Impact of Jaundice-Induced Elevated Red Blood Cell Distribution Width on Survival Outcomes in Pancreatic Cancer Patients: A Retrospective Cohort Study

Impact of Jaundice-Induced Elevated Red Blood Cell Distribution Width on Survival Outcomes in Pancreatic Cancer Patients: A Retrospective Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Impact of Jaundice-Induced Elevated Red Blood Cell Distribution Width on Survival Outcomes in Pancreatic Cancer Patients: A Retrospective Cohort Study Haoyu Wang, Wenying Zhou, Zhiwen Fu, Han Duan, Tian Miao, Yunbo Tan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8199768/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background To investigate the clinical significance of jaundice-related elevation in red cell distribution width (RDW) for predicting survival outcomes in patients with pancreatic cancer. Methods We established a multi-etiology cohort and stratified patients according to the underlying cause of disease and RDW status. Correlations between RDW and markers of inflammation, nutritional status, and bilirubin were examined. The independent prognostic impact of the “jaundice–RDW” phenotype on pancreatic cancer was evaluated using Kaplan–Meier survival curves and Cox proportional hazards models. Receiver operating characteristic (ROC) analyses were further performed to assess the ability of bilirubin to predict high RDW. Results RDW levels showed distinct distributions across different causes of jaundice; malignant obstructive jaundice, including pancreatic cancer, was associated with markedly higher RDW. In patients with pancreatic cancer, RDW was strongly positively correlated with total bilirubin (r > 0.73). Across tumor stages, patients with the “jaundice with high RDW” phenotype had the shortest median survival (11.0 months) and a substantially increased risk of death (HR = 2.65). Bilirubin demonstrated excellent discriminatory performance for high RDW status (AUC > 0.92). Conclusion In pancreatic cancer, the combination of jaundice and high RDW constitutes a simple and practical tool for risk stratification. Patients presenting with both jaundice and elevated RDW should be prioritized in clinical management, and more intensive, comprehensive therapeutic strategies should be considered to improve outcomes. This study provides readily applicable clinical parameters for precision prognostic assessment in pancreatic cancer. Pancreatic cancer Jaundice Red cell distribution width (RDW) Survival prognosis Risk stratification Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Pancreatic cancer is one of the most lethal malignancies worldwide, with a 5-year survival rate of less than 15%[ 1 ]. Driven by its rising incidence, it currently ranks second among causes of cancer-related death, surpassed only by lung cancer[ 2 ]. The extremely high mortality is largely attributable to difficulties in early detection and the limited availability of effective therapies. Clinically, obstructive jaundice is the predominant presenting symptom in approximately 90% of patients with pancreatic head cancer and develops in roughly 70% of all patients with pancreatic cancer as the tumor progresses. Patients with jaundice are consistently observed to have markedly shorter survival. Consequently, effective management of jaundice and accurate assessment of its prognostic implications have become critical for improving patient outcomes. Red cell distribution width (RDW) is a routine hematological index that reflects heterogeneity in red blood cell volume and has traditionally been used to classify different types of anemia[ 3 ]. Recent evidence indicates that elevated RDW is closely associated with disturbances in nutritional metabolism, systemic inflammation, and poor prognosis across a range of diseases and cancers, and has been regarded as an “integrative” indicator of systemic pathological derangements[ 4 – 7 ]. For instance, an RDW cutoff of 14.8% has been reported to distinguish benign from malignant causes of biliary obstruction, suggesting that increased RDW may serve as an auxiliary marker for etiological differentiation[ 8 ]. In hepatobiliary diseases, jaundice can drive abnormal increases in RDW by disrupting erythropoietic homeostasis through mechanisms such as intestinal microbiota imbalance and cholestatic liver injury[ 9 – 11 ]. These findings suggest that RDW may represent a key link between jaundice and clinical prognosis. In pancreatic cancer, the hemoglobin-to-RDW ratio has been associated with survival outcomes, with lower ratios predicting worse survival[ 12 ]. However, the underlying mechanisms remain unclear. It is unknown whether jaundice-induced elevations in RDW merely reflect overall disease severity or actively contribute to adverse prognosis. Based on these considerations, we propose the following central hypothesis: in pancreatic cancer, jaundice triggers systemic inflammatory responses, gut microbiota dysbiosis, and cholestatic liver injury, ultimately leading to disordered erythropoiesis and increased RDW. Elevated RDW, in turn, impairs antitumor immunity and exacerbates nutritional and metabolic disturbances (hypoalbuminemia), thereby creating a vicious cycle that accelerates disease progression and shortens survival. To test this hypothesis, we