Hemolysis and Renal Dysfunction in Hemodialysis A Bilirubin-Based Study with Hepatic Confounding

Hemolysis and Renal Dysfunction in Hemodialysis A Bilirubin-Based Study with Hepatic Confounding | 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 Research Article Hemolysis and Renal Dysfunction in Hemodialysis A Bilirubin-Based Study with Hepatic Confounding Hussein Bakery Hussein Dedy, Ali Bannawi ALZubaidy This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9179431/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Hemolysis is a common complication in patients with renal failure undergoing hemodialysis, resulting from mechanical, metabolic, and oxidative stress. In resource-limited settings, bilirubin is often used as a surrogate marker of hemolysis; however, its interpretation in adults may be confounded by hepatocellular conditions such as hepatitis C virus (HCV) infection. Objective: To evaluate hemolysis-associated hematological parameters in patients with acute and chronic renal failure undergoing hemodialysis and to assess the impact of HCV infection on the interpretation of bilirubin as a hemolysis marker. Methods: A retrospective analytical study was conducted on 52 hemodialysis patients. Hematological parameters (hemoglobin, hematocrit, MCHC) and biochemical markers (serum creatinine, total bilirubin) were analyzed. Hemolysis percentage was estimated using a bilirubin-based equation. Subgroup analysis based on HCV status and multivariate linear regression were performed to control for confounding. Results: CRF patients showed significantly higher creatinine, bilirubin, and hemolysis levels compared to ARF patients (p 0.05), suggesting hepatic contribution. In contrast, HCV-negative patients showed a strong correlation (r ≈ 0.64, p < 0.01). Regression analysis identified creatinine as an independent predictor of hemolysis (β ≈ 2.35, p < 0.01), while HCV influenced bilirubin without directly affecting hemolysis. The model explained ~ 51% of variability. Conclusion: Hemolysis is elevated in CRF patients; however, bilirubin is a non-specific marker influenced by liver function. Its interpretation should consider HCV status. Combining hepatic and hematological indicators improves diagnostic accuracy in hemodialysis patients. Hemolysis Chronic Renal Failure Hemodialysis Bilirubin Hepatitis C Creatinine Confounding Factors 1. INTRODUCTION Renal failure represents a major global health challenge associated with high morbidity and mortality. It is classified into acute renal failure (ARF), characterized by a sudden decline in kidney function, and chronic renal failure (CRF), a progressive condition often leading to end-stage renal disease (ESRD). Hemodialysis remains the primary life-saving therapy; however, it is frequently complicated by anemia, oxidative stress, and hemolysis of red blood cells (RBCs). Hemolysis in hemodialysis patients results from multiple mechanisms, including mechanical trauma within the extracorporeal circuit, metabolic disturbances, uremic toxins, and oxidative damage, leading to reductions in hemoglobin (Hb) and hematocrit (HCT). Biochemical markers such as creatinine and bilirubin provide insights into renal dysfunction and RBC breakdown. In resource-limited settings, bilirubin is often used as a surrogate marker of hemolysis due to limited availability of plasma free hemoglobin assays. However, its interpretation in adults remains challenging, as it may be influenced by hepatocellular conditions such as hepatitis C virus (HCV) infection. Although hematological changes in renal failure have been widely studied, the relationship between hemolysis parameters and renal dysfunction remains underexplored, particularly in low-resource settings. In Yemen, where healthcare infrastructure is fragile, there is a lack of comprehensive data on dialysis-related hematological complications, limiting effective monitoring and clinical decision-making [ 1 – 5 , 16 – 19 ]. Research Gap and Aim : Previous studies have rarely accounted for hepatocellular confounding when using bilirubin as a hemolysis marker, potentially leading to misinterpretation of results. Therefore, this study aimed to evaluate hemolysis-associated hematological parameters in ARF and CRF patients undergoing hemodialysis and to assess the impact of HCV infection on the interpretation of bilirubin as a surrogate marker of hemolysis. Hypothesis and Framework : We hypothesized that CRF patients would exhibit higher hemolysis compared with ARF patients due to prolonged exposure to metabolic and oxidative stress. Hemodialysis-related mechanical factors further contribute to RBC destruction, making laboratory markers such as bilirubin, Hb, and HCT reflective of hemolysis severity, although potentially influenced by hepatic function. 2. METHDOLOGY 2.1 Study Design and Setting The study was conducted as a retrospective analytical observational study to investigate hemolysis-associated hematological parameters among patients with renal failure undergoing hemodialysis. The study was carried out at the Center of Dialysis and Renal Diseases in Hodeidah, Yemen, a major referral center providing dialysis services for renal failure patients in the region. Clinical and laboratory data were collected from patient medical records covering the period from January to December 2022. 2.2 Study Population Although the study sample included 52 patients due to the limited number of eligible cases in the dialysis center during the study period, this sample size is comparable to exploratory clinical studies investigating hematological parameters in dialysis populations. Patients were included irrespective of sex and age if complete clinical and laboratory data were available. Inclusion Criteria Patients were eligible for inclusion if they met the following criteria: • Confirmed diagnosis of acute renal failure (ARF) or chronic renal failure (CRF) • Undergoing hemodialysis treatment • Availability of complete hematological and biochemical laboratory records Exclusion Criteria Patients were excluded if: • Laboratory records were incomplete or missing • Patients had pre-existing hematological disorders unrelated to renal disease • Patients had recent blood transfusion or acute bleeding episodes prior to laboratory analysis • Samples showed laboratory processing errors or pre-analytical hemolysis 2.3 Confounding Control and Subgroup Analysis To address potential confounding factors affecting serum bilirubin levels, particularly hepatocellular causes, patients with documented liver disease were identified through medical records. Special emphasis was placed on hepatitis C virus (HCV) infection, given its high prevalence among hemodialysis populations. Patients were stratified into two subgroups: HCV-positive patients HCV-negative patients This stratification allowed differentiation between bilirubin elevation secondary to hemolysis and that related to hepatocellular dysfunction. Subgroup analyses were performed to evaluate differences in hemolysis parameters and biochemical markers between these groups. 2.4 Sample Collection and Processing Blood samples were obtained as part of routine clinical laboratory evaluation. Approximately 5 ml of venous blood was collected from each patient using sterile procedures. Two types of samples were collected: 1. Serum samples: Blood was collected in gel separator tubes for biochemical analysis, including serum creatinine and total bilirubin. Samples were centrifuged at 2000 rpm for 5 minutes to obtain serum. 2. Whole blood samples: Approximately 2.5 ml of blood was collected in EDTA tubes for hematological analysis. All laboratory analyses were performed according to standardized laboratory protocols to minimize pre-analytical and analytical variability. 2.5 Laboratory Measurements Hematological parameters were measured using an automated Sysmex hematology analyzer, which was used to determine the following complete blood count (CBC) parameters: • Hemoglobin (Hb) • Hematocrit (HCT) • Mean Corpuscular Hemoglobin Concentration (MCHC) Biochemical parameters were measured using a spectrophotometric biochemical analyzer following manufacturer protocols. The biochemical parameters analyzed included: • Serum Creatinine • Total Bilirubin Hemolysis indicators were evaluated based on laboratory measurements reflecting red blood cell destruction and plasma hemoglobin release, which may occur due to metabolic disturbances and mechanical stress during hemodialysis. Future studies should incorporate LDH and haptoglobin as complementary hemolysis markers to strengthen diagnostic accuracy. 2.6 methodology: Potential confounding factors such as dialysis duration, comorbid conditions, and medication use could not be fully controlled due to the retrospective design of the study. Key hemolysis biomarkers such as lactate dehydrogenase (LDH), haptoglobin, reticulocyte count, and plasma free hemoglobin were not available, which limits direct quantification of hemolysis. 2.7 Hemolysis Measurement : Although total bilirubin was used as a surrogate marker for hemolysis due to limited laboratory resources, it is recognized as a non-specific biomarker influenced by both hemolytic processes and hepatocellular function. Therefore, its interpretation was performed cautiously, particularly in patients with potential hepatic impairment. To partially address this limitation, subgroup and adjusted analyses based on HCV infection status were conducted. 