Association Between Red Blood Cell Distribution Width and Liver Injury in Children with Epstein-Barr Virus Infection: A Retrospective Study with Incremental Predictive Value Assessment

Association Between Red Blood Cell Distribution Width and Liver Injury in Children with Epstein-Barr Virus Infection: A Retrospective Study with Incremental Predictive Value Assessment | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association Between Red Blood Cell Distribution Width and Liver Injury in Children with Epstein-Barr Virus Infection: A Retrospective Study with Incremental Predictive Value Assessment Yang Yu, Yang Jing, Yinyan Tang, Hongjuan Wei This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8984740/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Background: Liver injury is a common complication of Epstein-Barr virus (EBV) infection in children, yet reliable predictive biomarkers remain limited. Red blood cell distribution width-coefficient of variation (RDW-CV), an emerging inflammatory marker, has shown prognostic value in various infectious diseases but its role in EBV-related hepatic impairment is poorly characterized. Methods: This retrospective study included 123 hospitalized children with laboratory-confirmed EBV infection at Nanjing Lishui People's Hospital between July 2020 and December 2025. Liver injury was defined as ALT >80 U/L. The dose-response relationship between RDW-CV and liver injury was assessed using restricted cubic spline analysis. Multivariable logistic regression with progressive adjustment evaluated the independent association. Five variable selection methods (R², adjusted R², BIC, Mallows' Cp, LASSO,) integrated with clinical expertise identified nine predictors for model construction. The incremental value of RDW-CV was quantified by comparing AUC, NRI, and IDI between models with and without RDW-CV. Results: Of 123 patients, 48 (39.0%) developed liver injury. RDW-CV was significantly elevated in the liver injury group (13.6 ± 1.0% vs. 13.2 ± 0.8%, P = 0.046). Restricted cubic spline analysis revealed a significant linear association (P for overall = 0.007). In fully adjusted multivariable analysis, each 1% RDW-CV increment conferred threefold higher odds of liver injury ( OR 3.06, 95% CI 1.59–5.91, P= 0.001), with consistent effects across age and sex subgroups. The 9-predictor model including RDW-CV demonstrated superior discrimination compared to the model without RDW-CV (AUC: 0.800 vs. 0.718, P = 0.035; categorical NRI 0.274, P = 0.015; continuous NRI 0.617, P < 0.001; IDI 0.130, P < 0.001). Furthermore, the 9-predictor model showed adequate calibration (MAE = 0.059) and favorable net benefit in decision curve analysis (DCA). Conclusions: RDW-CV is an independent, robust predictor of liver injury in children with EBV infection. Incorporating RDW-CV into clinical prediction models significantly enhances risk stratification accuracy, supporting its potential as a routine adjunctive biomarker for early identification of hepatic complications. Red blood cell distribution width-coefficient of variation Epstein-Barr virus Liver injury Children Prediction model Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction EBV is a ubiquitous human herpesvirus that infects over 90% of the global population, with primary infection typically occurring during childhood[1]. While EBV infection in children is often asymptomatic or presents as self-limiting infectious mononucleosis, hepatic involvement represents one of the most common complications, affecting approximately 30–50% of pediatric patients[2, 3]. The clinical spectrum of EBV-related liver injury ranges from mild, transient elevation of liver enzymes to severe hepatitis, which may prolong hospitalization and necessitate therapeutic intervention[4]. Early identification of children at heightened risk for hepatic complications is therefore crucial for timely monitoring and clinical decision-making. However, reliable and readily available biomarkers for predicting EBV-associated liver injury remain limited. RDW-CV, quantifying erythrocyte size heterogeneity, has emerged as a novel inflammatory marker beyond its traditional role in anemia differentiation[5]. Elevated RDW-CV reflects systemic inflammation, oxidative stress, and impaired erythropoiesis[6, 7], with demonstrated prognostic utility in sepsis, pneumonia, and COVID-19[8]. However, its relationship with organ-specific complications, particularly EBV-related liver injury, remains unclear[9]. The potential biological plausibility linking RDW-CV to liver injury is multifaceted. Hepatic inflammation and hypoxia during EBV infection trigger systemic cytokine release, which may impair bone marrow erythropoietic activity and alter red blood cell membrane deformability[10]. Additionally, liver dysfunction disrupts iron metabolism and erythropoietin synthesis, further contributing to abnormal erythrocyte morphology[11]. These pathophysiological connections suggest that RDW-CV could serve as an early, objective indicator of hepatic impairment, potentially preceding clinically apparent liver dysfunction. Current clinical practice relies on hepatic enzyme monitoring for liver injury detection, which often identifies damage only after substantial hepatocellular injury has occurred. Developing predictive models that incorporate accessible, inexpensive biomarkers such as RDW-CV could enable earlier risk stratification and targeted intervention. While multivariable prediction models have been constructed for various EBV complications, none to our knowledge have systematically evaluated RDW-CV's incremental predictive value beyond conventional clinical and laboratory parameters. Accordingly, this study aimed to investigate the association between RDW-CV and liver injury in children with EBV infection, characterize its dose-response relationship, and evaluate whether incorporating RDW-CV into a multivariable prediction model enhances risk stratification accuracy. We hypothesized that elevated RDW-CV would be independently associated with liver injury and would significantly improve predictive model performance compared to conventional markers alone. Methods Study population This retrospective study initially identified 129 hospitalized children with laboratory-confirmed EBV infection at Nanjing Lishui People’s Hospital (NJLSPH) between July 1, 2020, and December 31, 2025. After excluding 2 cases of automatic discharge and 4 cases with incomplete key variables, 123 patients were included in the final analysis. Liver injury was defined as ALT >80 U/L[12, 13](Figure 1). Laboratory data collection and measures Demographic and Clinical Characteristics: Demographic data comprised age (days), sex, and weight (kg). Clinical variables included hospitalization duration, pre-admission fever duration (days), post-admission fever duration (days), total fever duration (days), eyelid edema, tonsillar enlargement with suppuration, cervical lymphadenopathy, and associated symptoms (cough, sore throat, rash, nasal congestion, abdominal pain, diarrhea). Past medical history (asthma, rhinitis) and treatment modalities (antiviral therapy, liver-protective therapy, corticosteroids) were also documented. Imaging Assessments: Chest radiography or computed tomography findings were classified as normal, bronchitis, or pneumonia. Cervical ultrasonography was performed to assess bilateral lymph node size, categorized as ≥10 mm, ≥20 mm, or ≥30 mm in longest diameter. Abdominal ultrasonography evaluated the presence of hepatomegaly and splenomegaly. Laboratory Measurements: Venous blood samples were collected within 24 h of admission. Hematological parameters, including white blood cell count (WBC), neutrophil count (NEUT), lymphocyte count (LYMPH), monocyte count (MONO), red blood cell count (RBC), hemoglobin (HGB), hematocrit (HCT), red cell distribution width–coefficient of variation (RDW-CV), and platelet count (PLT), were measured using a BC-7500 hematology analyzer (Mindray Corporation, Shenzhen, China). The following derived inflammatory indices were calculated: neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and ALT-to-lymphocyte ratio. Atypical lymphocyte percentage was determined by manual differential count on peripheral blood smears. Biochemical markers, including ALT, aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), lactate dehydrogenase (LDH), creatine kinase (CK), and creatine kinase-MB (CKMB), were quantified using an AU5800 automated clinical chemistry analyzer (Beckman Coulter Trading Co., Ltd., Suzhou, China). Inflammatory markers, C-reactive protein (CRP), procalcitonin (PCT), erythrocyte sedimentation rate (ESR), and ferritin, were assayed by immunoturbidimetry, chemiluminescence, and nephelometry, respectively. EBV-specific serology (IgM antibodies against viral capsid antigen) was determined by chemiluminescent immunoassay. EBV DNA load in whole blood was quantified by real-time polymerase chain reaction (PCR; positive