
==== Front
Blood Purif
Blood Purif
BPU
BPU
Blood Purification
0253-5068
1421-9735
S. Karger AG Basel, Switzerland

38901418
539786
10.1159/000539786
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Peritoneal Dialysis – Research Article
Nomogram to Estimate the Risk of Chronic Kidney Disease-Associated Pruritus in Patients with End-Stage Renal Disease Undergoing Peritoneal Dialysis: Model Development and Validation Study
Nomogram to Estimate CKD-Associated Pruritus in Peritoneal Dialysis Patients
2744870
Gu Wen a b
2744871
Zhang Ming a
2744872
Liang Changna b
2744873
Ma Shaohui a
2744874
Wang Xiaopei b
2744875
Yuan Huijie a
2744876
Luo Zhaoyao a
2744877
Lv Jing b
a Department of Medical Imaging, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
b Department of Nephrology, Kidney Hospital, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
Correspondence to: Jing Lv, drlvjing@163.com
20 6 2024
9 2024
53 9 755767
18 12 2023
10 6 2024
2024
© 2024 The Author(s). Published by S. Karger AG, Basel
2024
https://creativecommons.org/licenses/by-nc/4.0/ This article is licensed under the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC) (http://www.karger.com/Services/OpenAccessLicense). Usage and distribution for commercial purposes requires written permission.
Abstract

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.

Plain Language Summary

We conducted a study to better understand and predict the risk of CKD-associated pruritus, a common itching condition in patients with severe kidney disease undergoing peritoneal dialysis. Our research included an analysis of the risk factors associated with this condition. Using information from uremic pruritus patients, we developed a special tool that takes into account factors such as a patient’s age, certain blood markers, hemoglobin levels, residual urine volume, and renal Kt/V. This tool is designed to help doctors estimate a patient’s likelihood of developing CKD-associated pruritus. Our results show that this tool is not only accurate but also practical for use in medical settings, aiding doctors in making informed treatment decisions.

Keywords

End-stage renal disease
Chronic kidney disease-associated pruritus
Nomogram
Peritoneal dialysis
Interleukin-6
This work was supported by the National Natural Science Foundation of China (Grant No. 82,071,879) and the Natural Science Foundation of Shaanxi Province (Grant No. 2022JM-598).
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pmcIntroduction

Uremic pruritus (UP, also known as chronic kidney disease-associated pruritus [CKD-aP]) is one of many symptoms that often occur in patients with end-stage renal disease (ESRD) on maintenance dialysis [1]. Severe pruritus reduces the health-related quality of life of both hemodialysis (HD) and peritoneal dialysis (PD) patients and increases the risk of death [2, 3]. UP in dialysis patients has a long history and is reported to be as high as 50–60% [4]. A recent large-scale survey showed that >40% HD patients had CKD-aP [2]. Although there are fewer studies of pruritus in PD patients than HD patients, CKD-aP has been reported in PD patients [3, 4].

Despite the various treatment methods for CKD-aP, some patients still cannot be cured. The main reason is that the pathogenesis of CKD-aP is unclear [5]. Multiple parameters and factors have been assumed for the pathophysiology of CKD-aP, including immune-inflammatory factors, accumulation of uremic toxins, hyperparathyroidism, calcium and phosphate levels, nutritional status, dialysis-related factors, opioid receptor disorders, and neuropsychological factors [6–9]. By developing a clinical model, we can screen out the specific risk factors of ESRD patients’ pruritus, so that we can pay attention to this part of patients in advance and treat these patients according to the corresponding risk factors. Therefore, identifying the risk factors associated with CKD-aP is crucial for promptly diagnosing and treating the condition.

Developing an accurate multifeatured clinical model is beneficial for the diagnosis and treatment of CKD-aP. Studies have shown that the nomogram can be used as a tool for individualized disease diagnosis, and the prediction of disease risk has good practical value [10]. Recently, nomograms have been used to predict mortality in HD and PD patients [11, 12]. However, thus far, only a few clinical models have been used for the diagnosis and treatment of CKD-aP. Therefore, it is imperative to establish an individualized multiple parameters risk assessment model for PD patients with CKD-aP based on serum biochemical markers and neuropsychological factors.