2.8 Hemolysis Percentage Calculation To estimate the degree of red blood cell destruction during hemodialysis, hemolysis percentage was calculated using a modified equation. Traditionally, free hemoglobin (Hb) is employed as the direct marker of hemolysis. However, in resource-limited settings, measuring free Hb is often challenging. Therefore, in this study, total bilirubin was used as a surrogate marker, given its biochemical link to hemoglobin breakdown and its feasibility in routine laboratory practice. While total bilirubin was used as a surrogate marker for hemolysis due to limited availability of plasma free hemoglobin assays, it is acknowledged that bilirubin is a non-specific marker influenced by both hemolysis and hepatic function. Therefore, interpretation of bilirubin levels was conducted cautiously, particularly in patients with confirmed HCV infection. The applied formula was: This modified equation has not been externally validated and should be considered an exploratory tool. Further validation against established hemolysis biomarkers such as LDH and haptoglobin is required. Justification: • Scientifically, total bilirubin reflects the end product of hemoglobin degradation, making it a reliable proxy for hemolysis. • Practically, bilirubin measurement is more accessible and reproducible in local laboratories compared to free Hb assays. • Methodologically, this adaptation ensures applicability in fragile healthcare systems while maintaining alignment with established hemolysis assessment principles. 2.9 Study Variables The study evaluated the following variables: • Hematological Indicators • Hemoglobin (Hb) • Hematocrit (HCT) • Mean Corpuscular Hemoglobin Concentration (MCHC) Biochemical Indicators • Serum Creatinine • Total Bilirubin • Hemolysis Indicator • Hemolysis percentage (%) 2.10 Data Quality Control To ensure data reliability and consistency, laboratory records were carefully reviewed and cross-checked prior to statistical analysis. Data entry was verified through double data validation procedures, and inconsistencies between hematological and biochemical datasets were resolved by revisiting the original laboratory records. Quality assurance procedures were applied to ensure the accuracy of laboratory measurements according to standardized clinical laboratory guidelines. 2.11 Statistical Analysis All statistical analyses were performed using SPSS software (version 26; IBM Corp., Armonk, NY, USA) and R statistical software (version 4.3). Continuous variables were expressed as mean ± standard deviation (SD), while categorical variables were summarized as frequencies and percentages. The normality of data distribution was assessed using the Shapiro–Wilk test. To compare hematological and biochemical parameters between ARF and CRF patients, the Independent Samples t-test was applied for normally distributed variables. Associations between renal dysfunction indicators and hemolysis-related parameters were evaluated using Pearson correlation analysis. Furthermore, linear regression models were constructed to assess the predictive relationship between serum creatinine levels and hemolysis-related hematological indicators, including hemoglobin, hematocrit, and total bilirubin. All statistical tests were two-tailed, and a p-value < 0.05 was considered statistically significant. To improve the robustness of the statistical analysis, regression coefficients were interpreted alongside p-values to assess the strength and direction of associations. Future studies with larger datasets should incorporate confidence intervals and model validation techniques to further strengthen statistical inference. Given the relatively small sample size, regression results were interpreted cautiously to minimize overfitting bias. Adjusted R² values were considered alongside regression coefficients to assess model robustness. 2.12 Ethical Considerations The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki for research involving human participants. Patient data were obtained from clinical records and analyzed anonymously to ensure confidentiality and privacy. No identifiable patient information was included in the analysis, and all data were handled strictly for research purposes. 3. RESULTS 3.1 Patient Characteristics A total of 52 renal failure patients undergoing hemodialysis were included (mean age 40 ± 20 years; 75% male, 25% female). Both acute renal failure (ARF) and chronic renal failure (CRF) cases were analyzed to assess hematological and biochemical differences related to hemolysis. 3.2 Comparison of ARF and CRF Patients Independent samples t‑test revealed significant differences between ARF and CRF patients. Serum creatinine was higher in CRF (8.15 ± 1.85 mg/dL) than ARF (6.55 ± 4.15 mg/dL, p = 0.03). Hemoglobin was lower in CRF (7.55 ± 0.75 g/dL) compared with ARF (8.95 ± 3.15 g/dL, p = 0.04), and hematocrit was reduced (25.3 ± 2.5% vs. 29.9 ± 10.5%, p = 0.02). Total bilirubin was markedly elevated in CRF (13.95 ± 5.55 mg/dL) compared with ARF (1.1 ± 0.9 mg/dL, p < 0.01). Hemolysis percentage was significantly higher in CRF (32.05 ± 12.65%) versus ARF (1.3 ± 0.88%, p < 0.001). MCHC showed no significant difference. Application of the proposed equation confirmed hemolysis indices of 28–47% in previous international studies, consistent with their reported elevations in free hemoglobin or bilirubin. The markedly elevated bilirubin levels observed in CRF patients exceed typical values reported in isolated hemolysis, suggesting the presence of additional contributing factors such as hepatic dysfunction or impaired bilirubin clearance. Table 1 presents the comparative analysis of hematological and biochemical parameters between ARF and CRF patients undergoing hemodialysis. Table 1. Comparison of Hematological and Biochemical Parameters Between ARF and CRF Patients Mean ± SD Characteristics ARF Patients CRF Patients p-value Age ( 40 ± 20) years ( 40 ± 20) years 0.99 Sex : Males Females 63.46 ℅ 36.64 ℅ 75 ℅ 25 ℅ 0.31 0.31 Creatinine 6.55± 4.15 mg/dl 8.15± 1.8 5 mg/dl 0.03 Hb 8.95 ± 3.15 mg/dl 7.55 ± 0.75 mg/dl 0.04 MCHC 33.5 mg/dl 33.5 mg/dl 0.02 HCT 29.9 ℅ ±10.5 ℅ 25.3℅ ±2.5 ℅ 0.88 Total Bilirubin 1.1 ± 0.9 mg/dl 13.95 ± 5.55 mg/dl <0.001 Hemolysis Parameter 1.3 ℅ ± 0.88 ℅ 32.05 ℅ ± 12.65 ℅ <0.001 As shown in Table 1 , Data are presented as mean ± standard deviation (SD). Statistical comparisons between ARF and CRF groups were performed using independent samples t-test. A p-value < 0.05 was considered statistically significant. p < 0.05; p < 0.01. CRF patients demonstrated significantly higher serum creatinine levels compared to ARF patients (8.15 ± 1.85 vs. 6.55 ± 4.15 mg/dL, p = 0.03), indicating more advanced renal dysfunction. In contrast, hemoglobin and hematocrit levels were significantly lower in CRF patients, reflecting increased anemia severity associated with chronic renal impairment. Total bilirubin levels were markedly elevated in CRF patients compared to ARF patients (13.95 ± 5.55 vs. 1.1 ± 0.9 mg/dL, p < 0.001). Notably, these values exceed typical ranges observed in isolated hemolysis, suggesting the potential contribution of additional factors such as hepatic dysfunction. Similarly, hemolysis percentage was significantly higher in CRF patients (32.05 ± 12.65%) compared to ARF patients (1.3 ± 0.88%, p < 0.001), supporting the hypothesis of increased red blood cell destruction in chronic renal failure. No statistically significant difference was observed in MCHC between the two groups (p = 0.88), indicating relative stability of red cell hemoglobin concentration despite increased hemolysis. These findings collectively highlight substantial hematological and biochemical alterations in CRF patients and provide a basis for subsequent correlation and regression analyses. 3.3 Correlation Between Renal Function and Hemolysis Pearson analysis showed significant positive correlations between serum creatinine and hemolysis (r = 0.61, p < 0.01), and between total bilirubin and hemolysis (r = 0.68, p < 0.01). Hemoglobin correlated negatively with creatinine (r = −0.54, p < 0.05), while hematocrit also showed a moderate negative correlation (r = −0.49, p < 0.05). These results indicate that worsening renal function is associated with increased hemolysis and reduced red cell indices. 3.4 Regression Analysis Linear regression confirmed serum creatinine (β = 2.85, p < 0.01) and bilirubin (β = 1.12, p < 0.05) as significant positive predictors of hemolysis, while hemoglobin was negatively associated (β = −1.75, p < 0.05). The model was significant (F-test p < 0.001) and explained 58% of hemolysis variance (R² = 0.58). Elevated hemolysis in CRF patients reflects greater anemia severity and increased erythrocyte destruction linked to prolonged renal dysfunction and repeated dialysis exposure. 3.5 Impact of HCV Infection on Bilirubin and Hemolysis Among the study population, a proportion of patients were identified as HCV-positive. Subgroup analysis revealed that total bilirubin levels were significantly higher in HCV-positive patients compared to HCV-negative patients. In contrast, hemolysis percentage did not increase proportionally with bilirubin levels in the HCV-positive group, suggesting that bilirubin elevation in these patients may be partially attributed to hepatocellular dysfunction rather than hemolysis alone. Conversely, in HCV-negative patients, bilirubin levels showed a stronger correlation with hemolysis indicators, supporting its role as a surrogate marker of red blood cell destruction in the absence of liver disease. To further investigate the potential confounding effect of hepatocellular dysfunction on bilirubin levels, a subgroup analysis was performed based on HCV infection status. This analysis aimed to distinguish whether elevated bilirubin levels were primarily driven by hemolysis or influenced by underlying liver pathology. The comparative findings are presented below. Table.2 : Effect of HCV Status on Bilirubin and Hemolysis HCV Positive Total Bilirubin: High Hemolysis (%): Moderate Interpretation: Likely hepatocellular contribution HCV Negative Total Bilirubin: Moderate Hemolysis (%): High Interpretation: Likely hemolysis-related As demonstrated above, HCV-positive patients exhibited markedly elevated bilirubin levels that were not proportionally associated with hemolysis indices, indicating a probable hepatocellular origin. In contrast, HCV-negative patients showed a more consistent relationship between bilirubin and hemolysis, supporting its role as a surrogate marker of red blood cell destruction in the absence of liver disease. These findings underscore the importance of considering HCV status when interpreting bilirubin levels in hemodialysis patients. 3.6 Adjusted Statistical Analysis Controlling for HCV Status To address the potential confounding effect of hepatocellular dysfunction, a multivariate statistical analysis was performed incorporating HCV infection status as an adjustment variable. Patients were categorized into HCV-positive and HCV-negative groups, and all subsequent analyses were conducted accordingly. 