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Published on: July 3, 2013
Nomogram to Estimate the Risk of Chronic Kidney Disease-Associated Pruritus in Patients with End-Stage Renal Disease
Wen Gu1,2, Ming Zhang3, Changna Liang4
1Department of Medical Imaging, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China, desaix@stu.xjtu.edu.cn.
Introduction:
Chronic kidney disease-associated pruritus (CKD-aP) frequently occurs in patients with end-stage renal disease (ESRD) undergoing peritoneal dialysis (PD) and presents a therapeutic challenge to physicians owing to the diversity of its pathogenesis. Herein, we developed and validated a nomogram model for individualized risk estimation of CKD-aP and investigated the possible causes of CKD-aP in PD patients.
Methods:
We retrospectively screened patients with CKD-aP who underwent PD between 2021 and 2023 at the First Affiliated Hospital of Xi'an Jiaotong University Peritoneal Dialysis Center. Nomograms for each outcome were computed from multivariate logistic regression models with the least absolute shrinkage and selection operator regression and univariate logistic regression for variable selection. The discriminative ability was estimated by Harrell's C-index, and the accuracy was assessed graphically with a calibration curve plot. Models were validated internally using bootstrapping and externally by calculating their performance on a validation cohort. Decision curve analysis was used to assess the model's clinical usefulness.
Results:
In all, a total of 487 patients were entered in the analysis, including 325 in the development cohort and 162 in the validation cohort. The final nomogram incorporated five variables: age, interleukin-6, hemoglobin, residual urine volume, and renal Kt/V. The C-index of the model was 0.733 (95% CI: 0.679-0.787), and the calibration curve was a straight line with a slope close to 1. Both internal and external validations confirmed the model's good performance, with C-index of 0.725 (95% CI: 0.662-0.774) and 0.706 (95% CI: 0.623-0.789), respectively. Decision curve analysis showed that the nomogram had good clinical benefits.
Conclusion:
Our study proposes a nomogram model for CKD-aP risk assessment in ESRD patients with PD. This nomogram might help in clinical decision-making and evidence-based selection of therapy.
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