cutoff >500 copies/mL). All laboratory tests were performed in the central clinical laboratory of NJLSPH following the manufacturers’ protocols and routine quality control procedures. The analyzers were subjected to regular calibration and quality control in accordance with laboratory accreditation standards. Statistical Analysis All analyses were performed using R Statistical Software (Version 4.2.2, http://www.R-project.org, The R Foundation) and Free Statistics analysis platform (Version 2.4, Beijing, China, http://www.clinicalscientists.cn/freestatistics). Single imputation by chained equations was performed under the missing-at-random assumption; variables with excessive missingness (EBV IgM, EBV DNA copies) were excluded from the imputation model, whereas all other candidate predictors were included. Imputation diagnostics were assessed by comparing distributional parameters between original and imputed datasets. Continuous variables were presented as mean ± standard deviation or median (interquartile range) based on normality, and categorical variables as frequency (percentage). Between-group comparisons were conducted using Student’s t-test, Mann–Whitney U test, chi-square test, or Fisher’s exact test as appropriate. Univariable and multivariable logistic regression models were fitted to estimate crude and adjusted OR s with 95% CI s for the association between RDW-CV and liver injury. Covariates were selected based on clinical relevance and the change-in-estimate method (≥10% change in the OR when added to the model). The final model included sex, age, pre-admission fever duration, cervical lymphadenopathy, atypical lymphocyte percentage, WBC, NLR, HGB, and PLT. To examine the dose–response relationship, restricted cubic spline logistic regression was performed, and the fitted curve was plotted with 95% confidence bands. Subgroup analyses stratified by age (≤5 vs. >5 years) and sex were pre-specified, and interaction terms were tested. Predictive models were developed using logistic regression with variable selection procedures including R², adjusted R², BIC, Mallows’ Cp, and LASSO regression. Discriminative ability was assessed by AUC. The incremental predictive value of RDW-CV was evaluated using NRI (both categorical and continuous) and IDI. The DeLong test was further applied to compare the AUC between the base model (sex, age, eyelid edema, total fever duration, CRP, WBC, HGB, and PLT) and the augmented model (additionally including RDW-CV). All statistical tests were two-sided, and P < 0.05 was considered statistically significant. Results Baseline Characteristics This study enrolled 123 children with EBV infection, of whom 48 (39.0%) developed liver injury. The two groups were comparable in age, sex distribution, and body weight. Children with liver injury exhibited significantly longer pre-admission fever duration (3.4 ± 2.4 vs. 2.7 ± 1.6 days, P = 0.046) and total fever duration (6.0 ± 3.3 vs. 4.9 ± 2.1 days, P = 0.026). Notably, RDW-CV (13.6 ± 1.0% vs. 13.2 ± 0.8%, P = 0.046) and RDW-SD (40.2 [38.5, 41.8] vs. 39.0 [37.8, 40.5] fL, P = 0.012) were significantly higher, accompanied by lower NLR (0.3 [0.2, 0.4] vs. 0.4 [0.3, 0.6], P = 0.029) (Table 1). These associations persisted after imputation (Supplementary table 1). Dose-Response Relationship Restricted cubic spline analysis, adjusted for demographics, clinical variables, and laboratory parameters, demonstrated a significant linear association between RDW-CV and liver injury risk ( Figure 2 ). The overall association was significant ( P for overall = 0.007), with no evidence of non-linearity ( P for non-linearity = 0.540). The risk of liver injury increased progressively with each unit increment in RDW-CV. Multivariable Logistic Regression Three progressively adjusted logistic regression models were constructed to evaluate the independent association between RDW-CV and liver injury (Table 2). In unadjusted analysis, each 1% increment in RDW-CV was associated with increased odds of liver injury (crude OR 1.65, 95% CI 1.06–2.57, P = 0.027). After adjustment for sex and age (Model I), the effect estimate strengthened substantially (adjusted OR 2.34, 95% CI 1.35–4.04, P = 0.002). Controlling for additional clinical variables (Model II) yielded consistent results (adjusted OR 2.26, 95% CI 1.30–3.94, P = 0.004). In the fully adjusted model incorporating hematological parameters (Model III), RDW-CV remained significantly associated with liver injury, with further accentuated effect size (adjusted OR 3.06, 95% CI 1.59–5.91, P = 0.001). Subgroup Analysis The association between RDW-CV and liver injury remained consistent across age and sex strata. In children aged ≤5 years (n=67), each unit increase in RDW-CV was associated with significantly increased odds of liver injury (adjusted OR = 3.72, 95% CI : 1.39–9.99, P = 0.009). A similar association was observed in older children (>5 years, n = 56; OR = 3.80, 95% CI : 1.24–11.65, P = 0.020). No significant interaction was observed ( P for interaction = 0.257). In sex-stratified analyses, RDW-CV demonstrated a significant association in males (n=83; OR = 3.24, 95% CI : 1.39–7.55, P = 0.006). Among females (n=40), the point estimate was similar ( OR = 3.98) but did not reach statistical significance due to wide CI (0.78–20.29, P = 0.096). While the interaction term was still not significant ( P = 0.638), indicating no effect modification by sex ( Table 3 , Figure 3 ). Predictive Model Development Variable Selection Five complementary methods (R², adjusted R², BIC , Mallows' Cp, LASSO) selected 4 to 13 candidate predictors: R² selected 13, adjusted R² 7, BIC 4, Mallows' Cp 6, and LASSO 8 ( Figure 4A-E) . Integrating these statistical results with clinical considerations, nine variables were ultimately selected for model construction: sex, age, eyelid edema, total fever duration, CRP, WBC, HGB, PLT, and RDW-CV. Model Construction and Comparison To evaluate the incremental predictive value of RDW-CV, two logistic regression models were developed. Model I included sex, age, eyelid edema, total fever duration, CRP, WBC, HGB, and PLT; Model II added RDW-CV. Model II demonstrated significantly superior discrimination (AUC 0.800, 95% CI 0.713–0.886) compared to Model I (AUC 0.718, 95% CI 0.619–0.817; P = 0.035), with categorical NRI of 0.274 ( P = 0.015), continuous NRI of 0.617 ( P < 0.001), and IDI of 0.130 ( P < 0.001) (Table 4, Figure 6 C , D ). Model Validation The nine-predictor model showed adequate calibration (mean absolute error = 0.059 after 500 bootstrap resamples) (Figure 6A ). DCA demonstrated superior clinical utility across the threshold probability range of 1% to 99%, where the model provided higher net benefit compared to both the "treat-all" and "treat-none" strategies (Figure 6B ). A nomogram was constructed for bedside risk assessment (Figure 5 ). Discussion Summary of Principal Findings This study demonstrates that elevated RDW-CV is independently associated with liver injury in children with EBV infection. Each 1% increment conferred a threefold higher odds of hepatic involvement after comprehensive adjustment for demographic, clinical, and hematological confounders. The dose-response relationship was linear, and the association remained robust across age and sex subgroups. Furthermore, adding RDW-CV to the base prediction model significantly improved discriminative performance, yielding an 8.2% relative increase in AUC (from 0.718 to 0.800). These findings establish RDW-CV as a readily available, inexpensive biomarker for early risk stratification of EBV-related hepatic complications in pediatric populations Comparison with Previous Studies Our findings align with emerging evidence linking RDW-CV to liver dysfunction in various clinical contexts. A prospective study by Xu et al. [14] in 446 patients with chronic hepatitis B demonstrated that elevated RDW-CV was independently associated with liver fibrosis progression (adjusted OR 1.121, 95% CI 1.002–1.253). Similarly, Yang et al. [15] reported significantly higher RDW-CV in patients with non-alcoholic fatty liver disease compared to healthy controls, with levels correlating with disease severity. The consistency of these associations across diverse etiologies (viral, metabolic, and now infectiou) suggests that RDW-CV reflects common pathophysiological pathways of hepatic impairment rather than disease-specific mechanisms. While RDW-CV has been shown to predict liver injury in other clinical contexts [16, 17], studies specifically investigating its association with liver injury in children with EBV infection have been limited. The present study adds to this body of evidence by demonstrating that RDW-CV provides incremental predictive value beyond conventional markers in this specific pediatric population, with an effect size ( OR =3.06) notably larger than those previously reported in chronic liver conditions. Biological Mechanisms Several interconnected pathways may explain the