Clinical Correlates of the Haemoglobin to Red Cell Distribution Width Ratio in Bacterial Bloodstream Infections and Concomitant Kidney Dysfunction

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This study aimed to determine the clinical and laboratory correlates of the Hb/RDW ratio in patients with bacterial bloodstream infections and concomitant kidney dysfunction at Ndola Teaching Hospital. Methods We conducted a retrospective laboratory-based study of 136 patients with confirmed bacterial bloodstream infections and kidney dysfunction at a Ndola Teaching Hospital in Zambia. Multivariable linear regression was used to identify factors independently associated with the Hb/RDW ratio. Results A total of 136 patients with bloodstream infections and kidney dysfunction were included, with a median age in the older adult range; 44.9% were ≥ 60 years. Females comprised 52.9% of the cohort. Gram-negative bacteria predominated, particularly Enterobacter agglomerans (19.8%) and Escherichia coli (10.2%), while Staphylococcus aureus (18.3%) was the most common Gram-positive isolate. Multidrug-resistant organisms accounted for 61.8% of infections. In multivariable analysis, male sex was associated with lower Hb/RDW (β = −0.092, p = 0.020), and higher eGFR was associated with higher Hb/RDW (β = 0.003, p = 0.049). Other hematological and biochemical parameters showed no independent association. Conclusion Hb/RDW is independently associated with sex and renal function in patients with bloodstream infections and kidney dysfunction, suggesting its potential as a simple biomarker. Future studies should evaluate its prognostic value and utility in guiding early clinical interventions. Hb/RDW Bloodstream infection Kidney dysfunction Enterobacter agglomerans Staphylococcus aureus Figures Figure 1 Figure 2 Introduction Bacterial bloodstream infections (BSIs) are among the most serious infectious diseases encountered in clinical practice and are associated with high morbidity and mortality worldwide [ 1 , 2 ]. BSIs are caused by the invasion of normally sterile blood by pathogenic bacteria and often lead to systemic inflammatory responses, prolonged hospital stays, and adverse outcomes particularly in patients with underlying comorbidities such as kidney dysfunction [ 3 , 4 ]. Diagnosing and risk stratifying BSI remains challenging in many settings due to the limited availability of rapid and inexpensive biomarkers that reflect both infection severity and host response. Routine complete blood count (CBC) parameters, including red cell distribution width (RDW), have garnered interest as potential markers of illness severity in bacterial infections [ 5 ]. RDW, an indicator of red blood cell size variability, may be elevated in bacterial bloodstream infections as a result of inflammation, oxidative stress, and disrupted erythropoietic processes [ 6 ]. Elevated RDW has been shown to correlate with adverse outcomes in patients with bacterial bloodstream infection; for example, in Klebsiella pneumoniae BSI, higher RDW values were independently associated with increased in-hospital mortality [ 7 , 8 ]. Beyond RDW alone, ratios that integrate RDW with other hematological measures, such as the haemoglobin (Hb)/RDW ratio, may offer enhanced clinical insight by capturing both erythrocyte heterogeneity and haemoglobin status, which can be perturbed during bacterial infection and inflammation [ 9 ]. Studies exploring anaemia-related parameters, including the Hb/RDW ratio, have reported their potential utility in distinguishing bacterial from viral infections and in reflecting the host response to bacterial pathogens [ 10 ]. Despite these findings, the clinical and laboratory correlates of Hb/RDW remain underexplored specifically in patients with bacterial BSIs complicated by kidney dysfunction, a population in which systemic inflammation, anaemia, and renal impairment intersect and may collectively influence clinical course and outcomes. Understanding how Hb/RDW relates to host factors and laboratory indices in this context could enhance patient stratification and inform management strategies, particularly in settings with constrained access to advanced diagnostics [ 11 ]. This study aimed to determine the clinical and laboratory factors independently associated with the Hb/RDW ratio in patients with bacterial bloodstream infections and concomitant kidney dysfunction. Materials and Methods Study Design and Setting This was a laboratory-based, single-center retrospective cohort study conducted at Ndola Teaching Hospital (NTH), a tertiary referral center in Copperbelt Province, Zambia, which serves approximately 29,485 admissions annually and provides comprehensive clinical and diagnostic services, including microbiology and renal testing. The study included patients with confirmed bloodstream infections between January and December 2025. Data were extracted from the laboratory information system between 1st October 2025 and 11th January 2026. The study was conducted in accordance with the Declaration of Helsinki (2013) for human medical research. The study protocol was reviewed and approved by both the Mulungushi University School of Medicine and Health Sciences Research Ethics Committee and the National Health Research Ethics Board. Given the retrospective nature of the study using anonymized laboratory data, both ethics committees waived the requirement for informed consent. Study participants and eligibility The study included adult patients (≥ 18 years) with at least one laboratory-confirmed bloodstream infection and documented renal function results (serum creatinine) at the time of hospital admission. Patients were excluded if they had incomplete or missing laboratory records, duplicate blood culture isolates from the same infectious episode, organisms classified as contaminants, or an eGFR greater than 60 mL/min/1.73m². Contaminants were defined according to Clinical and Laboratory Standards Institute (CLSI) guidelines, including common skin commensals (e.g., coagulase-negative staphylococci, Corynebacterium spp., Bacillus spp., Micrococcus spp.) isolated from a single blood culture without clinical evidence of infection, or organisms inconsistent with the patient’s clinical presentation. Population A total of 582 positive blood culture entries recorded in DISA*Lab were reviewed. After excluding incomplete records, duplicate isolates, contaminants, and patients with an eGFR greater than 60 mL/min/1.73m², 136 participants with confirmed bloodstream infections were included in the final analysis. Sample Collection and Processing Blood samples were collected by trained phlebotomists from patients admitted to NTH in the Copperbelt Province of Zambia as part of routine clinical care for suspected bloodstream infections and renal function assessment. Peripheral venous blood was drawn, typically from the antecubital fossa, with the following volumes and purposes: approximately 10 mL for blood culture (inoculated into BACTEC aerobic and anaerobic bottles, Becton Dickinson, USA), 2–4 mL into EDTA tubes for complete blood count (including haemoglobin and red cell distribution width), and 3–5 mL into lithium heparin tubes for serum creatinine and other biochemical analyses. Samples were collected from patients in the emergency ward, general wards, and intensive care unit (ICU) and were transported immediately to the laboratory, which is located within the same hospital building. Transportation was carried out using a cold chain cooler box maintained at 2–8°C