Association Between Hemolysis Markers and Renal Dysfunction in Hemodialysis Patients A Bilirubin Based Retrospective Study

Association Between Hemolysis Markers and Renal Dysfunction in Hemodialysis Patients A Bilirubin Based Retrospective Study | 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 Association Between Hemolysis Markers and Renal Dysfunction in Hemodialysis Patients A Bilirubin Based Retrospective Study Hussein Bakery Hussein Dedy, Ali Bannawi ALZubaidy This is a preprint; it has not been peer reviewed by a journal.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-9060801/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 : Acute renal failure (ARF) and chronic renal failure (CRF) are associated with significant hematological disturbances, particularly anemia and hemolysis, in patients undergoing hemodialysis. Mechanical stress during dialysis, accumulation of uremic toxins, and metabolic imbalances contribute to red blood cell (RBC) damage. However, data on hemolysis-related parameters among renal failure patients in Yemen remain scarce. Objective: To evaluate hemolysis-associated hematological parameters and their relationship with renal dysfunction in ARF and CRF patients undergoing hemodialysis in Hodeidah, Yemen. Methods: A retrospective analytical study was conducted on 52 renal failure patients undergoing hemodialysis during 2022. Hematological parameters (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration) and biochemical indicators (serum creatinine, total bilirubin, hemolysis percentage) were measured using standardized hematology and biochemical analyzers. Statistical analysis included independent t-tests, Pearson correlation, and linear regression models, with significance set at p < 0.05. Results: CRF patients exhibited significantly higher serum creatinine (8.15 ± 1.85 mg/dl), total bilirubin (13.95 ± 5.55 mg/dl), and hemolysis percentage (32.05 ± 12.65%) compared with ARF patients (6.55 ± 4.15 mg/dl, 1.1 ± 0.9 mg/dl, and 1.3 ± 0.88%, respectively; p < 0.01). Hemoglobin and hematocrit values were lower in CRF patients (7.55 ± 0.75 g/dl and 25.3 ± 2.5%) than in ARF patients (8.95 ± 3.15 g/dl and 29.9 ± 10.5%; p < 0.05). Serum creatinine correlated positively with hemolysis (r = 0.61, p < 0.01), while hemoglobin and hematocrit showed negative correlations. These findings not only highlight laboratory abnormalities but also suggest potential clinical consequences such as increased transfusion requirements and altered erythropoietin dosing. Linear regression analysis demonstrated that serum creatinine was a significant predictor of hemolysis percentage (β = 2.85, p < 0.01), explaining approximately 58% of the variance in hemolysis levels (R² = 0.58). Conclusion: Hemolysis-related hematological abnormalities are significantly elevated in CRF patients compared with ARF patients. These findings suggest that worsening renal dysfunction is strongly associated with increased RBC destruction. Routine monitoring of hemolysis indicators alongside standard hematological tests may improve early detection and management of hematological complications in dialysis patients, particularly in resource-limited settings. Hemolysis Acute Renal Failure Chronic Renal Failure Creatinine Hemoglobin Hematological Parameters 1. INTRODUCTION Renal failure is a major global health challenge with high morbidity and mortality. It is classified into acute renal failure (ARF), marked by sudden decline in kidney function, and chronic renal failure (CRF), a progressive deterioration often leading to end‑stage renal disease (ESRD). Hemodialysis remains the primary life‑saving therapy, yet it is frequently complicated by anemia, oxidative stress, and hemolysis of red blood cells (RBCs). Hemolysis may result from mechanical trauma in the extracorporeal circuit, biochemical disturbances, uremic toxins, and oxidative damage, leading to reduced hemoglobin (Hb) and hematocrit (HCT). Biochemical markers such as creatinine and bilirubin provide additional insights into renal dysfunction and RBC breakdown. Although hematological changes in renal failure have been widely studied, the specific relationship between hemolysis parameters and renal dysfunction is underexplored in resource‑limited settings. Yemen, with fragile healthcare infrastructure, lacks comprehensive data on dialysis‑related hematological complications, hindering risk identification and optimization of supportive care [1–5, 16–19]. Recent studies (2023–2025) highlight oxidative stress and dialysis membrane biocompatibility as key contributors to hemolysis, linking these changes to patient morbidity and mortality. Research Gap and Aim Few studies have investigated hemolysis parameters in ARF and CRF patients undergoing hemodialysis in Yemen, or examined associations between creatinine, bilirubin, and hemolysis indicators. Addressing this gap is essential for improving monitoring and outcomes in fragile healthcare environments. Hypothesis and Framework We hypothesized that CRF patients would exhibit significantly higher hemolysis compared with ARF patients. Conceptually, renal dysfunction induces metabolic and oxidative stress, damaging erythrocyte membranes. Hemodialysis adds mechanical stress, further promoting RBC destruction. Thus, laboratory markers such as bilirubin, Hb, and HCT collectively reflect hemolysis severity associated with renal dysfunction. 