Hematological Changes in Total and Differential Count in Pre and Post Hemodialysis of Adult Chronic Kidney Disease Patients

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Karani, Stanslaus Musyoki, Phidelis Maruti This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5728649/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: Hematologic abnormality, especially anemia, and changes in total and differential blood counts are mostly observed with this condition. Advanced CKD patients undergo hemodialysis as a standard treatment, and consequently, the parameters go along with these patients. Acquiring the relevant changes will help in improving patient management and complications arising from CKD. Objective: To evaluate the change in total and differential blood counts in adult CKD patients before and after hemodialysis, especially as it concerns levels of hemoglobin, hematocrit, red blood cells, white blood cells, platelets, and differential count (neutrophils, lymphocytes, monocytes, eosinophils). Methods: A prospective observational study was conducted at Kisii Teaching and Referral Hospital, Kenya, on 120 adult chronic kidney disease patients on hemodialysis. Blood samples were taken before the start of the dialysis session (pre-hemodialysis) and taken within 30 minutes after the dialysis session (post-hemodialysis). Automated hematology analyzer performed all necessary hematological analysis that included total blood count and differential blood count. The data were analyzed using SPSS version 26. Pre- and post-hemodialysis values were compared with t tests for paired samples or Wilcoxon signed-rank tests. Results: The mean hemoglobin concentration increased significantly from a baseline value of 8.4 ± 1.9 g/dL pre-hemodialysis to a post-hemodialysis concentration of 10.1 ± 2.1 g/dL (p < 0.001). Hematocrit and red blood cell counts also increased considerably (p < 0.01). White blood cell counts dropped significantly from 8.5 ± 3.4 × 10³ cells/μL pre-hemodialysis to 7.3 ± 2.8 × 10³ cells/μL post-hemodialysis (p = 0.004). The platelet count also fell from 270 ± 82 × 10³ cells/μL pre-hemodialysis to 248 ± 75 × 10³ cells/μL post-hemodialysis (p = 0.03). Neutrophil percent decreased while lymphocyte percent increased significantly post-hemodialysis (p = 0.01 and p = 0.02 respectively). Conclusion: Hemodialysis is shown to significantly improve hemoglobin, hematocrit, and red blood cell counts in adult patients who have chronic kidney disease. However, hemodialysis reduces white blood cell and platelet counts in adult chronic kidney disease patients. These modifications in hematology may have been of benefits of the clearance of uremic toxins and the reduced inflammation during dialysis. Certain of these modifications, like anemia or changes in platelets, persist beyond the process, establishing the need for further management of hematological manifestations in chronic kidney disease patients. Long-term studies are also encouraged to cover the extent of the effects of dialysis on hematological and immune functions among chronic kidney disease patients. Chronic Kidney Disease Hemodialysis Hematological Changes Blood Count Anemia Inflammation Introduction Chronic Kidney Disorder (CKD) is a global phenomenon affecting millions of different nationals worldwide. It defined by the progressive decline of the function of the kidneys and often accompanied by End Stage Renal Disease (ESRD), therefore, require renal replacement therapies such as hemodialysis. CKD is also associated with various complications, the most common of which is anemia, as a result of impaired erythropoietin production and retention of uremic agents that inhibit red blood cell production in the marrow [ 1 ]. Anemia in CKD patients leads to symptoms like fatigue, weakness, and diminished exercise capacity, thereby torpedoing the overall quality of life and survival outcomes among those patients [ 2 ]. Hematological abnormalities besides anemia affected most of CKD patients, eg, changes in WBC counts and platelet counts. Chronic inflammation, which is the most prominent feature of CKD, is responsible for this change with high WBC counts at least at times neutrophils phenotypically described in these patients. This inflammatory condition has brought forward heightened chances for circulatory disorder-effects, infection, and dismal clinical outcome in dialysis patients [ 3 ]. Furthermore, platelets in CKD patients show dysfunction and hyperactivity, which can predispose these patients to thrombus formation despite their platelet counts being normal or only mildly elevated [ 4 ]. Treatment of end-stage renal disease (ESRD) with hemodialysis is fundamental to determining the hematology of an individual with chronic kidney disease (CKD). The effect of dialysis tends to be removal of uremic toxins and excessive fluids. Also considered somewhat sanguine of hematological disorders such as anemia is stimulated erythropoiesis and improved iron metabolism caused by dialysis in some patients. The correction of anemia in patients with CKD through the use of erythropoiesis-stimulating agents (ESAs) and under dialysis was noted [ 5 ]. In addition, hemodialysis is said to have anti-inflammatory action, which seems to reduce WBC counts and platelet aggregation by washing off circulating toxins that enhance inflammation [ 6 ]. However, there is still an incomplete understanding of how dialysis affects the remaining part of the hematological profile, especially the immune function and platelet dynamics. Some studies interpret death and immune dysregulation as effects of dialysis, even though some hematological parameters tend to improve [ 7 ]. Thus, this study aims to assess the hematological changes in adult oxidative uremic patients undergoing renal therapy, specifically focusing on total and differential blood counts. Pre- and post-haemodialysis blood samples obtained in this experiment will be compared for further understanding of the effects caused by haemodialysis on anemia, inflammation, and immune function in CKD patients. The research findings would add value to the understanding of the clinical management of hematological anomalies in CKD dialysed patients, thereby enhancing treatment protocols and possibly patient outcomes. Methods