to ensure sample stability. All samples were processed in the hospital's clinical laboratories (Microbiology, Hematology and Clinical Chemistry) within 2 hours of collection. Study variables The primary dependent variable was the Haemoglobin-to-Red Cell Distribution Width ratio (Hb/RDW). Independent variables included sociodemographic, microbiological, and laboratory parameters. Sociodemographic variables comprised age (18–39, 40–59, and ≥ 60 years) and sex (male or female). Microbiological variables included the type of bloodstream isolate (Gram-negative or Gram-positive), specific bacterial species, and multidrug-resistant organism status (yes/no). Laboratory variables included white cell count, neutrophil count, lymphocyte count, platelet count, serum creatinine, and estimated glomerular filtration rate (eGFR). Kidney dysfunction, defined as an eGFR of less than 60 mL/min/1.73 m² at hospital admission, was used as an inclusion criterion for study participants. Data analysis Data were exported from the laboratory information system Disa into a structured format and underwent initial cleaning in Microsoft Excel to ensure data integrity. All statistical analyses were performed using STATA Version 16 and GraphPad Prism Version 10. Descriptive statistics were presented as medians with interquartile ranges (IQR) for continuous variables and frequencies with percentages for categorical variables. The relationship between HB:RDW ratio and covariates was first assessed using simple linear regression. Variables showing a significance level of p < 0.05 in the univariate analysis were included in the multiple linear regression models. A p-value of < 0.05 was considered statistically significant in the multivariable models. Result Participant Characteristics A total of 136 patients with bloodstream infections and concurrent kidney dysfunction were included in the study. The median age was within the older adult range, with the largest proportion of participants (44.9%, n = 61) being 60 years or older, while those aged 40–59 and 18–39 constituted 27.9% (n = 38) and 27.2% (n = 37), respectively. There was a slight female predominance (52.9%, n = 72) compared to males (47.1%, n = 64) (Table 1 ). The majority of patients were HIV-negative (83.1%, n = 113). Patients were most commonly admitted from general wards (47.8%, n = 65), followed by the emergency/admission unit (39.0%, n = 53) and the intensive care unit (ICU) (13.2%, n = 18). Microbiological analysis of bloodstream isolates revealed a diverse range of pathogens. Gram-negative bacteria were frequently identified, with Enterobacter agglomerans (19.8%, n = 27) and Escherichia coli (10.2%, n = 14) being the most common. Among Gram-positive isolates, Staphylococcus aureus was predominant (18.3%, n = 25). A significant majority of infections (61.8%, n = 84) were caused by multidrug-resistant organisms. Laboratory parameters at presentation indicated systemic involvement. Haematological indices showed a median white cell count of 9.6 x 10 9 /L, with neutrophilia (median 7.2 x 10 9 /L) and lymphopenia (median 1.0 x 10 9 /L), resulting in an elevated derived neutrophil-to-lymphocyte ratio (median 4.4). Patients were anaemic, with a median haemoglobin level of 8.3 g/dL and an elevated red cell distribution width (median 16.6%). Markers of systemic inflammation, including the Systemic Immune-Inflammation Index (median 947.4), were notably high. Renal function was significantly impaired, with a median creatinine of 164 µmol/L and a median estimated glomerular filtration rate of 23 mL/min/1.73m 2 . Table 1 Participant Characteristics Variable Frequency (n) Percentage (%) Age, Years 18–39 37 27.2 40–59 38 27.9 ≥ 60 61 44.9 Sex Males 64 47.1 Females 72 52.9 HIV Positive 23 16.9 Negative 113 83.1 Care Unit Emergence/admission 53 39.0 General Wards 53 47.8 ICU 18 13.2 Bloodstream isolates Gram-negative bacteria Citrobacter species 1 0.7 Enterobacter agglomerans 27 19.8 Escherichia coli 14 10.2 Group D non enterococcus 1 0.7 Klebsiella oxytoca 2 1.4 Klebsiella pneumoniae 13 9.5 Morganella morganii 5 3.6 Proteus mirabilis 2 1.4 Salmonella species 7 5.1 Serratia marcescens 1 0.7 Yersinia enterocolytica 2 1.4 Hafnia alvei 1 0.7 Non-fermentative Acinetobacter baumannii 9 6.6 Burkholderia species 1 0.7 Pseudomonas aeruginosa 7 5.1 Gram-positive bacteria Streptococcus pneumoniae 8 5.8 Viridans streptococcus 2 1.4 á-haemolytic Streptococcus 3 2.2 Enterococcus faecalis 5 3.6 Staphylococcus aureus 25 18.3 Multidrug-resistant organisms Yes 84 61.8 No 52 38.2 Laboratory Parameters White Cell Count (×10 9 /L) 9.6 (5.2, 16.9) Haemoglobin (g/dL) 8.3 (6.4, 11.1) Red Cell Distribution Width (%) 16.6 (14.9, 19.7) Haemoglobin:Red Cell Distribution Width ratio 0.5 (0.3, 0.7) Platelets (×10 9 /L) 146 (83, 231) Neutrophils (×10 9 /L) 7.2 (3.9, 14.5) Lymphocytes (×10 9 /L) 1.0 (0.6, 1.4) Monocytes (×10 9 /L) 0.4 (0.2, 0.7) Derived Neutrophil-to-Lymphocyte Ratio 4.4 (2.3, 8.2) Systemic Immune-Inflammation Index 947.4 (451.2, 2247.5) Neutrophil-Platelet to Lymphocyte ratio 0.05 (0.03, 0.08) Creatinine (µmol/L) 164 (154.8, 585.7) Estimated GFR (mL/min/1.73m 2 ) 23 (8.5, 38) Data are presented as n (%) or median (interquartile range). Percentages may not sum to 100 due to rounding. Subcategories of bloodstream isolates are indented under their respective taxonomic groups. The total percentage for Gram-negative and Gram-positive bacteria exceeds 100% as some patients may have had polymicrobial infections. Simple Linear Regression of factors associated with Haemoglobin to Red Cell Distribution Width Ratio Scatter plots ( Fig. 2 ) illustrate the relationship between the HB:RDW ratio and (A) age, (B) total white blood cell count, (C) estimated glomerular filtration rate (eGFR), (D) platelet count, (E) neutrophil count, (F) lymphocyte count, (G) monocyte count, (H) derived neutrophil–lymphocyte ratio (dNLR), (I) systemic immune-inflammation index (SII), (J) neutrophil–platelet–lymphocyte ratio (NPL), and (K) serum creatinine levels. Linear regression lines with 95% confidence intervals are shown. Regression equations, coefficients of determination (R 2 ), and corresponding P-values are provided within each panel. Statistically significant associations were observed for white blood cell count, eGFR, platelet count, neutrophils, lymphocytes, monocytes, and creatinine, while no significant associations were observed for age, dNLR, SII and NPL ( p > 0.05) ( Fig. 2 ) . Multivariable linear regression of factors associated with Haemoglobin to Red Cell Distribution Width Ratio A multivariable linear regression model was constructed to identify factors independently associated with the Haemoglobin to Red Cell Distribution Width (Hb/RDW) ratio. As shown in Table 2 , after adjusting for other covariates, two parameters demonstrated a statistically significant independent association with the Hb/RDW ratio. Sex was significantly associated with the ratio (β = -0.092, 95% CI: -0.170 to -0.015, p = 0.0204). The negative beta coefficient indicates that, on average, male sex was associated with a lower Hb/RDW ratio compared to female sex. Renal function, as measured by the estimated glomerular filtration rate (eGFR), also showed a significant positive association (β = 0.003, 95% CI: 0.000 to 0.007, p = 0.0489). This indicates that a higher eGFR was associated with a higher Hb/RDW ratio in this cohort. Other laboratory parameters included in the model—white cell count, platelet count. neutrophil count, lymphocyte count, and creatinine, did not