Impact of Albumin Variability during the First Year on Prognosis in Peritoneal Dialysis Patients

Impact of Albumin Variability during the First Year on Prognosis in Peritoneal Dialysis Patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Impact of Albumin Variability during the First Year on Prognosis in Peritoneal Dialysis Patients Yonglong Min, Li Cheng, Hong Liu, Nan Jiang, Wenhui Qiu, Shuai Fu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7800922/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 05 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Purpose ​ This multicenter retrospective study aimed to evaluate the association between the coefficient of variation (CV) of serum albumin during the first year of peritoneal dialysis (PD) and clinical outcomes, including technique failure and all-cause mortality. Methods ​ We enrolled patients who initiated PD between January 1, 2018 and December 31, 2024 from three medical centers and maintained treatment for over one year. The albumin-CV was calculated from serial serum albumin measurements during the first year. Participants were categorized into low-, medium-, and high-variability groups based on albumin-CV tertiles. Primary endpoints were technique failure and all-cause mortality. Survival analyses were performed using Kaplan–Meier curves with log-rank tests. Multivariable Cox regression models were employed to assess independent associations. Results​ Among 759 included patients (mean age 57.81 ± 13.28 years; 58.8% male), 15.9% had diabetic nephropathy. After a median follow-up of 37 months, 66 technique failures and 170 all-cause deaths occurred. The high-albumin-variability group showed higher prevalence of diabetes and lower hemoglobin, albumin, calcium, and phosphorus levels (all p < 0.05). Albumin variability was significantly associated with all-cause mortality (log-rank χ² = 8.017, p = 0.013) but not with technique failure. After adjusting for confounders, albumin-CV remained an independent predictor of all-cause mortality ( HR = 1.039, 95% CI : 1.014–1.064, p = 0.002). Conclusion​ Higher variability in serum albumin during the first year of PD is independently associated with increased risk of all-cause mortality. Health sciences/Diseases Health sciences/Medical research Health sciences/Nephrology Health sciences/Risk factors Peritoneal dialysis Serum albumin coefficient of variation Prognosis All-cause mortality Technique failure Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Peritoneal dialysis (PD) serves as a major renal replacement therapy for patients with end-stage renal disease (ESRD), with global utilization exceeding 300,000 patients as of 2023, particularly widespread in developing countries [ 1 ]. Nonetheless, PD patients remain at considerable risk of technique failure, all-cause mortality, and complications such as malnutrition, cardiovascular events, and infections [ 2 , 3 ]. Serum albumin, a key marker of nutritional status, is strongly associated with clinical outcomes in this population [ 4 ]. Multiple studies have confirmed the prognostic value of albumin levels. A prospective cohort study demonstrated that each 1 g/dL increase in serum albumin one year after PD initiation was associated with an 8.7% reduction in all-cause mortality, particularly among patients with baseline hypoalbuminemia, suggesting that dynamic improvement correlates with long-term survival [ 5 ]. In hemodialysis (HD) patients, albumin variability has been identified as a risk factor for mortality, and emerging evidence indicates a similar pattern in PD populations. Albumin trajectory was shown to be a stronger predictor of survival than baseline values, with increasing levels linked to improved outcomes [ 6 ]. Although evidence remains limited in PD-specific settings, recent studies underscore the importance of dynamic albumin profiles. A 2025 study reported that variations in serum albumin and ferritin were predictive of early peritonitis risk among PD patients [ 7 ]. Another study from 2022 indicated that serum albumin levels after one year of PD were an independent predictor of long-term outcomes, outperforming baseline measurements [ 5 ]. These findings suggest that the coefficient of variation of albumin (albumin-CV) during the initial treatment period may reflect variations related to the malnutrition-inflammation complex syndrome (MICS), peritoneal membrane permeability, episodes of infection, and nutritional interventions, thereby influencing risks of technique failure and mortality. However, current literature predominantly focuses on static albumin measurements or HD populations. There is a lack of large-scale, multicenter studies evaluating albumin-CV during the first year of PD—a critical adaptation period often accompanied by nutritional instability and high rates of technical complications [ 8 – 11 ]. Assessing early albumin variability may improve risk stratification and guide timely interventions. This multicenter retrospective study aims to examine the impact of albumin-CV during the first year of PD on technique failure and all-cause mortality. Methods Study Design and Population This multicenter, retrospective cohort study enrolled patients from three peritoneal dialysis centers in Wuhan, China: Wuhan No. 1 Hospital, Central Hospital of Wuhan, and People’s Hospital of Huang Pi. Data were obtained from the Wuhan Peritoneal Dialysis Quality Control Platform database for all individuals who initiated PD between January 1, 2018, and December 31, 2023. Participants were followed from PD initiation until the occurrence of an outcome event or the end of the follow-up period on December 31, 2024, whichever occurred first.This study was performed in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants and/or their legal guardians.The study protocol was approved by the Ethics Committee of the Wuhan No. 1 Hospital (No. 128 [2024]).The inclusion criteria were as follows:Age 18 years or older;Newly commenced peritoneal dialysis (including continuous ambulatory peritoneal dialysis [CAPD]) during the study period;At least two recorded laboratory measurements during the first year after PD initiation.Exclusion criteria were as follows:Less than 12 months of follow-up;History of malignant tumor or severe digestive system disease at baseline;Transition from hemodialysis (HD) to PD with more than 3 months of prior HD treatment;Missing essential baseline or follow-up data that precluded valid analysis.A total of 759 patients met the criteria and were included in the final analysis.(Fig. 1 ) Data Collection and Variable Definitions Demographic and Clinical Baseline Characteristics The following demographic and clinical data were collected at the initiation of peritoneal dialysis (PD):Age at the start of PD therapy;Sex;with particular emphasis on the presence or absence of diabetes mellitus. Laboratory Parameters Laboratory data were obtained during the first year (0–12 months) following PD initiation. For each laboratory parameter, the mean value was calculated over this observation period. The following parameters were included:Weekly total urea clearance index(reported as mean Kt/V, Kt/V-M);Weekly total creatinine clearance rate(mean Ccr, Ccr-M);Serum albumin(mean albumin, ALB-M);Hemoglobin(mean hemoglobin,HB-M);Serum ferritin(mean ferritin,SF-M);Corrected serum calcium​ (mean corrected calcium,Ca-M);Serum phosphorus(mean phosphorus,P-M);Intact parathyroid hormone(mean iPTH,PTH-M). Data Processing and Missing Value Imputation To address the issue of randomly missing data (expected to be < 15% across variables), complete case analysis was avoided to prevent reduction in sample size and potential selection bias. Instead, missing values were imputed using the random forest algorithm to improve imputation accuracy. Transferrin saturation (TSAT) was excluded from the analytical dataset due to a high missing rate exceeding 40%. Medication-related data were not incorporated into the analysis owing to inconsistent reporting standards and variable data quality across participating centers. Statistical Analysis Statistical analysis was performed using SPSS 22.0 and GraphPad Prism 8 software. Continuous variables with a normal distribution were expressed as mean ± standard deviation (x̄ ± s ), and comparisons between the three groups were made using one-way analysis of variance (ANOVA). For non-normally distributed continuous variables, data were expressed as median ( P25, P75 ), and comparisons between the groups were performed using the Kruskal-Wallis H test. Categorical variables were presented as frequency (percentage) [n(%)] and compared using the chi-square ( χ ²) test.Kaplan-Meier survival curves were constructed to illustrate the technical survival and all-cause survival of each group (based on ALB-cv tertiles), and differences between the groups were compared using the log-rank test. A multivariate Cox proportional hazards regression model was used to assess the relationship between ALB-cv and prognosis, and hazard ratios ( HR ) along with their 95% confidence intervals ( CI ) were calculated. A p-value of < 0.05 was considered statistically significant. Results Baseline Characteristics of the Patients After applying the inclusion and exclusion criteria, a total of 759 PD patients were included in this study. The mean age was 57.81 ± 13.28 years, with 58.8% male and 15.9% with diabetic