constructed a retrospective cohort that included patients with jaundice of diverse etiologies. First, using a systematic stratified-analytic approach, we delineated the spectrum of associations between jaundice and RDW at a pan-disease level. We then focused specifically on pancreatic cancer to quantify the independent impact of the “jaundice–RDW” interaction phenotype on survival prognosis and to evaluate its value for risk stratification. The significance of this study lies in its establishment of a comprehensive evidence chain linking the clinical phenotype (jaundice), a readily obtainable biomarker (RDW), and prognostic outcomes across multiple disease categories. This work not only provides new insights into the prognostic role of jaundice but also offers clinicians a scientifically grounded and clinically feasible risk assessment tool. Ultimately, this research may facilitate more precise prognostic evaluation and individualized treatment planning in pancreatic cancer. 2. Methods 2.1 Study Design and Population This single-center retrospective cohort study included hospitalized patients treated at the First Affiliated Hospital of Dali University between February 2015 and October 2025. Follow-up was completed on October 30, 2025.(Ethics Number:DFY20250901001) 2.1.1 Inclusion Criteria 1. Pancreatic cancer: Diagnosis confirmed by pathological biopsy or by contrast-enhanced CT/MRI combined with tumor markers (CA19-9 ≥ 39 U/mL), in accordance with the 2025 edition of the Guidelines for Integrated Diagnosis and Treatment of Pancreatic Cancer issued by the Chinese Cancer Association (CACA). 2. Jaundice: Patients were classified as having prehepatic (e.g., hemolytic anemia), hepatic (e.g., viral hepatitis), posthepatic non-malignant (e.g., choledocholithiasis), or posthepatic malignant jaundice based on medical history and imaging (abdominal ultrasound, CT). Jaundice was confirmed by liver function tests showing total bilirubin (TBil) > 17.1 µmol/L. 3. Complete clinical data: Availability of tumor markers (CA19-9, CEA, CA125), liver function tests (TBil, albumin), baseline complete blood counts (RDW-CV, RDW-SD), and follow-up survival information. 2.1.2 Exclusion Criteria 1. Mixed etiologies: Jaundice attributable to two or more distinct causes (e.g., concomitant biliary obstruction and chronic liver disease), or cases in which the cause of jaundice was unclear. 2. Hematologic diseases affecting RDW: Acute leukemia, myelodysplastic syndromes, and other primary hematologic disorders known to cause marked abnormalities in RDW. 3. Recent medical interventions: Ongoing chemotherapy or radiotherapy (cancer patients were analyzed separately); use of erythropoietin within 1 month before admission; blood transfusion within 3 months before admission. 4. Incomplete data: Absence of TBil or baseline RDW measurements, or missing key clinical information required for etiological classification. 2.1.3 Grouping Strategy 2.1.3.1 Overall Cohort Stratification The entire cohort was categorized into five etiological groups: 1. Preh epatic disease group: Patients with hemolytic disorders (e.g., autoimmune hemolytic anemia, paroxysmal nocturnal hemoglobinuria) as the primary diagnosis. 2. Hepatic disease group: Patients with primary hepatic injury (e.g., cirrhosis, drug-induced liver injury, viral hepatitis). 3. Posthepatic non-malignant group: Patients with benign biliary obstruction (e.g., gallstones). 4. Other posthepatic cancer group: Patients with malignant biliary obstruction not caused by pancreatic cancer (e.g., cholangiocarcinoma, ampullary carcinoma). 5. Pancre atic cancer group: Patients with pathologically confirmed pancreatic cancer or those diagnosed by contrast-enhanced CT/MRI plus elevated tumor markers (CA19-9 ≥ 39 U/mL). 2.1.3.2. Subgroup Stratified Analysis To investigate the relationship between jaundice and RDW, patients in each etiological category were further stratified as follows: Jaundice Status: Total bilirubin (TBil) > 17.1µmol/L is used to define the jaundice subgroup. RDW Status: The high RDW category was characterized as patients with RDW-CV > 15.4% or RDW-SD > 46 fL. 2.1.3.3. Interaction Stratification in the Pancreatic Cancer Cohort To explore the prognostic effect of the interaction between jaundice and RDW in pancreatic cancer, patients in the pancreatic cancer group were divided into four subgroups: Group 1: Non-jaundiced + Normal RDW (TBil ≤ 17.1 µmol/L and RDW-CV ≤ 15.4%, RDW-SD ≤ 46 fL) Group 2: Non-jaundiced + High RDW (TBil ≤ 17.1 µmol/L and either RDW-CV > 15.4% or RDW-SD > 46 fL) Group 3: Jaundice + Normal RDW (TBil > 17.1 µmol/L and RDW-CV ≤ 15.4%, RDW-SD ≤ 46 fL) Group4: Jaundice + High RDW (TBil > 17.1 µmol/L and (RDW-CV > 15.4% or RDW-SD > 46 fL)) 2.2 Data Collection Data were extracted from the hospital’s electronic medical record (EMR) system and included: Baseline characteristics Age, sex, and comorbidities (hypertension, diabetes). Laboratory indicators First complete blood count after admission (RDW-CV, RDW-SD, mean corpuscular volume [MCV], C-reactive protein [CRP], neutrophil-to-lymphocyte ratio [NLR], hemoglobin), liver function tests (TBil, albumin, globulin), and tumor markers (CA19-9, CEA, CA125). Imaging and pathology Tumor staging for pancreatic cancer (AJCC 8th edition