In this study, 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. We hypothesized that the multiple parameters model can provide an accurate method for assessing CKD-aP and improve the evidence-based selection of therapy and clinical decision-making.

Materials and Methods

Data Collection and Study Population

The study was approved by the Ethics Committee of the First Affiliated Hospital of Xi’an Jiaotong University (application ID: XJTU1AF2021LSK-247). All methods were performed in accordance with the relevant guidelines and regulations or Declaration of Helsinki. All subjects provided written informed consent after the experimental procedures were fully explained. We screened regular follow-up patients who received maintenance PD from January 2021 to June 2022 and January 2023 to June 2023 at the First Affiliated Hospital of Xi’an Jiaotong University’s Peritoneal Dialysis Center.

All participants received a conventional glucose-based solution and a standard continuous ambulatory PD treatment schedule. Demographic, clinical information, and dialysis adequacy were recorded for each patient by chart review. The quality of PD was evaluated by calculating Kt/V, which when >1.7 was defined as adequate dialysis in participants [13]. All participants did not begin treatment for their pruritus at the time of their participation in the study, and the relevant treatment was carried out after basic clinical information was gathered. All participants were treated with erythropoietin when their hemoglobin level was less than 110 g/L and the target hemoglobin levels were maintained between 110 and 120 g/L [14].

The inclusion criteria were as follows: (a) Age: ≥18 years old, ≤70 years old; (b) confirmed clinical diagnosis of ESRD [15]; and (c) on continuous ambulatory peritoneal dialysis for more than 3 months. The exclusion criteria were as follows: (a) primary skin disorders (e.g., atopic dermatitis, allergy); (b) use of immunosuppressant drugs; (c) active infection or malignancy; (d) cholestatic liver disease or acute hepatitis; (e) patient refusal to participate; and (f) pregnant status. Ultimately, we screened 487 patients out of a total of 811 patients into this study (shown in Fig. 1).

Fig. 1. Flowchart of the study participants. ESRD, end-stage renal disease; PD, peritoneal dialysis; HD, hemodialysis.

Survey and Assessments

For evaluating pruritus status, patients were asked to estimate their pruritus intensity on a visual analog scale (VAS) (0 = no pruritus, 10 = worst pruritus) [16]. Patients without CKD-aP were defined by a score of 0. Beck Anxiety Inventory (BAI) self-reported questionnaires were administered to measure anxiety (21 items, score range 0–63, with higher scores indicating worse anxiety) [17]. Depression severity was assessed using the Beck Depression Inventory (BDI), a 21-item self-report scale widely recognized for measuring the intensity of depression symptoms, with each item rated on a scale of 0–3, resulting in a total score ranging from 0 to 63 [18].

Laboratory and Demographic Data Collection

Demographic and clinical information such as age, sex, body mass index, presence of diabetes mellitus, concurrent medications, causes of ESRD, peritoneal equilibration test, and underlying renal disease were recorded for each patient. Laboratory blood tests of CKD-aP patients included serum creatinine, blood urea nitrogen (BUN), hemoglobin, platelets, hematocrit, uric acid, albumin, prealbumin, fasting glucose, ferritin, high-sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), serum phosphorus, serum calcium, intact parathyroid hormone, total bilirubin, and alkaline phosphatase. All laboratory variables were obtained close to the time of participants’ entry into the study. All blood samples were collected in the same laboratory using the same method.

Statistical Analysis

Statistical analyses were conducted using R software (version 3.6.3; https://www.R-project.org) and Python software (version 3.7; https://www.python.org). For the binary prediction model, a well-known rule of thumb for the required sample size calculation has been described in a previous study [19]. According to the above formula, the minimum sample size is estimated to be 300. Ultimately, we screened 487 patients out of a total of 811 patients into this study.

A two-sample t test was used to compare various demographic data between patients with and without pruritus, and a Mann-Whitney U test was used for non-normally distributed data. The χ2 test was used to analyze categorical variables. Similarly, data are presented as mean (SD) for normally distributed continuous variables, as median (interquartile range [IQR]) for non-normally distributed continuous variables, and as number (percentage) for categorical variables. In addition, we used the Kolmogorov-Smirnov test to evaluate normality, Levene’s test to determine the equality of variances, and the variance inflation factor to evaluate the collinearity of variables. Two-sided p values of <0.05 were considered significant.