association between RDW-CV and liver injury in EBV infection. Hepatic inflammation during EBV infection triggers systemic release of pro-inflammatory cytokines, particularly interleukin-6 and tumor necrosis factor-α, which suppress bone marrow erythropoietic activity and alter red blood cell membrane deformability[18, 19]. Concurrently, EBV-induced hepatic dysfunction disrupts iron homeostasis and impairs hepatic erythropoietin synthesis, further contributing to abnormal erythrocyte morphology and increased size heterogeneity [20]. These mechanisms are supported by clinical evidence linking elevated RDW to liver fibrosis progression and synthetic dysfunction [21]. Notably, the persistence of the RDW-liver injury association after adjusting for HGB and other hematological parameters in our study suggests that this relationship reflects underlying inflammatory and metabolic perturbations rather than representing an anemic epiphenomenon. Robustness of Association: Confounding Adjustment and Subgroup Consistency The progressive effect amplification across adjustment stages (crude OR 1.65 → fully adjusted OR 3.06) reveals substantial negative confounding, indicating that demographic and inflammatory covariates initially masked the true RDW-CV–liver injury association. This relationship operates independently of conventional EBV severity markers (atypical lymphocytes, WBC, NLR), suggesting RDW-CV captures distinct pathophysiological information. Notably, the association persisted after HGB and PLT adjustment, arguing against mere anemia or hematological disturbance and instead implicating specific erythrocyte changes linked to hepatic inflammation. Validity is strengthened by demographic consistency. RDW-CV showed comparable effects in children ≤5 years (adjusted OR 3.72) and >5 years (adjusted OR 3.80), with no significant interaction ( P = 0.257). Similarly, consistent effects across sex ( P for interaction = 0.638) support uniform biological pathways. The robustness to confounding and consistency across subgroups establish RDW-CV as a reliable liver injury predictor in pediatric EBV infection, independent of age or sex. Incremental Predictive Value of RDW-CV From a predictive modeling perspective, our study addresses a critical gap in the literature. While previous investigations have examined RDW-CV as a diagnostic or prognostic marker in EBV infection, none have systematically evaluated its incremental predictive value beyond conventional clinical and laboratory parameters [22]. The addition of RDW-CV significantly improved model discrimination, increasing the AUC from 0.718 to 0.800 (8.2% relative improvement, P = 0.035). This was further supported by significant improvements in reclassification and discrimination metrics: categorical NRI of 0.274 ( P = 0.015), continuous NRI of 0.617 ( P < 0.001), and IDI of 0.130 ( P < 0.001) (Table 4). DCA confirmed the clinical utility of incorporating RDW-CV, demonstrating higher net benefit across threshold probabilities from 1% to 99% (Figure 6B). These findings have practical implications for resource allocation in pediatric emergency and infectious disease settings, where early identification of high-risk patients could facilitate targeted monitoring and preemptive hepatoprotective interventions. Strengths and Limitations This study has several strengths. First, we employed a comprehensive statistical approach, including imputation for missing data, restricted cubic splines for dose-response assessment, and progressive multivariable adjustment to evaluate robustness. Second, five variable selection methods (R², adjusted R², BIC, Mallows' Cp, and LASSO) were integrated with clinical expertise to construct a clinically relevant prediction model. Third, extensive model validation (encompassing AUC, calibration plots, and DCA) provided a holistic evaluation of model performance. However, several limitations warrant consideration. First, as a single-center retrospective study, our findings may be subject to selection bias and limited generalizability. The modest sample size, particularly in subgroup analyses (e.g., only 40 female patients), resulted in wide CI s and reduced statistical power in certain strata. Second, residual confounding cannot be excluded. However, an E-value of 2.89 indicates that an unmeasured confounder would need a 2.89-fold association with both RDW-CV and liver injury to nullify our findings, suggesting robustness. Third, RDW-CV was measured only at admission; longitudinal measurements might provide additional prognostic information and better characterize the temporal relationship with liver injury. Clinical Implications and Future Research In conclusion, this study establishes RDW-CV as an independent predictor of liver injury in children with EBV infection, demonstrating significant incremental predictive value. The linear dose-response relationship and consistent subgroup effects support integrating this routine parameter into clinical risk assessment. Future research should prospectively validate these findings and explore whether RDW-CV guided stratification improves outcomes. Mechanistic studies investigating the link between hepatic inflammation and erythrocyte heterogeneity could reveal novel therapeutic targets. Additionally, developing automated, EHR( Electronic Health Record) -integrated tools incorporating RDW-CV may facilitate real-time risk assessment in clinical practice. Conclusion Elevated RDW-CV is independently associated with increased risk of liver injury in children with EBV infection. Incorporating RDW-CV into multivariable prediction models significantly improves risk stratification accuracy compared to conventional markers alone. This inexpensive, routinely available biomarker holds promise for enhancing early identification of pediatric patients at heightened risk for hepatic complications, potentially enabling targeted monitoring and timely intervention to improve clinical outcomes. Abbreviations ALT: Alanine aminotransferase AST: Aspartate aminotransferase AUC: Area under the receiver operating characteristic curve BIC: Bayesian information criterion CI: Confidence interval CK: Creatine kinase CK-MB: Creatine kinase-MB Cp: Mallows’ Cp CRP: C-reactive protein DCA: Decision curve analysis DNA: Deoxyribonucleic acid EBV: Epstein-Barr virus EBV-VCA: Epstein-Barr virus viral capsid antigen EHR: Electronic Health Record ESR: Erythrocyte sedimentation rate GGT: Gamma-glutamyl transferase HCT: Hematocrit HGB: Hemoglobin IQR: Interquartile range LASSO: Least absolute shrinkage and selection operator LDH: Lactate dehydrogenase LYMPH: Lymphocyte count MLR: Monocyte-to-lymphocyte ratio MONO: Monocyte count NEUT: Neutrophil count NLR: Neutrophil-to-lymphocyte ratio OR: Odds ratio PCT: Procalcitonin PLR: Platelet-to-lymphocyte ratio PLT: Platelet count R²: Coefficient of determination RBC: Red blood cell count RDW-CV: Red blood cell distribution width-coefficient of variation RDW-SD: Red blood cell distribution width-standard deviation WBC: White blood cell count Declarations Authors' contributions Hongjuan Wei conceptualized and supervised the study, obtained ethical approval, and critically revised the manuscript. Yang Yu and Yang Jing contributed equally as co-first authors, performing data analysis, visualization, and initial drafting. Yinyan Tang assisted with data collection and validation. All authors reviewed and approved the final manuscript, had full access to the data, and accept responsibility for its integrity and accuracy. Hongjuan Wei is the guarantor. Ethics approval and consent to participate This study was approved by the Human Ethics Committee of Nanjing Lishui People's Hospital (approval number: 2026KY0210-01). All procedures performed were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments. Due to the retrospective nature of the study, the requirement for informed consent was waived by the committee. Declaration of competing interest The authors declare that they have no competing interests. Data availability The data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request. Funding information No funding support in the data collection, analysis or preparation of the manuscript. References Du Z, Yuan X, Yi J, Li M, Dai F, Liu X, et al. EBV and CMV Seroprevalence and Liver Injury Patterns Among Clinical Patients in Beijing: Differential Impact of Immunosuppression Status. J Med Virol. 2026;98:e70837. Tian S, Zheng J, Zhou Z, Yang Q, Sun B, Li Y, et al. Retrospective Review of Children Hospitalized for Epstein-Barr Virus-Related Infectious Mononucleosis. Pathogens. 2025;14:702. Wu Y, Huang J, Zheng W, Shi H, Wen Z, Jin L, et al. AIM2-mediated pyroptosis correlates with viral load and liver injury in children with primary Epstein-Barr virus infection. BMC Pediatr. 2026. Zhang C, Cui S, Mao G, Li G. 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Prognostic value of blood inflammatory composite markers in the survival of pediatric patients with secondary hemophagocytic lymphohistiocytosis. Front Pediatr. 