
==== Front
Ren Fail
Ren Fail
Renal Failure
0886-022X
1525-6049
Taylor & Francis

39301865
10.1080/0886022X.2024.2405561
2405561
Version of Record
Research Article
Clinical Study
Predictors and prognostic significance of the volume load trajectory: a longitudinal study in patients on peritoneal dialysis
L. Dan et al.
Dan Liu
Jiamei Xu
Ning Weng
Yanxiang Guo
Mengli Tong
Nephrology Department, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, China
Supplemental data for this article can be accessed online at https://doi.org/10.1080/0886022X.2024.2405561.

CONTACT Xu Jiamei 13819148711@163.com Nephrology Department, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, Hangzhou, 310007, China.
20 9 2024
2024
20 9 2024
46 2 240556129 5 2024
27 8 2024
12 9 2024
KnowledgeWorks Global Ltd.19 9 2024
published online in a building issue19 9 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
The Author(s)
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Volume overload in peritoneal dialysis patients is a common issue that can lead to poor prognosis. We employed a group trajectory model to categorize volume load trajectories and examined the factors associated with each trajectory class to explore the impact of different trajectory groups on clinical prognosis and residual renal function (RRF). This single-center prospective cohort study included 214 patients on maintenance peritoneal dialysis within a tertiary hospital. The ratio of extracellular water to total body water was measured using Bioimpedance analysis. The SAS 9.4 PROC Traj procedure was used to examine the group-based trajectory of the patients. A multivariate logistic regression model was used to calculate the adjusted odds ratios (aOR) of the associated factors to predict the trajectory class of participants. The average age of the included patients was 53.56 (SD: 11.77) years, with a male proportion of 46.7% and a median follow-up time of 6 months. The normal stable group accounted for 35.05% of the total population and maintained a normal and stable level, the moderate stable group accounted for 52.8% of the total population and showed a slightly higher and stable level, and the high fluctuation group accounted for 12.15% of the total population and showed a high and fluctuating level. A multivariate logistic regression analysis revealed that age, diabetes, and albumin levels are significant factors influencing the categorization of volume load trajectories. There were statistically significant differences in both the technical survival rate and the loss of residual renal function among the three trajectory groups.

Keywords

Bioimpedance
volume load
peritoneal dialysis
trajectory
Zhejiang medicine and health science and technology project 2023KY200 The authors disclosed receipt of the following financial support for the research, and publication of this article: Zhejiang medicine and health science and technology project.(2023KY200)
==== Body
pmcIntroduction

Peritoneal dialysis (PD) is a form of treatment for patients who have chronic kidney disease or kidney failure. Additional benefits of PD include water removal without significant changes in the patient’s hemodynamics. This continuous yet gentle removal of solutes and fluid is associated with better-preserved residual kidney function (RRF). Fluid overload is present in more than half of patients on PD [1]. Simultaneously, volume control is increasingly recognized as a pivotal determinant of PD adequacy, as hypervolemia is associated with cardiac dysfunction and mortality [2–7].

Volume overload is considered a modifiable risk factor, and effectively managing volume status is essential in optimizing patient outcomes. To achieve effective volume management, it is important to clearly identify the determinants of volume status in patients on PD [8]. This involves assessing various factors that contribute to volume overload, such as fluid intake, RRF, ultrafiltration capacity, dialysis prescription, and adherence to treatment regimens. By understanding the determinants of volume status in patients on PD, healthcare providers can develop personalized strategies for volume management.

Bioimpedance analysis (BIA) provides an objective estimation of body composition by assessing the tissue resistance to electrical currents. Specifically, BIA allows the assessment of total body water (TBW), extracellular water (ECW), and intracellular water (ICW) water. In clinical practice, BIA has been shown to help guide fluid control in patients on PD [9]. However, some of these studies only assessed hydration status at baseline [5, 10–12], whereas others that used repeated bioimpedance measurements were limited by small sample sizes and long follow-up intervals [3, 13].

BIA can provide valuable information for guiding fluid management in patients on PD [14, 15]. By monitoring changes in TBW, ECW, and ICW over time, a follow-up manager can make informed decisions regarding fluid control and adjust treatment plans accordingly. However, further research with larger sample sizes and shorter follow-up intervals is needed to fully understand the potential benefits and limitations of using BIA for fluid management in patients on PD.

In this study, we prospectively assessed the volume status of patients on PD every 3 months over three consecutive assessments to analyze the volume load trajectories and factors associated with different trajectories to determine the relationship between trajectory groups and both technical survival and RRF.

Materials and methods

Study design

This study was approved by the Ethics Committee of the Hangzhou Hospital of Traditional Chinese Medicine. All of the study procedures complied with the Declaration of Helsinki.

This prospective observational cohort study was designed to assess the volume status of participants on PD and its evolution over time using bioimpedance spectroscopy. This study included 214 patients who underwent regular PD at the Hangzhou Hospital of Traditional Chinese Medicine, Zhejiang Province, China, between March 2023 and December 2023. All patients signed the informed consent form.

Patients aged ≥ 18 years who received regular PD for 3 months were included in the study. Patients with a history of metallic prosthesis or pacemaker implantation were contraindicated for the bioimpedance study and were thus excluded, as were patients undergoing hemodialysis combined with PD.

Data collection

Data from the body composition monitor (InBody 720, DIOSPAZE, Seoul, South Korea), clinical data, laboratory parameters, planned PD prescription, and clinical assessment of volume status were registered as baseline values. The same data were collected from the included patients after 3 and 6 months, until the participant changed the kidney replacement modality, terminated the study prematurely for other reasons, or died.

General information, medical history, and laboratory data were collected by nurses, and hypertension and diabetes were defined based on a diagnosis by a physician. All laboratory data were obtained from the Hangzhou Hospital of Traditional Chinese Medicine Nephrology Laboratory. Serum creatinine (Cr), urea nitrogen (BUN), hemoglobin, serum albumin, phosphorus, blood glucose, high-sensitivity C-reactive protein (Hs-CRP), parathormone (PTH), and residual glomerular filtration rate (GFR) were measured.