TNM staging) and etiological diagnosis of jaundice (e.g., choledocholithiasis, cholangiocarcinoma). Survival outcomes Overall survival (OS), defined as the time from hospital admission or disease diagnosis to death from any cause or last follow-up. 2.3 Statistical Analysis All statistical analyses were performed using R software (version 4.5.1). A two-sided P value < 0.05 was considered statistically significant. Descriptive statistics Categorical variables were summarized as frequency (percentage, n/%), and continuous variables as mean ± standard deviation (mean ± SD). Between-group comparisons One-way analysis of variance (ANOVA) was used for continuous variables that met assumptions of normality and homogeneity of variance; Welch’s t-test was applied when variances were unequal. Pearson’s chi-square test was used for categorical variables. Correlation analysis Pearson correlation coefficients were calculated to assess linear relationships between RDW-CV, RDW-SD, TBil, albumin, and hemoglobin. Survival analysis Kaplan–Meier curves were constructed to compare survival among the four “jaundice–RDW” groups, and differences were evaluated using the log-rank test. Cox proportional hazards regression models (univariate and multivariate) were used to estimate the independent effects of RDW and jaundice on survival in pancreatic cancer, adjusting for age, sex, tumor stage, and albumin. Predictive performance ROC curves were generated to evaluate the ability of TBil to predict high RDW in pancreatic cancer, and the area under the curve (AUC), sensitivity, and specificity were calculated. An AUC > 0.9 was considered to indicate excellent predictive performance. 3. Results 3.1 Baseline Characteristics of the Overall Cohort Table 1 summarizes baseline characteristics for the 565 enrolled patients. Age, sex distribution, RDW indices, and TBil levels differed significantly across etiological groups (all P < 0.001). Table 1 Baseline Patient Characteristics by Etiology and Cancer Status Group Variable Overall a Pre-hepatic N = 81 a Hepatic N = 180 a Post-hepatic Non-cancer N = 194 a Other Post-hepatic cancer N = 37 a Pancreatic Cancer N = 73 a P-value b Age (years) 58.0 ± 16.2 48.6 ± 19.2 55.2 ± 15.5 61.2 ± 16.4 64.0 ± 7.8 63.7 ± 9.6 <0.001 Gender <0.001 Male 291.0 (51.5%) 22.0 (27.2%) 118.0 (65.6%) 90.0 (46.4%) 18.0 (48.6%) 43.0 (58.9%) Female 274.0 (48.5%) 59.0 (72.8%) 62.0 (34.4%) 104.0 (53.6%) 19.0 (51.4%) 30.0 (41.1%) RDW-CV (%) 15.6 ± 3.8 20.0 ± 5.4 15.7 ± 3.4 13.8 ± 1.8 15.6 ± 2.8 15.0 ± 2.6 <0.001 RDW-SD (fL) 52.6 ± 15.2 74.0 ± 24.3 53.4 ± 10.8 45.0 ± 5.3 49.9 ± 6.9 48.6 ± 8.7 <0.001 TBil (µmol/L) 72.9 ± 89.3 54.6 ± 41.6 46.0 ± 48.7 62.6 ± 63.7 187.9 ± 159.3 128.6 ± 136.5 <0.001 a Mean±SD or n(%) b One-way analysis of means; Pearson’s Chi-squared test The prehepatic group had the highest RDW values (RDW-CV: 20.0% ± 5.4%; RDW-SD: 74.0 ± 24.3 fL), whereas the posthepatic non-malignant group had the lowest (RDW-CV: 13.8% ± 1.8%; RDW-SD: 45.0 ± 5.3 fL). All posthepatic malignant groups (including pancreatic cancer and other posthepatic malignancies) exhibited significantly higher RDW levels than the posthepatic benign group, whereas RDW levels did not differ significantly between the malignant categories. Total bilirubin levels were significantly lower in all benign etiology groups (prehepatic, hepatic, and posthepatic non-malignant) than in the other posthepatic cancer group (187.9 ± 159.3 µmol/L) and the pancreatic cancer group (128.6 ± 136.5 µmol/L). Figure 1 illustrates RDW differences between jaundiced and non-jaundiced patients across etiological groups. In the posthepatic malignant group, RDW differed significantly between patients with and without jaundice (Group A, P = 0.0015). Statistically significant differences were also observed in the hepatic etiology group (P = 0.0237), whereas no significant differences were found in the prehepatic and posthepatic benign groups. In Group B, significant differences in RDW were observed between hepatic and posthepatic malignant etiologies (P < 0.001 and P = 0.0051, respectively), and within-group differences reached significance in the prehepatic group (P = 0.0155). The posthepatic benign group was the only group without notable RDW differences. In summary, markedly elevated RDW and hyperbilirubinemia were closely associated with malignant obstructive jaundice (particularly pancreatic cancer and other posthepatic malignancies), suggesting that RDW may play an important role in the pathophysiology of these conditions. 