nephropathy. The median follow-up time was 37 months. Based on ALB-cv tertiles, the patients were classified into three groups: low variability group (ALB-cv < 4%), medium variability group (4% ≤ ALB-cv < 8%), and high variability group (ALB-cv ≥ 8%). The proportion of diabetic patients in the high variability group (20.7%) was significantly higher than in the low variability group (10.9%) ( p = 0.010). The hemoglobin level in the high variability group (100.2 ± 14.6 g/L) was lower than in the low variability group (104.7 ± 17.2 g/L) ( p = 0.005). The serum albumin level in the high variability group (33.63 ± 4.71 g/L) was lower than in both the medium variability group (36.05 ± 4.16 g/L) and the low variability group (36.57 ± 3.75 g/L) ( p < 0.001). The serum calcium level in the high variability group (2.18 ± 0.17 mmol/L) was lower than in both the medium variability group (2.23 ± 0.18 mmol/L) and the low variability group (2.22 ± 0.17 mmol/L) ( p = 0.005). The serum phosphorus level in the high variability group (1.43 ± 0.29 mmol/L) was lower than in the medium variability group (1.52 ± 0.34 mmol/L) ( p = 0.006).There were no significant differences between the groups in terms of sex, age, residual renal function (Kt/V, creatinine clearance), ferritin, or parathyroid hormone levels ( p > 0.05)(Table 1 ). Table 1 Comparison of Baseline Levels of Patients with Different ALB-cv (N = 759) Variable Low Variability Group (N1 = 256) Medium Variability Group (N2 = 247) High Variability Group (N3 = 256) χ²/F/H p Sex Male 144 (56.2%) 141 (57.1%) 161 (62.9%) 2.754 0.252 Female 112 (43.8%) 106 (42.9%) 95 (37.1%) Diabetic Nephropathy (DN) Yes 28 (10.9%) 40 (16.2%) 53 (20.7%) a 9.127 0.010 No 228 (89.1%) 207 (83.8%) 203 (79.3%) Age 58.00 (48.00, 67.00) 59.00 (51.00, 67.00) 60.00 (50.00, 68.00) 1.367 0.505 Kt/V-M 1.83 (1.65, 2.12) 1.88 (1.70, 2.21) 1.85 (1.60, 2.13) 2.512 0.285 Ccr-M 67.35 (60.86, 78.82) 66.75 (58.50, 83.78) 67.62 (56.75, 82.89) 0.067 0.967 HB-M 104.67 ± 17.18 102.82 ± 13.97 100.23 ± 14.61 a 5.409 0.005 SF-M 132.16 (71.43, 187.63) 124.43 (63.10, 169.47) 123.23 (82.48, 195.62) 1.389 0.499 ALB-M 36.57 ± 3.75 36.05 ± 4.16 33.63 ± 4.71 ab 35.321 < 0.001 ALB-cv (%) 3.00 (2.00, 3.00) 6.00 (5.00, 7.00) a 12.00 (10.00, 16.00) ab 673.708 < 0.001 Ca-M (mmol/L) 2.22 ± 0.17 2.23 ± 0.18 2.18 ± 0.17 ab 5.285 0.005 P-M (mmol/L) 1.48 ± 0.29 1.52 ± 0.34 1.43 ± 0.29 b 5.126 0.006 PTH-M (mmol/L) 195.38 (116.23, 325.94) 188.66 (99.86, 334.79) 174.06 (103.95, 282.16) 3.600 0.165 a: Compared with Low Variability Group, p < 0.05; b: Compared with Medium Variability Group, p < 0.05. Comparison of Survival Curves Among Different ALB-cv Groups During the entire follow-up period, a total of 66 technical failure events and 170 all-cause mortality events occurred. Kaplan-Meier survival curve analysis revealed no significant difference in technical survival rates between the different serum albumin variability (ALB-cv) groups (Log-rank χ²=0.083, p = 0.669) (Fig. 2 ). However, there was a significant difference in all-cause survival rates among the ALB-cv groups (Log-rank χ ²=8.017, p = 0.013) (Fig. 3 ). Cox Regression Analysis of the Relationship Between ALB-cv and Patient Prognosis Regardless of adjustment for confounding factors, no association was found between ALB-cv and technical failure in PD patients ( p > 0.05). In the unadjusted model, ALB-cv was identified as a risk factor for all-cause mortality in PD patients (HR = 1.054, 95% CI 1.030–1.079, p < 0.001). After adjusting for age, sex, diabetes, total Kt/V (KtV-M), creatinine clearance (Ccr-M), hemoglobin (HB-M), ferritin (SF-M), corrected calcium (Ca-M), phosphorus (P-M), and parathyroid hormone (PTH-M), ALB-cv remained an independent risk factor for all-cause mortality in PD patients ( HR = 1.039, 95% CI 1.014–1.064, p = 0.002)(Table 2 ). Additionally, age (HR = 1.064, 95% CI 1.048–1.080, p < 0.001) and diabetes ( HR = 1.816, 95% CI 1.260–2.619, p = 0.001) were also identified as independent risk factors for all-cause mortality in PD patients(Fig. 4 ) Table 2 Cox Regression Analysis of the Relationship Between ALB-cv and Patient Prognosis Model Technical Failure All-Cause Mortality HR (95% CI ) p-value HR (95% CI ) p -value Unadjusted Model 1.012 (0.969–1.058) 0.580 1.054 (1.030–1.079) < 0.001 Model 1 1.009 (0.966–1.055) 0.681 1.044 (1.020–1.068) < 0.001 Model 2 1.008 (0.964–1.054) 0.727 1.040 (1.016–1.065) 0.001 Model 3 1.008 (0.964–1.053) 0.733 1.039 (1.015–1.064) 0.001 Model 4 0.998 (0.953–1.044) 0.918 1.039 (1.014–1.064) 0.002 Note : HR: Hazard Ratio; CI: Confidence Interval. Unadjusted Model : No adjustments for any variables. Model 1 : Adjusted for age and sex. Model 2 : Further adjusted for the presence of diabetes in addition to Model 1. Model 3 : Further adjusted for total Kt/V (KtV-M) and creatinine clearance (Ccr-M) in addition to Model 2. Model 4 : Further adjusted for hemoglobin (HB-M), ferritin (SF-M), corrected calcium (Ca-M), phosphorus (P-M), and parathyroid hormone (PTH-M) in addition to Model 3. Discussion This study demonstrates that the Albumin Coefficient of Variation (ALB-cv) is an independent predictor of all-cause mortality in peritoneal dialysis (PD) patients, while it does not significantly affect technical failure. In recent years, multiple studies have suggested that the dynamic or average values of serum albumin over time are more predictive of prognosis than a single baseline measurement. Previous cohort studies in PD patients have shown that time-averaged albumin (TA-ALB) and the trajectory of serum albumin levels provide superior predictive value for mortality risk compared to single measurements, indicating that dynamic indicators outperform baseline values in prognostic accuracy [ 5 ]. These findings align with our results, supporting the concept that "albumin stability/trend" serves as a more sensitive prognostic indicator [ 12 , 13 ]. Clinically, it is crucial not only to focus on a single or baseline serum albumin value but also to monitor dynamic measures such as ALB-cv, time-averaged albumin, or the achievement rate within the first year of treatment. These indicators should be used for early identification of high-risk patients and to guide timely interventions, including nutritional support, infection source identification, and the optimization of PD prescriptions [ 14 , 15 ]. Albumin is both a key indicator of nutritional status and a negative acute-phase reactant. Frequent or significant fluctuations in serum albumin levels often reflect recurrent inflammatory episodes, infections (such as peritonitis), insufficient nutritional intake, or metabolic imbalance, all of which contribute to an increased risk of mortality [ 16 , 17 ]. Furthermore, consistently low or repeatedly declining albumin levels accelerate muscle wasting, impair immune function, and increase the risk of infections and cardiovascular events. On the other hand, studies in the hemodialysis (HD) population have shown that high albumin variability (in the high variability group) is associated with an increased risk of all-cause mortality and adverse outcomes. Thus, not only is low albumin a risk factor, but "high albumin variability" itself is also a significant indicator of poor prognosis [ 18 , 19 ]. The study found that in the high ALB-cv group, the average serum albumin level declined, accompanied by lower levels of hemoglobin, calcium, and phosphorus. This suggests that ALB-cv reflects a composite pathological state: the interaction of malnutrition, chronic low-grade inflammation, and mineral metabolism disturbances. Clinical and epidemiological studies have highlighted that low albumin is closely associated with inflammation (CRP, white blood cell count), protein loss (dialysis-related or peritoneal protein loss), and insufficient nutritional intake. These conditions are all linked to an increased risk of cardiovascular complications and mortality [ 20 ]. In this study, ALB-cv was more effective in capturing fluctuations in albumin levels during follow-up, potentially providing a better reflection of the long-term "nutritional-inflammatory burden" than a single low value. Related studies have also shown that composite inflammatory/nutritional markers, such as NPAR (neutrophil percentage-to-albumin ratio), PAR, and HALP, provide incremental value in predicting prognosis in PD patients. The results of this study are logically consistent with these emerging indicators [ 21 – 23 ]. This study did not observe a significant association between ALB-cv and technical failure in peritoneal dialysis (PD). Technical failure is often influenced by dialysis-related complications (such as recurrent peritonitis, residual renal function decline, overload, or inadequate dialysis), as well as socioeconomic factors and patient compliance [ 24 ]. These factors may not necessarily be directly associated with short-term fluctuations in serum albumin. Additionally, many events leading to technical failure (such as severe peritonitis) may involve acute nutritional or metabolic deterioration prior to occurrence, but ALB-cv reflects early to mid-term albumin variability, which may limit its predictive ability for technical failure. Previous studies on albumin and dialysis modality switching or discontinuation have also shown variability, suggesting that the determinants of technical failure are complex and multifaceted [ 25 , 26 ]. This study shows