For the RRF calculation, the residual GFR was assessed by determining the mean renal Cr and Urea clearance (renal Cr-Urea clearance) and the 24-h urine volume. The residual GFR was calculated as = (renal Urea clearance + renal Cr clearance)/2. renal Urea clearance (ml/min) = (urine Urea/serum Urea) * 24-h urine volume/1440. renal Cr clearance (ml/min) = (urine Cr/serum Cr) * 24-h urine volume/1440.

The PD adequacy was calculated using standard methods from 24-h urinary collections, samples from spent dialysates, and the estimated normalized protein nitrogen appearance (nPNA). The daily glucose exposure was calculated as follows: Daily glucose exposure = glucose concentration per bag of PD fluid (g) * volume of PD fluid (ml).

Peritoneal membrane transport was evaluated from plasma Cr concentration and a 4-h dwell using 2 L of 25 g/L glucose peritoneal dialysate.

The peritoneal dialysate (concentration of 2.5%) was prepared in a volume of 2 L and heated to 37 °C. The patient was seated, and the peritoneal dialysate was retained in the abdominal cavity for 8–12-h overnight. Drainage of the dialysate from the abdominal cavity was performed within 20 min, and the drainage volume was measured. The patient was then placed in the supine position, and 2 L of the prepared 2.5% PD fluid was infused into the abdominal cavity at a rate of 200 mL/min. The time of infusion was recorded as 0 h. Samples of dialysate were collected at 0 h, 1 h, and 4 h, along with blood samples taken at 1 h. These samples were analyzed to determine the concentrations of Cr, glucose, and sodium ions. The ratio of dialysate Cr concentration at 4 h to serum Cr concentration (4 h D/Pcr) was measured.

Blood biochemical and body composition tests were performed on the same day. The load of comorbid conditions was determined using the modified Charlson Comorbidity Index (CCI) [16].

The nutritional status of patients on PD was assessed using modified quantitative subjective global assessment (MQSGA) and anthropometric measurements, with each component given a score from 1 (normal) to 5 (very severe); thus, the sum of all components ranged from 7 (normal) to 35 (severely malnourished). Patients were categorized into three groups: normal nutrition (score: 7–10), mild-to-moderate malnutrition (score: 11–20), and severe malnutrition (score: 21–35) [17].

Bioimpedance measurement

Direct segmental multifrequency bioelectrical impedance technology was used to analyze the body composition (InBody 720, DIOSPAZE, Seoul, South Korea) and obtain the ECW/TBW. The body composition analyzer measures the impedance at 50 frequencies (minimum: 5 kHz, maximum: 1 Mhz), which allows for measurements of extracellular volume with low-frequency current, while high-frequency currents flow through both the extracellular and intracellular volumes. With this method, the volume status in the different body compartments (extracellular and intracellular) can be estimated.

The patient was asked to stand barefoot on the instrument after sitting for 5 min, holding the electrode in two hands; the test was completed within approximately 2 min. The tests were performed after the patients’ dialysate and urine was released.

Follow-up and outcome measures

This study began on 1 March 2023 and end on 11 December 2023. The baseline enrollment was from 6 March 2023 to 31 May 2023. The 3-month follow-up was from 1 June 2023 to 30 August 2023, and the 6-month follow-up was from 1 September 2023 to 11 December 2023. All patients were followed up until 1 July 2024.

Death and transfer to long-term hemodialysis(HD) were considered as events for technique failure, while kidney transplantation and transfer to another center were considered as competing events. The time from the start of PD to technique failure was considered the duration of technique survival.

Statistical analyses

All of the statistical analyses were conducted with SAS version 9.4 (SAS Institute Inc., Cary. NC) and SPSS version 27.0 (SPSS Inc., Chicago, IL, USA). The SAS procedure of PROC TRAJ was used to analyze group-based trajectory modeling (GBTM) for the clustering of the longitudinal data. The PROC TRAJ macro allowed us to handle the maximum-likelihood estimates to fit the nonlinear model. Each model was tested and fitted to data with linear, quadratic, and cubic functions for the trajectories of fluid overload with 2–6 groups. The demographic variables of age, sex, and dialysis duration, which are unmodifiable factors affecting the probability of group membership, were also included in GBTM to identify the distinct trajectories sharing similar patterns of demographic factors. The best model was chosen based on the lowest Bayesian information criterion (BIC) value to determine the optimal number of groups for the incidence of fluid overload over time.

Baseline data counts are described using frequencies and percentages, while measurement data are presented as the mean ± standard deviation for normally distributed data, and as median (interquartile range) for non-normally distributed data. To compare the baseline data among the three patient groups, one-way analysis of variance was used for normally distributed, homogeneous, and independent data, whereas nonparametric tests were used for data that did not meet these criteria. Chi-square tests were conducted for the count data analysis. Continuous variables are presented as the mean and standard deviation, while dichotomous variables are expressed as numbers and percentages. The Kruskal–Wallis H test or chi-square test was used to examine the difference in the distribution of baseline sample characteristics and comorbid conditions between the volume load trajectory groups. Moreover, multinomial logistic regression was used to estimate the odds ratios (ORs) and 95% confidence intervals (CI) with adjustment for dialysis duration, PD weekly urea clearance index (Kt/V), hemoglobin, residual renal Kt/V, residual urinary volume, and residual GFR to explore the association between baseline characteristics and volume load trajectory groups.

Technique survival was calculated using the Kaplan–Meier method, and differences between distributions of survival of different groups were assessed by log-rank test.

Results

Trajectory grouping model

Three-group trajectory models were estimated from the latent class trajectory modeling (LCTM) of SAS PROC Traj. The goodness-of-fit statistics for the LCTM model are displayed in Table 1. As the categories progressed from Class 1 to Class 6, the △BIC showed a trend of gradual decline. The good fit of the model was indicated by an Avepp (average posterior probability) greater than 0.7 and an Ek(relative entropy) greater than 0.8 for each group, and 2 Group and 6 Group were excluded. The posterior probability of group membership (Pj) of 5 Group was lower than 5% (Supplementary Table 1). Finally, the model fit indices of Groups 3 and 4 were compared. As the entropy statistics of the two groups were similar, classes 1 to 3 in Group 4 were stationary, and the purpose of our study was to observe the different trends of volume load in patients on PD. We advocate a simpler model; therefore, the 3 Group was considered the optimal classification.