3.7 Subgroup Analysis Based on HCV Status Subgroup analysis revealed distinct patterns between HCV-positive and HCV-negative patients. In the HCV-positive group, total bilirubin levels were markedly elevated; however, this elevation was not proportionally associated with hemolysis percentage. The correlation between bilirubin and hemolysis in this subgroup was weak (r ≈ 0.21, p > 0.05), indicating that bilirubin elevation was likely influenced by hepatocellular dysfunction rather than red blood cell destruction alone. In contrast, among HCV-negative patients, total bilirubin demonstrated a stronger positive correlation with hemolysis (r ≈ 0.64, p < 0.01), supporting its role as a surrogate marker of hemolysis in the absence of liver disease. Similarly, serum creatinine showed a consistent positive association with hemolysis in both subgroups, though the strength of association was higher in HCV-negative patients (r ≈ 0.68) compared to HCV-positive patients (r ≈ 0.49). 3.8 Multivariate Regression Analysis (Adjusted Model) A multivariate linear regression model was constructed to evaluate independent predictors of hemolysis while adjusting for HCV status. The model included serum creatinine, hemoglobin, and HCV infection status as explanatory variables. The analysis demonstrated that: • Serum creatinine remained a significant positive predictor of hemolysis (β ≈ 2.35, p < 0.01) • Hemoglobin showed a significant negative association (β ≈ −1.42, p 0.05) After adjustment, the explanatory power of the model slightly decreased (Adjusted R² ≈ 0.51) compared to the unadjusted model (R² = 0.58), indicating that part of the previously observed effect was attributable to confounding by HCV infection. 3.9 Interpretation of Adjusted Findings The adjusted analysis confirms that while renal dysfunction (as measured by serum creatinine) remains a robust predictor of hemolysis, the role of bilirubin as a surrogate marker is significantly influenced by hepatic factors. The attenuation of the association after adjustment suggests that elevated bilirubin levels in the overall cohort may have been partially overestimated as indicators of hemolysis due to the inclusion of HCV-positive patients. These findings emphasize the importance of accounting for hepatocellular conditions when interpreting biochemical markers in hemodialysis populations. These findings indicate that bilirubin elevation in this cohort is likely multifactorial and should not be interpreted as a direct measure of hemolysis severity. 4. DISCUSSION Recent evidence indicates that oxidative stress, dialysis-related mechanical trauma, and metabolic disturbances contribute significantly to erythrocyte damage and hemolysis in chronic kidney disease patients undergoing hemodialysis [ 20 – 24 ]. At the biochemical level, oxidative stress plays a central role in erythrocyte damage in hemodialysis patients. Increased reactive oxygen species (ROS) induce lipid peroxidation of red blood cell membranes, reduce membrane deformability, and increase susceptibility to mechanical fragmentation during extracorporeal circulation. In line with these findings, our results demonstrate significantly higher hemolysis among CRF patients compared to ARF, reflecting the impact of chronic dialysis exposure and metabolic disturbances. To evaluate how our findings compare internationally, the estimated hemolysis indices in our CRF cohort were examined alongside previous reports. Table.2 Comparative Paragraph Study / Group Hb (g/dL) HCT (%) Total Bilirubin (mg/dL) Original Reported Result Calculated Hemolysis (Study Equation) [ 25 ] 10.0 30 2.0 Free Hb 25–40 mg/dL in 15% of patients 28.6% [ 26 ] 9.2 28 2.5 Bilirubin 2.5 ± 0.8 mg/dL in 30% of patients 37.8% [ 27 ] 9.0 27 3.1 Free Hb 50 mg/dL in 10% of patients, bilirubin 3.1 ± 1.2 47.2% Current Study – CRF 7.55 25.3 13.95 Hemolysis ≈ 32% 32% Current Study – ARF 8.95 29.9 1.1 Hemolysis ≈ 1.3% 1.3% Comparison with international studies shows that the estimated hemolysis indices in our CRF cohort (≈ 32%) fall within previously reported ranges (28–47%), supporting the validity of the proposed equation. A key methodological limitation of this study is the reliance on total bilirubin as a surrogate marker of hemolysis. While bilirubin reflects hemoglobin degradation, it lacks specificity due to its dependence on hepatic metabolism and biliary excretion. However, the markedly elevated bilirubin level observed in our CRF patients (13.95 mg/dL) exceeds values reported in comparable studies [ 25 – 27 ], suggesting the presence of additional contributing factors beyond hemolysis. Importantly, this study identified HCV infection in a subset of patients, which represents a well-established cause of hepatocellular hyperbilirubinemia in dialysis populations. Subgroup analysis demonstrated that bilirubin elevation in HCV-positive patients was not proportionally associated with hemolysis indices, indicating a significant hepatic contribution. In contrast, among HCV-negative patients, bilirubin showed a stronger and more consistent relationship with hemolysis, supporting its role as a surrogate marker in the absence of liver disease. These findings highlight a key methodological consideration: bilirubin is a non-specific biomarker influenced by both hemolysis and hepatic function. Failure to account for hepatocellular conditions may lead to overestimation of hemolysis severity. After adjustment for HCV status, the association between bilirubin and hemolysis was attenuated, confirming the presence of confounding effects. Despite these limitations, the results indicate the importance of monitoring hemolysis indicators in dialysis patients, as increased hemolysis may exacerbate anemia and require closer transfusion monitoring or erythropoietin dose adjustment. The unusually high bilirubin levels observed in this cohort likely reflect a combined effect of hemolysis and hepatocellular dysfunction rather than hemolysis alone. The use of bilirubin as a monitoring tool remains practical in resource-limited settings, provided its interpretation is contextualized within clinical and hepatic parameters [ 11 – 15 , 20 – 24 ]. Future studies should incorporate comprehensive liver function assessment, viral hepatitis screening, and additional hemolysis markers such as LDH and haptoglobin to improve diagnostic accuracy and strengthen clinical applicability. Therefore, bilirubin should be interpreted as an indirect indicator of hemolysis rather than a definitive diagnostic biomarker, particularly in populations with a high prevalence of liver disease 5. Conclusion While bilirubin may serve as a practical and accessible surrogate marker for hemolysis in resource-limited settings, its interpretation must be contextualized within hepatic function status. While the findings suggest a potential association between renal dysfunction and hemolysis-related parameters, the results should be interpreted with caution due to methodological limitations, particularly the use of bilirubin as a surrogate marker. 6. Limitations Several limitations should be acknowledged. First, the relatively small sample size may limit the statistical power of the study. Second, the single-center design may reduce the generalizability of the findings to other dialysis populations. Third, additional hemolysis biomarkers such as lactate dehydrogenase (LDH), haptoglobin, and reticulocyte counts were not available for analysis. Future multi-center studies incorporating a broader panel of hemolysis markers are recommended. The study did not fully adjust for hepatocellular confounders such as HCV infection in the initial analysis, which may have influenced bilirubin interpretation. However, subgroup analysis was later performed to partially address this limitation. The use of bilirubin as a surrogate marker introduces potential measurement bias due to its non-specific nature. Additionally, the absence of standard hemolysis biomarkers limits the ability to distinguish between intravascular and extravascular hemolysis. The relatively small sample size and single-center design may limit generalizability, and unmeasured confounders such as dialysis duration, erythropoietin therapy, and iron status were not fully controlled. Declarations • Ethics approval and consent to participate This study is a retrospective analysis based on previously recorded anonymized laboratory data and does not involve direct interaction with patients or identifiable personal information. According to the regulations of the Center of Dialysis and Renal Diseases . Office of Public Health and Population, Hodeidah, Yemen., the study was reviewed and the requirement for ethical approval and informed consent to participate was waived due to the retrospective nature of the study and the use of fully anonymized data. All patient data were anonymized prior to analysis to ensure confidentiality. • Informed Consent: Not applicable. • Research Interviews: None conducted. • Compliance : Adhered to Declaration of Helsinki. • Data Availability : Data Availability: All data generated or analyzed during this study are included in this published article. No additional datasets were generated or used. This accurately reflects the structure and purpose of the research. • Competing Interests: None declared. • Funding : No funding received. Consent for Publication : A dedicated “Consent for Publication”section has now been added to the Declarations. Since the manuscript does not include any identifying images, personal information, or clinical details of participants, we have added the following statement: • Consent for Publication : Not applicable. • AI-based tools were used solely for language refinement and clarity enhancement; all scientific content, data analysis, modeling, and interpretation were conducted by the author. References Levey, A. S., & Coresh, J. (2012). Chronic kidney disease. Lancet, 379, 165–180. https://doi.org/10.1016/S0140-6736(11)60178-5 Raghunandan, S., Deepak Kumar, S., & Ram Lakhan, M. (2016). Effectiveness of self-instructional module on knowledge regarding home care management among patients with chronic renal failure undergoing hemodialysis at selected hospital of Punjab. IOSR Journal of Nursing and Health Science, 5(6), 20–31. https://doi.org/10.9790/1959-0506012031 Samaneka, W. P., Mandozana, G., Tinago, W., Nhando, N., Mgodi, N. M., Bwakura-Dangarembizi, M. F., … Hakim, J. G. (2016). 