3.2 Comparison of RDW and Bilirubin Levels in Post-Hepatic Lesions As shown in Table 2 and Table 3 , RDW-CV (15.2% ± 2.7%), RDW-SD (49.1 ± 8.1 fL), and TBil (148.6 ± 146.6 µmol/L) were significantly higher in the posthepatic cancer group (n = 110) than in the posthepatic non-malignant group (n = 194) (all P < 0.001). Age and sex distribution did not differ significantly between these two groups. Table 2 Comparison Between Post-hepatic Non-cancer and Post-hepatic Cancers Variable Overall a Non-cancer N = 194 a Cancer N = 110 a P-value b Age(years) 62.1 ± 14.2 61.2 ± 16.4 63.8 ± 9.0 0.080 Gender 0.162 Male 151.0 (49.7%) 90.0 (46.4%) 61.0 (55.5%) Female 153.0 (50.3%) 104.0 (53.6%) 49.0 (44.5%) RDW-CV (%) 14.3 ± 2.3 13.8 ± 1.8 15.2 ± 2.7 <0.001 RDW-SD (fL) 46.5 ± 6.7 45.0 ± 5.3 49.1 ± 8.1 <0.001 Total Bilirubin (µmol/L) 93.7 ± 109.7 62.6 ± 63.7 148.6 ± 146.6 <0.001 a Mean±SD;n(%) b Welch Two-Sample t-test; Pearson's Chi-squared test Table 3 Comparison Between Other Post-hepatic Cancers and Pancreatic Cancer Variable Overall a Other Post-hepatic Cancers N = 37 a Pancreatic Cancer N = 73 a P-value b Age (years) 63.8 ± 9.0 64.0 ± 7.8 63.7 ± 9.6 0.841 Gender 0.413 Male 61.0 (55.5%) 18.0 (48.6%) 43.0 (58.9%) Female 49.0 (44.5%) 19.0 (51.4%) 30.0 (41.1%) RDW-CV (%) 15.2 ± 2.7 15.6 ± 2.8 15.0 ± 2.6 0.300 RDW-SD (fL) 49.1 ± 8.1 49.9 ± 6.9 48.6 ± 8.7 0.418 Total Bilirubin (µmol/L) 148.6 ± 146.6 187.9 ± 159.3 128.6 ± 136.5 0.058 a Mean ± SD; n (%) b Welch Two-Sample t-test; Pearson's Chi-squared test Within the posthepatic cancer group, RDW indices and TBil levels did not differ significantly between pancreatic cancer (n = 73) and other posthepatic malignancies (n = 37) (all P > 0.05). 3.3 Correlation between RDW and Clinical Indicators in the Pancreatic Cancer Subgroup In patients with pancreatic cancer, RDW was strongly positively correlated with TBil (RDW-CV: r = 0.764, P < 0.001; RDW-SD: r = 0.735, P < 0.001). RDW showed weak negative correlations with hemoglobin and albumin (both P < 0.001) and no significant correlations with tumor markers such as CEA, CA19-9, or CA125, or with classical inflammatory indices (Table 4 ). Table 4 Correlation Analysis Between RDW and Clinical Parameters in Pancreatic Cancer Pearson Correlation Coefficients Clinical Parameter RDW-CV RDW-SD Clinical Significance a r value P value r value P value TBil (µmol/L) 0.764 <0.001 0.735 <0.001 Strong correlation CA19-9 (U/mL) 0.057 0.633 0.087 0.469 No correlation CEA (ng/mL) -0.172 0.148 -0.128 0.283 No correlation CA125 (U/mL) -0.174 0.148 -0.149 0.215 No correlation Neutrophil-Lymphocyte Ratio 0.085 0.476 0.099 0.406 No correlation Platelet-Lymphocyte Ratio 0.262 0.025 0.214 0.069 No correlation C-reactive Protein (mg/L) 0.144 0.328 0.177 0.228 No correlation Albumin (g/L) -0.380 <0.001 -0.393 <0.001 Weak correlation Hemoglobin (g/L) -0.433 <0.001 -0.397 <0.001 Weak correlation Mean Corpuscular Volume (fL) -0.031 0.798 0.349 0.002 No correlation a Correlation strength: |r| ≥ 0.7 (Strong), 0.5 ≤ |r| < 0.7 (Moderate), 0.3 ≤|r| < 0.5 (Weak), |r| < 0.3 (No correlation) 3.4 Comparison of RDW and Clinical Parameters Across Jaundice Levels in pancreatic cancer In subgroup analyses based on TBil (threshold 17.1 µmol/L), patients with jaundice (n = 44) had significantly higher RDW-CV, RDW-SD, and TBil levels than those without jaundice (n = 28) (all P < 0.001), while albumin levels were significantly lower in the jaundiced group (P = 0.006). Hemoglobin and CA19-9 did not differ significantly between the two groups (Table 5 , Fig. 2 ). Table 5 Comparison of RDW and Clinical Parameters by TBil Level (Threshold: 17.1 µmol/L) Parameter Overall N = 72 a TBil < 17.1µmol/L N = 28 a TBil ≥ 17.1µmol/L N = 44 a P-value a RDW-CV (%) 15.0 ± 2.7 13.4 ± 1.3 16.0 ± 2.8 <0.001 RDW-SD (fL) 48.6 ± 8.7 42.7 ± 4.4 52.4 ± 8.7 <0.001 Total Bilirubin (µmol/L) 125.6 ± 134.9 10.8 ± 3.5 198.5 ± 126.7 <0.001 CA19-9 (U/mL) 345.2 ± 393.4 259.6 ± 370.2 399.7 ± 402.1 0.14 Albumin (g/L) 37.9 ± 5.9 40.0 ± 3.6 36.6 ± 6.6 0.006 Hemoglobin (g/L) 130.2 ± 20.1 133.2 ± 20.7 128.2 ± 19.7 0.3 a Mean±SD b Welch Two-Sample t-test 3.5 Survival Analysis in the Pancreatic Cancer Subgroup Stratified by "Jaundice-RDW" Quadrant survival analysis based on jaundice and RDW status revealed that only the “jaundice with high RDW” group (n = 35) had a significantly higher risk of death compared with the reference group (“no jaundice and normal RDW”, n = 24, median survival 18.0 months) (HR = 2.65, 95% CI: 1.22–5.75, P = 0.014; log-rank P = 0.028) and exhibited the shortest median survival (11.0 months). Survival did not differ significantly between the reference group and the other two groups (jaundice + normal RDW, non-jaundice + high RDW) (Table 6 ,Fig. 3 ). Table 6 Survival Analysis by Four-Group Classification Based on Jaundice and RDW Status a Group 1 (Non-jaundice + Normal RDW) as Reference Group N Median Survival (months) 95% CI HR vs Group 1 (95%) P Value Group 1: No jaundice + Normal RDW 24 18.0 (15.3-NA) 1 (Reference) - Group 2: Non-jaundice + High RDW 4 NR b NR 8.44(0.90-79.04) 0.062 Group 3: Jaundice + Normal RDW 10 16.0 (9.4–NA) 1.43 (0.50–4.05) 0.504 Group 4: Jaundice + High RDW 35 11.0 (7.8–17.8) 2.65 (1.22–5.75) 0.014 a Jaundice defined as total bilirubin (TBil) > 17.1 µmol/L; High red cell distribution width (RDW) defined as RDW-CV > 15.4% or RDW-SD > 46 fL b NR: Not Reached (median survival not reached during follow-up) 3.6 Independent Prognostic Factors in Pancreatic Cancer (Cox Regression) Cox regression identified RDW-CV (HR = 1.169, P = 0.012) and the albumin-to-globulin (A/G) ratio (HR = 3.332, P < 0.001) as significant prognostic risk factors for pancreatic cancer. In multivariate analysis, RDW-CV (HR = 1.728, P = 0.008) and A/G ratio (HR = 3.377, P = 0.007) remained independent prognostic indicators (Table 7 ). Table 7 Univariate and multivariate Cox regression analysis of prognostic factors in pancreatic cancer patients (n = 73) Clinical parameters Univariate analysis Multivariate analysis HR(95% CI) P HR (95% CI) P Gender 0.662 (0.341–1.286) 0.223 NA NA Age (years) 1.504 (0.784–2.885) 0.220 NA NA Tumor site 0.524 (0.248–1.106) 0.090 NA NA TNM staging 0.811 (0.368–1.785) 0.602 NA NA MCV 2.182 (0.656–7.257) 0.203 NA NA CA19-9 0.736 (0.377–1.436) 0.368 NA NA CA125 1.067 (0.563–2.022) 0.841 NA NA CEA 0.765 (0.269–2.176) 0.616 NA NA Globulin 0.483 (0.065–3.564) 0.475 NA NA Hemoglobin 1.269 (0.638–2.521) 0.497 NA NA CRP 1.12 (0.544–2.308) 0.758 NA NA NLR 1.681 (0.891–3.17) 0.109 NA NA Total Bilirubin (µmol/L) 1.002 (1–1.004) 0.060 0.999 (0.995–1.003) 0.578 RDW-CV(%) 1.159 (1.033–1.3) 0.012 1.729 (1.151–2.597) 0.008 RDW-SD (fL) 1.031 (0.994–1.068) 0.097 0.858 (0.765–0.962) 0.009 A/G ratio 3.332 (1.734–6.403) <0.001 3.377 (1.396–8.167) 0.007 Note: HR: Hazard Ratio; CI: Confidence Interval. Reference groups: Gender (Female), Tumor location (Head), TNM stage (I-II), all biomarkers (Normal). 3.7 Confounding Effects of Tumor Staging in Pancreatic Cancer The distribution of TNM stages (I–IV) did not differ significantly among the four “jaundice–RDW” groups (χ² = 11.31, P = 0.255), indicating that tumor stage was unlikely to be a major confounder in the observed survival differences (Table 8 ). Table 8 Distribution of Tumor Stages by Jaundice and RDW Groups Group Total n Stage I n(%) Stage II n(%) Stage III n(%) Stage IV n(%) NA n(%) Overall 73 15(20.5%) 31(42.5%) 8(11%) 15 (20.5%) 4 (5.5%) Non-jaundice + Normal RDW 24 3 (12.5%) 8 (33.3%) 3 (12.5%) 7 (29.2%) 3 (12.5%) Non-jaundice + High RDW 4 0 (0%) 1 (25%) 2(50%) 1(25%) 0(0%) Jaundice + Normal RDW 10 3 (30%) 5 (50%) 1(10%) 1 (10%) 0(0%) Jaundice + High RDW 35 9 (25.7%) 17 (48.6%) 2 (5.7%) 6 (17.1%) 1 (2.9%) Chi-square test:χ²= 11.31, df = 9, p = 0.255 RDW: Red cell distribution width; Normal RDW: RDW-CV ≤ 15.4% or RDW-SD ≤ 46; High RDW: RDW-CV > 15.4% or RDW-SD > 46 Jaundice: Total bilirubin > 17.1 µmol/L 3.8 Total bilirubin demonstrated outstanding predictive efficacy for high RDW status in Pancreatic Cancer TBil showed excellent predictive performance for high RDW status, with AUCs of 0.926 (95% CI: 0.858–0.993) for high RDW-CV and 0.923 (95% CI: 0.859–0.986) for high RDW-SD, and a sensitivity of 0.92 in both analyses (Fig. 4 ). 4. Discussion By systematically analyzing clinical data from 565 patients with jaundice of different etiologies, this study constructed a coherent logical framework from a pan-disease phenomenon to a pancreatic cancer–specific prognostic pattern. We demonstrated that distinct mechanisms of jaundice affect RDW in different etiological contexts and that jaundice-related RDW elevation has important prognostic implications in pancreatic cancer. 4.1 Pan-Disease Spectrum of RDW Associations Across Etiologies Comparison of baseline RDW across etiologies revealed a characteristic gradient (Table 1 , Fig. 1 ). Prehepatic diseases showed the highest RDW-CV (20.0%), followed by posthepatic malignancies (RDW-CV: 15.2%) and hepatic diseases (RDW-CV: 15.7%), whereas the posthepatic benign group had the lowest RDW-CV (13.8%). This pattern suggests that the impact of different diseases on erythrocyte dynamics is heterogeneous. We further evaluated whether the effect of jaundice on RDW differed across etiologies by comparing RDW levels between jaundiced and non-jaundiced patients within each group. The jaundiced subgroups had significantly higher RDW than the non-jaundiced subgroups in hepatic, posthepatic benign, and posthepatic malignant conditions (all P < 0.05), with the largest differences observed in the hepatic (P = 6 × 10⁻⁵) and malignant posthepatic groups (P = 0.0015). In contrast, RDW did not differ significantly between jaundiced and non-jaundiced patients in the prehepatic or posthepatic benign groups (P > 0.05). These findings indicate that the effect of jaundice on RDW elevation depends on specific pathophysiological pathways and is not uniformly present across all etiologies. On this basis, the principal mechanisms underlying RDW elevation in different etiologies can be summarized as follows: Prehepatic diseases: Predominant pattern of direct red blood cell destruction In prehepatic disorders, extensive hemolysis is the primary mechanism, leading to both increased RDW and elevated bilirubin. Prior studies have shown that RDW has early diagnostic value in hemolytic diseases, facilitating timely recognition of jaundice and hyperbilirubinemia[ 13 ]. This observation explains why RDW is markedly elevated in prehepatic disorders compared with other etiologies (Table 1 , Fig. 1 ), yet differences between jaundiced and non-jaundiced subgroups within this category are not statistically significant. By the time bilirubin rises sufficiently to manifest clinically as jaundice, hemolysis has typically reached an advanced stage. The bone marrow responds by rapidly releasing large numbers of immature erythrocytes to compensate for peripheral loss, creating a highly heterogeneous population of red blood cells and further increasing RDW. Thus, elevated RDW in prehepatic jaundice reflects classical disruption of the red blood cell life cycle. Hepatic disorders: Combined inflammatory and metabolic dysregulation In hepatic diseases, multiple factors contribute to RDW elevation. Hepatocyte injury induces inflammatory responses that interfere with erythropoietin (EPO) metabolism[ 14 , 15 ]. Portal hypertension and hypersplenism further shorten the lifespan of red blood cells[ 16 ]. In this setting, hyperbilirubinemia acts as an important synergistic factor. Elevated bilirubin, particularly unconjugated bilirubin, exerts cytotoxic effects and exacerbates oxidative damage to red blood cell membranes, thereby reducing their survival[ 17 ]. Together with underlying hepatic pathology, these processes collectively drive RDW elevation. Posthepatic malignant obstruction: Prominent inflammation–malnutrition axis Although RDW has been linked to various malignancies, the relationship between pancreatic cancer and RDW has not been extensively characterized. Our study focused on malignant obstructive jaundice caused by pancreatic cancer and demonstrated a particularly strong association between RDW and TBil. In pancreatic cancer, TBil showed excellent predictive performance for high RDW status (AUC > 0.92), and RDW was strongly positively correlated with TBil (r = 0.764, P < 0.001; Fig. 4 ). This suggests that the magnitude of RDW abnormality in malignant obstruction is directly reflected in bilirubin levels. Additionally, RDW was negatively correlated with hemoglobin and albumin, supporting a vicious cycle of “biliary obstruction → bile salt toxicity → disruption of the intestinal barrier/endotoxemia → systemic inflammatory activation → malabsorption → impaired erythropoiesis.” In this framework, jaundice is not merely a clinical manifestation but a fundamental pathophysiological driver of high RDW. 4.2 Clinical Translation: Prognostic Value of the “Jaundice–RDW” Interaction Model in Pancreatic Cancer The survival analyses (Fig. 3 , Table 6 ) revealed a clinically meaningful hierarchical pattern. The “jaundice with high RDW” group had a markedly poor prognosis, with a median survival of only 11.0 months and a 2.65-fold increased risk of death compared with the “no jaundice and normal RDW” group (HR = 2.65, 95% CI: 1.22–5.75, P = 0.014). After adjustment for confounders, multivariate Cox regression further confirmed RDW-CV as an independent prognostic factor (HR = 1.728, P = 0.008). Importantly, the prognostic significance of the jaundice–RDW interaction was not explained by differences in anatomical tumor burden. As shown in Table 8 , the distribution of TNM stages did not differ significantly among the four “jaundice–RDW” groups (P = 0.255), whereas survival did. This finding has two major implications. First, it effectively rules out tumor stage as the primary driver of intergroup survival differences, lending strong support to the independent prognostic value of the “jaundice + high RDW” phenotype. Second, it highlights a clinically relevant reality beyond TNM staging: patients in the “jaundice with high RDW” group (n = 35) were distributed across all stages (Stage I: 25.7%; Stage II: 48.6%; Stage IV: 17.1%), suggesting that once this phenotype develops, patients may enter a common adverse trajectory dominated by systemic pathophysiological disturbance, regardless of anatomical stage. Collectively, these findings strongly support our central hypothesis that systemic alterations induced by biliary obstruction, as reflected by elevated RDW, may exert a stronger influence on prognosis than tumor growth alone in the course of pancreatic cancer. In this context, host-related factors play a pivotal role. Clinically, patients with the “jaundice + high RDW” phenotype may benefit from more aggressive management strategies, including early and effective biliary drainage, enhanced nutritional support, and timely intervention targeting systemic inflammation. 4.3 From Biomarker to Active Effector: Hypothesized Role of Red Blood Cell–Derived Vesicles Our results raise a deeper scientific question that goes beyond the use of RDW as a prognostic marker: do elevated RDW values actively promote tumor progression through specific mechanisms? We put forward a novel hypothesis that red blood cell–derived extracellular vesicles (REVs) may serve as critical intermediaries linking red blood cell heterogeneity with malignant progression. Similar to extracellular vesicles (EVs) originating from other cell types, REVs participate in intercellular communication and contribute to a variety of physiological and pathological processes[ 18 ]. Increasing evidence suggests that REVs released from circulating erythrocytes have diagnostic and therapeutic potential in multiple diseases[ 19 ]. REVs have been implicated in thrombosis, hemostasis, infection, cancer, and inflammation[ 20 – 23 ]. They can serve as promising nanocarriers for drug delivery, but may also act as therapeutic targets or biomarkers for diagnosis and prognosis[ 24 ]. These observations indicate that REVs hold substantial potential for advancing precision medicine. In the context of obstructive jaundice, large-scale production of REVs may exacerbate tumor biological behavior through several potential mechanisms: (1) Immune regulation : REVs may be phagocytosed by tumor-associated macrophages, promoting their polarization toward an immunosuppressive M2 phenotype. (2) Reprogramming of iron metabolism : Iron-driven oxidative stress has been shown to promote tumor growth and metastasis by remodeling the extracellular matrix, suppressing immune responses, and inducing