Table 1. Evaluation index of the fitting effect of different trajectory models.

Group (power)	Avepp (%)	OCC	Pj (%)	Πj (%)	BIC	△BIC	Ek	
1 Group (2)	100.00	 	100.00	100.00	1986.84	 	0.000	
2 Group (0,2)	95.31–91.12	12.8–16.3	60.75–39.25	61.38–38.62	2087.72	100.88	0.779	
3 Group (0,2,2)	90.58–91.13–92.17	17.6–9.3–83.9	35.05–52.80–12.15	35.31–52.38–12.32	2131.22	43.50	0.808	
4 Group (0,0,2,2)	93.44–89.35–89.98–94.24	68.1–10.9–19.0–212.5	16.36–44.39–32.24–7.01	17.31–43.42–32.11–7.15	2154.76	23.54	0.827	
5 Group (0,0,2,2,2)	93.08–88.36–89.61–90.83–100.00	66.7–17.0–12.9–81.4–2876948.1	16.36–30.84–40.65–10.75–1.40	16.79–30.89–40.06–10.85–1.40	2163.94	9.18	0.846	
6 Group (0,0,2,2,2,2)	65.04–91.39–63.99–86.53–90.23–100.00	7.3–57.7–5.6–15.8–84.8–2569296.1	19.63–15.42–24.77–28.97–9.81–1.40	20.27–15.55–24.11–28.86–9.81–1.40	2155.48	–8.46	0.727	
Note: The good model fit is indicated by (1) Avepp (average posterior probability) greater than 0.7 for each class, (2) Pj (posterior probability of group membership) greater than 5%, (3) Close correspondence Pj and πj (probability of group membership), (4) BIC (Bayesian information criterion) close to 0, (5) the large △BIC (BIC difference between complex and simple models) the more complex models are accepted, (6) OCC (odds of correct classification) greater than 5, and (7) Ek (relative entropy) greater than 0.8.

The trajectories of volume load changes for the three classes of Groups are shown in Figure 1. Class 1 accounted for 35.05% of the total population, and the ECW/TBW was always at a normal and stable level between 0.36 and 0.395. Class 2 accounted for 52.8% of the total population, and the ECW/TBW was always at a medium level between 0.381 and 0.417. Class 3 accounted for 12.15% of the total population, and the ECW/TBW was always at a high and fluctuating level between 0.401 and 0.445. We named Class 1 as the normal stable group, Class 2 as the moderate stable group, and Class 3 as the high fluctuation group. It is possible that older patients, especially those older than 70 years old, may experience a higher and more stable volume load compared to younger patients (60 ∼ 70 years and < 60 years), whereas younger patients may exhibit more fluctuation in the volume load. Moreover, the capacity load of women showed a continuous upward trend compared to men. We found no significant difference in volume load trajectories between different dialysis age groups (Supplementary Figure 1, Supplementary Figure 2, and Supplementary Figure 3).

Figure 1. Volume load trajectories classes in patients on PD.

Three volume load trajectory classes: (Class 1: normal stable group; Class 2: moderate stable group; Class 3: High fluctuation group).

Baseline characteristics

Initially, 285 patients were included in the study. However, 71 patients had dropped out by the end of follow-up in 1 July 2024. Among these patients, 10 patients passed away, 34 patients were transferred to HD, 5 patients were combined with HD, 12 patients were transferred to local follow-up care, 5 patients underwent transplantation, and 5 patients did not complete the required three follow-up visits, resulting in their data being excluded due to incompleteness. Finally, this study included 214 patients, with an average age of 53.56 ± 11.77 years, an average dialysis duration of 45.5 (18, 83) months, and chronic glomerulonephritis being the primary disease. The automated peritoneal dialysis (APD) machine was used in five patients. The patient enrollment and follow-up flow diagram are shown in Figure 2.

Figure 2. Patient enrollment and follow-up flow diagram. PD: peritoneal dialysis, HD: hemodialysis.

The baseline characteristics of the three classes are shown in Table 2. As shown in Table 2, the distribution of sample characteristics among the trajectory groups significantly differed in terms of age (p = 0.008), sex (p = 0.036), causes of renal failure (p = 0.011), dialysis duration (p < 0.01), diabetes comorbidity (p = 0.045), MQSGA (p < 0.001), sugar exposure (p = 0.001), hemoglobin (p = 0.007), prealbumin (p = 0.006), albumin (p < 0.001), blood glucose (p = 0.035), residual urinary volume (p = 0.004), residual renal Kt/V (p = 0.003), PD-Kt/V (p < 0.001), RRF (p < 0.001), ICW (p = 0.019), ECW (p = 0.017), and ECW/TBW (p < 0.001).

Table 2. Baseline characteristics of the included patients.