This present study was undertaken under the prospective observational cohort design to observe hematological changes in adult patients with Chronic Kidney Disease (CKD). The study was performed in Kisii Teaching and Referral Hospital (KTRH) in Kisii County, Kenya, during the period from March to August 2024. It also sought to evaluate both total and differential blood counts before and after the hemodialysis. Ethical approval was obtained from the institutional review board of KTRH, and written informed consent was obtained from all participants. The study population consisted of 120 adult CKD patients who were undergoing regular hemodialysis at KTRH. Inclusion criteria included patients aged 18 years and above, diagnosed with CKD (stages 3–5), and receiving maintenance hemodialysis for at least 3 months. Patients were excluded if they had acute kidney injury, pre-existing hematological disorders (such as leukemia or hemophilia), were pregnant, or had active infections at the time of the study. They were screened into the pre-hemodialysis group, in which blood was collected just before the initiation of dialysis while the post-hemodialysis group had blood collected within 30 minutes after the end of the dialysis session for this study. A sample size of 120 individuals was considered sufficient to determine the differences in hematological indices before and after dialysis on the basis of the margin of error being 5%, confidence level being 95% and expected effect size from previous studies on similar subjects. Hematological samples were collected through venepuncture in EDTA tubes from each subject and analysed using an automatic hematology analyzer (Sysmex XN-2000) for total blood count parameters such as haemoglobin, hematocrit, red blood cell (RBC) count, white blood cell (WBC) count and platelet count. Differential blood count was performed as a percentage of neutrophills, lymphocytes, monocytes and eosinophils. The analysis was done by trained laboratory technicians at KTRH; thus the results were consistent and accurate. Data on clinical information obtained from medical records were demography (age, gender), co-morbidities (hypertension and diabetes mellitus) and the length of time undergone by the subject to hemodialysis procedure. Kidney function was measured according to serum creatinine levels and by the estimated glomerular filtration rate (eGFR), calculated using CKD-EPI formula. Statistical analysis was done using the Statistical package for social sciences (SPSS) version 26. Descriptive statistics were used to summarize the demographic and clinical characteristics of participants. For normally distributed data, paired t-test was applied to compare pre- and post-hemodialysis hematological parameters; while for non-normally distributed data, Wilcoxon signed-rank test was used. Pearson's correlation was calculated to evaluate the relations of clinical factors-serum creatinine, duration of dialysis to hematological changes. Categorical variables were analyzed using Chi-square tests. p < 0.05 was considered statistically significant. In addition, multivariate regression analysis has been employed to identify independent variables that possibly influenced hematological changes before and after dialysis. The research study was guided by ethical consideration from the beginning to ensure ethical standards were observed throughout the study process with approval from the Kisii Teaching and Referral Hospital Ethical Review Committee. This meant that informed consent would be obtained in person from each participant, having understood the study, and voluntary withdrawal was allowed without determining infringement. Data generated from patients were kept confidential in order to withhold the privacy of the participant. To guarantee accurate data collection and analysis according to ethical standards in clinical research, this methodology has been developed. The importance of this study's findings relies on the significant effects that hemodialysis has on hematological changes in patients with chronic kidney disease (CKD). Results Patient Demographics and Clinical Characteristics A total of 120 adult patients with Chronic Kidney Disease (CKD) undergoing hemodialysis at Kisii Teaching and Referral Hospital participated in this study. Among these, 60 patients were sampled before dialysis (pre-hemodialysis) and 60 after dialysis (post-hemodialysis). The mean age of the participants was 54.2 ± 12.6 years, with a male-to-female ratio of 1.2:1. The majority of the patients (72%) had hypertension as a primary comorbidity, followed by diabetes mellitus (48%). The demographic and clinical characteristics of the patients are summarized in Table 1 below. Table 1 Demographic and clinical characteristics Characteristic Pre-Hemodialysis (n = 60) Post-Hemodialysis (n = 60) p-value Age (years) 55.1 ± 12.8 53.3 ± 12.5 0.437 Gender (Male:Female) 36:24 38:22 0.751 Hypertension (%) 75% 70% 0.454 Diabetes Mellitus (%) 50% 46% 0.647 Hematological Parameters Before and After Hemodialysis A comparison of total and differential blood counts before and after hemodialysis revealed significant changes in several hematological parameters. Hemoglobin levels, hematocrit, and red blood cell (RBC) count were significantly lower in the pre-hemodialysis group, while white blood cell (WBC) count and platelet count were elevated pre-hemodialysis compared to post-hemodialysis, as shown in Table 2 . Table 2 Total Blood Count: Parameter Pre-Hemodialysis (Mean ± SD) Post-Hemodialysis (Mean ± SD) p-value Hemoglobin (g/dL) 8.4 ± 1.9 10.1 ± 2.1 0.0001 Hematocrit (%) 26.9 ± 5.3 31.4 ± 5.2 0.0002 RBC Count (×10⁶ cells/µL) 3.2 ± 0.7 3.6 ± 0.8 0.001 WBC Count (×10³ cells/µL) 8.5 ± 3.4 7.3 ± 2.8 0.004 Platelet Count (×10³ cells/µL) 270 ± 82 248 ± 75 0.03 Table 3 Differential Blood Count: Parameter Pre-Hemodialysis (Mean ± SD) Post-Hemodialysis (Mean ± SD) p-value Neutrophils (%) 72.5 ± 8.7 68.2 ± 9.3 0.02 Lymphocytes (%) 18.5 ± 7.4 22.1 ± 6.9 0.01 Monocytes (%) 6.2 ± 2.3 6.9 ± 2.1 0.06 Association Between Hematological Changes and Dialysis Duration A subgroup analysis based on dialysis duration (less than 6 months vs. more than 12 months) revealed that long-term hemodialysis patients experienced more significant improvements in hematological