that age and diabetes are independent risk factors for mortality, which is consistent with numerous PD survival analyses [ 27 , 28 ]. It is important to note that elderly patients may experience muscle mass decline, multiple comorbidities, and poor response to nutritional support, which could result in varying interpretations of serum albumin and its fluctuations across different age groups. Therefore, when conducting clinical risk stratification, the interaction between ALB-cv and immutable factors such as age and diabetes should be considered [ 13 , 29 ]. This study also has certain limitations. First, the retrospective cohort design may be subject to selection bias and residual confounding. Second, this study did not systematically include inflammatory markers (such as CRP, IL-6), peritoneal protein transport characteristics, dietary intake, and socioeconomic/lifestyle factors as potential confounders. These variables may play significant mediating or confounding roles in the relationship between ALB-cv and patient mortality. Furthermore, the estimation of ALB-cv is influenced by the sequence length and measurement frequency. Future prospective studies should standardize the measurement window and validate the generalizability of the ALB-cv threshold. Declarations Conflict of interest : The authors declare no competing interests. Funding: This research did not receive funding. Author Contribution Y. M. and L. C. designed the study, W.Q , S.F, and S.W. extracted and collated the data, Y. M. , H.L. and J.N. performed the analyses. Y. M. wrote the manuscript. W.C. , X.J. ,Y. Z. and F. X. reviewed and edited the manuscript. The manuscript has been approved by all the authors and is ready for publication. Acknowledgement we would like to extend our sincere gratitude to all team members for their diligent efforts in this study. Data Availability The data that support the fndings of this study are available from the corresponding author upon reasonable request. References Bello A K, Okpechi I G, Levin A, et al. An update on the global disparities in kidney disease burden and care across world countries and regions[J]. Lancet Glob Health, 2024,12(3):e382-e395. DOI: 10.1016/S2214-109X(23)00570-3. Guo S, Yang L, Zhu X, et al. Risk factors of different mortality periods in older patients with end-stage renal disease undergoing urgent-start peritoneal dialysis: a retrospective observational study[J]. BMC Geriatr, 2024,24(1):343. DOI: 10.1186/s12877-024-04931-4. Liu X, Huang R, Wu H, et al. Patient characteristics and risk factors of early and late death in incident peritoneal dialysis patients[J]. Sci Rep, 2016,6:32359. DOI: 10.1038/srep32359. Lin T Y, Chou H H, Hsieh T H, et al. Malnutrition is Associated with Increased Morbidity and Death in Dialysis Patients Undergoing Endovascular Therapy for Peripheral Artery Disease[J]. Eur J Vasc Endovasc Surg, 2022,64(2-3):225-233. DOI: 10.1016/j.ejvs.2022.03.045. Jin L, Wang X, Ma Y, et al. Serum albumin at 1 year after peritoneal dialysis predicts long-term outcomes on continuous ambulatory peritoneal dialysis[J]. Ren Fail, 2022,44(1):252-257. DOI: 10.1080/0886022X.2022.2033264. Wang X, Han Q, Wang T, et al. Serum albumin changes and mortality risk of peritoneal dialysis patients[J]. Int Urol Nephrol, 2020,52(3):565-571. DOI: 10.1007/s11255-020-02389-y. Li J, Liu Y, Lu Y, et al. Dynamic changes in serum albumin and ferritin indicate higher risk of early-onset peritonitis in peritoneal dialysis patients[J]. Ther Apher Dial, 2025. DOI: 10.1111/1744-9987.14244. Li M L, Bajaj A, Bhetuwal U, et al. Characteristics of registered research in peritoneal dialysis: Past and present[J]. Clin Nephrol, 2024,101(1):17-24. DOI: 10.5414/CN111162. Li P K, Chow K M, Van de Luijtgaarden M W, et al. Changes in the worldwide epidemiology of peritoneal dialysis[J]. Nat Rev Nephrol, 2017,13(2):90-103. DOI: 10.1038/nrneph.2016.181. See E J, Johnson D W, Hawley C M, et al. Risk Predictors and Causes of Technique Failure Within the First Year of Peritoneal Dialysis: An Australia and New Zealand Dialysis and Transplant Registry (ANZDATA) Study[J]. Am J Kidney Dis, 2018,72(2):188-197. DOI: 10.1053/j.ajkd.2017.10.019. Cho Y, See E J, Htay H, et al. Early Peritoneal Dialysis Technique Failure: Review[J]. Perit Dial Int, 2018,38(5):319-327. DOI: 10.3747/pdi.2018.00017. Chen C, Zhang J, Zhou Z, et al. Impact of serum albumin level and variability on short-term cardiovascular-related and all-cause mortality in patients on maintenance hemodialysis[J]. Medicine (Baltimore), 2021,100(43):e27666. DOI: 10.1097/MD.0000000000027666. Song P, Yang D, Li J, et al. Dynamic serum albumin and outcome of peritoneal dialysis patients: A retrospective study in China[J]. Front Med (Lausanne), 2022,9:917603. DOI: 10.3389/fmed.2022.917603. Jin L, Wang X, Ma Y, et al. Serum albumin at 1 year after peritoneal dialysis predicts long-term outcomes on continuous ambulatory peritoneal dialysis[J]. Ren Fail, 2022,44(1):252-257. DOI: 10.1080/0886022X.2022.2033264. Chiu P F, Tsai C C, Wu C L, et al. Trajectories of Serum Albumin Predict Survival of Peritoneal Dialysis Patients: A 15-year Follow-Up Study[J]. Medicine (Baltimore), 2016,95(12):e3202. DOI: 10.1097/MD.0000000000003202. Malho G A, Calcas M R, Ribeiro B, et al. Peritoneal Protein Loss, Inflammation, and Nutrition: Refuting Myths[J]. Front Med (Lausanne), 2022,9:884061. DOI: 10.3389/fmed.2022.884061. Huang Y T, Jiang M Y, Hwang J C. Albumin to prealbumin ratio in peritoneal dialysis patients: Clinical implication and outcome prediction[J]. PLoS One, 2022,17(11):e276159. DOI: 10.1371/journal.pone.0276159. Liu Y, Qiu Z, Shen G, et al. Associations between neutrophil-percentage-to-albumin ratio level and all-cause mortality and cardiovascular disease-cause mortality in general population: evidence from NHANES 1999-2010[J]. Front Cardiovasc Med, 2024,11:1393513. DOI: 10.3389/fcvm.2024.1393513. Castillo J C, Vesga J, Rivera A, et al. Variations in Serum Albumin Levels Over Time in Patients Treated With Conventional Hemodialysis or Expanded Hemodialysis: A Cohort Study[J]. Hemodial Int, 2025,29(3):327-334. DOI: 10.1111/hdi.13232. Ma X, Shi Y, Tao M, et al. Analysis of risk factors and outcome in peritoneal dialysis patients with early-onset peritonitis: a multicentre, retrospective cohort study[J]. BMJ Open, 2020,10(2):e29949. DOI: 10.1136/bmjopen-2019-029949. Xu M, Huan J, Zhu L, et al. The neutrophil percentage-to-albumin ratio is an independent risk factor for poor prognosis in peritoneal dialysis patients[J]. Ren Fail, 2024,46(1):2294149. DOI: 10.1080/0886022X.2023.2294149. Ma H, Chen J, Zhan X, et al. Platelet-to-albumin ratio: a potential biomarker for predicting all-cause and cardiovascular mortality in patients undergoing peritoneal dialysis[J]. BMC Nephrol, 2024,25(1):365. DOI: 10.1186/s12882-024-03792-8. Guo X, Song P, Qian Y, et al. The prognostic role of hemoglobin, albumin, lymphocyte, and platelets score in peritoneal dialysis[J]. Int Urol Nephrol, 2025. DOI: 10.1007/s11255-025-04679-9. Salani M. Strategies to Reduce Technique Failure in Peritoneal Dialysis[J]. Kidney360, 2025,6(4):496-497. DOI: 10.34067/KID.0000000778. Xie W, Qin L, Huang J, et al. Clinical risk factors for peritoneal dialysis withdrawal at different dialysis duration[J]. Ren Fail, 2023,45(2):2274965. DOI: 10.1080/0886022X.2023.2274965. Khan S F. Updates on Infectious and Other Complications in Peritoneal Dialysis: Core Curriculum 2023[J]. Am J Kidney Dis, 2023,82(4):481-490. DOI: 10.1053/j.ajkd.2023.03.011. Zhang J, Lu X, Li H, et al. Risk factors for mortality in patients undergoing peritoneal dialysis: a systematic review and meta-analysis[J]. Ren Fail, 2021,43(1):743-753. DOI: 10.1080/0886022X.2021.1918558. Lai K J, Hsieh Y P, Chiu P F, et al. Association of Albumin and Globulin with Mortality Risk in Incident Peritoneal Dialysis Patients[J]. Nutrients, 2022,14(14). DOI: 10.3390/nu14142850. Zoccali C, Mallamaci F, Adamczak M, et al. Cardiovascular complications in chronic kidney disease: a review from the European Renal and Cardiovascular Medicine Working Group of the European Renal Association[J]. Cardiovasc Res, 2023,119(11):2017-2032. DOI: 10.1093/cvr/cvad083. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 05 Apr, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 22 Dec, 2025 Reviews received at journal 21 Dec, 2025 Reviewers agreed at journal 30 Nov, 2025 Reviews received at journal 17 Nov, 2025 Reviewers agreed at journal 27 Oct, 2025 Reviewers invited by journal 14 Oct, 2025 Editor assigned by journal 14 Oct, 2025 Editor invited by journal 14 Oct, 2025 Submission checks completed at journal 10 Oct, 2025 First submitted to journal 10 Oct, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7800922","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":535358241,"identity":"21b061a5-b274-49e4-8350-646920a742d1","order_by":0,"name":"Yonglong Min","email":"","orcid":"","institution":"Wuhan No.1 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yonglong","middleName":"","lastName":"Min","suffix":""},{"id":535358242,"identity":"611b4387-a056-4e55-8e94-7a7d7daa2ccd","order_by":1,"name":"Li Cheng","email":"","orcid":"","institution":"Wuhan No.1 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Cheng","suffix":""},{"id":535358243,"identity":"1dd42f42-0688-4366-9252-68ddc228092a","order_by":2,"name":"Hong Liu","email":"","orcid":"","institution":"Wuhan No.1 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Liu","suffix":""},{"id":535358244,"identity":"9bc00c7a-452c-4512-b847-106638f50f99","order_by":3,"name":"Nan Jiang","email":"","orcid":"","institution":"Wuhan No.1 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Jiang","suffix":""},{"id":535358245,"identity":"190ff7ec-0a24-439e-b9a1-d97f9ad474e8","order_by":4,"name":"Wenhui Qiu","email":"","orcid":"","institution":"Wuhan No.1 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Wenhui","middleName":"","lastName":"Qiu","suffix":""},{"id":535358246,"identity":"d40bbd69-0b8d-4ea6-849f-bcb62db5ed05","order_by":5,"name":"Shuai Fu","email":"","orcid":"","institution":"Wuhan No.1 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Fu","suffix":""},{"id":535358247,"identity":"2e510f41-8a3f-48f2-a4e0-75cb6c2396f4","order_by":6,"name":"Sheng Wan","email":"","orcid":"","institution":"Wuhan No.1 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Sheng","middleName":"","lastName":"Wan","suffix":""},{"id":535358248,"identity":"f58c8de6-2bc9-4138-8bb3-834035355dcc","order_by":7,"name":"Wenli Chen","email":"","orcid":"","institution":"Central Hospital of Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Wenli","middleName":"","lastName":"Chen","suffix":""},{"id":535358249,"identity":"9e4275a2-5378-4bf1-b748-1c10f9fa7517","order_by":8,"name":"Xiaofei Jin","email":"","orcid":"","institution":"People’s Hospital of Huang Pi","correspondingAuthor":false,"prefix":"","firstName":"Xiaofei","middleName":"","lastName":"Jin","suffix":""},{"id":535358250,"identity":"91a17def-63a0-4dec-b420-3b950f0d327f","order_by":9,"name":"Yanmin Zhang","email":"","orcid":"","institution":"Wuhan No.1 Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yanmin","middleName":"","lastName":"Zhang","suffix":""},{"id":535358251,"identity":"fafa3a7d-2d04-4231-b425-3403d8f9c3ae","order_by":10,"name":"Fei Xiong","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA40lEQVRIiWNgGAWjYBACg8Mg0saGh1/+8IEDH34QrSUtTU5yBlviwZk9RGiRbABrOWxscIPH+DAHGxFa+NmZjz38ksCc2HC758NhBh4GeX6xA/i1sDGzpRvLJLAlNs45u+FwgQWD4czZCYS08JhJS/7gSWxmyN1weAYPQ4LBbQJa+EFaJBIkEtsYch4c5mEjQotkM4+Z5IcEA2MeiRwG4rQYHGZLk2ZISJCT4DlmAAxkCcJ+MTh/+Jjkj4T/PPbHmx9/+PDDRp5fmoAWEGDmQbAlCCsHAUZikskoGAWjYBSMYAAAmblEFdf8GlcAAAAASUVORK5CYII=","orcid":"","institution":"Wuhan No.1 Hospital","correspondingAuthor":true,"prefix":"","firstName":"Fei","middleName":"","lastName":"Xiong","suffix":""}],"badges":[],"createdAt":"2025-10-07 15:23:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7800922/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7800922/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-026-47169-3","type":"published","date":"2026-04-05T15:59:43+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":94625920,"identity":"330cfa4d-8963-4254-8647-93243192e674","added_by":"auto","created_at":"2025-10-29 04:46:31","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":286392,"visible":true,"origin":"","legend":"","description":"","filename":"Revisedmanuscript.docx","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/57256296067662a9dd1c3cb1.docx"},{"id":94625925,"identity":"4333e621-144f-42b3-b045-e44735f59421","added_by":"auto","created_at":"2025-10-29 04:46:31","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":11342,"visible":true,"origin":"","legend":"","description":"","filename":"6669db07f3bb45c9a13c639e8a411a3c.json","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/94311e702494864fc709dd5e.json"},{"id":94625926,"identity":"caba9218-8b51-4c4c-b291-9797e05b3e91","added_by":"auto","created_at":"2025-10-29 04:46:31","extension":"xml","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":86847,"visible":true,"origin":"","legend":"","description":"","filename":"6669db07f3bb45c9a13c639e8a411a3c1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/8dc0082d7e1c8aec35ae83d2.xml"},{"id":94625928,"identity":"3b4faaeb-a252-4e5b-b276-ce9e51f86e79","added_by":"auto","created_at":"2025-10-29 04:46:32","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":77254,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/dc57dc98be7ac74bc813d4a4.png"},{"id":94640029,"identity":"d43bc0ed-8dd3-41ca-a27a-0d68af74876c","added_by":"auto","created_at":"2025-10-29 07:47:55","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36413,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/a6c0ea8e14feccc8015f05e5.png"},{"id":94625931,"identity":"5f98e210-22fb-473c-b892-6ea033d6c08f","added_by":"auto","created_at":"2025-10-29 04:46:32","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36035,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/6e958d36d68ca731f13cd9ac.png"},{"id":94625935,"identity":"84b76aaf-f20b-4471-9347-1c2c4c11fde5","added_by":"auto","created_at":"2025-10-29 04:46:32","extension":"png","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":71894,"visible":true,"origin":"","legend":"","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/57c15c5c6b3ac79269496ccf.png"},{"id":94640224,"identity":"93b341d0-0bc3-4933-8b4b-b0c8fc3264b4","added_by":"auto","created_at":"2025-10-29 07:48:51","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":41634,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/1a1f1c4b7255eea568562a6d.png"},{"id":94640407,"identity":"41287038-af73-48c1-8f49-97251b828770","added_by":"auto","created_at":"2025-10-29 07:49:23","extension":"png","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":16671,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/e767add216536f1ecfe8e391.png"},{"id":94640015,"identity":"14b6c0a8-2d85-40f6-9abc-6f87630647bb","added_by":"auto","created_at":"2025-10-29 07:47:51","extension":"png","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":19046,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/5886bd3d3aba54cb2b10cc5a.png"},{"id":94625934,"identity":"0528586d-17d5-442b-8e4b-154d0a3450f2","added_by":"auto","created_at":"2025-10-29 04:46:32","extension":"png","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32553,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/d19174b548da6c12818be82c.png"},{"id":94625936,"identity":"65352522-1403-476a-a851-1fd14ffed1bd","added_by":"auto","created_at":"2025-10-29 04:46:32","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82600,"visible":true,"origin":"","legend":"","description":"","filename":"6669db07f3bb45c9a13c639e8a411a3c1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/418d082e19fc484ffcf0d394.xml"},{"id":94640440,"identity":"333c450b-5f3b-45df-aca8-2d4814e14725","added_by":"auto","created_at":"2025-10-29 07:49:29","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":96367,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/a8d8335c9238d90d97f4244b.html"},{"id":94640245,"identity":"271e039f-cca1-454b-8da2-6e6bc4931275","added_by":"auto","created_at":"2025-10-29 07:48:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":191178,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of Patient Enrollment.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/cadb4071500a402827a80e89.png"},{"id":94625922,"identity":"0348a450-39cc-4805-8c03-0f0330b62337","added_by":"auto","created_at":"2025-10-29 04:46:31","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":93681,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of Technical Survival Rates Among Different ALB-cv Groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/d4e7e492e01bceb80609ab3e.png"},{"id":94640235,"identity":"b6c46c5e-66a9-46bd-9053-bf438ac97b06","added_by":"auto","created_at":"2025-10-29 07:48:53","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":94892,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparison of All-Cause Survival Rates Among Different ALB-cv Groups\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/2db47b7f1b41cad2f28af315.png"},{"id":94639957,"identity":"cb7137de-cbb0-4aca-8b51-085b6abc36c1","added_by":"auto","created_at":"2025-10-29 07:47:02","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":127130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCox Regression Analysis of All-Cause Mortality After Adjustment\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/8398b608e4f20a879f76f6ff.png"},{"id":106343605,"identity":"24bc70e9-c41d-4f8a-9734-ed7fca11ecdd","added_by":"auto","created_at":"2026-04-07 16:07:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1217406,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7800922/v1/28a9b51e-1de8-4ae7-938d-41a37af3faf6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Albumin Variability during the First Year on Prognosis in Peritoneal Dialysis Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePeritoneal dialysis (PD) serves as a major renal replacement therapy for patients with end-stage renal disease (ESRD), with global utilization exceeding 300,000 patients as of 2023, particularly widespread in developing countries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Nonetheless, PD patients remain at considerable risk of technique failure, all-cause mortality, and complications such as malnutrition, cardiovascular events, and infections [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Serum albumin, a key marker of nutritional status, is strongly associated with clinical outcomes in this population [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMultiple studies have confirmed the prognostic value of albumin