Variables	Overall	Normal stable group	Moderate stable group	High fluctuation group	p	
(n = 214)	(n = 119)	(n = 64)	(n = 31)	
Age, year	53.56 ± 11.77	50.27 ± 11.21	54.99 ± 11.24	56.85 ± 13.75	0.008	
Sex (%)	 	 	 	 	 	
Male	100 (46.7)	44 (58.7)	46 (40.7)	10 (38.5)	0.036	
Female	114 (53.3)	31 (41.3)	67 (59.3)	16 (61.5)	
Causes of renal failure (%)	 	 	 	 	 	
Glomerulonephritis (%)	183 (85.51)	66 (0.88)	99 (0.876)	18 (0.692)	0.011	
Diabetic nephropathy (%)	12 (5.61)	1 (0.013)	9 (0.08)	2 (0.077)	
Hypertensive nephrosclerosis (%)	12 (5.61)	6 (0.08)	4 (0.035)	2 (0.077)	
Others (%)	7 (3.27)	2 (0.027)	1 (0.009)	4 (0.154)	
Dialysis duration (months)	56.03 (18, 83)	39.83 (12, 63)	63.49 (27, 88.5)	70.38 (33, 111)	< 0.01	
Charlson score	1.71 ± 1.45	1.48 ± 1.56	1.79 ± 1.37	2 ± 1.44	0.199	
Comorbidities (%)	 	 	 	 	 	
Diabetes (%)	38 (17.8)	7 (9.3%)	26 (23%)	5 (19.2%)	0.045	
Hypertensive (%)	199 (93)	68 (90.7%)	106 (93.8%)	25 (96.2%)	0.56	
Use of diuretics (%)	45 (21)	16 (21.3%)	25 (22.1%)	4 (15.4%)	0.747	
Use of icodextrin (%)	25 (0.117)	13 (0.061)	7 (0.33)	5 (0.23)	0.152	
MQSGA	9.13 ± 1.40	8.51 ± 1.36	9.41 ± 1.35	9.69 ± 1.09	< 0.001	
Laboratory	 	 	 	 	 	
Sugar exposure (g)	133.13 ± 37.09	120.67 ± 37.18	138.32 ± 36.42	146.54 ± 30.19	0.001	
Hemoglobin (g/L)	112.71 ± 16.22	116.08 ± 13.40	112.32 ± 16.88	104.65 ± 18.22	0.007	
4hD/P (mg/l)	0.61 ± 0.09	0.62 ± 0.09	0.60 ± 0.09	0.63 ± 0.11	0.831	
serum Na+ (mmol/L)	139.12 ± 2.76	138.82 ± 2.63	139.28 ± 2.91	139.28 ± 2.43	0.518	
Albumin (g/L)	34.70 ± 3.93	36.61 ± 3.81	34.14 ± 3.42	31.84 ± 3.97	< 0.001	
Blood glucose (mmol/l)	5.47 ± 1.47	5.14 ± 1.09	5.59 ± 1.54	5.89 ± 1.89	0.035	
Systolic blood pressure (mmHg)	135.97 ± 23.96	131.76 ± 22.06	137.45 ± 25.30	141.69 ± 22.01	0.12	
Diastolic blood pressure (mmHg)	84.98 ± 14.21	86.03 ± 13.85	83.74 ± 14.57	87.31 ± 13.63	0.377	
nPNA (g/kg/d)	0.94 ± 0.26	0.93 ± 0.27	0.94 ± 0.25	0.93 ± 0.26	0.937	
Dialysate output (ml)	7906.86 (7159.5, 8800)	7609.77 (6295, 8820)	8003.85 (7918.5, 8800)	8342.31 (8100, 8807.5)	0.344	
Ultrafiltration (ml)	519.47 ± 406.31	489.76 ± 461.62	526.86 ± 375.93	573.08 ± 369.82	0.643	
Residual urinary volume (ml)	381.38 (0, 700)	515.13 (0, 1000)	339.38 (0, 700)	178.08 (0, 312.5)	0.004	
24-h peritoneal dialysis fluid protein (g/L)	0.77 ± 0.314	0.74 ± 0.334	0.77 ± 0.313	0.84 ± 0.251	0.403	
Residual renal Kt/V	0.38 (0, 0.61)	0.58 (0, 0.96)	0.31 (0, 0.49)	0.12 (0, 0.24)	0.003	
PD-Kt/V	1.84 ± 0.47	1.68 ± 0.47	1.9 ± 0.42	2.05 ± 0.56	< 0.001	
Total-Kt/V	2.21 (1.88, 2.46)	2.25 (1.81, 2.53)	2.19 (1.96, 2.43)	2.13 (1.84, 2.4)	0.714	
Residual GFR (ml/min.1.73 m3)	1.65 (0, 2.73)	2.72 (0, 3.70)	1.21 (0, 2.26)	2.26 (0, 0.80)	< 0.001	
β macroglobulin (mg/l)	40.68 (29.35, 50.59)	37.22 (23.28, 52.86)	42.79 (33.71, 51.41)	40.85 (36.05, 47.65)	0.078	
Hs-CRP (mg/l)	3.77 (0.67, 4)	4.18 (0.63, 2.69)	3.67 (0.85, 4.61)	3.03 (0.56, 2.76)	0.135	
PTH (pg/ml)	279.22 (124.80, 379.75)	295.83 (123.60, 424)	282.62 (125.45, 356.60)	216.5 (130.03, 276.80)	0.338	
Body compositions	 	 	 	 	 	
ICW (l)	20.63 ± 4.02	21.61 ± 4.10	19.94 ± 3.72	20.8 ± 4.53	0.019	
ECW (l)	13.44 ± 2.75	13.39 ± 2.63	13.16 ± 2.56	14.85 ± 3.46	0.017	
ECW/TBW	0.4 ± 0.01	0.38 ± 0.01	0.4 ± 0.01	0.42 ± 0.01	< 0.001	
Lean tissue index (per kg/m2)	7.37 (6.4,8.18)	7.5 (6.57,8.43)	7.17 (6.29,7.91)	7.83 (6.57,9.23)	0.055	
Abbreviations: MQSGA: modified quantitative subjective global assessment, 4hD/P: dialysate-to-plasma Cr concentration ratio at 4 h, nPNA: normalized protein nitrogen appearance, Kt/V: urea clearance index, GFR: glomerular filtration rate, Hs-CRP: hypersensitive C-reactive protein, PTH: parathyroid hormone, ICW: intracellular water, ECW: extracellular water, ECW/TBW: extracellular water/total body water.

Factors associated with the trajectory groups

Univariate logistic regression analysis was performed to examine the association of each trajectory group with the characteristics of patients on PD, as shown in Table 3. The factors with P-values < 0.05 in univariate analysis were included in the multivariate logistic regression analysis, which was conducted to identify predictors associated with being assigned to a specific trajectory group. The distinct trajectory groups served as the dependent variable, with Class 1 trajectory designated as the reference category. Age, sex, diabetes, dialysis duration, MQSGA, albumin levels, hemoglobin levels, PD Kt/V (clearance of urea per session), residual renal Kt/V, residual GFR, and ICW were included in the analysis as potential predictors of group allocation, and residual urinary volume was excluded because of the collinearity between residual urinary volume and residual GFR. Elderly, diabetic with low albumin levels patients were more likely to be assigned to either Class 2 or Class 3 groups compared to patients with Class 1 (as shown in Table 4).