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 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. 2.4 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. 2.5 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. 2.6 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 direct biochemical link to hemoglobin breakdown and its feasibility in routine laboratory practice. The applied formula was: The approach allowed integration of biochemical (total bilirubin) and hematological parameters (Hb, HCT) to provide a quantitative measure of hemolysis. The calculated values were subsequently incorporated into correlation and regression analyses to examine the relationship between renal dysfunction indicators (serum creatinine, bilirubin) and hemolysis severity. 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. 2.7 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 (%) 2.8 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. 2.9 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. 2.10 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. Table 1 summarizes demographic and laboratory parameters, highlighting the marked differences between ARF and CRF groups. Table (1): Characteristic laboratory of ARF and CRF Patients . Mean ± SD Characteristics ARF Patients CRF Patients Age ( 40 ± 20) years ( 40 ± 20) years Sex : Males Females 63.46 ℅ 36.64 ℅ 75 ℅ 25 ℅ Creatinine 6.55± 4.15 mg/dl 8.15± 1.8 5 mg/dl Hb 8.95 ± 3.15 mg/dl 7.55 ± 0.75 mg/dl MCHC 33.5 mg/dl 33.5 mg/dl HCT 29.9 ℅ ±10.5 ℅ 25.3℅ ±2.5 ℅ Total Bilirubin 1.1 ± 0.9 mg/dl 13.95 ± 5.55 mg/dl Hemolysis Parameter 1.3 ℅ ± 0.88 ℅ 32.05 ℅ ± 12.65 ℅ As shown in Table 1, CRF patients had significantly higher serum creatinine and hemolysis values compared with ARF patients, while hemoglobin and hematocrit were lower. These results indicate that chronic renal failure is strongly associated with increased red blood cell destruction and anemia, supporting the 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. 4. DISCUSSION Recent studies show that oxidative stress, dialysis‑related mechanical trauma, and metabolic disturbances contribute to erythrocyte damage and hemolysis in chronic kidney disease patients undergoing hemodialysis, emphasizing the need to monitor hematological and biochemical markers for better anemia management [20–24]. ARF can be triggered by infectious outbreaks such as cholera, while CRF is often linked to dehydration, renal stones, and metabolic disturbances that worsen hematological complications [11–15]. Our findings provide quantitative evidence of increased hemolysis among CRF patients compared to ARF. These results are consistent with international reports: Clinical Nephrology observed hemolysis in 15% of patients with free Hb rising to 25–40 mg/dL [25]; American Journal of Kidney Diseases reported bilirubin elevations to 2.5 ± 0.8 mg/dL in 30% of patients [26]; and Nephrology Dialysis Transplantation documented free Hb reaching 50 mg/dL in 10% of patients, with bilirubin 3.1 ± 1.2 mg/dL [27]. Applying our equation to these datasets yielded hemolysis indices between 28–47%, closely matching the 32% observed in our CRF cohort. In contrast, ARF patients showed a markedly lower hemolysis rate (1.3%), highlighting a distinct local clinical pattern. 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% Although robust, these findings remain laboratory‑based and do not directly reflect clinical outcomes such as symptom severity, transfusion needs, or mortality. This methodological limitation restricts immediate translation into patient‑centered endpoints. Nevertheless, the elevated hemolysis percentages in CRF patients have important implications for dialysis protocols, as increased hemolysis may worsen anemia and necessitate closer Hb monitoring, timely EPO adjustment, and judicious transfusion use. Routine application of the bilirubin‑based hemolysis equation could serve as a practical monitoring tool, enabling early detection of red cell destruction and guiding dialysis adjustments. From a public health perspective, bilirubin measurement is cost‑effective and widely available, making it suitable for integration into national surveillance frameworks in resource‑limited settings. Such adaptation could strengthen early‑warning systems, improve patient safety, and align with WHO initiatives on anemia in CKD. The study’s results, while quantitative, highlight the potential to bridge laboratory indicators with practical interventions. Future research should explicitly connect hemolysis monitoring to patient‑centered outcomes and explore its role in shaping dialysis protocols and public health strategies. Our findings are consistent with previous reports linking CKD to anemia and increased red cell destruction, with similar observations in dialysis populations worldwide [11–15, 20–24]. The proposed bilirubin‑based estimation approach may represent a feasible monitoring strategy where plasma free hemoglobin assays are not readily available 5. Conclusion While the findings provide important preliminary evidence linking renal dysfunction to increased hemolysis, larger prospective studies are required to confirm these observations and evaluate their clinical implications. Adopt the bilirubin-based equation as a practical monitoring tool during dialysis sessions, given