Variable selection was carried out in four steps. First, we selected possible candidate variables based on clinical experience combined with previous studies. Second, we used the 10-fold least absolute shrinkage and selection operator (LASSO) regression to select the best potential risk variables (glmnet package in R) [20]. To obtain the best subset of predictors, the LASSO regression minimizes the error in prediction for a response variable by placing a penalty constraint to the model that forces regression coefficients for some variables toward zero. Variables with nonzero coefficients were selected based on the one standard error of the minimum criteria. Third, we included variables selected by LASSO regression to enter the logistic regression. Finally, all variables with a p < 0.05 in the univariate analysis were included in the multivariate logistic regression model. The stepwise selection was applied to determine which variables were included in the final multivariable logistic regression model.

Model building followed the results of the previous steps. The regression coefficient (β) of each variable and the odds ratio of the bilateral 95% confidence interval (CI) were calculated in multivariate logistic analysis. The nomogram model was plotted from the fitted multivariate logistic regression model (rms package in R). The discrimination of the model and its 95% CI was measured by ROC curve (sklearn library in Python) or the Harrell’s concordance index (C-index) (Hmisc package in R). In addition, the models’ accuracy was assessed graphically using a calibration curve plot. Next, we did an internal validation using a bootstrap sampling with 1,000 resampling processes to provide an unbiased estimate of model performance (rms and boot package in R). Last, decision curve analysis was used to determine the clinical net benefit associated with the use of the developed model of CKD-aP (rmda package in R) [21].

Results

Demographic, Clinical, and Neuropsychological Data

A total of 487 participants were enrolled in the study, and their complete data were collected. Demographic information and clinical characteristics of participants were summarized in Table 1. Overall, the median age was 47.00 (IQR: 37.00–58.00) years and the median dialysis duration was 27.37 (IQR: 9.15–55.36) months. Among them, 214 patients (43.94%) were female.

Table 1. Demographic and clinical characteristics of participants

Variables	Statistics (n = 487)	
Age, years	47.00 (37.00–58.00)	
Sex, n (%)	
 Male	273 (56.06)	
 Female	214 (43.94)	
BMI, kg/m2	22.31 (20.03–24.47)	
Dialysis duration, months	27.37 (9.15–55.36)	
Residual urine volume, mL	400.00 (20.00–800.00)	
eGFR, mL/min/1.73 m2	5.46 (4.42–7.09)	
Cause of chronic renal failure	
 Diabetes nephropathy	73	
 Hypertension nephrosclerosis	32	
 Glomerulonephritis	327	
 Interstitial nephritis	17	
 Lupus nephritis	4	
 ANCA-associated vasculitis	3	
 Other	31	
UP, n (%)	
 Yes	280 (57.50)	
 No	207 (42.51)	
IQR, interquartile range; SD, standard deviation; BMI, body mass index; eGFR, estimated glomerular filtration rate. Data are shown as mean (SD), median (IQR), and number (percentage).

Table 2 compares the demographic and clinical characteristics of the development (n = 325) and validation (n = 162) cohorts, showing no significant differences across key variables including age, sex, body mass index, dialysis duration, and various biochemical parameters such as creatinine, BUN, albumin, and phosphorus. Additionally, there were no significant differences in psychological assessment scores (BAI and BDI), indicating a homogeneous distribution between the cohorts and supporting the reliability of the data for further analysis.

Table 2. Demographic and clinical characteristics of participants in the development cohort and validation cohort