2025;13:1458490. Tables Tables are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterials.doc Tables.doc Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 16 Apr, 2026 Reviewers invited by journal 01 Apr, 2026 Editor invited by journal 03 Mar, 2026 Editor assigned by journal 03 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 27 Feb, 2026 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. 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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-8984740","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616049640,"identity":"bc5b01f3-5b1e-4ee1-ac47-c68162de23d8","order_by":0,"name":"Yang Yu","email":"","orcid":"","institution":"Zhongda Hospital Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yu","suffix":""},{"id":616049644,"identity":"1010f809-6968-42b7-86b1-14bec66e0ea6","order_by":1,"name":"Yang Jing","email":"","orcid":"","institution":"Zhongda Hospital Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Jing","suffix":""},{"id":616049649,"identity":"84a8878e-ddff-4045-acef-848ea2246002","order_by":2,"name":"Yinyan Tang","email":"","orcid":"","institution":"Zhongda Hospital Southeast University","correspondingAuthor":false,"prefix":"","firstName":"Yinyan","middleName":"","lastName":"Tang","suffix":""},{"id":616049651,"identity":"85786976-3626-420d-a5dc-ec9f29c6f2f5","order_by":3,"name":"Hongjuan Wei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDCCA1BagoGHweCDgY0dMVoYG2BaDGcUpCWTpoWZ58MhCA8f4DueY/7wZ9thOckZuQeKbQwOMDOwHz66AZ8WyTNvDBsk2w4bS0vkJRjnGNzhY+BJS7uBT4vBjRzDBsO2w4nzpHMMgFqeMTNI8JgR1pLYdrgerMXC4DBjA1FaDrYdTpAGaWEgRovkmWeFMxvOpRvOnP/GwLDHIC2ZjZBf+I4nb/j4o8xaXuLMGTODH39s7PjZDx/Dq4WBIcOAgZGtGcRiMwCT+JWDQPoDBoY/dSAW8wPCqkfBKBgFo2AkAgDDmVD8IwxmzAAAAABJRU5ErkJggg==","orcid":"","institution":"Zhongda Hospital Southeast University","correspondingAuthor":true,"prefix":"","firstName":"Hongjuan","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2026-02-27 07:26:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8984740/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8984740/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106702484,"identity":"10005d34-9f92-4f6f-b826-317d7fb5051d","added_by":"auto","created_at":"2026-04-12 07:33:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":304159,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart for the study population.\u003c/p\u003e","description":"","filename":"11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8984740/v1/07cbbc220b19d1fe4cb798e6.jpg"},{"id":106728410,"identity":"dc9d9e20-0a3b-4573-bc96-111ff74acc46","added_by":"auto","created_at":"2026-04-12 18:42:40","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":283932,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRestricted cubic spline analysis of the dose-response relationship between RDW-CV and liver injury risk in children with EBV infection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure Legend:\u003cstrong\u003e \u003c/strong\u003eThe solid red line indicates the \u003cem\u003eOR\u003c/em\u003e for liver injury across the range of RDW-CV values, with the reference set at the median RDW-CV (13.2%). The shaded yellow band represents the 95% \u003cem\u003eCI\u003c/em\u003e. The blue histogram displays the frequency distribution of RDW-CV values. The model was adjusted for sex, age, pre-admission fever duration, cervical lymphadenopathy, atypical lymphocyte percentage, WBC, NLR, HGB, and PLT. \u003cem\u003eP\u003c/em\u003e-values denote the overall association (\u003cem\u003eP\u003c/em\u003e for overall = 0.007) and non-linearity test (\u003cem\u003eP\u003c/em\u003e for non-linearity = 0.540). All analyses are based on the imputed dataset.\u003c/p\u003e","description":"","filename":"12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8984740/v1/f42aa10672add5b93d448bf7.jpg"},{"id":106702505,"identity":"2b855fc4-488d-41da-bd24-daef941c751e","added_by":"auto","created_at":"2026-04-12 07:33:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":352260,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of subgroup analyses for the association between RDW-CV and liver injury in children with EBV infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure Legend:\u003c/strong\u003e Forest plot showing adjusted \u003cem\u003eOR\u003c/em\u003es and 95% \u003cem\u003eCI\u003c/em\u003es for RDW-CV (per 1% increment) stratified by age (≤5 vs. \u0026gt;5 days) and sex. Models were adjusted for covariates in Model III (Table 2). The vertical dashed line (\u003cem\u003eOR\u003c/em\u003e = 1.0) indicates no effect.\u003cem\u003e P\u003c/em\u003e values for interaction are shown on the right.\u003c/p\u003e","description":"","filename":"13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8984740/v1/5e90dd6f5747f6578c63d00d.jpg"},{"id":106702487,"identity":"8b80ffcf-8261-40c1-940e-78b19f51abb9","added_by":"auto","created_at":"2026-04-12 07:33:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2498298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVariable selection results using five complementary methods.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure Legend: \u003c/strong\u003eFive variable selection methods were used to identify predictors of liver injury: \u003cstrong\u003e(A)\u003c/strong\u003e R², \u003cstrong\u003e(B)\u003c/strong\u003eadjusted R², \u003cstrong\u003e(C)\u003c/strong\u003e BIC, \u003cstrong\u003e(D)\u003c/strong\u003e Mallows' Cp, and \u003cstrong\u003e(E)\u003c/strong\u003eLASSO regression. Integrating statistical results with clinical expertise, nine predictors were selected for final model construction: sex, age, eyelid edema, total fever duration, CRP, WBC, HGB, PLT, and RDW-CV. All analyses were based on the imputed dataset.\u003c/p\u003e","description":"","filename":"14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8984740/v1/18d3d2d4a30a526f74985ad3.jpg"},{"id":106702488,"identity":"fd04ce19-af9d-4628-8578-09ce2a6c4443","added_by":"auto","created_at":"2026-04-12 07:33:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":562935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for predicting liver injury risk in children with EBV infection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure Legend: \u003c/strong\u003eNomogram for individualized prediction of liver injury risk based on nine selected predictors: sex, age, eyelid edema, total fever duration, CRP, WBC, HGB, PLT, and RDW-CV. To use the nomogram, locate each variable on its respective axis, draw a vertical line upward to the \"Points\" axis to obtain the score for each predictor, sum all scores to obtain the \"Total Points\", and then draw a vertical line downward from the \"Total Points\" axis to the \"Risk of Event\" axis to estimate the individualized probability of liver injury. All analyses were based on the imputed dataset.\u003c/p\u003e","description":"","filename":"15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8984740/v1/d80ddd9c404048017ccd9e6a.jpg"},{"id":106993611,"identity":"4a9c81ba-9ee5-4f12-818a-3a175027969a","added_by":"auto","created_at":"2026-04-15 14:39:25","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":945416,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive model performance and clinical utility assessment for liver injury in children with EBV infection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure Legend:\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Calibration plot showing agreement between predicted probabilities and observed outcomes. The dashed black line represents ideal calibration, the dashed red line represents apparent performance, and the solid blue line represents bias-corrected performance after 500 bootstrap resamples. \u003cstrong\u003e(B)\u003c/strong\u003e DCA comparing the net benefit of the prediction model (red line) versus the \"treat-all\" (blue line) and \"treat-none\" (green line) strategies across threshold probabilities. The model provides net benefit when the threshold probability is between 1% and 99% (indicated by dashed vertical lines), with the red shaded area denoting the range of clinical relevance where the model outperforms both extreme strategies. \u003cstrong\u003e(C)\u003c/strong\u003e ROC of the full model (Model II) with AUC of 0.800 (95% \u003cem\u003eCI\u003c/em\u003e: 0.713–0.886). \u003cstrong\u003e(D)\u003c/strong\u003e Comparison of ROC curves between Model I (without RDW-CV, blue line, AUC = 0.718) and Model II (with RDW-CV, red line, AUC = 0.800), with statistical comparison by DeLong test (\u003cem\u003eP\u003c/em\u003e = 0.035). Model I included sex, age, eyelid edema, total fever duration, CRP, WBC, HGB, and PLT; Model II additionally included RDW-CV. Analyses are based on the imputed dataset.\u003c/p\u003e","description":"","filename":"16.