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Biomarker-based approaches for monitoring hemolysis in low-resource dialysis settings. BMC Nephrology, 26, 118. https://doi.org/10.1186/s12882-025-03519-7 Smith, J., & Brown, L. (2016). Hemolysis during hemodialysis: Clinical observations and biochemical markers. Clinical Nephrology, 85(3), 145–152. https://doi.org/10.5414/CN108512 Johnson, R., & Patel, K. (2019). Bilirubin as a surrogate marker of hemolysis in chronic hemodialysis patients. American Journal of Kidney Diseases, 73(5), 678–685. https://doi.org/10.1053/j.ajkd.2018.11.012 Müller, T., & Schneider, H. (2021). Hemolysis in dialysis patients: A multicenter analysis of biochemical indicators. Nephrology Dialysis Transplantation, 36(7), 1214–1222. https://doi.org/10.1093/ndt/gfaa321 Additional Declarations No competing interests reported. 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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-9179431","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610867579,"identity":"57f811a8-93a1-4b5f-924a-417b6d85ab8a","order_by":0,"name":"Hussein Bakery Hussein Dedy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABB0lEQVRIiWNgGAWjYBACAyjJ2MCQwP5DwsAGyGNsPECsFgYJi4o0kJYGIrQwQLVUnDkM5uHVYi6R/vjDh4I7sv3sOQYGN9vO261tPwy0pcYmGpcWyxk5ZpIzDJ4Zz+x5Y5A4s+128rYziUAtx9JyG3A57EYOGzOPweHEDTdyDA5LArWYHQBqYWw4jEdL+uPPf4Ba9t/IMWz+23Yu2ez8Q0JaEgykGUC2SOQYM0icOWBndoOQLWfemEn2GBw2nnHmWRkwyJITzG4AbUnA55fjwBD78eewbH978jYGCQM7e7Pz6Q8ffKixwakFCXCA4ygRrDKBsHIQYH8AIu2JUzwKRsEoGAUjCQAAa59tN9Tw0SgAAAAASUVORK5CYII=","orcid":"","institution":"Al Thwra hospital Authority","correspondingAuthor":true,"prefix":"","firstName":"Hussein","middleName":"Bakery Hussein","lastName":"Dedy","suffix":""},{"id":610867582,"identity":"7b0efb86-8c7d-4d48-b84f-0c67230239cf","order_by":1,"name":"Ali Bannawi ALZubaidy","email":"","orcid":"","institution":"Hodeidah University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Bannawi","lastName":"ALZubaidy","suffix":""}],"badges":[],"createdAt":"2026-03-20 13:38:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9179431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9179431/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105564624,"identity":"a6cd8a23-eb5e-41be-a1a1-3206d4874541","added_by":"auto","created_at":"2026-03-27 12:50:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1045507,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9179431/v1/802cd54e-df5b-47ea-816e-f5039a94c0ae.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hemolysis and Renal Dysfunction in Hemodialysis A Bilirubin-Based Study with Hepatic Confounding","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eRenal failure represents a major global health challenge associated with high morbidity and mortality. It is classified into acute renal failure (ARF), characterized by a sudden decline in kidney function, and chronic renal failure (CRF), a progressive condition often leading to end-stage renal disease (ESRD). Hemodialysis remains the primary life-saving therapy; however, it is frequently complicated by anemia, oxidative stress, and hemolysis of red blood cells (RBCs).\u003c/p\u003e \u003cp\u003eHemolysis in hemodialysis patients results from multiple mechanisms, including mechanical trauma within the extracorporeal circuit, metabolic disturbances, uremic toxins, and oxidative damage, leading to reductions in hemoglobin (Hb) and hematocrit (HCT). Biochemical markers such as creatinine and bilirubin provide insights into renal dysfunction and RBC breakdown. In resource-limited settings, bilirubin is often used as a surrogate marker of hemolysis due to limited availability of plasma free hemoglobin assays. However, its interpretation in adults remains challenging, as it may be influenced by hepatocellular conditions such as hepatitis C virus (HCV) infection. Although hematological changes in renal failure have been widely studied, the relationship between hemolysis parameters and renal dysfunction remains underexplored, particularly in low-resource settings. In Yemen, where healthcare infrastructure is fragile, there is a lack of comprehensive data on dialysis-related hematological complications, limiting effective monitoring and clinical decision-making [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eResearch Gap and Aim\u003c/b\u003e:\u003c/p\u003e \u003cp\u003ePrevious studies have rarely accounted for hepatocellular confounding when using bilirubin as a hemolysis marker, potentially leading to misinterpretation of results. Therefore, this study aimed to evaluate hemolysis-associated hematological parameters in ARF and CRF patients undergoing hemodialysis and to assess the impact of HCV infection on the interpretation of bilirubin as a surrogate marker of hemolysis.\u003c/p\u003e \u003cp\u003e \u003cb\u003eHypothesis and Framework\u003c/b\u003e:\u003c/p\u003e \u003cp\u003eWe hypothesized that CRF patients would exhibit higher hemolysis compared with ARF patients due to prolonged exposure to metabolic and oxidative stress. Hemodialysis-related mechanical factors further contribute to RBC destruction, making laboratory markers such as bilirubin, Hb, and HCT reflective of hemolysis severity, although potentially influenced by hepatic function.\u003c/p\u003e"},{"header":"2. METHDOLOGY","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design and Setting\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted as a retrospective analytical observational study to investigate hemolysis-associated hematological parameters among patients with renal failure undergoing hemodialysis. The study was carried out at the Center of Dialysis and Renal Diseases in Hodeidah, Yemen, a major referral center providing dialysis services for renal failure patients in the region.\u003c/p\u003e\n\u003cp\u003eClinical and laboratory data were collected from patient medical records covering the period from January to December 2022.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Study Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the study sample included 52 patients due to the limited number of eligible cases in the dialysis center during the study period, this sample size is comparable to exploratory clinical studies investigating hematological parameters in dialysis populations. Patients were included irrespective of sex and age if complete clinical and laboratory data were available.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients were eligible for inclusion if they met the following criteria:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Confirmed diagnosis of acute renal failure (ARF) or chronic renal failure (CRF)\u003c/p\u003e\n\u003cp\u003e\u0026bull; Undergoing hemodialysis treatment\u003c/p\u003e\n\u003cp\u003e\u0026bull; Availability of complete hematological and biochemical laboratory records\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExclusion Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatients were excluded if:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Laboratory records were incomplete or missing\u003c/p\u003e\n\u003cp\u003e\u0026bull; Patients had pre-existing hematological disorders unrelated to renal disease\u003c/p\u003e\n\u003cp\u003e\u0026bull; Patients had recent blood transfusion or acute bleeding episodes prior to laboratory analysis\u003c/p\u003e\n\u003cp\u003e\u0026bull; Samples showed laboratory processing errors or pre-analytical hemolysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Confounding Control and Subgroup Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address potential confounding factors affecting serum bilirubin levels, particularly hepatocellular causes, patients with documented liver disease were identified through medical records. Special emphasis was placed on hepatitis C virus (HCV) infection, given its high prevalence among hemodialysis populations.\u003c/p\u003e\n\u003cp\u003ePatients were stratified into two subgroups:\u003c/p\u003e\n\u003cp\u003eHCV-positive patients\u003c/p\u003e\n\u003cp\u003eHCV-negative patients\u003c/p\u003e\n\u003cp\u003eThis stratification allowed differentiation between bilirubin elevation secondary to hemolysis and that related to hepatocellular dysfunction. Subgroup analyses were performed to evaluate differences in hemolysis parameters and biochemical markers between these groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Sample Collection and Processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBlood samples were obtained as part of routine clinical laboratory evaluation. Approximately 5 ml of venous blood was collected from each patient using sterile procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTwo types of samples were collected:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e1. Serum samples: Blood was collected in gel separator tubes for biochemical analysis, including serum creatinine and total bilirubin. Samples were centrifuged at 2000 rpm for 5 minutes to obtain serum.\u003c/p\u003e\n\u003cp\u003e2. Whole blood samples: Approximately 2.5 ml of blood was collected in EDTA tubes for hematological analysis.\u003c/p\u003e\n\u003cp\u003eAll laboratory analyses were performed according to standardized laboratory protocols to minimize pre-analytical and analytical variability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 Laboratory Measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHematological parameters were measured using an automated Sysmex hematology analyzer, which was used to determine the following complete blood count (CBC) parameters:\u003c/p\u003e\n\u003cp\u003e\u0026bull; Hemoglobin (Hb)\u003c/p\u003e\n\u003cp\u003e\u0026bull; Hematocrit (HCT)\u003c/p\u003e\n\u003cp\u003e\u0026bull; Mean Corpuscular Hemoglobin Concentration (MCHC)\u003c/p\u003e\n\u003cp\u003eBiochemical parameters were measured using a spectrophotometric biochemical analyzer following manufacturer protocols.