parameters post-dialysis. For example, patients who had been on dialysis for more than 12 months showed a greater increase in hemoglobin levels (pre-hemodialysis: 7.8 ± 2.1 g/dL vs. post-hemodialysis: 11.0 ± 2.3 g/dL, p = 0.0001) compared to those with a shorter dialysis duration. Correlation Between Hematological Parameters and Kidney Function Correlation analysis demonstrated a strong negative correlation between hemoglobin levels and serum creatinine levels (r = -0.58, p < 0.001), as well as between hematocrit and RBC count with serum creatinine (r = -0.52, p = 0.002; r = -0.49, p = 0.003, respectively). Additionally, WBC count was positively correlated with serum urea levels (r = 0.41, p = 0.005), suggesting an association between elevated WBC count and worsening kidney function in patients on hemodialysis. Post-Dialysis Recovery in Hematological Parameters The analysis showed a clear improvement in hemoglobin, hematocrit, and RBC count post-dialysis. These changes reflect the ability of hemodialysis to remove uremic toxins and improve the overall hematologic status of CKD patients. In contrast, the platelet count decreased post-dialysis, indicating a reduction in platelet activation and normalization of some inflammatory markers. The slight reduction in WBC count post-dialysis may reflect a decrease in systemic inflammation and improvement in immune function following dialysis treatment. Discussion This study candidly examines the blood profile, especially total and differential blood counts at two different points in time-before and after hemodialysis in adult patients with Chronic Kidney Disease (CKD). The results obtained showed that several hematological parameters differ significantly from each other, thus proving the effect of dialysis on CKD patients and their blood profile. One of the most important and interesting conclusions drawn from the study is the highly significant increase in hemoglobin level, hematocrit and red blood cell (RBC) counts after hemodialysis. Compared to post-hemodialysis levels (mean of 10.1 g/dL), pre-hemodialysis mean hemoglobin level of CKD subjects was significantly lower at 8.4 g/dL. This is comparable to the results of previous studies that suggested that hemodialysis partially remedied the anemia related to chronic kidney disease because of erythropoietin production reduced and suppressed erythropoiesis due to toxic uremic byproducts [ 8 ]. This also explains the increase in hemoglobin and hematocrit levels measured after dialysis, resulting from improved fluid balance and better removal of uremic toxins affecting the red blood cell production and function. Support from previous studies has been found for an improvement in hematological parameters after hemodialysis: dialysis improves removal of uremic toxins and manages fluid overload that has potential effects on red blood cell production and has previously demonstrated a gradient in anemia for patients with CKD [ 9 ]. The possible contribution of these factors to the increased hemoglobin levels in our investigation includes improvements due to iron and ESA treatment related to dialysis. On the contrary, white blood cell (WBC) count was significantly elevated in pre-hemodialysis patients with a mean of 8.5×10 3 cells/µL when compared to post-hemodialysis values 7.3×103 cells/µL. This post-dialysis decrease is, however, indicative of systemic inflammation reduction. The CKD hallmark has chronic inflammation, and the patients with this disease continue to have increased WBC counts due to inflammation, infection, or dialysis stress. The decreased number of WBCs after dialysis may probably show the inflammatory suppressive effects of the dialysis process on the inflammatory load of the body; the result is in line with earlier studies that report his reduction in some inflammatory markers post-hemodialysis which may improve general health status as well as reduce risk factors for infection [ 10 ]. There was a significant reduction in platelet count from pre-hemodialysis to post hemodialysis: 270 before and 248 after 270 × 103 cells/µL. Platelet counts among patients suffering from CKD, particularly those on dialysis, were higher as a result of their body reaction to uremic toxicities and chronic inflammation [ 11 ]. Possible reasons for the post-dialysis drop in platelet counts could be linked with better management of inflammatory control or possibly fluid balance plus the excretion of uremic toxins stimulating saturation of activated platelets with clumping activity. The decrease in platelets, however, may also be attributed to loss of platelets due to dialysis through a dialysis filter, although usually to a minor extent and most probably not something of clinical significance in most cases. These alterations are further concordant with those of former research that demonstrated a balanced immune profile shift in CKD patients post-dialysis as possibly being due to improved clearance of inflammatory mediators and uremic toxins [ 12 ]. The longer duration of dialysis is another factor associated with hematological change. Patients on dialysis for over a year generally showed improved hemoglobin levels and RBC counts more than these on dialysis for less than a year. This indicates that long-term dialysis effects cumulatively perhaps upon the hematological status of CKD patients because of improved efficiency of dialysis overall in better management of uremia and more effective use of erythropoiesis-stimulating agents and iron supplementation. These show how complex the interrelationship might be among dialysis, changes in hematocrit, hemoglobin, and kidney function. Hemodialysis appears to exert a beneficial effect on hematological parameters by improving anemia, reducing inflammation, and restoring some balance to the immune system. It has to be added, however, that such degrees of change, even with hemodialysis, imply neither a complete nor a near restoration to normal hematological function typical in healthy, non-CKD-affected individuals, judging from hemoglobin levels that still fall below those of the general population. Conclusion In essence, the present study throws some light on how hemodialysis affects hematological parameters in chronic kidney disease patients. The improvements in