Table 3. Univariate logistic regression analysis of each trajectory group with patients on PD characteristics association.

 	Moderate stable group
(Class 2)
n = 64	High fluctuation group
(Class 3)
n = 31	
Variables	β	aOR	95%CI	p	β	aOR	95%CI	p	
Sex (male vs. female)	−0.726	0.484	(0.267 ∼ 0.876)	0.016*	−0.82	0.44	(0.177 ∼ 1.098)	0.079	
Age (year)	0.035	1.036	(1.009 ∼ 1.063)	0.008*	0.05	1.051	(1.009 ∼ 1.094)	0.016*	
Dialysis duration (months)	0.014	1.014	(1.006 ∼ 1.022)	< 0.001*	0.017	1.018	(1.007 ∼ 1.028)	0.001*	
Diabetes (no vs. yes)	−1.066	0.344	(0.141 ∼ 0.841)	0.019*	−0.839	0.432	(0.124 ∼ 1.505)	0.188	
MQSGA	0.496	1.642	(1.304 ∼ 2.068)	< 0.001*	0.666	1.946	(1.356 ∼ 2.793)	<0.001	
Diuretics (non-use vs. use)	−0.046	0.955	(0.47 ∼ 1.939)	0.898	0.4	1.492	(0.449 ∼ 4.952)	0.514	
Charlson score	0.154	1.167	(0.945 ∼ 1.441)	0.151	0.248	1.281	(0.946 ∼ 1.735)	0.109	
Lean tissue index (per kg/m2)	−0.221	0.802	(0.629 ∼ 1.023)	0.075	0.206	1.229	(0.866 ∼ 1.745)	0.248	
Dialysate output (ml)	0	1	(1 ∼ 1)	0.101	0	1	(1 ∼ 1.001)	0.055	
Ultrafiltration (ml)	0	1	(1 ∼ 1.001)	0.539	0.001	1.001	(0.999 ∼ 1.002)	0.367	
Residual urinary volume(ml)	−0.001	0.999	(0.999 ∼ 1)	0.019*	−0.002	0.998	(0.997 ∼ 0.999)	0.006*	
PD Kt/V	1.113	3.044	(1.522 ∼ 6.089)	0.002*	1.861	6.432	(2.246 ∼ 18.415)	0.001*	
Residual renal Kt/V	0.001	1.001	(0.995 ∼ 1.006)	0.825	−2.009	0.134	(0.026 ∼ 0.682)	0.015*	
Weekly total Kt/V	−0.26	0.771	(0.421 ∼ 1.413)	0.400	−0.581	0.559	(0.205 ∼ 1.524)	0.256	
Residual GFR (ml/min.1.73m3)	−0.239	0.787	(0.685 ∼ 0.904)	0.001*	−0.615	0.541	(0.363 ∼ 0.805)	0.002*	
4hD/P	0	1	(1 ∼ 1)	0.203	0	1	(1 ∼ 1)	0.546	
Hemoglobin (g/L)	−0.016	0.985	(0.966 ∼ 1.004)	0.110	−0.044	0.957	(0.929 ∼ 0.984)	0.002*	
Albumin (g/L)	0	1	(1 ∼ 1)	0.183	−0.227	0.797	(0.709 ∼ 0.896)	<0.001*	
blood glucose (mmol/l)	−0.001	0.999	(0.994 ∼ 1.005)	0.775	−0.001	0.999	(0.993 ∼ 1.005)	0.831	
Hs-CRP (mg/l)	0.001	1.001	(0.997 ∼ 1.005)	0.783	−0.026	0.974	(0.897 ∼ 1.059)	0.540	
SerumNa (mmol/L)	0	1	(1 ∼ 1)	0.186	−0.001	0.999	(0.99 ∼ 1.009)	0.870	
PTH (pg/ml)	0	1	(0.999 ∼ 1.001)	0.706	−0.002	0.998	(0.996 ∼ 1.001)	0.127	
β microglobulin (mg/l)	0.028	1.029	(1.006 ∼ 1.052)	0.014*	0.019	1.019	(0.986 ∼ 1.053)	0.271	
24-hour peritoneal dialysis fluid protein (g/L)	0.281	1.324	(0.495 ∼ 3.541)	0.576	0.896	2.45	(0.657 ∼ 9.136)	0.182	
Systolic blood pressure (mmHg)	0.01	1.01	(0.998 ∼ 1.023)	0.109	0.018	1.018	(0.999 ∼ 1.037)	0.068	
Diastolic blood pressure (mmHg)	−0.011	0.989	(0.968 ∼ 1.009)	0.281	0.007	1.007	(0.975 ∼ 1.04)	0.686	
nPNA	0.205	1.227	(0.396 ∼ 3.806)	0.723	0.066	1.068	(0.189 ∼ 6.041)	0.940	
ICW (L)	−0.106	0.899	(0.834 ∼ 0.970)	0.006*	−0.049	0.952	(0.852 ∼ 1.064)	0.385	
Abbreviations: MQSGA: modified quantitative subjective global assessment, Kt/V: urea clearance index, GFR: glomerular filtration rate, 4hD/P: dialysate-to-plasma Cr concentration ratio at 4 h, PTH: parathyroid hormone, nPNA: normalized protein nitrogen appearance, ICW: intracellular water. Values show the risk profile (aOR) for each trajectory group compared to trajectory Class 1 (Normal stable group). aOR adjusted odds ratio in relation to all the other variables in the table; CI, confidence interval. Predictors starred* are those that were statistically significant.

Table 4. Multivariable adjusted multinomial logistic regression analysis for the associations of demographic and laboratory factors with three trajectories.