levels. A prospective cohort study demonstrated that each 1 g/dL increase in serum albumin one year after PD initiation was associated with an 8.7% reduction in all-cause mortality, particularly among patients with baseline hypoalbuminemia, suggesting that dynamic improvement correlates with long-term survival [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In hemodialysis (HD) patients, albumin variability has been identified as a risk factor for mortality, and emerging evidence indicates a similar pattern in PD populations. Albumin trajectory was shown to be a stronger predictor of survival than baseline values, with increasing levels linked to improved outcomes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough evidence remains limited in PD-specific settings, recent studies underscore the importance of dynamic albumin profiles. A 2025 study reported that variations in serum albumin and ferritin were predictive of early peritonitis risk among PD patients [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Another study from 2022 indicated that serum albumin levels after one year of PD were an independent predictor of long-term outcomes, outperforming baseline measurements [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These findings suggest that the coefficient of variation of albumin (albumin-CV) during the initial treatment period may reflect variations related to the malnutrition-inflammation complex syndrome (MICS), peritoneal membrane permeability, episodes of infection, and nutritional interventions, thereby influencing risks of technique failure and mortality.\u003c/p\u003e\u003cp\u003eHowever, current literature predominantly focuses on static albumin measurements or HD populations. There is a lack of large-scale, multicenter studies evaluating albumin-CV during the first year of PD\u0026mdash;a critical adaptation period often accompanied by nutritional instability and high rates of technical complications [\u003cspan additionalcitationids=\"CR9 CR10\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Assessing early albumin variability may improve risk stratification and guide timely interventions. This multicenter retrospective study aims to examine the impact of albumin-CV during the first year of PD on technique failure and all-cause mortality.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Design and Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis multicenter, retrospective cohort study enrolled patients from three peritoneal dialysis centers in Wuhan, China: Wuhan No. 1 Hospital, Central Hospital of Wuhan, and People\u0026rsquo;s Hospital of Huang Pi. Data were obtained from the Wuhan Peritoneal Dialysis Quality Control Platform database for all individuals who initiated PD between January 1, 2018, and December 31, 2023. Participants were followed from PD initiation until the occurrence of an outcome event or the end of the follow-up period on December 31, 2024, whichever occurred first.This study was performed in accordance with the principles of the Declaration of Helsinki. Informed consent was obtained from all participants and/or their legal guardians.The study protocol was approved by the Ethics Committee of the Wuhan No. 1 Hospital (No. 128 [2024]).The inclusion criteria were as follows:Age 18 years or older;Newly commenced peritoneal dialysis (including continuous ambulatory peritoneal dialysis [CAPD]) during the study period;At least two recorded laboratory measurements during the first year after PD initiation.Exclusion criteria were as follows:Less than 12 months of follow-up;History of malignant tumor or severe digestive system disease at baseline;Transition from hemodialysis (HD) to PD with more than 3 months of prior HD treatment;Missing essential baseline or follow-up data that precluded valid analysis.A total of 759 patients met the criteria and were included in the final analysis.(Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Collection and Variable Definitions\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eDemographic and Clinical Baseline Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe following demographic and clinical data were collected at the initiation of peritoneal dialysis (PD):Age at the start of PD therapy;Sex;with particular emphasis on the presence or absence of diabetes mellitus.\u003c/p\u003e\u003cp\u003e\u003cb\u003eLaboratory Parameters\u003c/b\u003e\u003c/p\u003e\u003cp\u003eLaboratory data were obtained during the first year (0\u0026ndash;12 months) following PD initiation. For each laboratory parameter, the mean value was calculated over this observation period. The following parameters were included:Weekly total urea clearance index(reported as mean Kt/V, Kt/V-M);Weekly total creatinine clearance rate(mean Ccr, Ccr-M);Serum albumin(mean albumin, ALB-M);Hemoglobin(mean hemoglobin,HB-M);Serum ferritin(mean ferritin,SF-M);Corrected serum calcium​ (mean corrected calcium,Ca-M);Serum phosphorus(mean phosphorus,P-M);Intact parathyroid hormone(mean iPTH,PTH-M).\u003c/p\u003e\u003cp\u003e\u003cb\u003eData Processing and Missing Value Imputation\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo address the issue of randomly missing data (expected to be \u0026lt;\u0026thinsp;15% across variables), complete case analysis was avoided to prevent reduction in sample size and potential selection bias. Instead, missing values were imputed using the random forest algorithm to improve imputation accuracy. Transferrin saturation (TSAT) was excluded from the analytical dataset due to a high missing rate exceeding 40%. Medication-related data were not incorporated into the analysis owing to inconsistent reporting standards and variable data quality across participating centers.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eStatistical analysis was performed using SPSS 22.0 and GraphPad Prism 8 software. Continuous variables with a normal distribution were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation \u003cem\u003e(x̄ \u0026plusmn; s\u003c/em\u003e), and comparisons between the three groups were made using one-way analysis of variance (ANOVA). For non-normally distributed continuous variables, data were expressed as median (\u003cem\u003eP25, P75\u003c/em\u003e), and comparisons between the groups were performed using the Kruskal-Wallis H test. Categorical variables were presented as frequency (percentage) [n(%)] and compared using the chi-square (\u003cem\u003eχ\u003c/em\u003e\u0026sup2;) test.Kaplan-Meier survival curves were constructed to illustrate the technical survival and all-cause survival of each group (based on ALB-cv tertiles), and differences between the groups were compared using the log-rank test. A multivariate Cox proportional hazards regression model was used to assess the relationship between ALB-cv and prognosis, and hazard ratios (\u003cem\u003eHR\u003c/em\u003e) along with their 95% confidence intervals (\u003cem\u003eCI\u003c/em\u003e) were calculated. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eBaseline Characteristics of the Patients\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAfter applying the inclusion and exclusion criteria, a total of 759 PD patients were included in this study. The mean age was 57.81\u0026thinsp;\u0026plusmn;\u0026thinsp;13.28 years, with 58.8% male and 15.9% with diabetic nephropathy. The median follow-up time was 37 months. Based on ALB-cv tertiles, the patients were classified into three groups: low variability group (ALB-cv\u0026thinsp;\u0026lt;\u0026thinsp;4%), medium variability group (4% \u0026le; ALB-cv\u0026thinsp;\u0026lt;\u0026thinsp;8%), and high variability group (ALB-cv\u0026thinsp;\u0026ge;\u0026thinsp;8%).\u003c/p\u003e\u003cp\u003eThe proportion of diabetic patients in the high variability group (20.7%) was significantly higher than in the low variability group (10.9%) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.010). The hemoglobin level in the high variability group (100.2\u0026thinsp;\u0026plusmn;\u0026thinsp;14.6 g/L) was lower than in the low variability group (104.7\u0026thinsp;\u0026plusmn;\u0026thinsp;17.2 g/L) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). The serum albumin level in the high variability group (33.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.71 g/L) was lower than in both the medium variability group (36.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.16 g/L) and the low variability group (36.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75 g/L) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The serum calcium level in the high variability group (2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 mmol/L) was lower than in both the medium variability group (2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18 mmol/L) and the low variability group (2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 mmol/L) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005). The serum phosphorus level in the high variability group (1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29 mmol/L) was lower than in the medium variability group (1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34 mmol/L) (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006).There were no significant differences between the groups in terms of sex, age, residual renal function (Kt/V, creatinine clearance), ferritin, or parathyroid hormone levels (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05)(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of Baseline Levels of Patients with Different ALB-cv (N\u0026thinsp;=\u0026thinsp;759)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow Variability Group (N1\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedium Variability Group (N2\u0026thinsp;=\u0026thinsp;247)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh Variability Group (N3\u0026thinsp;=\u0026thinsp;256)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003eχ\u0026sup2;/F/H\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e144 (56.