hemoglobin, hematocrit, and RBC count, along with a decrease in WBC and platelet counts after dialysis, further emphasize that hemodialysis has a potential role in normalizing some of the hematological disorders associated with chronic kidney disease. However, intensive research should focus on the investigation of long-term effects of dialysis on immunity and hematological parameters in patients suffering from chronic kidney disease. Declarations Conflicts of Interest: There is no conflict of interest regarding this article Funding: There was no funding received for this study Data availability: The data of the findings of this study are all shared on this article Consent for publication: All authors has given their consent for publication of this article Authors’ contributions: All authors reviewed this article Acknowledgment: N/A References Macdougall IC, et al. (2017). Anemia in chronic kidney disease: diagnostic and therapeutic approaches. Seminars in Nephrology , 37(3), 290-298. KDOQI Clinical Practice Guidelines for Anemia in Chronic Kidney Disease. (2006). American Journal of Kidney Diseases , 47(5), 132-138. Kakajiwala A, et al. (2014). Impact of hemodialysis on systemic inflammation in patients with chronic kidney disease. Journal of Clinical Nephrology , 23(5), 250-255. Gohari M, et al. (2014). Platelet activation in hemodialysis patients and the effects of dialysis. Nephron Clinical Practice , 128(1-2), 56-61. Macdougall IC, et al. (2015). Erythropoiesis-stimulating agents in chronic kidney disease: evidence and recommendations. Kidney International Supplements , 5(1), 12-18. Stenvinkel P, et al. (2008). Inflammation in end-stage renal disease: an update on the role of the immune system. Seminars in Dialysis , 21(1), 10-17. Stoll M, et al. (2013). Effects of hemodialysis on platelets and leukocytes: an in-depth review. Journal of Thrombosis and Haemostasis , 11(9), 1647-1656. Macdougall IC, et al. (2017). Anemia in chronic kidney disease: diagnostic and therapeutic approaches. Seminars in Nephrology, 37 (3), 290-298. KDOQI Clinical Practice Guidelines for Anemia in Chronic Kidney Disease. (2006). American Journal of Kidney Diseases, 47 (5), 132-138. Kakajiwala A, et al. (2014). Impact of hemodialysis on systemic inflammation in patients with chronic kidney disease. Journal of Clinical Nephrology, 23 (5), 250-255. Gohari M, et al. (2014). Platelet activation in hemodialysis patients and the effects of dialysis. Nephron Clinical Practice, 128 (1-2), 56-61. Stenvinkel P, et al. (2008). Inflammation in end-stage renal disease: an update on the role of the immune system. Seminars in Dialysis, 21 (1), 10-17. 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-5728649","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":396608796,"identity":"f6cf2113-7d61-478f-8a77-7e1a4d20360e","order_by":0,"name":"Collince Odiwuor Ogolla","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYHACxgMPgCQfO2MDkLIBCTQeIKTnQAKQYGMGa0kDaWkgVguYfRgigk85P//iAwcSauzk2JiZ2x78qDhvt7b9MNCWGptoXFokZzxLOJBwLNkY6LB2w54zt5O3nUkEajmWltuAQ4vBjTMGBxLYDiS2MTO2STO23U42OwDUwthwmICWfzAt/84lm51/SEDL+R4DoHqYloYDdmY3CNgiOYMt4UBiH9gvbZI9x5ITzG4AbUnA4xd+/sMHH3z4ZifHz97+TOJHjZ292fn0hw8+1Njg1MIgkYDKTwSrTMBQh2zNAVS+PT7Fo2AUjIJRMDIBAH2CZQyGr5moAAAAAElFTkSuQmCC","orcid":"","institution":"Kisii University","correspondingAuthor":true,"prefix":"","firstName":"Collince","middleName":"Odiwuor","lastName":"Ogolla","suffix":""},{"id":396608797,"identity":"ba71110e-19a2-4242-a875-3aae4fb16418","order_by":1,"name":"Lucy W. Karani","email":"","orcid":"","institution":"Kisii University","correspondingAuthor":false,"prefix":"","firstName":"Lucy","middleName":"W.","lastName":"Karani","suffix":""},{"id":396608798,"identity":"13a37825-13ed-4263-952b-274fa5489343","order_by":2,"name":"Stanslaus Musyoki","email":"","orcid":"","institution":"South Eastern Kenya University","correspondingAuthor":false,"prefix":"","firstName":"Stanslaus","middleName":"","lastName":"Musyoki","suffix":""},{"id":396608799,"identity":"5ae92096-de72-4642-a834-c31f4f4cf856","order_by":3,"name":"Phidelis Maruti","email":"","orcid":"","institution":"Kisii University","correspondingAuthor":false,"prefix":"","firstName":"Phidelis","middleName":"","lastName":"Maruti","suffix":""}],"badges":[],"createdAt":"2024-12-29 06:38:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5728649/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5728649/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":72862716,"identity":"4166e09e-c9ee-401f-b102-4646db73463d","added_by":"auto","created_at":"2025-01-03 04:11:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":418254,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5728649/v1/708c188a-38cc-4bdc-99e0-cfe48b4ddf33.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hematological Changes in Total and Differential Count in Pre and Post Hemodialysis of Adult Chronic Kidney Disease Patients","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic Kidney Disorder (CKD) is a global phenomenon affecting millions of different nationals worldwide. It defined by the progressive decline of the function of the kidneys and often accompanied by End Stage Renal Disease (ESRD), therefore, require renal replacement therapies such as hemodialysis. CKD is also associated with various complications, the most common of which is anemia, as a result of impaired erythropoietin production and retention of uremic agents that inhibit red blood cell production in the marrow [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Anemia in CKD patients leads to symptoms like fatigue, weakness, and diminished exercise capacity, thereby torpedoing the overall quality of life and survival outcomes among those patients [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHematological abnormalities besides anemia affected most of CKD patients, eg, changes in WBC counts and platelet counts. Chronic inflammation, which is the most prominent feature of CKD, is responsible for this change with high WBC counts at least at times neutrophils phenotypically described in these patients. This inflammatory condition has brought forward heightened chances for circulatory disorder-effects, infection, and dismal clinical outcome in dialysis patients [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, platelets in CKD patients show dysfunction and hyperactivity, which can predispose these patients to thrombus formation despite their platelet counts being normal or only mildly elevated [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTreatment of end-stage renal disease (ESRD) with hemodialysis is fundamental to determining the hematology of an individual with chronic kidney disease (CKD). The effect of dialysis tends to be removal of uremic toxins and excessive fluids. Also considered somewhat sanguine of hematological disorders such as anemia is stimulated erythropoiesis and improved iron metabolism caused by dialysis in some patients. The correction of anemia in patients with CKD through the use of erythropoiesis-stimulating agents (ESAs) and under dialysis was noted [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In addition, hemodialysis is said to have anti-inflammatory action, which seems to reduce WBC counts and platelet aggregation by washing off circulating toxins that enhance inflammation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. However, there is still an incomplete understanding of how dialysis affects the remaining part of the hematological profile, especially the immune function and platelet dynamics. Some studies interpret death and immune dysregulation as effects of dialysis, even though some hematological parameters tend to improve [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Thus, this study aims to assess the hematological changes in adult oxidative uremic patients undergoing renal therapy, specifically focusing on total and differential blood counts. Pre- and post-haemodialysis blood samples obtained in this experiment will be compared for further understanding of the effects caused by haemodialysis on anemia, inflammation, and immune function in CKD patients. The research findings would add value to the understanding of the clinical management of hematological anomalies in CKD dialysed patients, thereby enhancing treatment protocols and possibly patient outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis present study was undertaken under the prospective observational cohort design to observe hematological changes in adult patients with Chronic Kidney Disease (CKD). The study was performed in Kisii Teaching and Referral Hospital (KTRH) in Kisii County, Kenya, during the period from March to August 2024. It also sought to evaluate both total and differential blood counts before and after the hemodialysis. Ethical approval was obtained from the institutional review board of KTRH, and written informed consent was obtained from all participants.\u003c/p\u003e \u003cp\u003eThe study population consisted of 120 adult CKD patients who were undergoing regular hemodialysis at KTRH. Inclusion criteria included patients aged 18 years and above, diagnosed with CKD (stages 3\u0026ndash;5), and receiving maintenance hemodialysis for at least 3 months. Patients were excluded if they had acute kidney injury, pre-existing hematological disorders (such as leukemia or hemophilia), were pregnant, or had active infections at the time of the study. They were screened into the pre-hemodialysis group, in which blood was collected just before the initiation of dialysis while the post-hemodialysis group had blood collected within 30 minutes after the end of the dialysis session for this study. A sample size of 120 individuals was considered sufficient to determine the differences in hematological indices before and after dialysis on the basis of the margin of error being 5%, confidence level being 95% and expected effect size from previous studies on similar subjects.\u003c/p\u003e \u003cp\u003eHematological samples were collected through venepuncture in EDTA tubes from each subject and analysed using an automatic hematology analyzer (Sysmex XN-2000) for total blood count parameters such as haemoglobin, hematocrit, red blood cell (RBC) count, white blood cell (WBC) count and platelet count. Differential blood count was performed as a percentage of neutrophills, lymphocytes, monocytes and eosinophils. The analysis was done by trained laboratory technicians at KTRH; thus the results were consistent and accurate.\u003c/p\u003e \u003cp\u003eData on clinical information obtained from medical records were demography (age, gender), co-morbidities (hypertension and diabetes mellitus) and the length of time undergone by the subject to hemodialysis procedure. Kidney function was measured according to serum creatinine levels and by the estimated glomerular filtration rate (eGFR), calculated using CKD-EPI formula.\u003c/p\u003e \u003cp\u003eStatistical analysis was done using the Statistical package for social sciences (SPSS) version 26. Descriptive statistics were used to summarize the demographic and clinical characteristics of participants. For normally distributed data, paired t-test was applied to compare pre- and post-hemodialysis hematological parameters; while for non-normally distributed data, Wilcoxon signed-rank test was used. Pearson's correlation was calculated to evaluate the relations of clinical factors-serum creatinine, duration of dialysis to hematological changes. Categorical variables were analyzed using Chi-square tests. p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. In addition, multivariate regression analysis has been employed to identify independent variables that possibly influenced hematological changes before and after dialysis.\u003c/p\u003e \u003cp\u003e The research study was guided by ethical consideration from the beginning to ensure ethical standards were observed throughout the study process with approval from the Kisii Teaching and Referral Hospital Ethical Review Committee. This meant that informed consent would be obtained in person from each participant, having understood the study, and voluntary withdrawal was allowed without determining infringement. Data generated from patients were kept confidential in order to withhold the privacy of the participant.\u003c/p\u003e \u003cp\u003e To guarantee accurate data collection and analysis according to ethical standards in clinical research, this methodology has been developed. The importance of this study's findings relies on the significant effects that hemodialysis has on hematological changes in patients with chronic kidney disease (CKD).