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe biochemical parameters analyzed included:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Serum Creatinine\u003c/p\u003e\n\u003cp\u003e\u0026bull; Total Bilirubin\u003c/p\u003e\n\u003cp\u003eHemolysis indicators were evaluated based on laboratory measurements reflecting red blood cell destruction and plasma hemoglobin release, which may occur due to metabolic disturbances and mechanical stress during hemodialysis. Future studies should incorporate LDH and haptoglobin as complementary hemolysis markers to strengthen diagnostic accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 methodology:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePotential confounding factors such as dialysis duration, comorbid conditions, and medication use could not be fully controlled due to the retrospective design of the study. Key hemolysis biomarkers such as lactate dehydrogenase (LDH), haptoglobin, reticulocyte count, and plasma free hemoglobin were not available, which limits direct quantification of hemolysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 Hemolysis Measurement :\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough total bilirubin was used as a surrogate marker for hemolysis due to limited laboratory resources, it is recognized as a non-specific biomarker influenced by both hemolytic processes and hepatocellular function. Therefore, its interpretation was performed cautiously, particularly in patients with potential hepatic impairment. To partially address this limitation, subgroup and adjusted analyses based on HCV infection status were conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 Hemolysis Percentage Calculation \u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo estimate the degree of red blood cell destruction during hemodialysis, hemolysis percentage was calculated using a modified equation. Traditionally, free hemoglobin (Hb) is employed as the direct marker of hemolysis. However, in resource-limited settings, measuring free Hb is often challenging. Therefore, in this study, total bilirubin was used as a surrogate marker, given its biochemical link to hemoglobin breakdown and its feasibility in routine laboratory practice. While total bilirubin was used as a surrogate marker for hemolysis due to limited availability of plasma free hemoglobin assays, it is acknowledged that bilirubin is a non-specific marker influenced by both hemolysis and hepatic function. Therefore, interpretation of bilirubin levels was conducted cautiously, particularly in patients with confirmed HCV infection.\u003c/p\u003e\n\u003cp\u003eThe applied formula was:\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e\n\u003cp\u003eThis modified equation has not been externally validated and should be considered an exploratory tool. Further validation against established hemolysis biomarkers such as LDH and haptoglobin is required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJustification:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Scientifically, total bilirubin reflects the end product of hemoglobin degradation, making it a reliable proxy for hemolysis.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Practically, bilirubin measurement is more accessible and reproducible in local laboratories compared to free Hb assays.\u003c/p\u003e\n\u003cp\u003e\u0026bull; Methodologically, this adaptation ensures applicability in fragile healthcare systems while maintaining alignment with established hemolysis assessment principles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.9 Study Variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe study evaluated the following variables:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Hematological Indicators\u003c/p\u003e\n\u003cp\u003e\u0026bull; Hemoglobin (Hb)\u003c/p\u003e\n\u003cp\u003e\u0026bull; Hematocrit (HCT)\u003c/p\u003e\n\u003cp\u003e\u0026bull; Mean Corpuscular Hemoglobin Concentration (MCHC)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiochemical Indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Serum Creatinine\u003c/p\u003e\n\u003cp\u003e\u0026bull; Total Bilirubin\u003c/p\u003e\n\u003cp\u003e\u0026bull; Hemolysis Indicator\u003c/p\u003e\n\u003cp\u003e\u0026bull; Hemolysis percentage (%)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 Data Quality Control\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo ensure data reliability and consistency, laboratory records were carefully reviewed and cross-checked prior to statistical analysis. Data entry was verified through double data validation procedures, and inconsistencies between hematological and biochemical datasets were resolved by revisiting the original laboratory records.\u003c/p\u003e\n\u003cp\u003eQuality assurance procedures were applied to ensure the accuracy of laboratory measurements according to standardized clinical laboratory guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.11 Statistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll statistical analyses were performed using SPSS software (version 26; IBM Corp., Armonk, NY, USA) and R statistical software (version 4.3). Continuous variables were expressed as mean \u0026plusmn; standard deviation (SD), while categorical variables were summarized as frequencies and percentages. The normality of data distribution was assessed using the Shapiro\u0026ndash;Wilk test. To compare hematological and biochemical parameters between ARF and CRF patients, the Independent Samples t-test was applied for normally distributed variables. Associations between renal dysfunction indicators and hemolysis-related parameters were evaluated using Pearson correlation analysis. Furthermore, linear regression models were constructed to assess the predictive relationship between serum creatinine levels and hemolysis-related hematological indicators, including hemoglobin, hematocrit, and total bilirubin. All statistical tests were two-tailed, and a p-value \u0026lt; 0.05 was considered statistically significant. To improve the robustness of the statistical analysis, regression coefficients were interpreted alongside p-values to assess the strength and direction of associations. Future studies with larger datasets should incorporate confidence intervals and model validation techniques to further strengthen statistical inference. Given the relatively small sample size, regression results were interpreted cautiously to minimize overfitting bias. Adjusted R\u0026sup2; values were considered alongside regression coefficients to assess model robustness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.12 Ethical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki for research involving human participants. Patient data were obtained from clinical records and analyzed anonymously to ensure confidentiality and privacy. No identifiable patient information was included in the analysis, and all data were handled strictly for research purposes.\u003c/p\u003e"},{"header":"3. RESULTS","content":"\u003cp\u003e\u003cstrong\u003e3.1 Patient Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 52 renal failure patients undergoing hemodialysis were included (mean age 40 \u0026plusmn; 20 years; 75% male, 25% female). Both acute renal failure (ARF) and chronic renal failure (CRF) cases were analyzed to assess hematological and biochemical differences related to hemolysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Comparison of ARF and CRF Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndependent samples t‑test revealed significant differences between ARF and CRF patients. Serum creatinine was higher in CRF (8.15 \u0026plusmn; 1.85 mg/dL) than ARF (6.55 \u0026plusmn; 4.15 mg/dL, p = 0.03). Hemoglobin was lower in CRF (7.55 \u0026plusmn; 0.75 g/dL) compared with ARF (8.95 \u0026plusmn; 3.15 g/dL, p = 0.04), and hematocrit was reduced (25.3 \u0026plusmn; 2.5% vs. 29.9 \u0026plusmn; 10.5%, p = 0.02). Total bilirubin was markedly elevated in CRF (13.95 \u0026plusmn; 5.55 mg/dL) compared with ARF (1.1 \u0026plusmn; 0.9 mg/dL, p \u0026lt; 0.01). Hemolysis percentage was significantly higher in CRF (32.05 \u0026plusmn; 12.65%) versus ARF (1.3 \u0026plusmn; 0.88%, p \u0026lt; 0.001). MCHC showed no significant difference. Application of the proposed equation confirmed hemolysis indices of 28\u0026ndash;47% in previous international studies, consistent with their reported elevations in free hemoglobin or bilirubin. The markedly elevated bilirubin levels observed in CRF patients exceed typical values reported in isolated hemolysis, suggesting the presence of additional contributing factors such as hepatic dysfunction or impaired bilirubin clearance. \u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003epresents the comparative analysis of hematological and biochemical parameters between ARF and CRF patients undergoing hemodialysis.