its accessibility, cost-effectiveness, and reproducibility in local laboratories. Incorporate this equation into national dialysis protocols to establish standardized quality measures and strengthen patient safety across dialysis centers. Link hemolysis monitoring to clinical outcomes such as transfusion requirements, erythropoietin (EPO) dosing, and mortality rates, thereby enhancing the clinical relevance of laboratory findings. Expand future research to include larger, multi-center cohorts and additional hemolysis markers (e.g., LDH, haptoglobin) to improve diagnostic accuracy and generalizability. Integrate results into public health strategies, using bilirubin-based monitoring as part of early-warning systems for dialysis complications, aligning with global initiatives to reduce anemia and improve outcomes in chronic kidney disease. 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. 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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Oxidative stress and erythrocyte damage in chronic kidney disease patients undergoing hemodialysis. Kidney International Reports, 10(2), 215–224. https://doi.org/10.1016/j.ekir.2024.11.012 Nakamura, H., Sato, Y., & Tanaka, M. (2025). Hemolysis and hematological complications during maintenance hemodialysis: Mechanisms and clinical implications. Clinical Kidney Journal, 18(1), 45–54. https://doi.org/10.1093/ckj/sfad210 Rodríguez, L., Gómez, J., & Martínez, P. (2026). Hematological alterations and hemolysis markers in chronic kidney disease patients receiving hemodialysis. Nephrology Dialysis Transplantation, 41(3), 522–530. https://doi.org/10.1093/ndt/gfae112 Chen, X., Li, Y., & 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 Williams, D., Carter, S., & 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 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-9060801","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":606619532,"identity":"67a49dec-f6e6-4d39-be98-933b9c49c748","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":606619533,"identity":"7596056a-baa7-4113-975b-ff76dfef356f","order_by":1,"name":"Ali Bannawi ALZubaidy","email":"","orcid":"","institution":"Faculty of Medicine \u0026Health Sciences, Hodeidah University","correspondingAuthor":false,"prefix":"","firstName":"Ali","middleName":"Bannawi","lastName":"ALZubaidy","suffix":""}],"badges":[],"createdAt":"2026-03-07 21:08:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9060801/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9060801/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104865821,"identity":"51b3674d-0d71-48fd-92f5-8951bfc25691","added_by":"auto","created_at":"2026-03-18 06:57:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":778511,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9060801/v1/302f84d0-735e-404f-8255-32e80da48389.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Hemolysis Markers and Renal Dysfunction in Hemodialysis Patients A Bilirubin Based Retrospective Study","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eRenal failure is a major global health challenge with high morbidity and mortality. It is classified into acute renal failure (ARF), marked by sudden decline in kidney function, and chronic renal failure (CRF), a progressive deterioration often leading to end‑stage renal disease (ESRD). Hemodialysis remains the primary life‑saving therapy, yet it is frequently complicated by anemia, oxidative stress, and hemolysis of red blood cells (RBCs). Hemolysis may result from mechanical trauma in the extracorporeal circuit, biochemical disturbances, uremic toxins, and oxidative damage, leading to reduced hemoglobin (Hb) and hematocrit (HCT). Biochemical markers such as creatinine and bilirubin provide additional insights into renal dysfunction and RBC breakdown. Although hematological changes in renal failure have been widely studied, the specific relationship between hemolysis parameters and renal dysfunction is underexplored in resource‑limited settings. Yemen, with fragile healthcare infrastructure, lacks comprehensive data on dialysis‑related hematological complications, hindering risk identification and optimization of supportive care [1\u0026ndash;5, 16\u0026ndash;19]. Recent studies (2023\u0026ndash;2025) highlight oxidative stress and dialysis membrane biocompatibility as key contributors to hemolysis, linking these changes to patient morbidity and mortality.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eResearch Gap and Aim\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eFew studies have investigated hemolysis parameters in ARF and CRF patients undergoing hemodialysis in Yemen, or examined associations between creatinine, bilirubin, and hemolysis indicators. Addressing this gap is essential for improving monitoring and outcomes in fragile healthcare environments.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eHypothesis and Framework\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eWe hypothesized that CRF patients would exhibit significantly higher hemolysis compared with ARF patients. Conceptually, renal dysfunction induces metabolic and oxidative stress, damaging erythrocyte membranes. Hemodialysis adds mechanical stress, further promoting RBC destruction. Thus, laboratory markers such as bilirubin, Hb, and HCT collectively reflect hemolysis severity associated with renal dysfunction.