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e141 (57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e161 (62.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.754\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.252\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e112 (43.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e106 (42.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95 (37.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiabetic Nephropathy (DN)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28 (10.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (16.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53 (20.7%) a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.127\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e228 (89.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e207 (83.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e203 (79.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e58.00 (48.00, 67.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.00 (51.00, 67.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e60.00 (50.00, 68.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.367\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.505\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKt/V-M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.83 (1.65, 2.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.88 (1.70, 2.21)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.85 (1.60, 2.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e2.512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.285\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCcr-M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e67.35 (60.86, 78.82)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e66.75 (58.50, 83.78)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e67.62 (56.75, 82.89)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.067\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.967\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHB-M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e104.67\u0026thinsp;\u0026plusmn;\u0026thinsp;17.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e102.82\u0026thinsp;\u0026plusmn;\u0026thinsp;13.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e100.23\u0026thinsp;\u0026plusmn;\u0026thinsp;14.61 a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.409\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSF-M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e132.16 (71.43, 187.63)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e124.43 (63.10, 169.47)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e123.23 (82.48, 195.62)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e1.389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.499\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALB-M\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36.57\u0026thinsp;\u0026plusmn;\u0026thinsp;3.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e36.05\u0026thinsp;\u0026plusmn;\u0026thinsp;4.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e33.63\u0026thinsp;\u0026plusmn;\u0026thinsp;4.71 ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35.321\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eALB-cv (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.00 (2.00, 3.00)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.00 (5.00, 7.00) a\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12.00 (10.00, 16.00) ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e673.708\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCa-M (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 ab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP-M (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.52\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.43\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29 b\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.126\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePTH-M (mmol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e195.38 (116.23, 325.94)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e188.66 (99.86, 334.79)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e174.06 (103.95, 282.16)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e3.600\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.165\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ea: Compared with Low Variability Group, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; b: Compared with Medium Variability Group, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of Survival Curves Among Different ALB-cv Groups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDuring the entire follow-up period, a total of 66 technical failure events and 170 all-cause mortality events occurred. Kaplan-Meier survival curve analysis revealed no significant difference in technical survival rates between the different serum albumin variability (ALB-cv) groups (Log-rank χ\u0026sup2;=0.083, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.669) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). However, there was a significant difference in all-cause survival rates among the ALB-cv groups (Log-rank \u003cem\u003eχ\u003c/em\u003e\u0026sup2;=8.017, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.013) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eCox Regression Analysis of the Relationship Between ALB-cv and Patient Prognosis\u003c/b\u003e\u003c/p\u003e\u003cp\u003eRegardless of adjustment for confounding factors, no association was found between ALB-cv and technical failure in PD patients (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In the unadjusted model, ALB-cv was identified as a risk factor for all-cause mortality in PD patients (HR\u0026thinsp;=\u0026thinsp;1.054, 95% CI 1.030\u0026ndash;1.079, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). After adjusting for age, sex, diabetes, total Kt/V (KtV-M), creatinine clearance (Ccr-M), hemoglobin (HB-M), ferritin (SF-M), corrected calcium (Ca-M), phosphorus (P-M), and parathyroid hormone (PTH-M), ALB-cv remained an independent risk factor for all-cause mortality in PD patients (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.039, 95% \u003cem\u003eCI\u003c/em\u003e 1.014\u0026ndash;1.064, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002)(Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAdditionally, age (HR\u0026thinsp;=\u0026thinsp;1.064, 95% CI 1.048\u0026ndash;1.080, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and diabetes (\u003cem\u003eHR\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.816, 95% \u003cem\u003eCI\u003c/em\u003e 1.260\u0026ndash;2.619, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) were also identified as independent risk factors for all-cause mortality in PD patients(Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCox Regression Analysis of the Relationship Between ALB-cv and Patient Prognosis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eModel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTechnical Failure\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAll-Cause Mortality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u003cem\u003eHR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eHR\u003c/em\u003e (95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUnadjusted Model\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.012 (0.969\u0026ndash;1.058)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.580\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.054 (1.030\u0026ndash;1.079)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.009 (0.966\u0026ndash;1.055)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.044 (1.020\u0026ndash;1.068)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.008 (0.964\u0026ndash;1.054)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.040 (1.016\u0026ndash;1.065)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.008 (0.964\u0026ndash;1.053)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.733\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.039 (1.015\u0026ndash;1.064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eModel 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.998 (0.953\u0026ndash;1.044)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.918\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.039 (1.014\u0026ndash;1.064)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eNote\u003c/b\u003e: HR: Hazard Ratio; CI: Confidence Interval.\u003cb\u003eUnadjusted Model\u003c/b\u003e: No adjustments for any variables.\u003cb\u003eModel 1\u003c/b\u003e: Adjusted for age and sex.