show statistically significant independent associations with the Hb/RDW ratio in this analysis (all p > 0.05). Table 2 Multivariable linear regression of factors associated with Haemoglobin to Red Cell Distribution Width Ratio Parameter Beta (β) 95% L. Limit 95% U. Limit P-value Sex -0.092 -0.170 -0.015 0.0204 White Cell count (×10 9 /L) -0.019 -0.050 0.012 0.2188 Platelets (×10 9 /L) 0.000 -0.001 0.000 0.0974 Neutrophils (×10 9 /L) 0.015 -0.016 0.047 0.3381 Lymphocytes (×10 9 /L) 0.001 -0.047 0.050 0.9529 Creatinine (µmol/L) 0.000 0.000 0.000 0.1844 eGFR (mL/min/1.73m 2 ) 0.003 0.000 0.007 0.0489 β, unstandardized regression coefficient; CI, confidence interval; eGFR, estimated glomerular filtration rate. Discussion In this study of 136 patients with bacterial bloodstream infections and concurrent kidney dysfunction, the Haemoglobin-to-Red Cell Distribution Width ratio (Hb/RDW) was independently associated with sex and renal function. Male sex was linked to lower Hb/RDW, while higher estimated glomerular filtration rate (eGFR) corresponded to higher Hb/RDW. These results underscore the potential utility of Hb/RDW as a simple, inexpensive biomarker reflecting both host and kidney-related factors, which may aid in risk stratification and early clinical decision-making in patients with bloodstream infections. Our microbiological findings showed a predominance of Gram-negative bacteria, particularly Enterobacter agglomerans and Escherichia coli , in patients with bloodstream infections and concurrent kidney dysfunction. This pattern aligns with evidence that individuals with renal impairment are at increased risk of bloodstream infections, and Gram-negative organisms often predominate in this vulnerable population [ 12 , 13 ]. In nephrology and dialysis cohorts, Gram-negative bacteria such as E. coli and Pseudomonas aeruginosa accounted for a large proportion of BSIs among patients with chronic kidney disease, with high levels of antimicrobial resistance observed in these isolates [ 14 ]. A retrospective study of bloodstream infections in patients with kidney disease similarly found that Gram-negative bacilli comprised over half of all isolates, with E. coli being the most common pathogen [ 13 ]. The high proportion of multidrug-resistant organisms (61.8%) in our cohort further underscores the growing challenge of antimicrobial resistance in patients with renal dysfunction, complicating empiric therapy and heightening the risk of adverse outcomes [ 15 ]. The observed association between lower Hb/RDW and male sex may reflect sex-related differences in erythropoiesis, haemoglobin levels, and inflammatory responses, as seen in studies exploring RDW’s prognostic implications across clinical conditions [ 16 – 18 ]. Heterogeneous associations between RDW and mortality by sex have been reported in critically ill patients, highlighting that RDW’s predictive value may differ between males and females[ 19 ]. Meanwhile, the positive correlation between eGFR and Hb/RDW in our cohort suggests that declining kidney function is linked to alterations in red blood cell morphology and anaemia, consistent with evidence demonstrating that elevated RDW is associated with adverse renal outcomes and deterioration of kidney function in patients with chronic kidney disease[ 20 ]. Prior study has further shown that RDW and related ratios are elevated in inflammatory states and in the context of renal impairment, and that higher RDW predicts mortality in conditions involving systemic infection and organ dysfunction, including bacterial bloodstream infection cohorts [ 7 ]. Therefore, these studies support the potential utility of RDW-based metrics as inexpensive, readily available prognostic markers in patients with bacterial bloodstream infections complicated by kidney dysfunction. To our knowledge, this is the first study in the region to comprehensively examine the relationship between Hb/RDW and bacterial bloodstream infections in patients with kidney dysfunction. The study benefits from a well-defined cohort with laboratory-confirmed infections and detailed haematological and renal data. However, the retrospective, single-center design limits generalizability, and causality cannot be inferred. Furthermore, clinical outcomes such as mortality, ICU admission, or length of hospital stay were not evaluated, which limits the ability to directly assess the prognostic value of Hb/RDW in this population. Our findings suggest that Hb/RDW may be a useful adjunct biomarker in patients with bacterial bloodstream infections and kidney dysfunction, helping to identify those at higher risk of complications. Future prospective studies should evaluate the prognostic value of Hb/RDW for mortality, treatment response, and hospital outcomes, as well as explore its integration into clinical decision-making alongside other laboratory and clinical markers. Conclusion In patients with bacterial bloodstream infections and concurrent kidney dysfunction, Hb/RDW is independently associated with sex and renal function. These findings suggest that Hb/RDW may serve as a simple, inexpensive biomarker to identify patients at higher risk of complications. Future prospective studies should evaluate its prognostic value for mortality, treatment response, and clinical outcomes, and explore its integration into early risk stratification strategies in resource-limited settings. Declarations Ethics approval and consent to participate Ethical approval for this study was obtained from the Mulungushi University School of Medicine and Health Sciences Research Ethics Committee (IRB: 00012281; FWA: 00028888; Ref. No. : SMHS-MU1-2025-37) on 17 March 2025 and the National Health Research Ethics Board (Ref: NHRA-2246/20/05/2025) on 5 June 2025. Permission to conduct the study was granted by Ndola Teaching Hospital. All extracted data were de-identified to protect participant confidentiality, and no personally identifiable information was collected. The requirement for written or verbal informed consent was waived by both the Mulungushi University School of Medicine and Health Sciences Research Ethics Committee and the National Health Research Ethics Board due to the retrospective nature of the study. Clinical Trial number Not applicable (N/A). Consent for publication Not applicable. Competing interests The authors have no conflicts of interest to declare. Funding Not applicable. Author Contribution JN and DC conceived the Study. JN oversaw Data acquisition. JN and DC conducted the formal analysis. JN, DC, JC, KB, AM and SKM wrote the original draft. All authors contributed to the article edits and approved the final manuscript. Acknowledgments The authors would like to thank the management of Ndola Teaching Hospital for granting permission to conduct this study in the Department of Pathology. We also extend our gratitude to the students from the Department of Biomedical Sciences, Chikankata College of Biomedical Sciences, Mazabuka, Zambia—Exildah Sichone, Luswepo Namwinga, and Mwaka Mpela—for their valuable assistance during data collection. Data Availability The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. References Bai AD, Daneman N, Brown KA, Boyd JG, Gill SS. Long-term morbidity and mortality of patients who survived past 30 days from bloodstream infection: A population-based retrospective cohort study. J Infect. 2024;89(5):106283. Goto M, Al-Hasan MN. 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BMC Gastroenterol [Internet]. 