\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• Confirmed diagnosis of acute renal failure (ARF) or chronic renal failure (CRF)\u003c/p\u003e\n\u003cp\u003e• Undergoing hemodialysis treatment\u003c/p\u003e\n\u003cp\u003e• 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• Laboratory records were incomplete or missing\u003c/p\u003e\n\u003cp\u003e• Patients had pre-existing hematological disorders unrelated to renal disease\u003c/p\u003e\n\u003cp\u003e• Patients had recent blood transfusion or acute bleeding episodes prior to laboratory analysis\u003c/p\u003e\n\u003cp\u003e• Samples showed laboratory processing errors or pre-analytical hemolysis\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 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\u003e2.4 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• Hemoglobin (Hb)\u003c/p\u003e\n\u003cp\u003e• Hematocrit (HCT)\u003c/p\u003e\n\u003cp\u003e• 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• Serum Creatinine\u003c/p\u003e\n\u003cp\u003e• 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\u003e2.5 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.6 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 direct biochemical link to hemoglobin breakdown and its feasibility in routine laboratory practice.\u003c/p\u003e\n\u003cp\u003eThe applied formula was:\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"297\" height=\"88\" 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alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003eThe approach allowed integration of biochemical (total bilirubin) and hematological parameters (Hb, HCT) to provide a quantitative measure of hemolysis. The calculated values were subsequently incorporated into correlation and regression analyses to examine the relationship between renal dysfunction indicators (serum creatinine, bilirubin) and hemolysis severity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJustification:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e• Scientifically, total bilirubin reflects the end product of hemoglobin degradation, making it a reliable proxy for hemolysis.\u003c/p\u003e\n\u003cp\u003e• Practically, bilirubin measurement is more accessible and reproducible in local laboratories compared to free Hb assays.\u003c/p\u003e\n\u003cp\u003e• Methodologically, this adaptation ensures applicability in fragile healthcare systems while maintaining alignment with established hemolysis assessment principles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.7 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• Hematological Indicators\u003c/p\u003e\n\u003cp\u003e• Hemoglobin (Hb)\u003c/p\u003e\n\u003cp\u003e• Hematocrit (HCT)\u003c/p\u003e\n\u003cp\u003e• Mean Corpuscular Hemoglobin Concentration (MCHC)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiochemical Indicators\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e• Serum Creatinine\u003c/p\u003e\n\u003cp\u003e• Total Bilirubin\u003c/p\u003e\n\u003cp\u003e• Hemolysis Indicator\u003c/p\u003e\n\u003cp\u003e• Hemolysis percentage (%)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.8 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\u003e2.9 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 ± 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 \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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.10 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. Table 1 summarizes demographic and laboratory parameters, highlighting the marked differences between ARF and CRF groups.\u003c/p\u003e\n\u003cp\u003eTable (1): \u0026nbsp;Characteristic laboratory \u0026nbsp;of ARF and CRF \u0026nbsp;Patients .\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003eMean\u0026nbsp;\u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eARF Patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCRF Patients\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e( 40 \u0026plusmn; 20) years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e( 40 \u0026plusmn; 20) years\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSex :\u003c/p\u003e\n \u003cp\u003eMales\u003c/p\u003e\n \u003cp\u003eFemales\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cbr\u003e\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\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e75 ℅\u003c/p\u003e\n \u003cp\u003e25 ℅\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCreatinine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e6.55\u0026plusmn; 4.15 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.15\u0026plusmn; 1.8 5 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHb\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.95 \u0026plusmn; 3.15 mg/dl\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.55 \u0026plusmn; 0.75 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMCHC\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.5 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e33.5 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHCT\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.9 ℅ \u0026plusmn;10.5 ℅\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.3℅ \u0026plusmn;2.5 ℅\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Bilirubin\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1 \u0026plusmn; 0.9 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.95 \u0026plusmn; 5.55 mg/dl\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHemolysis Parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3 ℅ \u0026plusmn; 0.88 ℅\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32.05 ℅ \u0026plusmn; 12.65 ℅\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAs shown in Table 1, CRF patients had significantly higher serum creatinine and hemolysis values compared with ARF patients, while hemoglobin and hematocrit were lower. These results indicate that chronic renal failure is strongly associated with increased red blood cell destruction and anemia, supporting the 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"},{"header":"4. DISCUSSION","content":"\u003cp\u003eRecent studies show that oxidative stress, dialysis‑related mechanical trauma, and metabolic disturbances contribute to erythrocyte damage and hemolysis in chronic kidney disease patients undergoing hemodialysis, emphasizing the need to monitor hematological and biochemical markers for better anemia management [20–24]. ARF can be triggered by infectious outbreaks such as cholera, while CRF is often linked to dehydration, renal stones, and metabolic disturbances that worsen hematological complications [11–15]. Our findings provide quantitative evidence of increased hemolysis among CRF patients compared to ARF. These results are consistent with international reports: Clinical Nephrology observed hemolysis in 15% of patients with free Hb rising to 25–40 mg/dL [25]; American Journal of Kidney Diseases reported bilirubin elevations to 2.5 ± 0.8 mg/dL in 30% of patients [26]; and Nephrology Dialysis Transplantation documented free Hb reaching 50 mg/dL in 10% of patients, with bilirubin 3.1 ± 1.2 mg/dL [27]. Applying our equation to these datasets yielded hemolysis indices between 28–47%, closely matching the 32% observed in our CRF cohort. In contrast, ARF patients showed a markedly lower hemolysis rate (1.3%), highlighting a distinct local clinical pattern.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable.2 Comparative Paragraph\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"588\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eStudy / Group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHb (g/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHCT (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTotal Bilirubin (mg/dL)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eOriginal Reported Result\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCalculated Hemolysis (Study\u0026nbsp;Equation)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[25]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFree Hb 25–40 mg/dL in 15% of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28.6%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[26]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBilirubin 2.5 ± 0.8 mg/dL in 30% of patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e37.8%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e[27]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e9.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFree Hb 50 mg/dL in 10% of patients, bilirubin 3.1 ± 1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e47.2%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCurrent Study – CRF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e7.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e25.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e13.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHemolysis ≈ 32%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e32%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCurrent Study – ARF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e8.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e29.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHemolysis ≈ 1.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e1.3%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Although robust, these findings remain laboratory‑based and do not directly reflect clinical outcomes such as symptom severity, transfusion needs, or mortality. This methodological limitation restricts immediate translation into patient‑centered endpoints. Nevertheless, the elevated hemolysis percentages in CRF patients have important implications for dialysis protocols, as increased hemolysis may worsen anemia and necessitate closer Hb monitoring, timely EPO adjustment, and judicious transfusion use. Routine application of the bilirubin‑based hemolysis equation could serve as a practical monitoring tool, enabling early detection of red cell destruction and guiding dialysis adjustments. From a public health perspective, bilirubin measurement is cost‑effective and widely available, making it suitable for integration into national surveillance frameworks in resource‑limited settings. Such adaptation could strengthen early‑warning systems, improve patient safety, and align with WHO initiatives on anemia in CKD. The study’s results, while quantitative, highlight the potential to bridge laboratory indicators with practical interventions. Future research should explicitly connect hemolysis monitoring to patient‑centered outcomes and explore its