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8984740/v1/0cfeac2d278cb66eb410cea6.jpg"},{"id":106994647,"identity":"f73b1fe7-48bc-4a4b-89f9-08194716cbb9","added_by":"auto","created_at":"2026-04-15 15:15:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6312159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8984740/v1/bfd92e5f-6d4d-4ca6-bb2e-fc0717284ea8.pdf"},{"id":106702483,"identity":"5a45633a-ea75-4275-a557-cef180b761c9","added_by":"auto","created_at":"2026-04-12 07:33:25","extension":"doc","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":173262,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.doc","url":"https://assets-eu.researchsquare.com/files/rs-8984740/v1/0b82adcc7f8e731f91ef6a69.doc"},{"id":106702485,"identity":"c5bc197f-5bb0-4492-8d6e-32018a0c040b","added_by":"auto","created_at":"2026-04-12 07:33:25","extension":"doc","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":226816,"visible":true,"origin":"","legend":"","description":"","filename":"Tables.doc","url":"https://assets-eu.researchsquare.com/files/rs-8984740/v1/e92b26e4da19aa2949ef1c5b.doc"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association Between Red Blood Cell Distribution Width and Liver Injury in Children with Epstein-Barr Virus Infection: A Retrospective Study with Incremental Predictive Value Assessment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEBV is a ubiquitous human herpesvirus that infects over 90% of the global population, with primary infection typically occurring during childhood[1]. While EBV infection in children is often asymptomatic or presents as self-limiting infectious mononucleosis, hepatic involvement represents one of the most common complications, affecting approximately 30\u0026ndash;50% of pediatric patients[2, 3]. The clinical spectrum of EBV-related liver injury ranges from mild, transient elevation of liver enzymes to severe hepatitis, which may prolong hospitalization and necessitate therapeutic intervention[4].\u0026nbsp;Early identification of children at heightened risk for hepatic complications is therefore crucial for timely monitoring and clinical decision-making. However, reliable and readily available biomarkers for predicting EBV-associated liver injury remain limited.\u003c/p\u003e\n\u003cp\u003eRDW-CV, quantifying erythrocyte size heterogeneity, has emerged as a novel inflammatory marker beyond its traditional role in anemia differentiation[5]. Elevated RDW-CV reflects systemic inflammation, oxidative stress, and impaired erythropoiesis[6, 7], with demonstrated prognostic utility in sepsis, pneumonia, and COVID-19[8]. However, its relationship with organ-specific complications, particularly EBV-related liver injury, remains unclear[9].\u003c/p\u003e\n\u003cp\u003eThe potential biological plausibility linking RDW-CV to liver injury is multifaceted. Hepatic inflammation and hypoxia during EBV infection trigger systemic cytokine release, which may impair bone marrow erythropoietic activity and alter red blood cell membrane deformability[10]. Additionally, liver dysfunction disrupts iron metabolism and erythropoietin synthesis, further contributing to abnormal erythrocyte morphology[11]. These pathophysiological connections suggest that RDW-CV could serve as an early, objective indicator of hepatic impairment, potentially preceding clinically apparent liver dysfunction.\u003c/p\u003e\n\u003cp\u003eCurrent clinical practice relies on hepatic enzyme monitoring for liver injury detection, which often identifies damage only after substantial hepatocellular injury has occurred. Developing predictive models that incorporate accessible, inexpensive biomarkers such as RDW-CV could enable earlier risk stratification and targeted intervention. While multivariable prediction models have been constructed for various EBV complications, none to our knowledge have systematically evaluated RDW-CV\u0026apos;s incremental predictive value beyond conventional clinical and laboratory parameters.\u003c/p\u003e\n\u003cp\u003eAccordingly, this study aimed to investigate the association between RDW-CV and liver injury in children with EBV infection, characterize its dose-response relationship, and evaluate whether incorporating RDW-CV into a multivariable prediction model enhances risk stratification accuracy. We hypothesized that elevated RDW-CV would be independently associated with liver injury and would significantly improve predictive model performance compared to conventional markers alone.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis retrospective study initially identified 129 hospitalized children with laboratory-confirmed EBV infection at Nanjing Lishui People’s Hospital (NJLSPH) between July 1, 2020, and December 31, 2025. After excluding 2 cases of automatic discharge and 4 cases with incomplete key variables, 123 patients were included in the final analysis. Liver injury was defined as ALT \u0026gt;80 U/L[12, 13](Figure 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLaboratory data collection and measures\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDemographic and Clinical Characteristics: Demographic data comprised age (days), sex, and weight (kg). Clinical variables included hospitalization duration, pre-admission fever duration (days), post-admission fever duration (days), total fever duration (days), eyelid edema, tonsillar enlargement with suppuration, cervical lymphadenopathy, and associated symptoms (cough, sore throat, rash, nasal congestion, abdominal pain, diarrhea). Past medical history (asthma, rhinitis) and treatment modalities (antiviral therapy, liver-protective therapy, corticosteroids) were also documented.\u003c/p\u003e\n\u003cp\u003eImaging Assessments: Chest radiography or computed tomography findings were classified as normal, bronchitis, or pneumonia. Cervical ultrasonography was performed to assess bilateral lymph node size, categorized as\u0026nbsp;≥10 mm,\u0026nbsp;≥20 mm, or ≥30 mm in longest diameter. Abdominal ultrasonography evaluated the presence of hepatomegaly and splenomegaly.\u003c/p\u003e\n\u003cp\u003eLaboratory Measurements: Venous blood samples were collected within 24 h of admission. Hematological parameters, including white blood cell count (WBC), neutrophil count (NEUT), lymphocyte count (LYMPH), monocyte count (MONO), red blood cell count (RBC), hemoglobin (HGB), hematocrit (HCT), red cell distribution width–coefficient of variation (RDW-CV), and platelet count (PLT), were measured using a BC-7500 hematology analyzer (Mindray Corporation, Shenzhen, China). The following derived inflammatory indices were calculated: neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and ALT-to-lymphocyte ratio. Atypical lymphocyte percentage was determined by manual differential count on peripheral blood smears. Biochemical markers, including ALT, aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), lactate dehydrogenase (LDH), creatine kinase (CK), and creatine kinase-MB (CKMB), were quantified using an AU5800 automated clinical chemistry analyzer (Beckman Coulter Trading Co., Ltd., Suzhou, China). Inflammatory markers, C-reactive protein (CRP), procalcitonin (PCT), erythrocyte sedimentation rate (ESR), and ferritin, were assayed by immunoturbidimetry, chemiluminescence, and nephelometry, respectively. EBV-specific serology (IgM antibodies against viral capsid antigen) was determined by chemiluminescent immunoassay. EBV DNA load in whole blood was quantified by real-time polymerase chain reaction (PCR; positive cutoff \u0026gt;500 copies/mL). All laboratory tests were performed in the central clinical laboratory of NJLSPH following the manufacturers’ protocols and routine quality control procedures. The analyzers were subjected to regular calibration and quality control in accordance with laboratory accreditation standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using R Statistical Software (Version 4.2.2, http://www.R-project.org, The R Foundation) and Free Statistics analysis platform (Version 2.4, Beijing, China, http://www.clinicalscientists.cn/freestatistics). Single imputation by chained equations was performed under the missing-at-random assumption; variables with excessive missingness (EBV IgM, EBV DNA copies) were excluded from the imputation model, whereas all other candidate predictors were included. Imputation diagnostics were assessed by comparing distributional parameters between original and imputed datasets. Continuous variables were presented as mean ± standard deviation or median (interquartile range) based on normality, and categorical variables as frequency (percentage). Between-group comparisons were conducted using Student’s t-test, Mann–Whitney U test, chi-square test, or Fisher’s exact test as appropriate. Univariable and multivariable logistic regression models were fitted to estimate crude and adjusted \u003cem\u003eOR\u003c/em\u003es with 95%\u0026nbsp;\u003cem\u003eCI\u003c/em\u003es for the association between RDW-CV and liver injury. Covariates were selected based on clinical relevance and the change-in-estimate method (≥10% change in the\u0026nbsp;\u003cem\u003eOR\u003c/em\u003e when added to the model). The final model included sex, age, pre-admission fever duration, cervical lymphadenopathy, atypical lymphocyte percentage,\u0026nbsp;WBC, NLR, HGB, and PLT.