Variables	Development cohort (n = 325)	Validation cohort (n = 162)	p value	
Age, years	47.00 (37.00–57.00)	50.00 (38.00–60.00)	0.194	
Sex, n (%)			0.164	
 Male	175 (53.85)	98 (60.50)		
 Female	150 (46.15)	64 (39.51)		
BMI, kg/m2	22.27 (19.81–24.39)	22.48 (20.31–24.94)	0.249	
VAS (scores)	2.00 (0.00–3.00)	2 (0.00–4.00)	0.419	
Dialysis duration, months	31.97 (9.70–56.78)	23.09 (8.59–53.88)	0.232	
Total, kt/V	1.97 (1.73–2.25)	2.00 (1.75–2.28)	0.359	
Renal, kt/V	0.27 (0.00–0.57)	0.36 (0.01–0.70)	0.168	
Dialytic, kt/Va	1.62 (1.34–1.92)	1.59 (1.33–1.91)	0.684	
Residual urine volume, mL	400.00 (0.00–750.00)	400.00 (30.00–800.00)	0.962	
Hematocrit, %a	33.66 (5.03)	33.56 (5.00)	0.832	
Hemoglobin, g/La	109.22 (16.71)	107.53 (15.40)	0.282	
Platelets, 103/μL	211.00 (173.00–259.00)	214.00 (165.00–273.00)	0.783	
Creatinine, mg/dL	9.52 (7.58–11.71)	8.89 (7.29–12.02)	0.329	
Uric acid, mg/dL	6.14 (5.50–6.86)	6.44 (5.53–7.13)	0.111	
BUN, mg/dL	46.76 (39.84–53.96)	49.76 (39.65–58.60)	0.092	
Albumin, g/dL	3.69 (3.43–4.01)	3.60 (3.32–3.94)	0.096	
Prealbumin, mg/L	34.35 (29.19–40.93)	33.56 (28.97–38.13)	0.195	
Glucose, mg/dL	91.44 (84.42–104.2)	91.80 (82.44–114.12)	0.641	
Ferritin, μg/L	194.00 (108.00–322.00)	185.00 (93.70–384.00)	0.969	
hsCRP, mg/L	2.01 (0.82–5.61)	2.23 (1.04–5.88)	0.178	
IL-6, pg/mL	7.21 (5.25–11.36)	6.98 (4.33–11.40)	0.144	
Phosphorus, mg/dL	4.40 (3.84–5.12)	4.65 (3.88–5.55)	0.117	
Calcium albumin adjusted, mg/dL	8.92 (8.46–9.30)	8.84 (8.45–9.13)	0.154	
Ca × Pb	39.25 (32.49–47.62)	41.11 (32.79–50.67)	0.265	
Intact parathyroid hormone, pg/mL	355.90 (229.90–505.40)	351.50 (232.10–509.00)	0.840	
Total bilirubin, mg/dL	0.38 (0.30–0.48)	0.37 (0.29–0.45)	0.256	
Alkaline phosphatase, U/L	97.00 (76.00–126.00)	93.00 (72.00–120.00)	0.153	
Diabetes mellitus, n (%)	45 (13.85)	28 (17.28)	0.371	
BAI (scores)	6.00 (2.00–12.00)	3.00 (1.00–4.00)	0.572	
BDI (scores)	8.00 (3.00–15.00)	10.00 (5.00–16.00)	0.833	
IQR, interquartile range; SD, standard deviation; BMI, body mass index; hsCRP, high-sensitivity C-reactive protein; BUN, blood urea nitrogen; IL-6, interleukin-6; BAI, Beck Anxiety Inventory; BDI, Beck Depression Inventory; VAS, visual analog scale.

Data are shown as amean (SD), median (IQR), and number (percentage).

bProduct of albumin-adjusted serum calcium and serum phosphorus.

In addition, the demographic and clinical characteristics of participants with and without CKD-aP are presented in Table 3. We found that the median age of the CKD-aP group was greater than that of the non-CKD-aP group (p = 0.002). The proportion of female patients in the pruritic and non-pruritic groups was 41.6% and 52.1%, respectively. However, there were no significant differences in sex (p = 0.06) between patients with and without pruritus.

Table 3. Demographic and clinical characteristics of participants with and without CKD-aP in the development cohort