role in shaping dialysis protocols and public health strategies. Our findings are consistent with previous reports linking CKD to anemia and increased red cell destruction, with similar observations in dialysis populations worldwide [11–15, 20–24]. The proposed bilirubin‑based estimation approach may represent a feasible monitoring strategy where plasma free hemoglobin assays are not readily available\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWhile the findings provide important preliminary evidence linking renal dysfunction to increased hemolysis, larger prospective studies are required to confirm these observations and evaluate their clinical implications. Adopt the bilirubin-based equation as a practical monitoring tool during dialysis sessions, given its accessibility, cost-effectiveness, and reproducibility in local laboratories. Incorporate this equation into national dialysis protocols to establish standardized quality measures and strengthen patient safety across dialysis centers. Link hemolysis monitoring to clinical outcomes such as transfusion requirements, erythropoietin (EPO) dosing, and mortality rates, thereby enhancing the clinical relevance of laboratory findings. Expand future research to include larger, multi-center cohorts and additional hemolysis markers (e.g., LDH, haptoglobin) to improve diagnostic accuracy and generalizability. Integrate results into public health strategies, using bilirubin-based monitoring as part of early-warning systems for dialysis complications, aligning with global initiatives to reduce anemia and improve outcomes in chronic kidney disease.\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.\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, Acute Renal Failure, Chronic Renal Failure, Creatinine, Hemoglobin, Hematological Parameters ","lastPublishedDoi":"10.21203/rs.3.rs-9060801/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9060801/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground : \u003c/strong\u003eAcute renal failure (ARF) and chronic renal failure (CRF) are associated with significant hematological disturbances, particularly anemia and hemolysis, in patients undergoing hemodialysis. Mechanical stress during dialysis, accumulation of uremic toxins, and metabolic imbalances contribute to red blood cell (RBC) damage. However, data on hemolysis-related parameters among renal failure patients in Yemen remain scarce.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo evaluate hemolysis-associated hematological parameters and their relationship with renal dysfunction in ARF and CRF patients undergoing hemodialysis in Hodeidah, Yemen.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eA retrospective analytical study was conducted on 52 renal failure patients undergoing hemodialysis during 2022. Hematological parameters (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration) and biochemical indicators (serum creatinine, total bilirubin, hemolysis percentage) were measured using standardized hematology and biochemical analyzers. Statistical analysis included independent t-tests, Pearson correlation, and linear regression models, with significance set at p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eCRF patients exhibited significantly higher serum creatinine (8.15 ± 1.85 mg/dl), total bilirubin (13.95 ± 5.55 mg/dl), and hemolysis percentage (32.05 ± 12.65%) compared with ARF patients (6.55 ± 4.15 mg/dl, 1.1 ± 0.9 mg/dl, and 1.3 ± 0.88%, respectively; p \u0026lt; 0.01). Hemoglobin and hematocrit values were lower in CRF patients (7.55 ± 0.75 g/dl and 25.3 ± 2.5%) than in ARF patients (8.95 ± 3.15 g/dl and 29.9 ± 10.5%; p \u0026lt; 0.05). Serum creatinine correlated positively with hemolysis (r = 0.61, p \u0026lt; 0.01), while hemoglobin and hematocrit showed negative correlations. These findings not only highlight laboratory abnormalities but also suggest potential clinical consequences such as increased transfusion requirements and altered erythropoietin dosing. Linear regression analysis demonstrated that serum creatinine was a significant predictor of hemolysis percentage (β = 2.85, p \u0026lt; 0.01), explaining approximately 58% of the variance in hemolysis levels (R² = 0.58).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eHemolysis-related hematological abnormalities are significantly elevated in CRF patients compared with ARF patients. These findings suggest that worsening renal dysfunction is strongly associated with increased RBC destruction. Routine monitoring of hemolysis indicators alongside standard hematological tests may improve early detection and management of hematological complications in dialysis patients, particularly in resource-limited settings.\u003c/p\u003e","manuscriptTitle":"Association Between Hemolysis Markers and Renal Dysfunction in Hemodialysis Patients A Bilirubin Based Retrospective Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 13:01:31","doi":"10.21203/rs.3.rs-9060801/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 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T18:43:32+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 13:01:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9060801","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9060801","identity":"rs-9060801","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}