2025 Dec 19 [cited 2026 Jan 19]; Available from: https://doi.org/10.1186/s12876-025-04549-9 Deng X, Gao B, Wang F, Zhao M, hui, Wang J, Zhang L. Red Blood Cell Distribution Width Is Associated With Adverse Kidney Outcomes in Patients With Chronic Kidney Disease. Front Med [Internet]. 2022 Jun 9 [cited 2025 Jul 1];9. Available from: https://www.frontiersin.org/journals/medicine/articles/ 10.3389/fmed.2022.877220/full Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-8642275","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589153392,"identity":"a35b44e8-ebde-42e1-9777-81a3dfc6ec72","order_by":0,"name":"John 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Sciences","correspondingAuthor":false,"prefix":"","firstName":"Alick","middleName":"","lastName":"Mwambungu","suffix":""},{"id":589153399,"identity":"4fc497b0-c28b-490a-96d8-1948f42efcb6","order_by":5,"name":"Sepiso K Masenga","email":"","orcid":"","institution":"Vanderbilt University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Sepiso","middleName":"K","lastName":"Masenga","suffix":""}],"badges":[],"createdAt":"2026-01-19 18:29:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8642275/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8642275/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102441737,"identity":"5bea17c0-8bc9-48c7-a32e-8ebd7cb3c517","added_by":"auto","created_at":"2026-02-11 16:59:20","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82868,"visible":true,"origin":"","legend":"\u003cp\u003eEligibility flow diagram\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8642275/v1/69bc981429d1055498cab0e8.png"},{"id":102441738,"identity":"b9e9cc17-bd0e-468e-be36-3f067369c912","added_by":"auto","created_at":"2026-02-11 16:59:20","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":43979,"visible":true,"origin":"","legend":"\u003cp\u003eSimple Linear Regression of factors associated with Haemoglobin to Red Cell Distribution Width Ratio\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8642275/v1/797d432922da152e4772cfac.png"},{"id":105363155,"identity":"4b23dbd0-1666-42ef-80c4-663a65d38940","added_by":"auto","created_at":"2026-03-25 08:13:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1020949,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8642275/v1/eccf0add-65b7-4e9f-8b4b-f06c263a1fcc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical Correlates of the Haemoglobin to Red Cell Distribution Width Ratio in Bacterial Bloodstream Infections and Concomitant Kidney Dysfunction","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBacterial bloodstream infections (BSIs) are among the most serious infectious diseases encountered in clinical practice and are associated with high morbidity and mortality worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. BSIs are caused by the invasion of normally sterile blood by pathogenic bacteria and often lead to systemic inflammatory responses, prolonged hospital stays, and adverse outcomes particularly in patients with underlying comorbidities such as kidney dysfunction [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Diagnosing and risk stratifying BSI remains challenging in many settings due to the limited availability of rapid and inexpensive biomarkers that reflect both infection severity and host response.\u003c/p\u003e \u003cp\u003eRoutine complete blood count (CBC) parameters, including red cell distribution width (RDW), have garnered interest as potential markers of illness severity in bacterial infections [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. RDW, an indicator of red blood cell size variability, may be elevated in bacterial bloodstream infections as a result of inflammation, oxidative stress, and disrupted erythropoietic processes [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Elevated RDW has been shown to correlate with adverse outcomes in patients with bacterial bloodstream infection; for example, in Klebsiella \u003cem\u003epneumoniae\u003c/em\u003e BSI, higher RDW values were independently associated with increased in-hospital mortality [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBeyond RDW alone, ratios that integrate RDW with other hematological measures, such as the haemoglobin (Hb)/RDW ratio, may offer enhanced clinical insight by capturing both erythrocyte heterogeneity and haemoglobin status, which can be perturbed during bacterial infection and inflammation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Studies exploring anaemia-related parameters, including the Hb/RDW ratio, have reported their potential utility in distinguishing bacterial from viral infections and in reflecting the host response to bacterial pathogens [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite these findings, the clinical and laboratory correlates of Hb/RDW remain underexplored specifically in patients with bacterial BSIs complicated by kidney dysfunction, a population in which systemic inflammation, anaemia, and renal impairment intersect and may collectively influence clinical course and outcomes. Understanding how Hb/RDW relates to host factors and laboratory indices in this context could enhance patient stratification and inform management strategies, particularly in settings with constrained access to advanced diagnostics [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. This study aimed to determine the clinical and laboratory factors independently associated with the Hb/RDW ratio in patients with bacterial bloodstream infections and concomitant kidney dysfunction.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis was a laboratory-based, single-center retrospective cohort study conducted at Ndola Teaching Hospital (NTH), a tertiary referral center in Copperbelt Province, Zambia, which serves approximately 29,485 admissions annually and provides comprehensive clinical and diagnostic services, including microbiology and renal testing. The study included patients with confirmed bloodstream infections between January and December 2025. Data were extracted from the laboratory information system between 1st October 2025 and 11th January 2026. The study was conducted in accordance with the Declaration of Helsinki (2013) for human medical research. The study protocol was reviewed and approved by both the Mulungushi University School of Medicine and Health Sciences Research Ethics Committee and the National Health Research Ethics Board. Given the retrospective nature of the study using anonymized laboratory data, both ethics committees waived the requirement for informed consent.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy participants and eligibility\u003c/h3\u003e\n\u003cp\u003eThe study included adult patients (\u0026ge;\u0026thinsp;18 years) with at least one laboratory-confirmed bloodstream infection and documented renal function results (serum creatinine) at the time of hospital admission. Patients were excluded if they had incomplete or missing laboratory records, duplicate blood culture isolates from the same infectious episode, organisms classified as contaminants, or an eGFR greater than 60 mL/min/1.73m\u0026sup2;. Contaminants were defined according to Clinical and Laboratory Standards Institute (CLSI) guidelines, including common skin commensals (e.g., coagulase-negative staphylococci, \u003cem\u003eCorynebacterium\u003c/em\u003e spp., \u003cem\u003eBacillus\u003c/em\u003e spp., \u003cem\u003eMicrococcus\u003c/em\u003e spp.) isolated from a single blood culture without clinical evidence of infection, or organisms inconsistent with the patient\u0026rsquo;s clinical presentation.