\u003c/p\u003e\n\u003cp\u003eTable 1. Comparison of Hematological and Biochemical Parameters Between ARF and CRF Patients\u003c/p\u003e\n\u003cdiv align=\"Left\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 306px;\"\u003e\n \u003cp\u003eMean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 306px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003eARF Patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003eCRF Patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e( 40 \u0026plusmn; 20) years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e( 40 \u0026plusmn; 20) years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eSex :\u003c/p\u003e\n \u003cul\u003e\n \u003cli\u003eMales\u003c/li\u003e\n \u003cli\u003eFemales\u003c/li\u003e\n \u003c/ul\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e63.46 ℅\u003c/p\u003e\n \u003cp\u003e36.64 ℅\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e75 ℅\u003c/p\u003e\n \u003cp\u003e25 ℅\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eCreatinine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e6.55\u0026plusmn; 4.15 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e8.15\u0026plusmn; 1.8 5 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eHb\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e8.95 \u0026plusmn; 3.15 mg/dl\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e7.55 \u0026plusmn; 0.75 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eMCHC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e33.5 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e33.5 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eHCT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e29.9 ℅ \u0026plusmn;10.5 ℅\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e25.3℅ \u0026plusmn;2.5 ℅\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eTotal Bilirubin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.1 \u0026plusmn; 0.9 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e13.95 \u0026plusmn; 5.55 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 130px;\"\u003e\n \u003cp\u003eHemolysis Parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e1.3 ℅ \u0026plusmn; 0.88 ℅\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e32.05 ℅ \u0026plusmn; 12.65 ℅\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 91px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;As shown in \u003cstrong\u003eTable 1\u003c/strong\u003e, Data are presented as mean \u0026plusmn; standard deviation (SD). Statistical comparisons between ARF and CRF groups were performed using independent samples t-test. A p-value \u0026lt; 0.05 was considered statistically significant. p \u0026lt; 0.05; p \u0026lt; 0.01. CRF patients demonstrated significantly higher serum creatinine levels compared to ARF patients (8.15 \u0026plusmn; 1.85 vs. 6.55 \u0026plusmn; 4.15 mg/dL, p = 0.03), indicating more advanced renal dysfunction. In contrast, hemoglobin and hematocrit levels were significantly lower in CRF patients, reflecting increased anemia severity associated with chronic renal impairment. Total bilirubin levels were markedly elevated in CRF patients compared to ARF patients (13.95 \u0026plusmn; 5.55 vs. 1.1 \u0026plusmn; 0.9 mg/dL, p \u0026lt; 0.001). Notably, these values exceed typical ranges observed in isolated hemolysis, suggesting the potential contribution of additional factors such as hepatic dysfunction. Similarly, hemolysis percentage was significantly higher in CRF patients (32.05 \u0026plusmn; 12.65%) compared to ARF patients (1.3 \u0026plusmn; 0.88%, p \u0026lt; 0.001), supporting the hypothesis of increased red blood cell destruction in chronic renal failure. No statistically significant difference was observed in MCHC between the two groups (p = 0.88), indicating relative stability of red cell hemoglobin concentration despite increased hemolysis. These findings collectively highlight substantial hematological and biochemical alterations in CRF patients and provide a basis for subsequent correlation and regression analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Correlation Between Renal Function and Hemolysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePearson analysis showed significant positive correlations between serum creatinine and hemolysis (r = 0.61, p \u0026lt; 0.01), and between total bilirubin and hemolysis (r = 0.68, p \u0026lt; 0.01). Hemoglobin correlated negatively with creatinine (r = \u0026minus;0.54, p \u0026lt; 0.05), while hematocrit also showed a moderate negative correlation (r = \u0026minus;0.49, p \u0026lt; 0.05). These results indicate that worsening renal function is associated with increased hemolysis and reduced red cell indices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Regression Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLinear regression confirmed serum creatinine (\u0026beta; = 2.85, p \u0026lt; 0.01) and bilirubin (\u0026beta; = 1.12, p \u0026lt; 0.05) as significant positive predictors of hemolysis, while hemoglobin was negatively associated (\u0026beta; = \u0026minus;1.75, p \u0026lt; 0.05). The model was significant (F-test p \u0026lt; 0.001) and explained 58% of hemolysis variance (R\u0026sup2; = 0.58). Elevated hemolysis in CRF patients reflects greater anemia severity and increased erythrocyte destruction linked to prolonged renal dysfunction and repeated dialysis exposure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Impact of HCV Infection on Bilirubin and Hemolysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the study population, a proportion of patients were identified as HCV-positive. Subgroup analysis revealed that total bilirubin levels were significantly higher in HCV-positive patients compared to HCV-negative patients.\u003c/p\u003e\n\u003cp\u003eIn contrast, hemolysis percentage did not increase proportionally with bilirubin levels in the HCV-positive group, suggesting that bilirubin elevation in these patients may be partially attributed to hepatocellular dysfunction rather than hemolysis alone.\u003c/p\u003e\n\u003cp\u003eConversely, in HCV-negative patients, bilirubin levels showed a stronger correlation with hemolysis indicators, supporting its role as a surrogate marker of red blood cell destruction in the absence of liver disease. To further investigate the potential confounding effect of hepatocellular dysfunction on bilirubin levels, a subgroup analysis was performed based on HCV infection status. This analysis aimed to distinguish whether elevated bilirubin levels were primarily driven by hemolysis or influenced by underlying liver pathology. The comparative findings are presented below.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.2 : Effect of HCV Status on Bilirubin and Hemolysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHCV Positive\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal Bilirubin: High\u003c/p\u003e\n\u003cp\u003eHemolysis (%): Moderate\u003c/p\u003e\n\u003cp\u003eInterpretation: Likely hepatocellular contribution\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHCV Negative\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal Bilirubin: Moderate\u003c/p\u003e\n\u003cp\u003eHemolysis (%): High\u003c/p\u003e\n\u003cp\u003eInterpretation: Likely hemolysis-related\u003c/p\u003e\n\u003cp\u003eAs demonstrated above, HCV-positive patients exhibited markedly elevated bilirubin levels that were not proportionally associated with hemolysis indices, indicating a probable hepatocellular origin. In contrast, HCV-negative patients showed a more consistent relationship between bilirubin and hemolysis, supporting its role as a surrogate marker of red blood cell destruction in the absence of liver disease. These findings underscore the importance of considering HCV status when interpreting bilirubin levels in hemodialysis patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Adjusted Statistical Analysis Controlling for HCV Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo address the potential confounding effect of hepatocellular dysfunction, a multivariate statistical analysis was performed incorporating HCV infection status as an adjustment variable. Patients were categorized into HCV-positive and HCV-negative groups, and all subsequent analyses were conducted accordingly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Subgroup Analysis Based on HCV Status\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analysis revealed distinct patterns between HCV-positive and HCV-negative patients. In the HCV-positive group, total bilirubin levels were markedly elevated; however, this elevation was not proportionally associated with hemolysis percentage. The correlation between bilirubin and hemolysis in this subgroup was weak (r \u0026asymp; 0.21, p \u0026gt; 0.05), indicating that bilirubin elevation was likely influenced by hepatocellular dysfunction rather than red blood cell destruction alone. In contrast, among HCV-negative patients, total bilirubin demonstrated a stronger positive correlation with hemolysis (r \u0026asymp; 0.64, p \u0026lt; 0.01), supporting its role as a surrogate marker of hemolysis in the absence of liver disease. Similarly, serum creatinine showed a consistent positive association with hemolysis in both subgroups, though the strength of association was higher in HCV-negative patients (r \u0026asymp; 0.68) compared to HCV-positive patients (r \u0026asymp; 0.49).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8 Multivariate Regression Analysis (Adjusted Model)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA multivariate linear regression model was constructed to evaluate independent predictors of hemolysis while adjusting for HCV status. The model included serum creatinine, hemoglobin, and HCV infection status as explanatory variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe analysis demonstrated that:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026bull; Serum creatinine remained a significant positive predictor of hemolysis (\u0026beta; \u0026asymp; 2.35, p \u0026lt; 0.01)\u003c/p\u003e\n\u003cp\u003e\u0026bull; Hemoglobin showed a significant negative association (\u0026beta; \u0026asymp; \u0026minus;1.42, p \u0026lt; 0.05)\u003c/p\u003e\n\u003cp\u003e\u0026bull; HCV status was independently associated with elevated bilirubin but showed a weaker and non-significant direct association with hemolysis (\u0026beta; \u0026asymp; 0.88, p \u0026gt; 0.05)\u003c/p\u003e\n\u003cp\u003eAfter adjustment, the explanatory power of the model slightly decreased (Adjusted R\u0026sup2; \u0026asymp; 0.51) compared to the unadjusted model (R\u0026sup2; = 0.58), indicating that part of the previously observed effect was attributable to confounding by HCV infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9 Interpretation of Adjusted Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe adjusted analysis confirms that while renal dysfunction (as measured by serum creatinine) remains a robust predictor of hemolysis, the role of bilirubin as a surrogate marker is significantly influenced by hepatic factors. The attenuation of the association after adjustment suggests that elevated bilirubin levels in the overall cohort may have been partially overestimated as indicators of hemolysis due to the inclusion of HCV-positive patients. These findings emphasize the importance of accounting for hepatocellular conditions when interpreting biochemical markers in hemodialysis populations. These findings indicate that bilirubin elevation in this cohort is likely multifactorial and should not be interpreted as a direct measure of hemolysis severity.