Variables	Patients with CKD-aP (n = 185)	Patients without CKD-aP (n = 140)	p value	
Age, years	48.00 (39.00–59.00)	44.00 (34.00–54.00)	0.002**	
Sex, n (%)			0.060	
 Male	108 (58.38)	67 (47.86)		
 Female	77 (41.62)	73 (52.14)		
BMI, kg/m2	22.49 (19.92–24.45)	21.97 (19.81–23.88)	0.178	
VAS (scores)	3.00 (2.00–5.00)	–	–	
Dialysis duration, months	32.63 (9.84–59.97)	30.13 (9.70–52.40)	0.695	
Total kt/V	1.94 (1.70–2.22)	2.00 (1.77–2.28)	0.179	
Renal kt/V	0.19 (0.00–0.52)	0.32 (0.08–0.61)	0.015*	
Dialytic kt/Va	1.66 (0.40)	1.63 (0.41)	0.557	
Residual urine volume, mL	300.00 (0.00–700.00)	500.00 (150.00–800.00)	0.013*	
Hematocrit, %a	33.38 (5.19)	34.04 (4.78)	0.242	
Hemoglobin, g/La	106.96 (16.89)	112.20 (15.98)	0.005**	
Platelets, 103/μL	203.00 (162.00–257.00)	218.00 (184.00–261.00)	0.064	
Creatinine, mg/dL	9.41 (7.52–11.89)	9.72 (8.12–11.50)	0.651	
Uric acid, mg/dL	6.05 (5.46–6.82)	6.24 (5.53–6.92)	0.218	
BUN, mg/dL	47.26 (40.12–55.24)	45.86 (39.82–52.34)	0.279	
Albumin, g/dL	3.68 (3.43–3.98)	3.71 (3.48–4.04)	0.355	
Prealbumin, mg/L	33.12 (27.43–40.48)	35.74 (31.28–40.26)	0.034*	
Glucose, mg/dL	92.7 (85.68–110.34)	88.56 (82.26–97.74)	0.002**	
Ferritin, μg/L	182.00 (107.00–366.00)	199.00 (116.00–296.00)	0.757	
hsCRP, mg/L	2.29 (1.060–6.99)	1.40 (0.66–3.83)	<0.001***	
IL-6, pg/mL	9.29 (6.12–14.10)	5.80 (4.33–8.49)	<0.001***	
Phosphorus, mg/dL	4.34 (3.84–5.12)	4.46 (3.88–5.15)	0.856	
Calcium albumin adjusted, mg/dL	8.96 (8.48–9.32)	8.88 (8.48–8.92)	0.582	
Ca × Pb	38.56 (33.48–46.00)	39.43 (34.34–45.88)	0.992	
Intact parathyroid hormone, pg/mL	357.80 (237.40–513.40)	352.80 (226.70–503.30)	0.735	
Total bilirubin, mg/dL	0.37 (0.29–0.46)	0.42 (0.33–0.49)	0.060	
Alkaline phosphatase, U/L	93.00 (73.00–122.00)	99.00 (81.00–129.00)	0.115	
Diabetes mellitus, n (%)	28 (15.14)	17 (12.14)	0.439	
BAI (scores)	6.00 (2.00–12.00)	3.00 (1.00–4.00)	<0.001***	
BDI (scores)	8.00 (3.00–15.00)	10.00 (5.00–16.00)	0.091	
IQR, interquartile range; SD, standard deviation; BMI, body mass index; hsCRP, high-sensitivity C-reactive protein; BUN, blood urea nitrogen; IL-6, interleukin-6; BAI, Beck Anxiety Inventory; BDI, Beck Depression Inventory; VAS, visual analog scale; CKD-aP, chronic kidney disease-associated pruritus.

Data are shown as amean (SD), median (IQR), and number (percentage).

bProduct of albumin-adjusted serum calcium and serum phosphorus.

* p < 0.05.

** p < 0.01.

*** p < 0.001.

Variable Selection

A total of 29 demographic and clinical variables were included in the LASSO regression (shown in Table 3). After LASSO penalty selection, 29 variables were reduced to 5 variables with non-zero coefficients (shown in Fig. 2a, b). These variables included age, IL-6, hemoglobin, platelets, and BAI. Next, the prognostic value of these 5 variables was evaluated by using a univariate (shown in Table 4) and multivariate logistic regression model. Finally, based on clinical expertise, we revised the model to include residual urine volume and renal Kt/V and to exclude BAI. The modified model included age, IL-6, hemoglobin, residual urine volume, and renal Kt/V, to further explore the impact of residual renal function factors on CKD-aP (shown in Table 5).

Fig. 2. Features were selected using the LASSO model. a The LASSO regression was used to select informative features via 10-fold cross-validation. The model feature selection is based on the minimum distance of the SE. b LASSO coefficient profiles of the 29 features. LASSO, the least absolute shrinkage and selection operator.