\u003c/p\u003e\n\u003ch3\u003ePopulation\u003c/h3\u003e\n\u003cp\u003e A total of 582 positive blood culture entries recorded in DISA*Lab were reviewed. After excluding incomplete records, duplicate isolates, contaminants, and patients with an eGFR greater than 60 mL/min/1.73m\u0026sup2;, 136 participants with confirmed bloodstream infections were included in the final analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eSample Collection and Processing\u003c/h3\u003e\n\u003cp\u003eBlood samples were collected by trained phlebotomists from patients admitted to NTH in the Copperbelt Province of Zambia as part of routine clinical care for suspected bloodstream infections and renal function assessment. Peripheral venous blood was drawn, typically from the antecubital fossa, with the following volumes and purposes: approximately 10 mL for blood culture (inoculated into BACTEC aerobic and anaerobic bottles, Becton Dickinson, USA), 2\u0026ndash;4 mL into EDTA tubes for complete blood count (including haemoglobin and red cell distribution width), and 3\u0026ndash;5 mL into lithium heparin tubes for serum creatinine and other biochemical analyses. Samples were collected from patients in the emergency ward, general wards, and intensive care unit (ICU) and were transported immediately to the laboratory, which is located within the same hospital building. Transportation was carried out using a cold chain cooler box maintained at 2\u0026ndash;8\u0026deg;C to ensure sample stability. All samples were processed in the hospital's clinical laboratories (Microbiology, Hematology and Clinical Chemistry) within 2 hours of collection.\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStudy variables\u003c/h2\u003e \u003cp\u003eThe primary dependent variable was the Haemoglobin-to-Red Cell Distribution Width ratio (Hb/RDW). Independent variables included sociodemographic, microbiological, and laboratory parameters. Sociodemographic variables comprised age (18\u0026ndash;39, 40\u0026ndash;59, and \u0026ge;\u0026thinsp;60 years) and sex (male or female). Microbiological variables included the type of bloodstream isolate (Gram-negative or Gram-positive), specific bacterial species, and multidrug-resistant organism status (yes/no). Laboratory variables included white cell count, neutrophil count, lymphocyte count, platelet count, serum creatinine, and estimated glomerular filtration rate (eGFR). Kidney dysfunction, defined as an eGFR of less than 60 mL/min/1.73 m\u0026sup2; at hospital admission, was used as an inclusion criterion for study participants.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData were exported from the laboratory information system Disa into a structured format and underwent initial cleaning in Microsoft Excel to ensure data integrity. All statistical analyses were performed using STATA Version 16 and GraphPad Prism Version 10. Descriptive statistics were presented as medians with interquartile ranges (IQR) for continuous variables and frequencies with percentages for categorical variables. The relationship between HB:RDW ratio and covariates was first assessed using simple linear regression. Variables showing a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were included in the multiple linear regression models. A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant in the multivariable models.\u003c/p\u003e \u003c/div\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eParticipant Characteristics\u003c/h2\u003e \u003cp\u003eA total of 136 patients with bloodstream infections and concurrent kidney dysfunction were included in the study. The median age was within the older adult range, with the largest proportion of participants (44.9%, n\u0026thinsp;=\u0026thinsp;61) being 60 years or older, while those aged 40\u0026ndash;59 and 18\u0026ndash;39 constituted 27.9% (n\u0026thinsp;=\u0026thinsp;38) and 27.2% (n\u0026thinsp;=\u0026thinsp;37), respectively. There was a slight female predominance (52.9%, n\u0026thinsp;=\u0026thinsp;72) compared to males (47.1%, n\u0026thinsp;=\u0026thinsp;64) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The majority of patients were HIV-negative (83.1%, n\u0026thinsp;=\u0026thinsp;113). Patients were most commonly admitted from general wards (47.8%, n\u0026thinsp;=\u0026thinsp;65), followed by the emergency/admission unit (39.0%, n\u0026thinsp;=\u0026thinsp;53) and the intensive care unit (ICU) (13.2%, n\u0026thinsp;=\u0026thinsp;18). Microbiological analysis of bloodstream isolates revealed a diverse range of pathogens. Gram-negative bacteria were frequently identified, with \u003cem\u003eEnterobacter agglomerans\u003c/em\u003e (19.8%, n\u0026thinsp;=\u0026thinsp;27) and Escherichia coli (10.2%, n\u0026thinsp;=\u0026thinsp;14) being the most common. Among Gram-positive isolates, Staphylococcus aureus was predominant (18.3%, n\u0026thinsp;=\u0026thinsp;25). A significant majority of infections (61.8%, n\u0026thinsp;=\u0026thinsp;84) were caused by multidrug-resistant organisms. Laboratory parameters at presentation indicated systemic involvement. Haematological indices showed a median white cell count of 9.6 x 10\u003csup\u003e9\u003c/sup\u003e/L, with neutrophilia (median 7.2 x 10\u003csup\u003e9\u003c/sup\u003e/L) and lymphopenia (median 1.0 x 10\u003csup\u003e9\u003c/sup\u003e/L), resulting in an elevated derived neutrophil-to-lymphocyte ratio (median 4.4). Patients were anaemic, with a median haemoglobin level of 8.3 g/dL and an elevated red cell distribution width (median 16.6%). Markers of systemic inflammation, including the Systemic Immune-Inflammation Index (median 947.4), were notably high. Renal function was significantly impaired, with a median creatinine of 164 \u0026micro;mol/L and a median estimated glomerular filtration rate of 23 mL/min/1.73m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParticipant Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge, Years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e18\u0026ndash;39\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e40\u0026ndash;59\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026ge;\u0026thinsp;60\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHIV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePositive\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNegative\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCare Unit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEmergence/admission\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGeneral Wards\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eICU\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBloodstream isolates\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGram-negative bacteria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCitrobacter species\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnterobacter agglomerans\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEscherichia coli\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGroup D non enterococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella oxytoca\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eKlebsiella pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMorganella morganii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProteus mirabilis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSalmonella species\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSerratia marcescens\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYersinia enterocolytica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHafnia alvei\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNon-fermentative\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAcinetobacter baumannii\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBurkholderia species\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGram-positive bacteria\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eStreptococcus pneumoniae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eViridans streptococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026aacute;-haemolytic Streptococcus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEnterococcus faecalis\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eStaphylococcus aureus\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMultidrug-resistant organisms\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eYes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e61.