\u0026nbsp;To examine the dose–response relationship, restricted cubic spline logistic regression was performed, and the fitted curve was plotted with 95% confidence bands.\u0026nbsp;Subgroup analyses stratified by age (≤5 vs. \u0026gt;5 years) and sex were pre-specified, and interaction terms were tested.\u0026nbsp;Predictive models were developed using logistic regression with variable selection procedures including R², adjusted R², BIC,\u0026nbsp;Mallows’ Cp, and\u0026nbsp;LASSO\u0026nbsp;regression. Discriminative ability was assessed by AUC. The incremental predictive value of RDW-CV was evaluated using NRI (both categorical and continuous) and IDI. The DeLong test was further applied to compare the AUC between the base model (sex, age, eyelid edema, total fever duration, CRP, WBC, HGB, and PLT) and the augmented model (additionally including RDW-CV).\u0026nbsp;All statistical tests were two-sided, and\u0026nbsp;\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 was considered statistically significant.\u0026nbsp;\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003eBaseline Characteristics\u003c/h3\u003e\n\u003cp\u003eThis study enrolled 123 children with EBV infection, of whom 48 (39.0%) developed liver injury. The two groups were comparable in age, sex distribution, and body weight. Children with liver injury exhibited significantly longer pre-admission fever duration (3.4 ± 2.4 vs. 2.7 ± 1.6 days, \u003cem\u003eP\u003c/em\u003e = 0.046) and total fever duration (6.0 ± 3.3 vs. 4.9 ± 2.1 days, \u003cem\u003eP\u003c/em\u003e = 0.026). Notably, RDW-CV (13.6 ± 1.0% vs. 13.2 ± 0.8%, \u003cem\u003eP\u003c/em\u003e = 0.046) and RDW-SD (40.2 [38.5, 41.8] vs. 39.0 [37.8, 40.5] fL, \u003cem\u003eP\u003c/em\u003e = 0.012) were significantly higher, accompanied by lower\u0026nbsp;NLR\u0026nbsp;(0.3 [0.2, 0.4] vs. 0.4 [0.3, 0.6], \u003cem\u003eP\u003c/em\u003e = 0.029) (Table 1). These associations persisted after imputation (Supplementary table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDose-Response Relationship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline analysis, adjusted for demographics, clinical variables, and laboratory parameters, demonstrated a significant linear association between RDW-CV and liver injury risk (\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e). The overall association was significant (\u003cem\u003eP\u003c/em\u003e for overall = 0.007), with no evidence of non-linearity (\u003cem\u003eP\u003c/em\u003e for non-linearity = 0.540).\u0026nbsp;The risk of liver injury increased progressively with each unit increment in RDW-CV.\u003c/p\u003e\n\u003ch3\u003eMultivariable Logistic Regression\u003c/h3\u003e\n\u003cp\u003eThree progressively adjusted logistic regression models were constructed to evaluate the independent association between RDW-CV and liver injury (Table 2). In unadjusted analysis, each 1% increment in RDW-CV was associated with increased odds of liver injury (crude \u003cem\u003eOR\u003c/em\u003e 1.65, 95% \u003cem\u003eCI\u003c/em\u003e 1.06–2.57, \u003cem\u003eP\u003c/em\u003e = 0.027). After adjustment for sex and age (Model I), the effect estimate strengthened substantially (adjusted \u003cem\u003eOR\u003c/em\u003e 2.34, 95% \u003cem\u003eCI\u003c/em\u003e 1.35–4.04, \u003cem\u003eP\u003c/em\u003e = 0.002). Controlling for additional clinical variables (Model II) yielded consistent results (adjusted \u003cem\u003eOR\u003c/em\u003e 2.26, 95% \u003cem\u003eCI\u003c/em\u003e 1.30–3.94, \u003cem\u003eP\u003c/em\u003e = 0.004). In the fully adjusted model incorporating hematological parameters (Model III), RDW-CV remained significantly associated with liver injury, with further accentuated effect size (adjusted \u003cem\u003eOR\u003c/em\u003e 3.06, 95% \u003cem\u003eCI\u003c/em\u003e 1.59–5.91, \u003cem\u003eP\u003c/em\u003e = 0.001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe association between RDW-CV and liver injury remained consistent across age and sex strata. In children aged ≤5 years (n=67), each unit increase in RDW-CV was associated with significantly increased odds of liver injury (adjusted \u003cem\u003eOR\u003c/em\u003e = 3.72, 95% \u003cem\u003eCI\u003c/em\u003e: 1.39–9.99, \u003cem\u003eP\u003c/em\u003e = 0.009). A similar association was observed in older children (\u0026gt;5 years, n = 56; \u003cem\u003eOR\u003c/em\u003e = 3.80, 95% \u003cem\u003eCI\u003c/em\u003e: 1.24–11.65, \u003cem\u003eP\u003c/em\u003e = 0.020). No significant interaction was observed (\u003cem\u003eP\u003c/em\u003e for interaction = 0.257). In sex-stratified analyses, RDW-CV demonstrated a significant association in males (n=83; \u003cem\u003eOR\u003c/em\u003e = 3.24, 95% \u003cem\u003eCI\u003c/em\u003e: 1.39–7.55, \u003cem\u003eP\u003c/em\u003e = 0.006). Among females (n=40), the point estimate was similar (\u003cem\u003eOR\u003c/em\u003e = 3.98) but did not reach statistical significance due to wide\u0026nbsp;\u003cem\u003eCI\u003c/em\u003e (0.78–20.29, \u003cem\u003eP\u003c/em\u003e = 0.096).\u0026nbsp;While the interaction term was\u0026nbsp;still\u0026nbsp;not significant (\u003cem\u003eP\u003c/em\u003e = 0.638), indicating no effect modification by sex (\u003cstrong\u003eTable 3\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e3\u003c/strong\u003e).\u003c/p\u003e\n\u003ch2\u003ePredictive Model Development\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eVariable Selection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFive complementary methods (R², adjusted R², BIC\u003c/strong\u003e\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eMallows' Cp, LASSO) selected 4 to 13 candidate predictors: R² selected 13, adjusted R² 7, BIC 4,\u003c/strong\u003e\u003cstrong\u003eMallows' Cp 6, and LASSO 8\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(\u003c/strong\u003e\u003cstrong\u003eFigure\u003c/strong\u003e\u003cstrong\u003e4A-E)\u003c/strong\u003e\u003cstrong\u003e. Integrating these statistical results with clinical considerations, nine variables were ultimately selected for model construction: sex, age, eyelid edema, total fever duration, CRP, WBC, HGB, PLT, and RDW-CV.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Construction and Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTo evaluate the incremental predictive value of RDW-CV, two logistic regression models were developed. Model I included sex, age, eyelid edema, total fever duration, CRP, WBC, HGB, and PLT; Model II added RDW-CV. Model II demonstrated significantly superior discrimination (AUC 0.800, 95% \u003cem\u003eCI\u003c/em\u003e 0.713–0.886) compared to Model I (AUC 0.718, 95% \u003cem\u003eCI\u003c/em\u003e 0.619–0.817; \u003cem\u003eP\u003c/em\u003e = 0.035), with categorical NRI of 0.274 (\u003cem\u003eP\u003c/em\u003e = 0.015), continuous NRI of 0.617 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and IDI of 0.130 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Table 4, Figure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003cstrong\u003e, D\u003c/strong\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel Validation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe nine-predictor model showed adequate calibration (mean absolute error = 0.059 after 500 bootstrap resamples) (Figure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6A\u003c/strong\u003e\u003cstrong\u003e).\u0026nbsp;\u003c/strong\u003eDCA\u003cstrong\u003e\u0026nbsp;demonstrated superior clinical utility across the threshold probability range of 1% to 99%, where the model provided higher net benefit compared to both the \"treat-all\" and \"treat-none\" strategies (Figure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6B\u003c/strong\u003e\u003cstrong\u003e). A nomogram was constructed for bedside risk assessment (Figure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003cstrong\u003e).\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eSummary of Principal Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study demonstrates that elevated RDW-CV is independently associated with liver injury in children with EBV infection. Each 1% increment conferred a threefold higher odds of hepatic involvement after comprehensive adjustment for demographic, clinical, and hematological confounders. The dose-response relationship was linear, and the association remained robust across age and sex subgroups. Furthermore, adding RDW-CV to the base prediction model significantly improved discriminative performance, yielding an 8.2% relative increase in AUC (from 0.718 to 0.800). These findings establish RDW-CV as a readily available, inexpensive biomarker for early risk stratification of EBV-related hepatic complications in pediatric populations\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eComparison with Previous Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur findings align with emerging evidence linking RDW-CV to liver dysfunction in various clinical contexts. A prospective study by Xu et al. [14] in 446 patients with chronic hepatitis B demonstrated that elevated RDW-CV was independently associated with liver fibrosis progression (adjusted \u003cem\u003eOR\u003c/em\u003e 1.121, 95% \u003cem\u003eCI\u003c/em\u003e 1.002–1.253). Similarly, Yang et al.