Table 4. Univariate logistic analysis of risk factors for CKD-aP

Variables	Univariate analysis	
OR	95% CI	p value	
Age (years)	1.03	(1.01–1.05)	0.001**	
Hemoglobin (g/L)	0.98	(0.97–0.99)	0.006**	
Platelets (103/μL)	1.00	(0.99–1.00)	0.163	
IL-6 (pg/mL)	1.16	(1.10–1.23)	<0.001***	
BAI (scores)	1.13	(1.08–1.18)	<0.001***	
IL-6, interleukin-6; BAI, Beck Anxiety Inventory; OR, odds ratio; CI, confidence interval; CKD-aP, chronic kidney disease-associated pruritus.

** p < 0.01.

*** p < 0.001.

Table 5. Multivariate logistic regression analysis of risk factors for CKD-aP

Variables	β	SE	OR	95% CI	p value	
Age (years)	0.03	0.01	1.03	(1.01–1.05)	0.011*	
Hemoglobin (g/L)	−0.02	0.01	0.98	(0.97–1.00)	0.009**	
Interleukin-6 (pg/mL)	0.13	0.03	1.14	(1.09–1.21)	<0.001***	
Residual urine volume (mL)	−0.01	0.01	0.99	(0.99–1.01)	0.349	
Renal kt/V	0.48	0.74	1.62	(0.38–7.00)	0.518	
SE, standard error; OR, odds ratio; CI, confidence interval; CKD-aP, chronic kidney disease-associated pruritus.

* p < 0.05.

** p < 0.01.

*** p < 0.001.

Construction of Model

A total of five selected variables including age, IL-6, hemoglobin, residual urine volume, and renal kt/V, which were independent risk factors for CKD-aP, were used to construct the nomogram (shown in Fig. 3). The nomogram was used to evaluate the risk of CKD-aP in ESRD patients based on their various features. The total score was obtained by summing the scores of each variable projected to points, and then the total score was projected onto the corresponding risk axis to calculate the final probability of CKD-aP.

Fig. 3. Nomogram for predicting risk probability of CKD-aP. Drawing a straight line on every characteristic each time to the points axis to calculate their score. The points for each characteristic are summed together to generate a total-points score to evaluate the risk of CKD-aP. CKD-aP, chronic kidney disease-associated pruritus; HB, hemoglobin; IL-6, interleukin-6.

Model Assessment and Validation

The assessment of the model was measured by discrimination and clinical usefulness, and the model was internally validated by bootstrapping with 1,000 resamples. The area under the receiver operating characteristic curve of the CKD-aP risk-prediction model was 0.733 (95% CI: 0.679–0.787) (shown in Fig. 4a). The CKD-aP model showed high discrimination, with a C-index of 0.733 (95% CI: 0.679–0.787). Similar C-indices were achieved at internal validation 0.725 (95% CI: 0.662–0.774) after bootstrapping with 1,000 resamples and externally validation 0.706 (95% CI: 0.623–0.789) (shown in Fig. 5a). Furthermore, the plot of calibration shows that the model has good consistency in development cohort (shown in Fig. 4b) and validation cohort (shown in Fig. 5b). Finally, DCA was used to evaluate the clinical utility of the model; hence, the decision curve shows that at most risk threshold probabilities, the CKD-aP model has a better net benefit than the “all-treatment” and “none-treatment” models (shown in Fig. 6).

Fig. 4. Model performance of CKD-aP. a ROC curve of the CKD-aP model in which the AUC was 0.733 (95% CI: 0.679–0.787). b Calibration curves of the model for CKD-aP. The diagonal dotted line represents the ideal curve, and the predicted probabilities are almost the same as the observed probabilities. AUC, area under the receiver operating characteristic curve; CKD-aP, chronic kidney disease-associated pruritus; ROC, receiver operating characteristic.

Fig. 5. Model performance of validation cohort. a ROC curve of the model in which the AUC was 0.706 (95% CI: 0.623–0.789). b Calibration curves of the model for validation cohort. The diagonal dotted line represents the ideal curve, and the predicted probabilities are almost the same as the observed probabilities. AUC, area under the receiver operating characteristic curve; CKD-aP, chronic kidney disease-associated pruritus; ROC, receiver operating characteristic.

Fig. 6. DCA for the CKD-aP model. The decision curve shows that at most risk threshold probabilities, the CKD-aP model has a better net benefit than the “all-treatment” and “none-treatment” models. CKD-aP, chronic kidney disease-associated pruritus.