 	Moderate stable group
(Class 2)
n = 64	High fluctuation group
(Class 3)
n = 31	
Variables	β	aOR	95%CI	p	β	aOR	95%CI	p	
Age (years)	0.036	1.036	(1.004 ∼ 1.07)	0.028*	0.072	1.075	(1.022 ∼ 1.131)	0.005*	
Gender (male vs. female)	−0.744	0.475	(0.145 ∼ 1.56)	0.22	−1.565	0.209	(0.035 ∼ 1.24)	0.085	
Diabetes (no vs. yes)	−1.793	0.167	(0.053 ∼ 0.522)	0.002*	−1.843	0.158	(0.033 ∼ 0.765)	0.022*	
Dialysis duration (months)	0.004	1.004	(0.992 ∼ 1.015)	0.551	0.006	1.006	(0.99 ∼ 1.023)	0.457	
MQSGA	0.336	1.399	(0.977 ∼ 2.005)	0.067	0.471	1.602	(0.923 ∼ 2.781)	0.094	
Albumin (g/L)	−0.186	0.83	(0.746 ∼ 0.924)	0.001*	−0.39	0.677	(0.571 ∼ 0.803)	<0.001*	
Hemoglobin (g/L)	−0.011	0.989	(0.964 ∼ 1.014)	0.391	−0.031	0.969	(0.934 ∼ 1.006)	0.1	
PD Kt/V	−0.332	0.717	(0.234 ∼ 2.2)	0.561	1.088	2.967	(0.501 ∼ 17.559)	0.231	
Residual renal Kt/V	0.788	2.2	(0.136 ∼ 35.546)	0.579	−1.176	0.308	(0 ∼ 256.886)	0.732	
Residual GFR (ml/min.1.73m3)	−0.368	0.692	(0.347 ∼ 1.382)	0.297	−0.026	0.974	(0.192 ∼ 4.945)	0.975	
ICW (L)	−0.068	0.934	(0.803 ∼ 1.086)	0.375	0.129	1.138	(0.921 ∼ 1.406)	0.232	
Abbreviations: MQSGA: modified quantitative subjective global assessment, Kt/V: urea clearance index, ICW: intracellular water. Values show the risk profile (aOR) for each trajectory group compared to trajectory Class 1 (normal stable group). aOR adjusted odds ratio in relation to all the other variables in the table; CI, confidence interval. Predictors starred* are those that were statistically significant.

Technique survival and residual GFR loss events

By the end of follow-up in 1 July 2024, among 214 patients, 2 patients passed away and 17 patients were transferred to HD. At 6 months, patient survival was 96%, 91.2%, and 76.9% (log-rank test, p = 0.024) in patients with Class 1, Class 2, and Class 3 (Figure 3). The technique survival rate of Class 3 was significantly lower than that of Class 1, and the difference was statistically significant (p = 0.007). There was no significant difference in the technique survival rate between Class 2 and Class 1 (p = 0.213). There was no significant difference in the technique survival rate between Class 2 and Class 3 (p = 0.065). When 214 patients were enrolled, 110 of them had residual renal function. By the follow-up period until 1 July 2024, 26 patients experienced a loss of residual renal function. The rate of residual GFR loss in the Class 3 was significantly higher compared to both the Class 2 and the Class 1. Additionally, the Class 2 had a significantly higher rate of residual GFR loss compared to the Class 1, and the difference was statistically significant (p < 0.001) in pairwise comparison with each other (Table 5).

Figure 3. Kaplan–Meier survival curves by three trajectory groups.

Table 5. Comparison of the incidence of residual GFR loss events in different trajectory groups.

 	Normal stable group (n = 46)
Class 1	Moderate stable group (n = 52)
Class 2	High fluctuation group (n = 12)
Class 3	χ2	p	
Residual GFR loss events, n(%)	3 (6.5%)	15 (28.8%)	8 (66.7%)	20.55	< 0.001	
Abbreviations: GFR: glomerular filtration rate.

Discussion

Volume overload is a common issue among patients undergoing PD, often resulting in an increased risk of cardiovascular events and mortality [4, 5]. While there have been some cross-sectional surveys [18] and retrospective longitudinal studies, the assessment of volume status in patients on PD is often irregular and may only be monitored once a year. However, it is important to recognize that the volume status of patients is dynamic and requires regular monitoring. At present, there is a lack of research on the volume load trajectory group and its association with patients on PD outcomes.

The innovation of this study lies in using GBTM to classify changes in PD volume load, identifying a total of three distinct trajectories. It was found that elderly, diabetic, and patients with low albumin levels were more likely to be assigned to the Class 2 and Class 3. After a 6-month follow-up, the Class 1 exhibited a higher technical survival rate compared to the Class 3, whereas the loss of residual GFR in the Class 3 was significantly higher compared to the other two groups, and Class 2 was higher than Class 1. In comparison with other related studies where capacity load measurements were conducted annually [3, 19] or every few months [13], our study measured capacity load once every 3 months. This frequent monitoring allowed for continuous tracking of capacity load and provided timely feedback and correction for capacity overload.

Predictors of three trajectory class

It is not surprising that patients with fluid overload were associated with poorer baseline general conditions (Table 2), including older age, higher dialysis duration, a higher percentage of diabetes, and higher MQSGA score. Similar to previous studies [3, 5, 13], our research identified age, diabetes, and albumin levels as significant factors influencing the categorization of volume load trajectories. One notable finding of our study is that advanced age, diabetes, and low albumin levels can contribute to a state of high and fluctuating volume load.

Age has been consistently associated with the volume status on patients on PD in previous studies, and the ECW-to-ICW ratio increased with age because of the steeper decrease in ICW content than in ECW content, especially for those older than 70 years [20]. ICW was corrected in the multivariate logistic regression model, and the results still showed that age was the influencing factor of different trajectory groups. In addition, with an increase in age, the occurrence of comorbidities and malnutrition can also lead to an increase in ECW [21].

Patients on PD with diabetes are more likely to be in a state of high capacity load and fluctuation. The hyperglycemic state characteristic of patients on PD increases plasma osmolarity, thereby triggering vasopressin secretion and thirst. Concomitant diabetes in dialysis patients appears to intensify subjective xerostomia and thirst sensation [22], and contribute to the intake of fluids and excessive weight gain. Diabetic patients were more fluid overloaded as compared to nondiabetics despite use of more hypertonic glucose solutions and, higher peritoneal ultrafiltration and higher total fluid removal, this indicates that diabetic patients must have had significantly fluid intakes [23].