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNo\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLaboratory Parameters\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eWhite Cell Count (\u0026times;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e9.6 (5.2, 16.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHaemoglobin (g/dL)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8.3 (6.4, 11.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRed Cell Distribution Width (%)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e16.6 (14.9, 19.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHaemoglobin:Red Cell Distribution Width ratio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.5 (0.3, 0.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlatelets (\u0026times;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e146 (83, 231)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNeutrophils (\u0026times;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7.2 (3.9, 14.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLymphocytes (\u0026times;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1.0 (0.6, 1.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMonocytes (\u0026times;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.4 (0.2, 0.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eDerived Neutrophil-to-Lymphocyte Ratio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4.4 (2.3, 8.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSystemic Immune-Inflammation Index\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e947.4 (451.2, 2247.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNeutrophil-Platelet to Lymphocyte ratio\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.05 (0.03, 0.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eCreatinine (\u0026micro;mol/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e164 (154.8, 585.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEstimated GFR (mL/min/1.73m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e23 (8.5, 38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eData are presented as n (%) or median (interquartile range). Percentages may not sum to 100 due to rounding. Subcategories of bloodstream isolates are indented under their respective taxonomic groups. The total percentage for Gram-negative and Gram-positive bacteria exceeds 100% as some patients may have had polymicrobial infections.\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 \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSimple Linear Regression of factors associated with Haemoglobin to Red Cell Distribution Width Ratio\u003c/h2\u003e \u003cp\u003eScatter plots \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e illustrate the relationship between the HB:RDW ratio and (A) age, (B) total white blood cell count, (C) estimated glomerular filtration rate (eGFR), (D) platelet count, (E) neutrophil count, (F) lymphocyte count, (G) monocyte count, (H) derived neutrophil\u0026ndash;lymphocyte ratio (dNLR), (I) systemic immune-inflammation index (SII), (J) neutrophil\u0026ndash;platelet\u0026ndash;lymphocyte ratio (NPL), and (K) serum creatinine levels. Linear regression lines with 95% confidence intervals are shown. Regression equations, coefficients of determination (R\u003csup\u003e2\u003c/sup\u003e), and corresponding P-values are provided within each panel. Statistically significant associations were observed for white blood cell count, eGFR, platelet count, neutrophils, lymphocytes, monocytes, and creatinine, while no significant associations were observed for age, dNLR, SII and NPL (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultivariable linear regression of factors associated with Haemoglobin to Red Cell Distribution Width Ratio\u003c/h2\u003e \u003cp\u003eA multivariable linear regression model was constructed to identify factors independently associated with the Haemoglobin to Red Cell Distribution Width (Hb/RDW) ratio. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, after adjusting for other covariates, two parameters demonstrated a statistically significant independent association with the Hb/RDW ratio. Sex was significantly associated with the ratio (β = -0.092, 95% CI: -0.170 to -0.015, p\u0026thinsp;=\u0026thinsp;0.0204). The negative beta coefficient indicates that, on average, male sex was associated with a lower Hb/RDW ratio compared to female sex. Renal function, as measured by the estimated glomerular filtration rate (eGFR), also showed a significant positive association (β\u0026thinsp;=\u0026thinsp;0.003, 95% CI: 0.000 to 0.007, p\u0026thinsp;=\u0026thinsp;0.0489). This indicates that a higher eGFR was associated with a higher Hb/RDW ratio in this cohort. Other laboratory parameters included in the model\u0026mdash;white cell count, platelet count. neutrophil count, lymphocyte count, and creatinine, did not show statistically significant independent associations with the Hb/RDW ratio in this analysis (all p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eMultivariable linear regression of factors associated with Haemoglobin to Red Cell Distribution Width Ratio\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\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\u003eBeta (β)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% L. Limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% U. Limit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\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\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0204\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite Cell count \u003cem\u003e(\u0026times;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.2188\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlatelets \u003cem\u003e(\u0026times;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0974\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeutrophils \u003cem\u003e(\u0026times;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.3381\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLymphocytes \u003cem\u003e(\u0026times;10\u003c/em\u003e\u003csup\u003e\u003cem\u003e9\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.9529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCreatinine \u003cem\u003e(\u0026micro;mol/L)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.1844\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR \u003cem\u003e(mL/min/1.73m\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.0489\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eβ, unstandardized regression coefficient; CI, confidence interval; eGFR, estimated glomerular filtration rate.