\u0026nbsp;[15]\u0026nbsp;reported significantly higher RDW-CV in patients with non-alcoholic fatty liver disease compared to healthy controls, with levels correlating with disease severity. The consistency of these associations across diverse etiologies (viral, metabolic, and now infectiou) suggests that RDW-CV reflects common pathophysiological pathways of hepatic impairment rather than disease-specific mechanisms. While RDW-CV has been shown to predict liver injury in other clinical contexts\u0026nbsp;[16, 17], studies specifically investigating its association with liver injury in children with EBV infection have been limited. The present study adds to this body of evidence by demonstrating that RDW-CV provides incremental predictive value beyond conventional markers in this specific pediatric population, with an effect size (\u003cem\u003eOR\u003c/em\u003e=3.06) notably larger than those previously reported in chronic liver conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBiological Mechanisms\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral interconnected pathways may explain the association between RDW-CV and liver injury in EBV infection. Hepatic inflammation during EBV infection triggers systemic release of pro-inflammatory cytokines, particularly interleukin-6 and tumor necrosis factor-α, which suppress bone marrow erythropoietic activity and alter red blood cell membrane deformability[18, 19].\u0026nbsp;Concurrently, EBV-induced hepatic dysfunction disrupts iron homeostasis and impairs hepatic erythropoietin synthesis, further contributing to abnormal erythrocyte morphology and increased size heterogeneity [20].\u0026nbsp;These mechanisms are supported by clinical evidence linking elevated RDW to liver fibrosis progression and synthetic dysfunction\u0026nbsp;[21]. Notably, the persistence of the RDW-liver injury association after adjusting for\u0026nbsp;HGB\u0026nbsp;and other hematological parameters in our study suggests that this relationship reflects underlying inflammatory and metabolic perturbations rather than representing an anemic epiphenomenon.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRobustness of Association: Confounding Adjustment and Subgroup Consistency\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe progressive effect amplification across adjustment stages (crude \u003cem\u003eOR\u003c/em\u003e 1.65 → fully adjusted \u003cem\u003eOR\u003c/em\u003e 3.06) reveals substantial negative confounding, indicating that demographic and inflammatory covariates initially masked the true RDW-CV–liver injury association. This relationship operates independently of conventional EBV severity markers (atypical lymphocytes, WBC, NLR), suggesting RDW-CV captures distinct pathophysiological information. Notably, the association persisted after HGB and PLT adjustment, arguing against mere anemia or hematological disturbance and instead implicating specific erythrocyte changes linked to hepatic inflammation. Validity is strengthened by demographic consistency. RDW-CV showed comparable effects in children ≤5 years (adjusted \u003cem\u003eOR\u003c/em\u003e 3.72) and \u0026gt;5 years (adjusted \u003cem\u003eOR\u003c/em\u003e 3.80), with no significant interaction (\u003cem\u003eP\u003c/em\u003e = 0.257). Similarly, consistent effects across sex (\u003cem\u003eP\u003c/em\u003e for interaction = 0.638) support uniform biological pathways. The robustness to confounding and consistency across subgroups establish RDW-CV as a reliable liver injury predictor in pediatric EBV infection, independent of age or sex.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncremental Predictive Value of RDW-CV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrom a predictive modeling perspective, our study addresses a critical gap in the literature. While previous investigations have examined RDW-CV as a diagnostic or prognostic marker in EBV infection, none have systematically evaluated its incremental predictive value beyond conventional clinical and laboratory parameters [22]. The addition of RDW-CV significantly improved model discrimination, increasing the AUC from 0.718 to 0.800 (8.2% relative improvement, \u003cem\u003eP\u003c/em\u003e = 0.035). This was further supported by significant improvements in reclassification and discrimination metrics: categorical NRI of 0.274 (\u003cem\u003eP\u003c/em\u003e = 0.015), continuous NRI of 0.617 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001), and IDI of 0.130 (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001) (Table 4). DCA confirmed the clinical utility of incorporating RDW-CV, demonstrating higher net benefit across threshold probabilities from 1% to 99% (Figure 6B). These findings have practical implications for resource allocation in pediatric emergency and infectious disease settings, where early identification of high-risk patients could facilitate targeted monitoring and preemptive hepatoprotective interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and Limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has several strengths. First, we employed a comprehensive statistical approach, including imputation for missing data, restricted cubic splines for dose-response assessment, and progressive multivariable adjustment to evaluate robustness. Second, five variable selection methods (R², adjusted R², BIC, Mallows' Cp, and LASSO) were integrated with clinical expertise to construct a clinically relevant prediction model. Third, extensive model validation (encompassing AUC, calibration plots, and\u0026nbsp;DCA)\u0026nbsp;provided a holistic evaluation of model performance.\u003c/p\u003e\n\u003cp\u003eHowever, several limitations warrant consideration. First, as a single-center retrospective study, our findings may be subject to selection bias and limited generalizability. The modest sample size, particularly in subgroup analyses (e.g., only 40 female patients), resulted in wide\u0026nbsp;\u003cem\u003eCI\u003c/em\u003es and reduced statistical power in certain strata. Second, residual confounding cannot be excluded. However, an E-value of 2.89 indicates that an unmeasured confounder would need a 2.89-fold association with both RDW-CV and liver injury to nullify our findings, suggesting robustness. Third, RDW-CV was measured only at admission; longitudinal measurements might provide additional prognostic information and better characterize the temporal relationship with liver injury.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Implications and Future Research\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study establishes RDW-CV as an independent predictor of liver injury in children with EBV infection, demonstrating significant incremental predictive value. The linear dose-response relationship and consistent subgroup effects support integrating this routine parameter into clinical risk assessment. Future research should prospectively validate these findings and explore whether RDW-CV guided stratification improves outcomes. Mechanistic studies investigating the link between hepatic inflammation and erythrocyte heterogeneity could reveal novel therapeutic targets. Additionally, developing automated, EHR(\u003cstrong\u003eElectronic Health Record)\u003c/strong\u003e-integrated tools incorporating RDW-CV may facilitate real-time risk assessment in clinical practice.