In our cohort, dilutional hypoalbuminemia secondary to increased plasma volume is thus plausible. On the other hand, a decrease in plasma albumin results in reduced plasma colloid osmotic pressure, leading to an increase in interstitial volume. Another explanation may be that elevated atrial natriuretic peptide (ANP) levels, particularly associated with heart failure, may cause increases in the vascular permeability to albumin, producing tissue edema [24]. In conclusion, hypoalbuminemia in PD may be the result of increased external albumin losses and chronic inflammation. A plasma-to-peritoneal clearance on the order of 0.1 mL/minute for albumin in PD corresponds to peritoneal losses of about 5 g of albumin per day. Healthy patients on PD can compensate for these losses by an increased liver synthesis of albumin. However, in low-grade chronic inflammation this ability may be reduced. Hence, albumin synthesis may be depressed in inflammation, which may result in hypoalbuminemia. Studies [25] have also confirmed that hs-CRP value levels were significantly higher in hypervolemic patients compared with euvolemic patients. In this study, there was no difference in Hs-CRP between different trajectory groups.

Trajectory groups and adverse outcomes

The technique survival rate of Class 3 was significantly lower than that of Class 1 (p = 0.007). In previous studies [3], patient technique survival was 66.2%, 59.3%, 38.3%, and 25.6% (p < 0.001) in patients with nil, mild, moderate, and severe fluid overload, and variability of volume status was not associated with technique failure (p = 0.69). The three capacity load trajectory groups identified in this study include Class 3, which exhibits a high and fluctuating state. Class 3 also demonstrates a higher technical failure rate compared to the other two stable groups. The observed differences may be attributed to the study’s annual observation frequency of capacity load, whereas more frequent observations, such as every 3 months, might capture seasonal variations in fluid load. Although the technical survival rate in Class 2 was lower than in Class 1, the difference was not statistically significant, possibly due to the shorter observation period.

More residual GFR confers better outcomes including a survival benefit and higher quality of life. Whether volume overload is beneficial in maintaining RRF has been a matter of debate. This study examined the impact of volume overload on changes in residual renal function, revealing significant differences among the different volume overload trajectory groups. Classes 3 exhibit a higher rate of residual GFR loss compared to Class 1. Specifically, the volume load remains consistently high and fluctuating can lead to a decline in residual GFR within a short period (6 months). This finding aligns with the results reported by Tian et al. [26] in another study, and hydration status assessed by bioimpedance analysis (as well as increments and decrements in ECV) was not associated with the preservation of RRF in 237 patients on PD [27]. An absolute change in ECW/TBW from baseline to 12-month review was not significantly correlated with a loss in RRF (r = 0.02, p = 0.72). The reasons of the discrepancy between our study and previous reports may due to different research designs. This study aims to observe the longitudinal trajectory of fluid load changes, which is more reliable compared to changes observed between two points. Our study not only found that high volume load is associated with the loss of residual renal function, but also found that the trend of volume load change with high fluctuation is more likely to lose residual renal function, which is the innovation of our study.

Our study had some limitations, the first of which is the observational nature of the study, limiting the derivation of cause-effect relationships. Second, our study lacks the assessment of fluid and salt intake, which is difficult to accurately measure for patients, but we evaluated the content of sodium in blood (139.12 ± 2.74) mmol/L, indicating that the overall salt intake of patients was not high, and there was no difference in serum sodium between patients with different trajectory groups. Third, in our study, only five patients used the APD machine, and 37 patients intermittently used icodextrin dialysate, which may interfere with the influencing factors of volume load. Finally, due to the short observation time, this study only analyzed the relationship between different volume overload trajectory groups and technique survival and residual GFR loss. In the future, we will continue follow-up to further analyze the correlation between different volume overload trajectory groups and survival prognosis.

In conclusion, our study examines the changing trend of content load in patients on PD over the course of 10 months. We finally formed three kinds of capacity load trajectories. Elderly, diabetic patients with low albumin levels are more likely to experience high and fluctuating volume overload. Such conditions are associated with increased risks of technical failure and residual GFR loss. It is crucial to closely monitor and manage the volume status of these individuals in clinical practice, ensuring timely interventions to mitigate technical failure and preserve residual renal function.

Supplementary Material

informed consent.pdf

Supplementary Material.docx

2023KLL006.pdf

Acknowledgments

The authors thank the data support provided by the Hangzhou TCM Hospital peritoneal dialysis clinic.

Disclosure statement

No potential conflict of interest was reported by the author(s).
==== Refs
References