genetic alterations[ 25 ]. As a major source of extracellular iron, massive release of REVs may induce iron overload in tumor cells, leading to genomic instability and enhanced tumor progression. (3) Intercellular communication : Through paracrine signaling, specific miRNAs carried by REVs (e.g., miR-451a) may alter the gene expression profiles of pancreatic cancer cells, thereby influencing their invasive and metastatic potential. The coexistence of marked survival differences and comparable stage distributions among the “jaundice–RDW” groups provides an ideal clinical model for the REV hypothesis. We speculate that REV-mediated systemic effects may represent a distinct malignant driver that is only partially related to anatomical tumor stage. This could explain why patients with the “jaundice + high RDW” phenotype have similarly poor outcomes across stages: they may share a tumor-promoting microenvironment and systemic milieu shaped by carriers such as REVs. 4.4 Innovation and Limitations This study has several innovative aspects. First, it systematically compares the mechanisms by which different etiologies of jaundice affect RDW across disease categories. Second, focusing on pancreatic cancer, it demonstrates that the interaction between jaundice and RDW independently influences prognosis regardless of tumor stage and establishes a coherent clinical evidence chain linking “obstructive jaundice → elevated RDW → poor prognosis.” Third, it prospectively proposes REVs as a potential mechanistic bridge connecting red blood cell heterogeneity to tumor progression. Several limitations should be acknowledged. The retrospective design may introduce selection bias. The relatively small sample sizes in the non-jaundiced + high RDW group and the other posthepatic malignancy group may limit statistical power for certain comparisons. Direct measurements of intermediate factors such as REVs and specific inflammatory cytokines were not available. In addition, the impact of treatments such as biliary drainage and chemotherapy on RDW dynamics and prognosis was not fully evaluated. Future research should therefore focus on: (1) validating the prognostic value of the “jaundice–RDW” stratification system in prospective, multicenter cohorts; (2) isolating and characterizing REVs from plasma of jaundiced patients using animal models and co-culture systems to directly test their biological functions and molecular mechanisms; and (3) dynamically monitoring changes in RDW and REVs during treatment to assess their potential as biomarkers for therapeutic response and recurrence surveillance. 5. Conclusion Through pan-disease comparisons spanning prehepatic (dominant red blood cell destruction), hepatic (combined inflammatory and metabolic disturbances), and posthepatic malignant obstruction (driven by severe inflammation and malnutrition) etiologies, this study systematically elucidates how diverse causes of jaundice affect RDW. Crucially, we show that, beyond tumor stage, the combination of jaundice and elevated RDW independently predicts prognosis in pancreatic cancer. In this context, RDW is elevated from a conventional adjunct index to a core biomarker representing host-related factors, with REVs proposed as a potential mechanistic link between red blood cell heterogeneity and tumor progression. This conceptual shift—from clinical observation to mechanistic hypothesis—broadens our understanding of pancreatic cancer pathophysiology and opens new avenues for developing prognostic models that transcend purely morphological staging and for designing treatment strategies that explicitly target host factors. Abbreviations RDW Red cell distribution width CT computed tomography MRI magnetic resonance imaging ROC Receiver operating characteristic TBil Total Bilirubin MCV mean corpuscular volume CRP C-reactive protein NLR neutrophil-to-lymphocyte ratio ANOVA One-way analysis of variance OS Overall survival AUC Area under the curve HR hazard ratio CI confidence interval REVs red blood cell–derived extracellular vesicles Declarations Ethics approval and consent to participate This study was conducted in accordance with the Declaration of Helsinki and was approved by the Medical Ethics Committee of The First Affiliated Hospital of Dali University (Ethics Number: DFY20250901001). Consent for publication Not applicable. Data accessibility The data and figures generated and analyzed during this study are available from the corresponding authors upon reasonable request. Conflict of interest The authors declare that they have no competing interests Funding This work is supported by Yunnan Provincial Foundation Joint Program for Local Undergraduate Universities, (202101BA070001-276) Author Contribution H.W. and W.Z. contributed equally to this work. They were jointly responsible for the conception and design of the study, performed data analysis and interpretation, and drafted the initial manuscript. Z.F., H.D., and T.M. contributed substantially to patient data acquisition, curation, and validation. Y.T. provided critical supervision and assisted in project administration and manuscript revision. L.J., as the corresponding author, secured funding and ethical approval, provided overall scientific direction, supervised all stages of the research, and