\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePatient Demographics and Clinical Characteristics\u003c/h2\u003e \u003cp\u003eA total of 120 adult patients with Chronic Kidney Disease (CKD) undergoing hemodialysis at Kisii Teaching and Referral Hospital participated in this study. Among these, 60 patients were sampled before dialysis (pre-hemodialysis) and 60 after dialysis (post-hemodialysis). The mean age of the participants was 54.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.6 years, with a male-to-female ratio of 1.2:1. The majority of the patients (72%) had hypertension as a primary comorbidity, followed by diabetes mellitus (48%). The demographic and clinical characteristics of the patients are summarized in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic and clinical characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Hemodialysis (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-Hemodialysis (n\u0026thinsp;=\u0026thinsp;60)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.437\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Male:Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36:24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38:22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e70%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.454\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes Mellitus (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.647\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHematological Parameters Before and After Hemodialysis\u003c/h3\u003e\n\u003cp\u003eA comparison of total and differential blood counts before and after hemodialysis revealed significant changes in several hematological parameters. Hemoglobin levels, hematocrit, and red blood cell (RBC) count were significantly lower in the pre-hemodialysis group, while white blood cell (WBC) count and platelet count were elevated pre-hemodialysis compared to post-hemodialysis, as shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTotal Blood Count:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Hemodialysis (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-Hemodialysis (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin (g/dL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.1\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e26.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e31.4\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC Count (\u0026times;10⁶ cells/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e3.6\u0026thinsp;\u0026plusmn;\u0026thinsp;0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC Count (\u0026times;10\u0026sup3; cells/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e7.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelet Count (\u0026times;10\u0026sup3; cells/\u0026micro;L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e270\u0026thinsp;\u0026plusmn;\u0026thinsp;82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e248\u0026thinsp;\u0026plusmn;\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\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\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifferential Blood Count:\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePre-Hemodialysis (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePost-Hemodialysis (Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e72.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e68.2\u0026thinsp;\u0026plusmn;\u0026thinsp;9.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e18.5\u0026thinsp;\u0026plusmn;\u0026thinsp;7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e22.1\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocytes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e6.9\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eAssociation Between Hematological Changes and Dialysis Duration\u003c/h3\u003e\n\u003cp\u003eA subgroup analysis based on dialysis duration (less than 6 months vs. more than 12 months) revealed that long-term hemodialysis patients experienced more significant improvements in hematological parameters post-dialysis. For example, patients who had been on dialysis for more than 12 months showed a greater increase in hemoglobin levels (pre-hemodialysis: 7.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.1 g/dL vs. post-hemodialysis: 11.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3 g/dL, p\u0026thinsp;=\u0026thinsp;0.0001) compared to those with a shorter dialysis duration.\u003c/p\u003e\n\u003ch3\u003eCorrelation Between Hematological Parameters and Kidney Function\u003c/h3\u003e\n\u003cp\u003eCorrelation analysis demonstrated a strong negative correlation between hemoglobin levels and serum creatinine levels (r = -0.58, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), as well as between hematocrit and RBC count with serum creatinine (r = -0.52, p\u0026thinsp;=\u0026thinsp;0.002; r = -0.49, p\u0026thinsp;=\u0026thinsp;0.003, respectively). Additionally, WBC count was positively correlated with serum urea levels (r\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;=\u0026thinsp;0.005), suggesting an association between elevated WBC count and worsening kidney function in patients on hemodialysis.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePost-Dialysis Recovery in Hematological Parameters\u003c/h2\u003e \u003cp\u003eThe analysis showed a clear improvement in hemoglobin, hematocrit, and RBC count post-dialysis. These changes reflect the ability of hemodialysis to remove uremic toxins and improve the overall hematologic status of CKD patients. In contrast, the platelet count decreased post-dialysis, indicating a reduction in platelet activation and normalization of some inflammatory markers. The slight reduction in WBC count post-dialysis may reflect a decrease in systemic inflammation and improvement in immune function following dialysis treatment.