\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"},{"header":"Discussion","content":"\u003cp\u003eIn this study of 136 patients with bacterial bloodstream infections and concurrent kidney dysfunction, the Haemoglobin-to-Red Cell Distribution Width ratio (Hb/RDW) was independently associated with sex and renal function. Male sex was linked to lower Hb/RDW, while higher estimated glomerular filtration rate (eGFR) corresponded to higher Hb/RDW. These results underscore the potential utility of Hb/RDW as a simple, inexpensive biomarker reflecting both host and kidney-related factors, which may aid in risk stratification and early clinical decision-making in patients with bloodstream infections.\u003c/p\u003e \u003cp\u003eOur microbiological findings showed a predominance of Gram-negative bacteria, particularly \u003cem\u003eEnterobacter agglomerans\u003c/em\u003e and \u003cem\u003eEscherichia coli\u003c/em\u003e, in patients with bloodstream infections and concurrent kidney dysfunction. This pattern aligns with evidence that individuals with renal impairment are at increased risk of bloodstream infections, and Gram-negative organisms often predominate in this vulnerable population [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In nephrology and dialysis cohorts, Gram-negative bacteria such as \u003cem\u003eE. coli\u003c/em\u003e and \u003cem\u003ePseudomonas aeruginosa\u003c/em\u003e accounted for a large proportion of BSIs among patients with chronic kidney disease, with high levels of antimicrobial resistance observed in these isolates [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A retrospective study of bloodstream infections in patients with kidney disease similarly found that Gram-negative bacilli comprised over half of all isolates, with \u003cem\u003eE. coli\u003c/em\u003e being the most common pathogen [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The high proportion of multidrug-resistant organisms (61.8%) in our cohort further underscores the growing challenge of antimicrobial resistance in patients with renal dysfunction, complicating empiric therapy and heightening the risk of adverse outcomes [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe observed association between lower Hb/RDW and male sex may reflect sex-related differences in erythropoiesis, haemoglobin levels, and inflammatory responses, as seen in studies exploring RDW\u0026rsquo;s prognostic implications across clinical conditions [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Heterogeneous associations between RDW and mortality by sex have been reported in critically ill patients, highlighting that RDW\u0026rsquo;s predictive value may differ between males and females[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Meanwhile, the positive correlation between eGFR and Hb/RDW in our cohort suggests that declining kidney function is linked to alterations in red blood cell morphology and anaemia, consistent with evidence demonstrating that elevated RDW is associated with adverse renal outcomes and deterioration of kidney function in patients with chronic kidney disease[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Prior study has further shown that RDW and related ratios are elevated in inflammatory states and in the context of renal impairment, and that higher RDW predicts mortality in conditions involving systemic infection and organ dysfunction, including bacterial bloodstream infection cohorts [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, these studies support the potential utility of RDW-based metrics as inexpensive, readily available prognostic markers in patients with bacterial bloodstream infections complicated by kidney dysfunction.\u003c/p\u003e \u003cp\u003eTo our knowledge, this is the first study in the region to comprehensively examine the relationship between Hb/RDW and bacterial bloodstream infections in patients with kidney dysfunction. The study benefits from a well-defined cohort with laboratory-confirmed infections and detailed haematological and renal data. However, the retrospective, single-center design limits generalizability, and causality cannot be inferred. Furthermore, clinical outcomes such as mortality, ICU admission, or length of hospital stay were not evaluated, which limits the ability to directly assess the prognostic value of Hb/RDW in this population.\u003c/p\u003e \u003cp\u003eOur findings suggest that Hb/RDW may be a useful adjunct biomarker in patients with bacterial bloodstream infections and kidney dysfunction, helping to identify those at higher risk of complications. Future prospective studies should evaluate the prognostic value of Hb/RDW for mortality, treatment response, and hospital outcomes, as well as explore its integration into clinical decision-making alongside other laboratory and clinical markers.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn patients with bacterial bloodstream infections and concurrent kidney dysfunction, Hb/RDW is independently associated with sex and renal function. These findings suggest that Hb/RDW may serve as a simple, inexpensive biomarker to identify patients at higher risk of complications. Future prospective studies should evaluate its prognostic value for mortality, treatment response, and clinical outcomes, and explore its integration into early risk stratification strategies in resource-limited settings.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e Ethical approval for this study was obtained from the Mulungushi University School of Medicine and Health Sciences Research Ethics Committee (IRB: 00012281; FWA: 00028888; Ref. No. : SMHS-MU1-2025-37) on 17 March 2025 and the National Health Research Ethics Board (Ref: NHRA-2246/20/05/2025) on 5 June 2025. Permission to conduct the study was granted by Ndola Teaching Hospital. All extracted data were de-identified to protect participant confidentiality, and no personally identifiable information was collected. The requirement for written or verbal informed consent was waived by both the Mulungushi University School of Medicine and Health Sciences Research Ethics Committee and the National Health Research Ethics Board due to the retrospective nature of the study.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eClinical Trial number\u003c/h2\u003e \u003cp\u003eNot applicable (N/A).\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent for publication\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJN and DC conceived the Study. JN oversaw Data acquisition. JN and DC conducted the formal analysis. JN, DC, JC, KB, AM and SKM wrote the original draft. All authors contributed to the article edits and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors would like to thank the management of Ndola Teaching Hospital for granting permission to conduct this study in the Department of Pathology. We also extend our gratitude to the students from the Department of Biomedical Sciences, Chikankata College of Biomedical Sciences, Mazabuka, Zambia\u0026mdash;Exildah Sichone, Luswepo Namwinga, and Mwaka Mpela\u0026mdash;for their valuable assistance during data collection.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBai AD, Daneman N, Brown KA, Boyd JG, Gill SS. Long-term morbidity and mortality of patients who survived past 30 days from bloodstream infection: A population-based retrospective cohort study. J Infect. 2024;89(5):106283.