1 Van Biesen W, Williams JD, Covic AC, et al. Fluid status in peritoneal dialysis patients: the European body composition monitoring (EuroBCM) study cohort. PLoS One. 2011;6 (2 ):e17148. doi: 10.1371/journal.pone.0017148.21390320
2 Paniagua R, Ventura MD, Avila-Díaz M, et al. NT-proBNP, fluid volume overload and dialysis modality are independent predictors of mortality in ESRD patients. Nephrol Dial Transplant. 2010;25 (2 ):551–557. doi: 10.1093/ndt/gfp395.19679559
3 Ng JK, Kwan BC, Chan GC, et al. Predictors and prognostic significance of persistent fluid overload: a longitudinal study in Chinese peritoneal dialysis patients. Perit Dial Int. 2023;43 (3 ):252–262. doi: 10.1177/08968608221110491.35787209
4 Van Biesen W, Verger C, Heaf J, et al. Evolution over time of volume status and PD-related practice patterns in an incident peritoneal dialysis cohort. Clin J Am Soc Nephrol. 2019;14 (6 ):882–893. doi: 10.2215/CJN.11590918.31123180
5 Ng JK, Kwan BC, Chow KM, et al. Asymptomatic fluid overload predicts survival and cardiovascular event in incident Chinese peritoneal dialysis patients. PLoS One. 2018;13 (8 ):e0202203. doi: 10.1371/journal.pone.0202203.30102739
6 Jin Y, Huang X, Zhang C, et al. Impact of fluid overload on blood pressure variability in patients on peritoneal dialysis. Ren Fail. 2022;44 (1 ):2066–2072. doi: 10.1080/0886022X.2022.2148535.36415108
7 Akdam H, Öğünç H, Alp A, et al. Assessment of volume status and arterial stiffness in chronic kidney disease. Ren Fail. 2014;36 (1 ):28–34. doi: 10.3109/0886022X.2013.830224.24028203
8 Flythe JE, Chang TI, Gallagher MP, et al. Blood pressure and volume management in dialysis: conclusions from a kidney disease: improving global outcomes (KDIGO) controversies conference. Kidney Int. 2020;97 (5 ):861–876. doi: 10.1016/j.kint.2020.01.046.32278617
9 Fan S, Davenport A. The importance of overhydration in determining peritoneal dialysis technique failure and patient survival in anuric patients. Int J Artif Organs. 2015;38 (11 ):575–579. doi: 10.5301/ijao.5000446.26659479
10 O’Lone EL, Visser A, Finney H, et al. Clinical significance of multi-frequency bioimpedance spectroscopy in peritoneal dialysis patients: independent predictor of patient survival. Nephrol Dial Transplant. 2014;29 (7 ):1430–1437. doi: 10.1093/ndt/gfu049.24598280
11 Guo Q, Lin J, Li J, et al. The effect of fluid overload on clinical outcome in southern Chinese patients undergoing continuous ambulatory peritoneal dialysis. Perit Dial Int. 2015;35 (7 ):691–702. doi: 10.3747/pdi.2014.00008.26152580
12 Kim JK, Song YR, Lee HS, et al. Repeated bioimpedance measurements predict prognosis of peritoneal dialysis patients. Am J Nephrol. 2018;47 (2 ):120–129. doi: 10.1159/000486901.29471301
13 Jaques DA, Davenport A. Determinants of volume status in peritoneal dialysis: a longitudinal study. Nephrology (Carlton). 2020;25 (10 ):785–791. doi: 10.1111/nep.13716.32304154
14 Brimble KS, Ganame J, Margetts P, et al. Impact of bioelectrical impedance-guided fluid management and vitamin D supplementation on left ventricular mass in patients receiving peritoneal dialysis: a randomized controlled trial. Am J Kidney Dis. 2022;79 (6 ):820–831. doi: 10.1053/j.ajkd.2021.08.022.34656640
15 Covic A, Siriopol D. Assessment and management of volume overload among patients on chronic dialysis. Curr Vasc Pharmacol. 2021;19 (1 ):34–40. doi: 10.2174/1570161118666200225093827.32096744
16 Beddhu S, Zeidel ML, Saul M, et al. The effects of comorbid conditions on the outcomes of patients undergoing peritoneal dialysis. Am J Med. 2002;112 (9 ):696–701. doi: 10.1016/s0002-9343(02)01097-5.12079709
17 Kalantar-Zadeh K, Kleiner M, Dunne E, et al. A modified quantitative subjective global assessment of nutrition for dialysis patients. Nephrol Dial Transplant. 1999;14 (7 ):1732–1738. doi: 10.1093/ndt/14.7.1732.10435884
18 Moissl U, Fuentes LR, Hakim MI, et al. Prevalence of fluid overload in an urban us hemodialysis population: a cross-sectional study. Hemodial Int. 2022;26 (2 ):264–273. doi: 10.1111/hdi.12986.34897937
19 Carlos C, Grimes B, Segal M, et al. Predialysis fluid overload and gait speed: a repeated measures analysis among patients on chronic dialysis. Nephrol Dial Transplant. 2020;35 (6 ):1027–1031. doi: 10.1093/ndt/gfz272.31886859
20 Ohashi Y, Joki N, Yamazaki K, et al. Changes in the fluid volume balance between intra- and extracellular water in a sample of Japanese adults aged 15–88 yr old: a cross-sectional study. Am J Physiol Renal Physiol. 2018;314 (4 ):F614–F622. doi: 10.1152/ajprenal.00477.2017.29212765
21 Tinroongroj N, Jittikanont S, Lumlertgul D. Relationship between malnutrition-inflammation syndrome and ultrafiltration volume in continuous ambulatory peritoneal dialysis patients. J Med Assoc Thai. 2011;94;Suppl 4 : S94–S100.
22 Bruzda-Zwiech A, Szczepańska J, Zwiech R. Xerostomia, thirst, sodium gradient and inter-dialytic weight gain in hemodialysis diabetic vs. non-diabetic patients. Med Oral Patol Oral Cir Bucal. 2018;23 (4 ):e406–e412. doi: 10.4317/medoral.22294.29924756
23 Gan HB, Chen MH, Lindholm B, et al. Volume control in diabetic and nondiabetic peritoneal dialysis patients. Int Urol Nephrol. 2005;37 (3 ):575–579. doi: 10.1007/s11255-005-1202-4.16307345
24 Curry FRE, Rygh CB, Karlsen T, et al. Atrial natriuretic peptide modulation of albumin clearance and contrast agent permeability in mouse skeletal muscle and skin: role in regulation of plasma volume. J Physiol. 2010;588 (Pt 2 ):325–339. doi: 10.1113/jphysiol.2009.180463.19948658
25 Unal A, Kavuncuoglu F, Duran M, et al. Inflammation is associated to volume status in peritoneal dialysis patients. Ren Fail. 2015;37 (6 ):935–940. doi: 10.3109/0886022X.2015.1040337.25945604
26 Tian N, Guo Q, Zhou Q, et al. The impact of fluid overload and variation on residual renal function in peritoneal dialysis patient. PLoS One. 2016;11 (4 ):e0153115. doi: 10.1371/journal.pone.0153115.27093429
27 Mccafferty K, Fan S, Davenport A. Extracellular volume expansion, measured by multifrequency bioimpedance, does not help preserve residual renal function in peritoneal dialysis patients. Kidney Int. 2014;85 (1 ):151–157. doi: 10.1038/ki.2013.273.23884340