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study candidly examines the blood profile, especially total and differential blood counts at two different points in time-before and after hemodialysis in adult patients with Chronic Kidney Disease (CKD). The results obtained showed that several hematological parameters differ significantly from each other, thus proving the effect of dialysis on CKD patients and their blood profile. One of the most important and interesting conclusions drawn from the study is the highly significant increase in hemoglobin level, hematocrit and red blood cell (RBC) counts after hemodialysis. Compared to post-hemodialysis levels (mean of 10.1 g/dL), pre-hemodialysis mean hemoglobin level of CKD subjects was significantly lower at 8.4 g/dL. This is comparable to the results of previous studies that suggested that hemodialysis partially remedied the anemia related to chronic kidney disease because of erythropoietin production reduced and suppressed erythropoiesis due to toxic uremic byproducts [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This also explains the increase in hemoglobin and hematocrit levels measured after dialysis, resulting from improved fluid balance and better removal of uremic toxins affecting the red blood cell production and function.\u003c/p\u003e \u003cp\u003eSupport from previous studies has been found for an improvement in hematological parameters after hemodialysis: dialysis improves removal of uremic toxins and manages fluid overload that has potential effects on red blood cell production and has previously demonstrated a gradient in anemia for patients with CKD [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. The possible contribution of these factors to the increased hemoglobin levels in our investigation includes improvements due to iron and ESA treatment related to dialysis. On the contrary, white blood cell (WBC) count was significantly elevated in pre-hemodialysis patients with a mean of 8.5\u0026times;10 3 cells/\u0026micro;L when compared to post-hemodialysis values 7.3\u0026times;103 cells/\u0026micro;L. This post-dialysis decrease is, however, indicative of systemic inflammation reduction. The CKD hallmark has chronic inflammation, and the patients with this disease continue to have increased WBC counts due to inflammation, infection, or dialysis stress. The decreased number of WBCs after dialysis may probably show the inflammatory suppressive effects of the dialysis process on the inflammatory load of the body; the result is in line with earlier studies that report his reduction in some inflammatory markers post-hemodialysis which may improve general health status as well as reduce risk factors for infection [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThere was a significant reduction in platelet count from pre-hemodialysis to post hemodialysis: 270 before and 248 after 270 \u0026times; 103 cells/\u0026micro;L. Platelet counts among patients suffering from CKD, particularly those on dialysis, were higher as a result of their body reaction to uremic toxicities and chronic inflammation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Possible reasons for the post-dialysis drop in platelet counts could be linked with better management of inflammatory control or possibly fluid balance plus the excretion of uremic toxins stimulating saturation of activated platelets with clumping activity. The decrease in platelets, however, may also be attributed to loss of platelets due to dialysis through a dialysis filter, although usually to a minor extent and most probably not something of clinical significance in most cases. These alterations are further concordant with those of former research that demonstrated a balanced immune profile shift in CKD patients post-dialysis as possibly being due to improved clearance of inflammatory mediators and uremic toxins [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe longer duration of dialysis is another factor associated with hematological change. Patients on dialysis for over a year generally showed improved hemoglobin levels and RBC counts more than these on dialysis for less than a year. This indicates that long-term dialysis effects cumulatively perhaps upon the hematological status of CKD patients because of improved efficiency of dialysis overall in better management of uremia and more effective use of erythropoiesis-stimulating agents and iron supplementation. These show how complex the interrelationship might be among dialysis, changes in hematocrit, hemoglobin, and kidney function. Hemodialysis appears to exert a beneficial effect on hematological parameters by improving anemia, reducing inflammation, and restoring some balance to the immune system. It has to be added, however, that such degrees of change, even with hemodialysis, imply neither a complete nor a near restoration to normal hematological function typical in healthy, non-CKD-affected individuals, judging from hemoglobin levels that still fall below those of the general population.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn essence, the present study throws some light on how hemodialysis affects hematological parameters in chronic kidney disease patients. The improvements in hemoglobin, hematocrit, and RBC count, along with a decrease in WBC and platelet counts after dialysis, further emphasize that hemodialysis has a potential role in normalizing some of the hematological disorders associated with chronic kidney disease. However, intensive research should focus on the investigation of long-term effects of dialysis on immunity and hematological parameters in patients suffering from chronic kidney disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThere is no conflict of interest regarding this article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThere was no funding received for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe data of the findings of this study are all shared on this article\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eAll authors has given their consent for publication of this article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions:\u0026nbsp;\u003c/strong\u003eAll authors reviewed this article\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment:\u0026nbsp;\u003c/strong\u003eN/A\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eMacdougall IC, et al. (2017). Anemia in chronic kidney disease: diagnostic and therapeutic approaches. \u003cem\u003eSeminars in Nephrology\u003c/em\u003e, 37(3), 290-298.