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoto M, Al-Hasan MN. Overall burden of bloodstream infection and nosocomial bloodstream infection in North America and Europe. Clin Microbiol Infect Off Publ Eur Soc Clin Microbiol Infect Dis. 2013;19(6):501\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCosta SP, Carvalho CM. Burden of bacterial bloodstream infections and recent advances for diagnosis. Pathog Dis. 2022;80(1):ftac027.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlannery AH, Li X, Delozier NL, Toto RD, Moe OW, Yee J, et al. Sepsis-Associated Acute Kidney Disease and Long-term Kidney Outcomes. Kidney Med. 2021;3(4):507\u0026ndash;e5141.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMahmood NA, Mathew J, Kang B, DeBari VA, Khan MA. Broadening of the red blood cell distribution width is associated with increased severity of illness in patients with sepsis. Int J Crit Illn Inj Sci. 2014;4(4):278\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgnello L, Giglio RV, Bivona G, Scazzone C, Gambino CM, Iacona A, et al. The Value of a Complete Blood Count (CBC) for Sepsis Diagnosis and Prognosis. Diagnostics. 2021;11(10):1881.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Ao T, Hu M, Zhen P. Association Between Red Cell Distribution Width and Mortality in Patients with Klebsiella pneumoniae Bloodstream Infection: A Cohort Study. Infect Drug Resist. 2025;18:5961\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLubis AP, Hamdi T, Radiansyah A. Correlation Between Red Cell Distribution Width (RDW) and Sequential Organ Failure Assessment (SOFA) Score in Sepsis Patients. JAI J Anestesiol Indones. 2024;16(2):150\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQin K, Su Y, Ding N. Red cell distribution width and clinical outcomes in sepsis patients infected with Escherichia coli using data from MIMIC-IV. Eur J Med Res. 2025;30(1):580.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSteuerman Y, Wasserman A, Zeltser D, Shapira I, Trotzky D, Halpern P, et al. Anemia measurements to distinguish between viral and bacterial infections in the emergency department. Eur J Clin Microbiol Infect Dis Off Publ Eur Soc Clin Microbiol. 2019;38(12):2331\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSunkonkit K, Chai-adisaksopha C, Natesirinilkul R, Phinyo P, Trongtrakul K. Admission Red Blood Cell Distribution Width and Mean Platelet Volume as Predictors of Mortality in the Pediatric Intensive Care Unit: A Five-Year Single-Center Retrospective Study. J Clin Med. 2025;14(11):3839.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDagasso G, Conley J, Steele L, Parfitt EEC, Pasquill K, Laupland KB. Risk of bloodstream infection in patients with renal dysfunction: a population-based cohort study. Epidemiol Infect. 2020;148:e105.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRojas L, Mu\u0026ntilde;oz P, Kestler M, Arroyo D, Guembe M, Rodr\u0026iacute;guez-Cr\u0026eacute;ixems M, et al. Bloodstream infections in patients with kidney disease: risk factors for poor outcome and mortality. J Hosp Infect. 2013;85(3):196\u0026ndash;205.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKamori D, Henry E, Shedura VJ, Kikoth R, Seguni N, Kibwana UO, et al. 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Am J Mens Health. 2026;20(1):15579883251414645.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurphy WG. The sex difference in haemoglobin levels in adults - mechanisms, causes, and consequences. Blood Rev. 2014;28(2):41\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu J, Song Y, Zhang D, Ji J. Sex differences in the association between red cell distribution width and 30-day mortality in critically ill patients with acute cholangitis: a retrospective cohort study. BMC Gastroenterol [Internet]. 2025 Dec 19 [cited 2026 Jan 19]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12876-025-04549-9\u003c/span\u003e\u003cspan address=\"10.1186/s12876-025-04549-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeng X, Gao B, Wang F, Zhao M, hui, Wang J, Zhang L. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.frontiersin.org/journals/medicine/articles/\u003c/span\u003e\u003cspan address=\"https://www.frontiersin.org/journals/medicine/articles/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmed.2022.877220/full\u003c/span\u003e\u003cspan address=\"10.3389/fmed.2022.877220/full\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"Hb/RDW, Bloodstream infection, Kidney dysfunction, Enterobacter agglomerans, Staphylococcus aureus","lastPublishedDoi":"10.21203/rs.3.rs-8642275/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8642275/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe Haemoglobin-to-Red Cell Distribution Width ratio (Hb/RDW) is an emerging marker of inflammation and clinical risk, but its correlates in patients with bacterial bloodstream infections and kidney dysfunction remain poorly defined in low-resource settings. This study aimed to determine the clinical and laboratory correlates of the Hb/RDW ratio in patients with bacterial bloodstream infections and concomitant kidney dysfunction at Ndola Teaching Hospital.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe conducted a retrospective laboratory-based study of 136 patients with confirmed bacterial bloodstream infections and kidney dysfunction at a Ndola Teaching Hospital in Zambia. Multivariable linear regression was used to identify factors independently associated with the Hb/RDW ratio.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 136 patients with bloodstream infections and kidney dysfunction were included, with a median age in the older adult range; 44.9% were \u0026ge;\u0026thinsp;60 years. Females comprised 52.9% of the cohort. Gram-negative bacteria predominated, particularly \u003cem\u003eEnterobacter agglomerans\u003c/em\u003e (19.8%) and \u003cem\u003eEscherichia coli\u003c/em\u003e (10.2%), while \u003cem\u003eStaphylococcus aureus\u003c/em\u003e (18.3%) was the most common Gram-positive isolate. Multidrug-resistant organisms accounted for 61.8% of infections. In multivariable analysis, male sex was associated with lower Hb/RDW (β = \u0026minus;0.092, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), and higher eGFR was associated with higher Hb/RDW (β\u0026thinsp;=\u0026thinsp;0.003, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). Other hematological and biochemical parameters showed no independent association.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eHb/RDW is independently associated with sex and renal function in patients with bloodstream infections and kidney dysfunction, suggesting its potential as a simple biomarker. Future studies should evaluate its prognostic value and utility in guiding early clinical interventions.\u003c/p\u003e","manuscriptTitle":"Clinical Correlates of the Haemoglobin to Red Cell Distribution Width Ratio in Bacterial Bloodstream Infections and Concomitant Kidney Dysfunction","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-11 16:59:16","doi":"10.21203/rs.3.rs-8642275/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":"15e87687-0a47-44a0-b62e-ef67dba1257f","owner":[],"postedDate":"February 11th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T08:12:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-11 16:59:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8642275","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8642275","identity":"rs-8642275","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}