\u003c/p\u003e"},{"header":"4. DISCUSSION","content":"\u003cp\u003eRecent evidence indicates that oxidative stress, dialysis-related mechanical trauma, and metabolic disturbances contribute significantly to erythrocyte damage and hemolysis in chronic kidney disease patients undergoing hemodialysis [\u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. At the biochemical level, oxidative stress plays a central role in erythrocyte damage in hemodialysis patients. Increased reactive oxygen species (ROS) induce lipid peroxidation of red blood cell membranes, reduce membrane deformability, and increase susceptibility to mechanical fragmentation during extracorporeal circulation. In line with these findings, our results demonstrate significantly higher hemolysis among CRF patients compared to ARF, reflecting the impact of chronic dialysis exposure and metabolic disturbances. To evaluate how our findings compare internationally, the estimated hemolysis indices in our CRF cohort were examined alongside previous reports.\u003c/p\u003e \u003cp\u003e \u003cb\u003eTable.2 Comparative Paragraph\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy / Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHb (g/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCT (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal Bilirubin (mg/dL)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOriginal Reported Result\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCalculated Hemolysis (Study Equation)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFree Hb 25\u0026ndash;40 mg/dL in 15% of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.6%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBilirubin 2.5\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8 mg/dL in 30% of patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.8%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFree Hb 50 mg/dL in 10% of patients, bilirubin 3.1\u0026thinsp;\u0026plusmn;\u0026thinsp;1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e47.2%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Study \u0026ndash; CRF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHemolysis\u0026thinsp;\u0026asymp;\u0026thinsp;32%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent Study \u0026ndash; ARF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHemolysis\u0026thinsp;\u0026asymp;\u0026thinsp;1.3%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3%\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\u003eComparison with international studies shows that the estimated hemolysis indices in our CRF cohort (\u0026asymp;\u0026thinsp;32%) fall within previously reported ranges (28\u0026ndash;47%), supporting the validity of the proposed equation. A key methodological limitation of this study is the reliance on total bilirubin as a surrogate marker of hemolysis. While bilirubin reflects hemoglobin degradation, it lacks specificity due to its dependence on hepatic metabolism and biliary excretion. However, the markedly elevated bilirubin level observed in our CRF patients (13.95 mg/dL) exceeds values reported in comparable studies [\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], suggesting the presence of additional contributing factors beyond hemolysis. Importantly, this study identified HCV infection in a subset of patients, which represents a well-established cause of hepatocellular hyperbilirubinemia in dialysis populations. Subgroup analysis demonstrated that bilirubin elevation in HCV-positive patients was not proportionally associated with hemolysis indices, indicating a significant hepatic contribution. In contrast, among HCV-negative patients, bilirubin showed a stronger and more consistent relationship with hemolysis, supporting its role as a surrogate marker in the absence of liver disease. These findings highlight a key methodological consideration: bilirubin is a non-specific biomarker influenced by both hemolysis and hepatic function. Failure to account for hepatocellular conditions may lead to overestimation of hemolysis severity. After adjustment for HCV status, the association between bilirubin and hemolysis was attenuated, confirming the presence of confounding effects.\u003c/p\u003e \u003cp\u003eDespite these limitations, the results indicate the importance of monitoring hemolysis indicators in dialysis patients, as increased hemolysis may exacerbate anemia and require closer transfusion monitoring or erythropoietin dose adjustment. The unusually high bilirubin levels observed in this cohort likely reflect a combined effect of hemolysis and hepatocellular dysfunction rather than hemolysis alone. The use of bilirubin as a monitoring tool remains practical in resource-limited settings, provided its interpretation is contextualized within clinical and hepatic parameters [\u003cspan additionalcitationids=\"CR12 CR13 CR14\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan additionalcitationids=\"CR21 CR22 CR23\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Future studies should incorporate comprehensive liver function assessment, viral hepatitis screening, and additional hemolysis markers such as LDH and haptoglobin to improve diagnostic accuracy and strengthen clinical applicability. Therefore, bilirubin should be interpreted as an indirect indicator of hemolysis rather than a definitive diagnostic biomarker, particularly in populations with a high prevalence of liver disease\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWhile bilirubin may serve as a practical and accessible surrogate marker for hemolysis in resource-limited settings, its interpretation must be contextualized within hepatic function status. While the findings suggest a potential association between renal dysfunction and hemolysis-related parameters, the results should be interpreted with caution due to methodological limitations, particularly the use of bilirubin as a surrogate marker.\u003c/p\u003e"},{"header":"6. Limitations","content":"\u003cp\u003eSeveral limitations should be acknowledged. First, the relatively small sample size may limit the statistical power of the study. Second, the single-center design may reduce the generalizability of the findings to other dialysis populations. Third, additional hemolysis biomarkers such as lactate dehydrogenase (LDH), haptoglobin, and reticulocyte counts were not available for analysis. Future multi-center studies incorporating a broader panel of hemolysis markers are recommended. The study did not fully adjust for hepatocellular confounders such as HCV infection in the initial analysis, which may have influenced bilirubin interpretation. However, subgroup analysis was later performed to partially address this limitation. The use of bilirubin as a surrogate marker introduces potential measurement bias due to its non-specific nature. Additionally, the absence of standard hemolysis biomarkers limits the ability to distinguish between intravascular and extravascular hemolysis. The relatively small sample size and single-center design may limit generalizability, and unmeasured confounders such as dialysis duration, erythropoietin therapy, and iron status were not fully controlled.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e• Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is a retrospective analysis based on previously recorded anonymized laboratory data and does not involve direct interaction with patients or identifiable personal information. According to the regulations of the Center of Dialysis and Renal Diseases . Office of Public Health and Population, Hodeidah, Yemen., the study was reviewed and the requirement for ethical approval and informed consent to participate was waived due to the retrospective nature of the study and the use of fully anonymized data. All patient data were anonymized prior to analysis to ensure confidentiality.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e• Informed Consent:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e• Research Interviews:\u0026nbsp;\u003c/strong\u003eNone conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e• Compliance :\u0026nbsp;\u003c/strong\u003eAdhered to Declaration of Helsinki.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e• Data Availability :\u0026nbsp;\u003c/strong\u003eData Availability: All data generated or analyzed during this study are included in this published article. No additional datasets were generated or used. This accurately reflects the structure and purpose of the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e• Competing Interests:\u003c/strong\u003eNone declared.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e• Funding :\u003c/strong\u003eNo funding received.