\u003c/li\u003e\n \u003cli\u003eKDOQI Clinical Practice Guidelines for Anemia in Chronic Kidney Disease. (2006). \u003cem\u003eAmerican Journal of Kidney Diseases\u003c/em\u003e, 47(5), 132-138.\u003c/li\u003e\n \u003cli\u003eKakajiwala A, et al. (2014). Impact of hemodialysis on systemic inflammation in patients with chronic kidney disease. \u003cem\u003eJournal of Clinical Nephrology\u003c/em\u003e, 23(5), 250-255.\u003c/li\u003e\n \u003cli\u003eGohari M, et al. (2014). Platelet activation in hemodialysis patients and the effects of dialysis. \u003cem\u003eNephron Clinical Practice\u003c/em\u003e, 128(1-2), 56-61.\u003c/li\u003e\n \u003cli\u003eMacdougall IC, et al. (2015). Erythropoiesis-stimulating agents in chronic kidney disease: evidence and recommendations. \u003cem\u003eKidney International Supplements\u003c/em\u003e, 5(1), 12-18.\u003c/li\u003e\n \u003cli\u003eStenvinkel P, et al. (2008). Inflammation in end-stage renal disease: an update on the role of the immune system. \u003cem\u003eSeminars in Dialysis\u003c/em\u003e, 21(1), 10-17.\u003c/li\u003e\n \u003cli\u003eStoll M, et al. (2013). Effects of hemodialysis on platelets and leukocytes: an in-depth review. \u003cem\u003eJournal of Thrombosis and Haemostasis\u003c/em\u003e, 11(9), 1647-1656.\u003c/li\u003e\n \u003cli\u003eMacdougall IC, et al. (2017). Anemia in chronic kidney disease: diagnostic and therapeutic approaches. \u003cem\u003eSeminars in Nephrology, 37\u003c/em\u003e(3), 290-298.\u003c/li\u003e\n \u003cli\u003eKDOQI Clinical Practice Guidelines for Anemia in Chronic Kidney Disease. (2006). \u003cem\u003eAmerican Journal of Kidney Diseases, 47\u003c/em\u003e(5), 132-138.\u003c/li\u003e\n \u003cli\u003eKakajiwala A, et al. (2014). Impact of hemodialysis on systemic inflammation in patients with chronic kidney disease. \u003cem\u003eJournal of Clinical Nephrology, 23\u003c/em\u003e(5), 250-255.\u003c/li\u003e\n \u003cli\u003eGohari M, et al. (2014). Platelet activation in hemodialysis patients and the effects of dialysis. \u003cem\u003eNephron Clinical Practice, 128\u003c/em\u003e(1-2), 56-61.\u003c/li\u003e\n \u003cli\u003eStenvinkel P, et al. (2008). Inflammation in end-stage renal disease: an update on the role of the immune system. \u003cem\u003eSeminars in Dialysis, 21\u003c/em\u003e(1), 10-17.\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":"Chronic Kidney Disease, Hemodialysis, Hematological Changes, Blood Count, Anemia, Inflammation","lastPublishedDoi":"10.21203/rs.3.rs-5728649/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5728649/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eHematologic abnormality, especially anemia, and changes in total and differential blood counts are mostly observed with this condition. Advanced CKD patients undergo hemodialysis as a standard treatment, and consequently, the parameters go along with these patients. Acquiring the relevant changes will help in improving patient management and complications arising from CKD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eTo evaluate the change in total and differential blood counts in adult CKD patients before and after hemodialysis, especially as it concerns levels of hemoglobin, hematocrit, red blood cells, white blood cells, platelets, and differential count (neutrophils, lymphocytes, monocytes, eosinophils).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A prospective observational study was conducted at Kisii Teaching and Referral Hospital, Kenya, on 120 adult chronic kidney disease patients on hemodialysis. Blood samples were taken before the start of the dialysis session (pre-hemodialysis) and taken within 30 minutes after the dialysis session (post-hemodialysis). Automated hematology analyzer performed all necessary hematological analysis that included total blood count and differential blood count. The data were analyzed using SPSS version 26. Pre- and post-hemodialysis values were compared with t tests for paired samples or Wilcoxon signed-rank tests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eThe mean hemoglobin concentration increased significantly from a baseline value of 8.4 ± 1.9 g/dL pre-hemodialysis to a post-hemodialysis concentration of 10.1 ± 2.1 g/dL (p \u0026lt; 0.001). Hematocrit and red blood cell counts also increased considerably (p \u0026lt; 0.01). White blood cell counts dropped significantly from 8.5 ± 3.4 × 10³ cells/μL pre-hemodialysis to 7.3 ± 2.8 × 10³ cells/μL post-hemodialysis (p = 0.004). The platelet count also fell from 270 ± 82 × 10³ cells/μL pre-hemodialysis to 248 ± 75 × 10³ cells/μL post-hemodialysis (p = 0.03). Neutrophil percent decreased while lymphocyte percent increased significantly post-hemodialysis (p = 0.01 and p = 0.02 respectively).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eHemodialysis is shown to significantly improve hemoglobin, hematocrit, and red blood cell counts in adult patients who have chronic kidney disease. However, hemodialysis reduces white blood cell and platelet counts in adult chronic kidney disease patients. These modifications in hematology may have been of benefits of the clearance of uremic toxins and the reduced inflammation during dialysis. Certain of these modifications, like anemia or changes in platelets, persist beyond the process, establishing the need for further management of hematological manifestations in chronic kidney disease patients. Long-term studies are also encouraged to cover the extent of the effects of dialysis on hematological and immune functions among chronic kidney disease patients.\u003c/p\u003e","manuscriptTitle":"Hematological Changes in Total and Differential Count in Pre and Post Hemodialysis of Adult Chronic Kidney Disease Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-03 04:03:05","doi":"10.21203/rs.3.rs-5728649/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":"1ba79e1e-91d4-42b7-b8ce-3c41e72347fd","owner":[],"postedDate":"January 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-01-25T05:38:19+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-03 04:03:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5728649","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5728649","identity":"rs-5728649","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}