Discussion

In this study, we analyzed a total of 487 ESRD patients undergoing PD to develop and validate a nomogram model of CKD-aP and investigate the possible causes. Our nomograms for CKD-aP combined the demographic, clinical, and neuropsychological variables and showed good discriminative ability, accuracy, and clinical utility. Moreover, internal validation showed good consistency between the training and validation sets. Furthermore, our results highlighted that the five variables, namely age, IL-6, hemoglobin levels, residual urine volume, and renal Kt/V are considered risk factors for CKD-aP.

At present, despite the successful management of traditional CKD-aP risk factors including serum phosphorus, serum calcium, intact parathyroid hormone, and adequacy of dialysis in a significant number of patients undergoing PD, many still experience CKD-aP [5]. This also suggests that with the advancement of PD technology and health management, the risk factors for UP are likely to change accordingly. Our nomogram model contributes to the identification of non-traditional risk factors for CKD-aP in PD patients, such as IL-6, hemoglobin, residual urine volume, and renal Kt/V, presenting a fresh perspective for clinical management. By utilizing our model, high-risk CKD-aP patients can be identified early, allowing for targeted interventions such as psychological support, adjustment of erythropoietin dosage, and mitigation of micro-inflammation [22, 23].

Age as a basic demographic data may play an important role in the occurrence of CKD-aP. Our study showed that the median age of the CKD-aP group was greater than that of the non-CKD-aP group, and age was a risk factor for CKD-aP. A previous cohort study of CKD-aP in PD patients also found that participants with CKD-aP were older than those without [24]. Similarly, a 2022 study of 80 PD patients found that the mean age of the CKD-aP group was 54.27 years, while that of the non-CKD-aP group was 51.24 years [25]. However, these studies likely did not identify age as a risk factor for CKD-aP because the sample size was not as large as our study or because the study population was different.

Previous studies have shown that CKD-aP may be associated with up-regulation of micro-inflammation in which IL-6 levels were found to be significantly elevated in HD patients with CKD-aP [9]. In addition, some researchers have confirmed that renal injury can induce local and systemic IL-6 elevation in patients [26]. Moreover, it was confirmed by basic experiments that IL-6 was an important molecule for capsaicin-induced itch in mice [27]. The relationship between serum levels of inflammatory markers and CKD-aP has been inconsistent in numerous related studies. The possible reasons for the above different results are the different dialysis methods (HD or PD), the difference in the population size (dozens to hundreds), and the baseline status (i.e., cause of chronic renal failure, region, and nation) of the participant. Generally, serum levels of inflammatory markers such as hsCRP, IL-6, and ferritin have been found to be higher in CKD-aP patients with HD, while serum levels of albumin and transferrin have been shown to be decreased in such patients [8, 9, 28]. Likewise, in PD patients, researchers have found that higher hsCRP levels were independent determinants of higher VAS scores of pruritus intensity [24]. In our study, IL-6 and hsCRP levels were higher in PD patients with CKD-aP versus those without CKD-aP. However, no statistically significant differences were detected between serum levels of ferritin and albumin in the CKD-aP and non-CKD-aP patients in our study. Furthermore, in the final results, IL-6 was included in the features of the model while hsCRP was not, suggesting that IL-6 was a better predictor of CKD-aP risk than hsCRP in our model.

Our findings show that the level of hemoglobin is a risk factor for CKD-aP. Hemoglobin levels are generally analyzed in studies of CKD-aP; most studies have shown that the level of hemoglobin in CKD-aP patients is lower than that in non-CKD-aP patients, but these results are not statistically significant [29, 30]. In another study, the authors used erythropoietin to treat 10 patients with CKD-aP and found that eight of them had marked reductions in their pruritus scores during erythropoietin therapy. Unlike our results, the improvement was not related to the change in hemoglobin level [22]. In recent years, several studies used neuroimaging technology to explore the relationship between hemoglobin levels and brain function and structural damage in ESRD patients. A part of these findings indicates that the reduction of hemoglobin can affect the function and structure of different regions of the brain, thereby causing sensorimotor and cognitive abnormalities [31, 32]. It is well known that the itch sensation is finally transmitted to the brain through the central neural, and any changes in this process may cause a change in patient’s itching sensation [33]. Therefore, whether the brain changes caused by the reduction of hemoglobin levels are related to CKD-aP needs further research.