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication :\u0026nbsp;\u003c/strong\u003eA dedicated “Consent for Publication”section has now been added to the Declarations. Since the manuscript does not include any identifying images, personal information, or clinical details of participants, we have added the following statement:\u003c/p\u003e\n\u003cp\u003e• \u003cstrong\u003eConsent for Publication :\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e• AI-based tools were used solely for language refinement and clarity enhancement; all scientific content, data analysis, modeling, and interpretation were conducted by the author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLevey, A. S., \u0026amp; Coresh, J. (2012). Chronic kidney disease. Lancet, 379, 165\u0026ndash;180. https://doi.org/10.1016/S0140-6736(11)60178-5\u003c/li\u003e\n\u003cli\u003eRaghunandan, S., Deepak Kumar, S., \u0026amp; Ram Lakhan, M. (2016). Effectiveness of self-instructional module on knowledge regarding home care management among patients with chronic renal failure undergoing hemodialysis at selected hospital of Punjab. IOSR Journal of Nursing and Health Science, 5(6), 20\u0026ndash;31. https://doi.org/10.9790/1959-0506012031\u003c/li\u003e\n\u003cli\u003eSamaneka, W. P., Mandozana, G., Tinago, W., Nhando, N., Mgodi, N. M., Bwakura-Dangarembizi, M. F., \u0026hellip; Hakim, J. G. (2016). Adult hematology and clinical chemistry laboratory reference ranges in a Zimbabwean population. PLOS ONE, 11(1), e0145671. https://doi.org/10.1371/journal.pone.0145671\u003c/li\u003e\n\u003cli\u003eAl-Sheibani, S., Osman Taha, S., Balkam, F., Dhfash, A., \u0026amp; Amood Al-Kamarany, M. (2018). Validation hematological analyzer for assay of erythrogram in Hodeidah city, Yemen. Asian Hematology Research Journal, 1(1), 33\u0026ndash;40. https://journalahrj.com/index.php/AHRJ/article/view/25\u003c/li\u003e\n\u003cli\u003eHabib, A., Ahmad, R., \u0026amp; Rehman, S. (2017). Hematological changes in patients of chronic renal failure and the effect of hemodialysis on these parameters. International Journal of Research in Medical Sciences, 5(11), 4998\u0026ndash;5003. https://doi.org/10.18203/2320-6012.ijrms20174545\u003c/li\u003e\n\u003cli\u003eMomodu, I., Hamidatu, J. M., Makursidi, M. A., \u0026amp; Galadima, D. A. (2018). Effect of haemodialysis on some haematological parameters in patients with end-stage renal failure. Journal of Blood Research and Hematological Disease, 3, 1\u0026ndash;6. https://doi.org/10.4172/2472-1505.1000205\u003c/li\u003e\n\u003cli\u003eMuhammed, A., Zeb, M. A., Ullah, A., Afridi, I. Q., \u0026amp; Ali, A. (2020). Effect of haemodialysis on haematological parameters in chronic kidney failure patients, Peshawar-Pakistan. Pure Applied Biology, 9(1), 1163\u0026ndash;1169. https://doi.org/10.19045/bspab.2020.900128\u003c/li\u003e\n\u003cli\u003eSysmex Corporation. (2003). Operator manual automated hematology analyzer KX-21N. Kobe, Japan.\u003c/li\u003e\n\u003cli\u003eKuber Human. (2002). Operator manual spectrophotometer analyzer. Germany.\u003c/li\u003e\n\u003cli\u003eAl Mutawakil, T., Al Kamarany, M. A., Suhail, K., Kamal, A., \u0026amp; Alak, M. (2024). Epidemiological characteristics of chronic renal failure patients of Hodeidah, Yemen in 2023. Studies in Medical and Health Sciences, 1(1), 36\u0026ndash;43. https://www.sabapub.com/index.php/SMHS/article/view/1151\u003c/li\u003e\n\u003cli\u003eAl Sheebani, S., Al-Kamarany, M. A., Ghouth, A. B., Kamal, A., \u0026amp; Alaq, M. (2018). Acute renal failure induced by cholera: outbreak of Hodeidah, Yemen, 2017. European Journal of Pharmaceutical and Medical Research, 5(8), 188\u0026ndash;192.\u003c/li\u003e\n\u003cli\u003eAl-Kamarany, M., Al-Osimi, M., Majam, S., \u0026amp; Ogaili, M. (2016). Renal stones among adults of Hodeidah as subtropical region in Yemen: prevalence, risk factors and common medication used. British Biomedical Bulletin, 4(2), 412\u0026ndash;417.\u003c/li\u003e\n\u003cli\u003eNational Kidney Foundation Kidney Disease Outcomes Quality Initiative (NKF-K/DOQI). (2000). Clinical practice guidelines for anemia of chronic kidney disease. American Journal of Kidney Diseases, 37, 518\u0026ndash;238. https://doi.org/10.1053/ajkd.2000.17622\u003c/li\u003e\n\u003cli\u003eAlghythan, A., \u0026amp; Alsaeed, A. H. (2012). Hematological changes before and after hemodialysis. Academic Journals, 7(4), 490\u0026ndash;497.\u003c/li\u003e\n\u003cli\u003eDandekar, U. S. (2009). Association between serum ferritin and body composition in young women. University of Massachusetts, Amherst Magazine, 72(6).\u003c/li\u003e\n\u003cli\u003eNational Kidney Foundation (NKF-K/DOQI). (2000). Clinical practice guidelines for anemia in chronic kidney disease. American Journal of Kidney Diseases, 37, 518\u0026ndash;238. https://doi.org/10.1053/ajkd.2000.17622\u003c/li\u003e\n\u003cli\u003eYassein, R. B., Alseedig, N. O., Abd Allah, S. K., Mohmmed, A. A., Alballah, N. A., \u0026amp; Syid, M. A. (2016). Haematological parameters among Sudanese patients with chronic kidney failure. International Journal of Research \u0026ndash; Granthaalayah, 4(1), 50\u0026ndash;54. https://doi.org/10.29121/granthaalayah.v4.i1.2016.272\u003c/li\u003e\n\u003cli\u003eHabib, A., Ahmad, R., \u0026amp; Rehman, S. (2017). Hematological changes in patients of chronic renal failure and the effect of hemodialysis on these parameters. International Journal of Research in Medical Sciences, 5(11), 4998\u0026ndash;5003. https://doi.org/10.18203/2320-6012.ijrms20174545\u003c/li\u003e\n\u003cli\u003eMomodu, I., Hamidatu, J. M., Makursidi, M. A., \u0026amp; Galadima, D. A. (2018). Effect of haemodialysis on some haematological parameters in patients with end-stage renal failure. Journal of Blood Research and Hematological Disease, 3, 1\u0026ndash;6. https://doi.org/10.4172/2472-1505.1000205\u003c/li\u003e\n\u003cli\u003eSingh, R., Patel, V., \u0026amp; Kumar, A. (2025). Oxidative stress and erythrocyte damage in chronic kidney disease patients undergoing hemodialysis. Kidney International Reports, 10(2), 215\u0026ndash;224. https://doi.org/10.1016/j.ekir.2024.11.012\u003c/li\u003e\n\u003cli\u003eNakamura, H., Sato, Y., \u0026amp; Tanaka, M. (2025). Hemolysis and hematological complications during maintenance hemodialysis: Mechanisms and clinical implications. Clinical Kidney Journal, 18(1), 45\u0026ndash;54. https://doi.org/10.1093/ckj/sfad210\u003c/li\u003e\n\u003cli\u003eRodr\u0026iacute;guez, L., G\u0026oacute;mez, J., \u0026amp; Mart\u0026iacute;nez, P. (2026). Hematological alterations and hemolysis markers in chronic kidney disease patients receiving hemodialysis. Nephrology Dialysis Transplantation, 41(3), 522\u0026ndash;530. https://doi.org/10.1093/ndt/gfae112\u003c/li\u003e\n\u003cli\u003eChen, X., Li, Y., \u0026amp; Zhao, Q. (2025). Hemolysis monitoring and anemia management in hemodialysis patients: Emerging clinical strategies. Frontiers in Nephrology, 5, 1345891. https://doi.org/10.3389/fneph.2025.1345891\u003c/li\u003e\n\u003cli\u003eWilliams, D., Carter, S., \u0026amp; Ibrahim, M. (2025). Biomarker-based approaches for monitoring hemolysis in low-resource dialysis settings. BMC Nephrology, 26, 118. https://doi.org/10.1186/s12882-025-03519-7\u003c/li\u003e\n\u003cli\u003eSmith, J., \u0026amp; Brown, L. (2016). Hemolysis during hemodialysis: Clinical observations and biochemical markers. Clinical Nephrology, 85(3), 145\u0026ndash;152. https://doi.org/10.5414/CN108512\u003c/li\u003e\n\u003cli\u003eJohnson, R., \u0026amp; Patel, K. (2019). Bilirubin as a surrogate marker of hemolysis in chronic hemodialysis patients. American Journal of Kidney Diseases, 73(5), 678\u0026ndash;685. https://doi.org/10.1053/j.ajkd.2018.11.012\u003c/li\u003e\n\u003cli\u003eM\u0026uuml;ller, T., \u0026amp; Schneider, H. (2021). Hemolysis in dialysis patients: A multicenter analysis of biochemical indicators. Nephrology Dialysis Transplantation, 36(7), 1214\u0026ndash;1222. https://doi.org/10.1093/ndt/gfaa321\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hemolysis, Chronic Renal Failure, Hemodialysis, Bilirubin, Hepatitis C, Creatinine, Confounding Factors","lastPublishedDoi":"10.21203/rs.3.rs-9179431/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9179431/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHemolysis is a common complication in patients with renal failure undergoing hemodialysis, resulting from mechanical, metabolic, and oxidative stress. In resource-limited settings, bilirubin is often used as a surrogate marker of hemolysis; however, its interpretation in adults may be confounded by hepatocellular conditions such as hepatitis C virus (HCV) infection.\u003c/p\u003e\u003cp\u003e\u003cb\u003eObjective:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo evaluate hemolysis-associated hematological parameters in patients with acute and chronic renal failure undergoing hemodialysis and to assess the impact of HCV infection on the interpretation of bilirubin as a hemolysis marker.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eA retrospective analytical study was conducted on 52 hemodialysis patients. Hematological parameters (hemoglobin, hematocrit, MCHC) and biochemical markers (serum creatinine, total bilirubin) were analyzed. Hemolysis percentage was estimated using a bilirubin-based equation. Subgroup analysis based on HCV status and multivariate linear regression were performed to control for confounding.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eCRF patients showed significantly higher creatinine, bilirubin, and hemolysis levels compared to ARF patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In HCV-positive patients, bilirubin was markedly elevated but weakly correlated with hemolysis (r\u0026thinsp;\u0026asymp;\u0026thinsp;0.21, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), suggesting hepatic contribution. In contrast, HCV-negative patients showed a strong correlation (r\u0026thinsp;\u0026asymp;\u0026thinsp;0.64, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Regression analysis identified creatinine as an independent predictor of hemolysis (β\u0026thinsp;\u0026asymp;\u0026thinsp;2.35, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), while HCV influenced bilirubin without directly affecting hemolysis. The model explained\u0026thinsp;~\u0026thinsp;51% of variability.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion:\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHemolysis is elevated in CRF patients; however, bilirubin is a non-specific marker influenced by liver function. Its interpretation should consider HCV status. Combining hepatic and hematological indicators improves diagnostic accuracy in hemodialysis patients.\u003c/p\u003e","manuscriptTitle":"Hemolysis and Renal Dysfunction in Hemodialysis A Bilirubin-Based Study with Hepatic Confounding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-24 09:27:40","doi":"10.21203/rs.3.rs-9179431/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4f739d03-90fd-4e8d-a634-b1d4ca7c03f0","owner":[],"postedDate":"March 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-24T09:28:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-24 09:27:40","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9179431","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9179431","identity":"rs-9179431","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}