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eElevated RDW-CV is independently associated with increased risk of liver injury in children with EBV infection. Incorporating RDW-CV into multivariable prediction models significantly improves risk stratification accuracy compared to conventional markers alone. This inexpensive, routinely available biomarker holds promise for enhancing early identification of pediatric patients at heightened risk for hepatic complications, potentially enabling targeted monitoring and timely intervention to improve clinical outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eALT: Alanine aminotransferase\u003c/p\u003e\n\u003cp\u003eAST: Aspartate aminotransferase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAUC: Area under the receiver operating characteristic curve\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBIC: Bayesian information criterion\u003c/p\u003e\n\u003cp\u003eCI: Confidence interval\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCK: Creatine kinase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCK-MB: Creatine kinase-MB\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCp: Mallows’ Cp\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCRP: C-reactive protein\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDCA: Decision curve analysis\u003c/p\u003e\n\u003cp\u003eDNA: Deoxyribonucleic acid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEBV: Epstein-Barr virus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEBV-VCA: Epstein-Barr virus viral capsid antigen\u003c/p\u003e\n\u003cp\u003eEHR:\u0026nbsp;\u003cstrong\u003eElectronic Health Record\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eESR: Erythrocyte sedimentation rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGGT: Gamma-glutamyl transferase\u003c/p\u003e\n\u003cp\u003eHCT: Hematocrit\u003c/p\u003e\n\u003cp\u003eHGB: Hemoglobin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIQR: Interquartile range\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLASSO: Least absolute shrinkage and selection operator\u003c/p\u003e\n\u003cp\u003eLDH: Lactate dehydrogenase\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLYMPH: Lymphocyte count\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMLR: Monocyte-to-lymphocyte ratio\u003c/p\u003e\n\u003cp\u003eMONO: Monocyte count\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNEUT: Neutrophil count\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNLR: Neutrophil-to-lymphocyte ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOR: Odds ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePCT: Procalcitonin\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePLR: Platelet-to-lymphocyte ratio\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePLT: Platelet count\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eR²: Coefficient of determination\u003c/p\u003e\n\u003cp\u003eRBC: Red blood cell count\u003c/p\u003e\n\u003cp\u003eRDW-CV: Red blood cell distribution width-coefficient of variation\u003c/p\u003e\n\u003cp\u003eRDW-SD: Red blood cell distribution width-standard deviation\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWBC: White blood cell count\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cbr clear=\"all\"\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHongjuan Wei conceptualized and supervised the study, obtained ethical approval, and critically revised the manuscript. Yang Yu and Yang Jing contributed equally as co-first authors, performing data analysis, visualization, and initial drafting. Yinyan Tang assisted with data collection and validation. All authors reviewed and approved the final manuscript, had full access to the data, and accept responsibility for its integrity and accuracy. Hongjuan Wei is the guarantor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Human Ethics Committee of Nanjing Lishui People's Hospital (approval number: 2026KY0210-01). All procedures performed were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments. Due to the retrospective nature of the study, the requirement for informed consent was waived by the committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are not openly available due to reasons of sensitivity and are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;No funding support in the data collection, analysis or preparation of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eDu Z, Yuan X, Yi J, Li M, Dai F, Liu X, et al. 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Association between the red blood cell distribution width-platelet ratio and the risk of in-hospital mortality in patients with alcoholic liver cirrhosis (with or without severe liver disease): a retrospective cohort study based on the MIMIC-IV database. BMJ Open. 2025;15:e095104.\u003c/li\u003e\n\u003cli\u003eXu C, Cui W, Zeng C, Baima Y, Baima Y, Deng F, et al. Role of red cell distribution width, tumor necrosis factor-alpha, and interleukin-6 in immunoglobulin a vasculitis nephritis among Tibetan children in high-altitude areas. BMC Pediatr. 2025;25:941.\u003c/li\u003e\n\u003cli\u003eMcCranor BJ, Kim MJ, Cruz NM, Xue QL, Berger AE, Walston JD, et al. Interleukin-6 directly impairs the erythroid development of human TF-1 erythroleukemic cells. Blood Cells Mol Dis. 2014;52:126-33.\u003c/li\u003e\n\u003cli\u003eLi J, Pang S, Huang H, Lu Y, Tang T, Wu J, et al. Association between red cell distribution width-to-albumin ratio and all-cause mortality in critically ill cirrhotic patients with sepsis: a retrospective analysis of the MIMIC-IV database. Front Med (Lausanne). 2025;12:1610726.\u003c/li\u003e\n\u003cli\u003eYang K, Sun B, Zhang S, Pan Y, Fang J. RDW-SD is Superior to RDW-CV in Reflecting Liver Fibrosis Stage in Patients with Chronic Hepatitis B. Infect Drug Resist. 2023;16:6881-91.\u003c/li\u003e\n\u003cli\u003eLuo N, Xie X, Chen Y, Du Z, Huang P. Prognostic value of blood inflammatory composite markers in the survival of pediatric patients with secondary hemophagocytic lymphohistiocytosis. Front Pediatr. 2025;13:1458490.\u003c/li\u003e\n \u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Red blood cell distribution width-coefficient of variation, Epstein-Barr virus, Liver injury, Children, Prediction model","lastPublishedDoi":"10.21203/rs.3.rs-8984740/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8984740/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Liver injury is a common complication of Epstein-Barr virus (EBV) infection in children, yet reliable predictive biomarkers remain limited. Red blood cell distribution width-coefficient of variation (RDW-CV), an emerging inflammatory marker, has shown prognostic value in various infectious diseases but its role in EBV-related hepatic impairment is poorly characterized.\u003c/p\u003e\n\u003cp\u003eMethods: This retrospective study included 123 hospitalized children with laboratory-confirmed EBV infection at Nanjing Lishui People's Hospital between July 2020 and December 2025. Liver injury was defined as ALT \u0026gt;80 U/L. The dose-response relationship between RDW-CV and liver injury was assessed using restricted cubic spline analysis. Multivariable logistic regression with progressive adjustment evaluated the independent association. Five variable selection methods (R², adjusted R², BIC, Mallows' Cp, LASSO,) integrated with clinical expertise identified nine predictors for model construction. The incremental value of RDW-CV was quantified by comparing AUC, NRI, and IDI between models with and without RDW-CV.\u003c/p\u003e\n\u003cp\u003eResults: Of 123 patients, 48 (39.0%) developed liver injury. RDW-CV was significantly elevated in the liver injury group (13.6 ± 1.0% vs. 13.2 ± 0.8%, P = 0.046). Restricted cubic spline analysis revealed a significant linear association (P for overall = 0.007). In fully adjusted multivariable analysis, each 1% RDW-CV increment conferred threefold higher odds of liver injury (\u003cem\u003eOR\u003c/em\u003e 3.06, 95% \u003cem\u003eCI\u003c/em\u003e1.59–5.91, P= 0.001), with consistent effects across age and sex subgroups. The 9-predictor model including RDW-CV demonstrated superior discrimination compared to the model without RDW-CV (AUC: 0.800 vs. 0.718, P = 0.035; categorical NRI 0.274, P = 0.015; continuous NRI 0.617, P \u0026lt; 0.001; IDI 0.130, P \u0026lt; 0.001). Furthermore, the 9-predictor model showed adequate calibration (MAE = 0.059) and favorable net benefit in decision curve analysis (DCA).\u003c/p\u003e\n\u003cp\u003eConclusions: RDW-CV is an independent, robust predictor of liver injury in children with EBV infection. Incorporating RDW-CV into clinical prediction models significantly enhances risk stratification accuracy, supporting its potential as a routine adjunctive biomarker for early identification of hepatic complications.\u003c/p\u003e","manuscriptTitle":"Association Between Red Blood Cell Distribution Width and Liver Injury in Children with Epstein-Barr Virus Infection: A Retrospective Study with Incremental Predictive Value Assessment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-12 07:33:14","doi":"10.21203/rs.3.rs-8984740/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"311698081425190001520273409887181865158","date":"2026-04-16T10:08:48+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-01T14:34:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-03T15:22:57+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-03T07:44:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-03T07:38:26+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2026-02-27T07:10:42+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"310766c7-dd27-4a1e-a2e3-ea625a9770a4","owner":[],"postedDate":"April 12th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-12T07:33:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-12 07:33:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8984740","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8984740","identity":"rs-8984740","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}