Similarly, the risk factors determined by our model also offer a new perspective on the treatment of CKD-aP. First, “age” in our model is a non-modifiable factor linked to CKD-aP risk. Although we cannot change age, understanding its influence helps clinicians stratify patients’ risk. Older patients may require closer monitoring for CKD-aP, and awareness of this relationship can guide discussions and interventions tailored to the needs of each age-group. Additionally, JAK (Janus kinase) is a type of non-receptor tyrosine kinase that includes JAK1, JAK2, JAK3, and Tyk2 (tyrosine kinase 2). The in vitro data indicate that IL-6 signaling is suppressed by all JAK inhibitors [34]. Baricitinib, an oral JAK inhibitor, has been demonstrated to be effective in treating atopic dermatitis, showcasing its capability to swiftly ameliorate pruritus and skin manifestations. Consequently, considering baricitinib as a potential therapeutic option for CKD-aP presents a novel approach [35]. Furthermore, in patients with ESRD undergoing maintenance PD, long-term regulation of hemoglobin levels is essential. Therefore, adjusting erythropoietin dosage and the patient’s dietary regimen can be considered to manage their hemoglobin levels. These methods may represent potential avenues for future pruritus management in clinical settings.

Additionally, in our study, the renal Kt/V and residual urine volume of pruritic patients were significantly lower than those of non-pruritic patients, suggesting that residual renal function may be related to pruritus. However, these two variables were not included in the model during the variable selection stage. Furthermore, consistent with some previous studies, there were no significant statistical differences in Kt/V between pruritic and non-pruritic patients [9, 25]. Therefore, we hypothesize that PD can compensate for the deficiencies in toxin clearance due to reduced renal function, thereby mitigating the impact of residual renal function. Nevertheless, considering the clinical importance of residual renal function, we included renal Kt/V and residual urine volume in the final model.

The strengths of the present study are as follows. First, to our knowledge, we used nomograms for the first time to construct a risk assessment model for CKD-aP. Second, our model candidate variables cover a wide range and were narrowed down by utilizing LASSO regression and multivariate logistic regression analysis. Third, in the same type of research on CKD-aP, we collected a large sample size of data, which increases the credibility of our findings.

Our study also has some limitations. First, although we used bootstrap method to validate the model internally, it would be better to have an independent cohort outside of the First Affiliated Hospital of Xi’an Jiaotong University Peritoneal Dialysis Center to validate the nomogram externally. Second, our study focused on CKD-aP patients with PD, but a large proportion of CKD-aP patients are treated with HD, so these patients may also need to be included in future studies. Third, our study primarily focuses on a specific ethnic group, potentially limiting the generalizability of our findings to other ethnicities. Future research encompassing diverse populations would enhance the broader applicability of our conclusions.

Conclusion

We developed and validated a nomogram model to estimate the risk of CKD-aP in patients with PD. Additionally, the results of our study highlight some key risk factors of CKD-aP including age, IL-6, hemoglobin, residual urine volume, and renal Kt/V. These findings contribute to the selection of therapy and clinical decision-making in CKD-aP.

Acknowledgments

We would like to thank all participants and staff involved in this research.

Statement of Ethics

All research procedures were approved by the Medical Ethics Review Board of the First Affiliated Hospital of the Medical College in Xi’an Jiaotong University (application ID: XJTU1AF2021LSK-247). All methods were performed in accordance with the relevant guidelines and regulations or Declaration of Helsinki. All subjects provided written informed consent after the experimental procedures were fully explained.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding Sources

This work was supported by the National Natural Science Foundation of China (Grant No. 82,071,879) and the Natural Science Foundation of Shaanxi Province (Grant No. 2022JM-598).

Author Contributions

W.G., J.L., and M.Z. contributed to the research idea and study design. W.G. and H.Y. performed the data acquisition and analysis. W.G. and S.M. wrote the manuscript. C.L., Z.L., and X.W. interpreted the data and managed patient recruitment. All authors reviewed and approved the final manuscript.

Data Availability Statement

The data that support the findings of this study are not publicly available due to their containing information that could compromise the privacy of research participants but are available from the corresponding author J.L. upon reasonable request.
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