\u003cb\u003eModel 2\u003c/b\u003e: Further adjusted for the presence of diabetes in addition to Model 1.\u003cb\u003eModel 3\u003c/b\u003e: Further adjusted for total Kt/V (KtV-M) and creatinine clearance (Ccr-M) in addition to Model 2.\u003cb\u003eModel 4\u003c/b\u003e: Further adjusted for hemoglobin (HB-M), ferritin (SF-M), corrected calcium (Ca-M), phosphorus (P-M), and parathyroid hormone (PTH-M) in addition to Model 3.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that the Albumin Coefficient of Variation (ALB-cv) is an independent predictor of all-cause mortality in peritoneal dialysis (PD) patients, while it does not significantly affect technical failure. In recent years, multiple studies have suggested that the dynamic or average values of serum albumin over time are more predictive of prognosis than a single baseline measurement. Previous cohort studies in PD patients have shown that time-averaged albumin (TA-ALB) and the trajectory of serum albumin levels provide superior predictive value for mortality risk compared to single measurements, indicating that dynamic indicators outperform baseline values in prognostic accuracy [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. These findings align with our results, supporting the concept that \"albumin stability/trend\" serves as a more sensitive prognostic indicator [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Clinically, it is crucial not only to focus on a single or baseline serum albumin value but also to monitor dynamic measures such as ALB-cv, time-averaged albumin, or the achievement rate within the first year of treatment. These indicators should be used for early identification of high-risk patients and to guide timely interventions, including nutritional support, infection source identification, and the optimization of PD prescriptions [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlbumin is both a key indicator of nutritional status and a negative acute-phase reactant. Frequent or significant fluctuations in serum albumin levels often reflect recurrent inflammatory episodes, infections (such as peritonitis), insufficient nutritional intake, or metabolic imbalance, all of which contribute to an increased risk of mortality [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, consistently low or repeatedly declining albumin levels accelerate muscle wasting, impair immune function, and increase the risk of infections and cardiovascular events. On the other hand, studies in the hemodialysis (HD) population have shown that high albumin variability (in the high variability group) is associated with an increased risk of all-cause mortality and adverse outcomes. Thus, not only is low albumin a risk factor, but \"high albumin variability\" itself is also a significant indicator of poor prognosis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe study found that in the high ALB-cv group, the average serum albumin level declined, accompanied by lower levels of hemoglobin, calcium, and phosphorus. This suggests that ALB-cv reflects a composite pathological state: the interaction of malnutrition, chronic low-grade inflammation, and mineral metabolism disturbances. Clinical and epidemiological studies have highlighted that low albumin is closely associated with inflammation (CRP, white blood cell count), protein loss (dialysis-related or peritoneal protein loss), and insufficient nutritional intake. These conditions are all linked to an increased risk of cardiovascular complications and mortality [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In this study, ALB-cv was more effective in capturing fluctuations in albumin levels during follow-up, potentially providing a better reflection of the long-term \"nutritional-inflammatory burden\" than a single low value. Related studies have also shown that composite inflammatory/nutritional markers, such as NPAR (neutrophil percentage-to-albumin ratio), PAR, and HALP, provide incremental value in predicting prognosis in PD patients. The results of this study are logically consistent with these emerging indicators [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study did not observe a significant association between ALB-cv and technical failure in peritoneal dialysis (PD). Technical failure is often influenced by dialysis-related complications (such as recurrent peritonitis, residual renal function decline, overload, or inadequate dialysis), as well as socioeconomic factors and patient compliance [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These factors may not necessarily be directly associated with short-term fluctuations in serum albumin. Additionally, many events leading to technical failure (such as severe peritonitis) may involve acute nutritional or metabolic deterioration prior to occurrence, but ALB-cv reflects early to mid-term albumin variability, which may limit its predictive ability for technical failure. Previous studies on albumin and dialysis modality switching or discontinuation have also shown variability, suggesting that the determinants of technical failure are complex and multifaceted [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study shows that age and diabetes are independent risk factors for mortality, which is consistent with numerous PD survival analyses [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. It is important to note that elderly patients may experience muscle mass decline, multiple comorbidities, and poor response to nutritional support, which could result in varying interpretations of serum albumin and its fluctuations across different age groups. Therefore, when conducting clinical risk stratification, the interaction between ALB-cv and immutable factors such as age and diabetes should be considered [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThis study also has certain limitations. First, the retrospective cohort design may be subject to selection bias and residual confounding. Second, this study did not systematically include inflammatory markers (such as CRP, IL-6), peritoneal protein transport characteristics, dietary intake, and socioeconomic/lifestyle factors as potential confounders. These variables may play significant mediating or confounding roles in the relationship between ALB-cv and patient mortality. Furthermore, the estimation of ALB-cv is influenced by the sequence length and measurement frequency. Future prospective studies should standardize the measurement window and validate the generalizability of the ALB-cv threshold.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eConflict of interest :\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e\u003cp\u003eThis research did not receive funding.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eY. M. and L. C. designed the study, W.Q , S.F, and S.W. extracted and collated the data, Y. M. , H.L. and J.N. performed the analyses. Y. M. wrote the manuscript. W.C. , X.J. ,Y. Z. and F. X. reviewed and edited the manuscript. The manuscript has been approved by all the authors and is ready for publication.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003ewe would like to extend our sincere gratitude to all team members for their diligent efforts in this study.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data that support the fndings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBello A K, Okpechi I G, Levin A, et al. An update on the global disparities in kidney disease burden and care across world countries and regions[J]. Lancet Glob Health, 2024,12(3):e382-e395. DOI: 10.1016/S2214-109X(23)00570-3.\u003c/li\u003e\n\u003cli\u003eGuo S, Yang L, Zhu X, et al. Risk factors of different mortality periods in older patients with end-stage renal disease undergoing urgent-start peritoneal dialysis: a retrospective observational study[J]. BMC Geriatr, 2024,24(1):343. DOI: 10.1186/s12877-024-04931-4.\u003c/li\u003e\n\u003cli\u003eLiu X, Huang R, Wu H, et al. Patient characteristics and risk factors of early and late death in incident peritoneal dialysis patients[J]. Sci Rep, 2016,6:32359. DOI: 10.1038/srep32359.\u003c/li\u003e\n\u003cli\u003eLin T Y, Chou H H, Hsieh T H, et al. Malnutrition is Associated with Increased Morbidity and Death in Dialysis Patients Undergoing Endovascular Therapy for Peripheral Artery Disease[J]. Eur J Vasc Endovasc Surg, 2022,64(2-3):225-233. DOI: 10.1016/j.ejvs.2022.03.045.\u003c/li\u003e\n\u003cli\u003eJin L, Wang X, Ma Y, et al. Serum albumin at 1 year after peritoneal dialysis predicts long-term outcomes on continuous ambulatory peritoneal dialysis[J]. Ren Fail, 2022,44(1):252-257. DOI: 10.1080/0886022X.2022.2033264.\u003c/li\u003e\n\u003cli\u003eWang X, Han Q, Wang T, et al. Serum albumin changes and mortality risk of peritoneal dialysis patients[J]. Int Urol Nephrol, 2020,52(3):565-571. DOI: 10.1007/s11255-020-02389-y.\u003c/li\u003e\n\u003cli\u003eLi J, Liu Y, Lu Y, et al. Dynamic changes in serum albumin and ferritin indicate higher risk of early-onset peritonitis in peritoneal dialysis patients[J]. Ther Apher Dial, 2025. DOI: 10.1111/1744-9987.14244.\u003c/li\u003e\n\u003cli\u003eLi M L, Bajaj A, Bhetuwal U, et al. Characteristics of registered research in peritoneal dialysis: Past and present[J]. Clin Nephrol, 2024,101(1):17-24. DOI: 10.5414/CN111162.