Vurea in peritoneal dialysis patients
Danfeng Zhang1,2,3, Tian Zhao3, Liting Gao1,2
1Department of Nephrology, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
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.