\u003c/li\u003e\n\u003cli\u003eLi P K, Chow K M, Van de Luijtgaarden M W, et al. Changes in the worldwide epidemiology of peritoneal dialysis[J]. Nat Rev Nephrol, 2017,13(2):90-103. DOI: 10.1038/nrneph.2016.181.\u003c/li\u003e\n\u003cli\u003eSee E J, Johnson D W, Hawley C M, et al. Risk Predictors and Causes of Technique Failure Within the First Year of Peritoneal Dialysis: An Australia and New Zealand Dialysis and Transplant Registry (ANZDATA) Study[J]. Am J Kidney Dis, 2018,72(2):188-197. DOI: 10.1053/j.ajkd.2017.10.019.\u003c/li\u003e\n\u003cli\u003eCho Y, See E J, Htay H, et al. Early Peritoneal Dialysis Technique Failure: Review[J]. Perit Dial Int, 2018,38(5):319-327. DOI: 10.3747/pdi.2018.00017.\u003c/li\u003e\n\u003cli\u003eChen C, Zhang J, Zhou Z, et al. Impact of serum albumin level and variability on short-term cardiovascular-related and all-cause mortality in patients on maintenance hemodialysis[J]. Medicine (Baltimore), 2021,100(43):e27666. DOI: 10.1097/MD.0000000000027666.\u003c/li\u003e\n\u003cli\u003eSong P, Yang D, Li J, et al. Dynamic serum albumin and outcome of peritoneal dialysis patients: A retrospective study in China[J]. Front Med (Lausanne), 2022,9:917603. DOI: 10.3389/fmed.2022.917603.\u003c/li\u003e\n\u003cli\u003eJin L, Wang X, Ma Y, et al. Serum albumin at 1 year after peritoneal dialysis predicts long-term outcomes on continuous ambulatory peritoneal dialysis[J]. Ren Fail, 2022,44(1):252-257. DOI: 10.1080/0886022X.2022.2033264.\u003c/li\u003e\n\u003cli\u003eChiu P F, Tsai C C, Wu C L, et al. Trajectories of Serum Albumin Predict Survival of Peritoneal Dialysis Patients: A 15-year Follow-Up Study[J]. Medicine (Baltimore), 2016,95(12):e3202. DOI: 10.1097/MD.0000000000003202.\u003c/li\u003e\n\u003cli\u003eMalho G A, Calcas M R, Ribeiro B, et al. Peritoneal Protein Loss, Inflammation, and Nutrition: Refuting Myths[J]. Front Med (Lausanne), 2022,9:884061. DOI: 10.3389/fmed.2022.884061.\u003c/li\u003e\n\u003cli\u003eHuang Y T, Jiang M Y, Hwang J C. Albumin to prealbumin ratio in peritoneal dialysis patients: Clinical implication and outcome prediction[J]. PLoS One, 2022,17(11):e276159. DOI: 10.1371/journal.pone.0276159.\u003c/li\u003e\n\u003cli\u003eLiu Y, Qiu Z, Shen G, et al. Associations between neutrophil-percentage-to-albumin ratio level and all-cause mortality and cardiovascular disease-cause mortality in general population: evidence from NHANES 1999-2010[J]. Front Cardiovasc Med, 2024,11:1393513. DOI: 10.3389/fcvm.2024.1393513.\u003c/li\u003e\n\u003cli\u003eCastillo J C, Vesga J, Rivera A, et al. Variations in Serum Albumin Levels Over Time in Patients Treated With Conventional Hemodialysis or Expanded Hemodialysis: A Cohort Study[J]. Hemodial Int, 2025,29(3):327-334. DOI: 10.1111/hdi.13232.\u003c/li\u003e\n\u003cli\u003eMa X, Shi Y, Tao M, et al. Analysis of risk factors and outcome in peritoneal dialysis patients with early-onset peritonitis: a multicentre, retrospective cohort study[J]. BMJ Open, 2020,10(2):e29949. DOI: 10.1136/bmjopen-2019-029949.\u003c/li\u003e\n\u003cli\u003eXu M, Huan J, Zhu L, et al. The neutrophil percentage-to-albumin ratio is an independent risk factor for poor prognosis in peritoneal dialysis patients[J]. Ren Fail, 2024,46(1):2294149. DOI: 10.1080/0886022X.2023.2294149.\u003c/li\u003e\n\u003cli\u003eMa H, Chen J, Zhan X, et al. Platelet-to-albumin ratio: a potential biomarker for predicting all-cause and cardiovascular mortality in patients undergoing peritoneal dialysis[J]. BMC Nephrol, 2024,25(1):365. DOI: 10.1186/s12882-024-03792-8.\u003c/li\u003e\n\u003cli\u003eGuo X, Song P, Qian Y, et al. The prognostic role of hemoglobin, albumin, lymphocyte, and platelets score in peritoneal dialysis[J]. Int Urol Nephrol, 2025. DOI: 10.1007/s11255-025-04679-9.\u003c/li\u003e\n\u003cli\u003eSalani M. Strategies to Reduce Technique Failure in Peritoneal Dialysis[J]. Kidney360, 2025,6(4):496-497. DOI: 10.34067/KID.0000000778.\u003c/li\u003e\n\u003cli\u003eXie W, Qin L, Huang J, et al. Clinical risk factors for peritoneal dialysis withdrawal at different dialysis duration[J]. Ren Fail, 2023,45(2):2274965. DOI: 10.1080/0886022X.2023.2274965.\u003c/li\u003e\n\u003cli\u003eKhan S F. Updates on Infectious and Other Complications in Peritoneal Dialysis: Core Curriculum 2023[J]. Am J Kidney Dis, 2023,82(4):481-490. DOI: 10.1053/j.ajkd.2023.03.011.\u003c/li\u003e\n\u003cli\u003eZhang J, Lu X, Li H, et al. Risk factors for mortality in patients undergoing peritoneal dialysis: a systematic review and meta-analysis[J]. Ren Fail, 2021,43(1):743-753. DOI: 10.1080/0886022X.2021.1918558.\u003c/li\u003e\n\u003cli\u003eLai K J, Hsieh Y P, Chiu P F, et al. Association of Albumin and Globulin with Mortality Risk in Incident Peritoneal Dialysis Patients[J]. Nutrients, 2022,14(14). DOI: 10.3390/nu14142850.\u003c/li\u003e\n\u003cli\u003eZoccali C, Mallamaci F, Adamczak M, et al. Cardiovascular complications in chronic kidney disease: a review from the European Renal and Cardiovascular Medicine Working Group of the European Renal Association[J]. Cardiovasc Res, 2023,119(11):2017-2032. DOI: 10.1093/cvr/cvad083.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Peritoneal dialysis, Serum albumin, coefficient of variation, Prognosis, All-cause mortality, Technique failure","lastPublishedDoi":"10.21203/rs.3.rs-7800922/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7800922/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e​\u003c/p\u003e\n\u003cp\u003eThis multicenter retrospective study aimed to evaluate the association between the coefficient of variation (CV) of serum albumin during the first year of peritoneal dialysis (PD) and clinical outcomes, including technique failure and all-cause mortality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e​\u003c/p\u003e\n\u003cp\u003eWe enrolled patients who initiated PD between January 1, 2018 and December 31, 2024 from three medical centers and maintained treatment for over one year. The albumin-CV was calculated from serial serum albumin measurements during the first year. Participants were categorized into low-, medium-, and high-variability groups based on albumin-CV tertiles. Primary endpoints were technique failure and all-cause mortality. Survival analyses were performed using Kaplan–Meier curves with log-rank tests. Multivariable Cox regression models were employed to assess independent associations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults​\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong 759 included patients (mean age 57.81 ± 13.28 years; 58.8% male), 15.9% had diabetic nephropathy. After a median follow-up of 37 months, 66 technique failures and 170 all-cause deaths occurred. The high-albumin-variability group showed higher prevalence of diabetes and lower hemoglobin, albumin, calcium, and phosphorus levels (all \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05). Albumin variability was significantly associated with all-cause mortality (log-rank χ² = 8.017, \u003cem\u003ep \u003c/em\u003e= 0.013) but not with technique failure. After adjusting for confounders, albumin-CV remained an independent predictor of all-cause mortality (\u003cem\u003eHR\u003c/em\u003e = 1.039, 95% \u003cem\u003eCI\u003c/em\u003e: 1.014–1.064,\u003cem\u003e p\u003c/em\u003e = 0.002).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion​\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHigher variability in serum albumin during the first year of PD is independently associated with increased risk of all-cause mortality.\u003c/p\u003e","manuscriptTitle":"Impact of Albumin Variability during the First Year on Prognosis in Peritoneal Dialysis Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-29 04:46:27","doi":"10.21203/rs.3.rs-7800922/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-22T07:55:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-21T09:55:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122304857735709095090103240945916518029","date":"2025-12-01T04:10:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-18T04:50:33+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"23957588378022172034518393476105138986","date":"2025-10-27T07:17:19+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-15T00:40:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-14T23:59:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-14T14:28:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-10T16:04:46+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-10T15:54:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"414ddc8b-ce51-4fd6-a5c7-0a0f6980531a","owner":[],"postedDate":"October 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":56910110,"name":"Health sciences/Diseases"},{"id":56910111,"name":"Health sciences/Medical research"},{"id":56910112,"name":"Health sciences/Nephrology"},{"id":56910113,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-04-07T16:03:15+00:00","versionOfRecord":{"articleIdentity":"rs-7800922","link":"https://doi.org/10.1038/s41598-026-47169-3","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-04-05 15:59:43","publishedOnDateReadable":"April 5th, 2026"},"versionCreatedAt":"2025-10-29 04:46:27","video":"","vorDoi":"10.1038/s41598-026-47169-3","vorDoiUrl":"https://doi.org/10.1038/s41598-026-47169-3","workflowStages":[]},"version":"v1","identity":"rs-7800922","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7800922","identity":"rs-7800922","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}