critically revised the manuscript for important intellectual content. All authors reviewed and approved the final version of the manuscript. Acknowledgement This study was funded in part by the Yunnan Provincial Foundation Joint Program for Local Undergraduate Universities (Grant No. 202101BA070001-276).The authors gratefully acknowledge the clinical and administrative staff of the First Affiliated Hospital of Dali University for their invaluable contributions to patient care and data curation. We extend our sincere appreciation to the Department of General Surgery for their collaborative support. We are deeply indebted to the patients and their families whose participation made this study possible. Data Availability The data and figures generated and analyzed during this study are available from the corresponding authors upon reasonable request. References Stoop TF, Javed AA, Oba A, Koerkamp BG, Seufferlein T, Wilmink JW, Besselink MG. Pancreatic cancer. Lancet. 2025;405:1182–202. Wenzel P, Mogler C, Görgülü K, Algül H. Pancreatic Cancer: Current Concepts, Trends, and Future Directions. Turk J Gastroenterol. 2024;36:69–81. Bessman JD, Gilmer PR Jr., Gardner FH. Improved classification of anemias by MCV and RDW. Am J Clin Pathol. 1983;80:322–6. García-Escobar A, Lázaro-García R, Goicolea-Ruigómez J, González-Casal D, Fontenla-Cerezuela A, Soto N, González-Panizo J, Datino T, Pizarro G, Moreno R, Cabrera J. Red Blood Cell Distribution Width is a Biomarker of Red Cell Dysfunction Associated with High Systemic Inflammation and a Prognostic Marker in Heart Failure and Cardiovascular Disease: A Potential Predictor of Atrial Fibrillation Recurrence. High Blood Press Cardiovasc Prev. 2024;31:437–49. Li N, Zhou H, Tang Q. Red Blood Cell Distribution Width: A Novel Predictive Indicator for Cardiovascular and Cerebrovascular Diseases. Dis Markers. 2017;2017:7089493. Cetinkaya E, Senol K, Saylam B, Tez M. Red cell distribution width to platelet ratio: new and promising prognostic marker in acute pancreatitis. World J Gastroenterol. 2014;20:14450–4. Cao W, Shao Y, Wang N, Jiang Z, Yu S, Wang J. Pretreatment red blood cell distribution width may be a potential biomarker of prognosis in urologic cancer: a systematic review and meta-analysis. Biomark Med. 2022;16:1289–300. Beyazit Y, Kekilli M, Ibis M, Kurt M, Sayilir A, Onal IK, Purnak T, Oztas E, Tas A, Yesil Y, Arhan M. Can red cell distribution width help to discriminate benign from malignant biliary obstruction? A retrospective single center analysis. Hepatogastroenterology. 2012;59:1469–73. Tan M, Liu B, You R, Huang Q, Lin L, Cai D, Yang R, Li D, Huang H. Red Blood Cell Distribution Width as a Potential Valuable Survival Predictor in Hepatitis B Virus-related Hepatocellular Carcinoma. Int J Med Sci. 2023;20:976–84. Li X, Xu H, Gao P. Increased red cell distribution width predicts severity of drug-induced liver injury: a retrospective study. Sci Rep. 2021;11:773. Wang XD, Zhao ZZ, Yang XY, Bao R, Wang YY, Lan Y, Quan ZY, Wang JF, Bian JJ. Association Between Red Cell Distribution Width and Liver Injury after Cardiac and Aortic Aneurysm Surgery with Cardiopulmonary Bypass. J Cardiothorac Vasc Anesth. 2024;38:3065–75. Zhou G, Yang L, Lu Y, Lu G. Prognostic value of hemoglobin to red blood cell distribution width ratio in pancreatic ductal adenocarcinoma: a retrospective study. BMC Gastroenterol. 2024;24:288. Lin H, Luo P, Liu C, Lin X, Que C, Zhong W. The application value of mean red blood cell volume and red blood cell volume distribution width combined with total serum bilirubin in the early screening of neonatal hemolytic disease. BMC Pediatr. 2023;23:19. He M, Wei W, Zhang Y, Xiang Z, Peng D, Kasimumali A, Rong S. Gut microbial metabolites SCFAs and chronic kidney disease. J Transl Med. 2024;22:172. Li B, You Z, Xiong XZ, Zhou Y, Wu SJ, Zhou RX, Lu J, Cheng NS. Elevated red blood cell distribution width predicts poor prognosis in hilar cholangiocarcinoma. Oncotarget. 2017;8:109468–77. Koschade SE, Moser LM, Sokolovskiy A, Michael FA, Serve H, Brandts CH, Finkelmeier F, Zeuzem S, Trebicka J, Ferstl P, Ballo O. Bone Marrow Assessment in Liver Cirrhosis Patients with Otherwise Unexplained Peripheral Blood Cytopenia. J Clin Med 2023, 12. Zelenka J, Lenícek M, Muchová L, Jirsa M, Kudla M, Balaz P, Zadinová M, Ostrow JD, Wong RJ, Vítek L. Highly sensitive method for quantitative determination of bilirubin in biological fluids and tissues. J Chromatogr B Analyt Technol Biomed Life Sci. 2008;867:37–42. Georgatzakou HT, Fortis SP, Papageorgiou EG, Antonelou MH, Kriebardis AG. Blood Cell-Derived Microvesicles in Hematological Diseases and beyond. Biomolecules 2022, 12. Ma SR, Xia HF, Gong P, Yu ZL. Red Blood Cell-Derived Extracellular Vesicles: An Overview of Current Research Progress, Challenges, and Opportunities. Biomedicines 2023, 11. Cai N, Zhan X, Zhang Q, Di H, Chen C, Hu Y, Yan X. Red Blood Cell-Derived Small Extracellular Vesicles Inhibit Influenza Virus through Surface-Displayed Sialic Acids. Angew Chem Int Ed Engl. 2025;64:e202413946. Peng B, Yang Y, Wu Z, Tan R, Pham TT, Yeo EYM, Pirisinu M, Jayasinghe MK, Pham TC, Liang K, et al. Red blood cell extracellular vesicles deliver therapeutic siRNAs to skeletal muscles for treatment of cancer cachexia. Mol Ther. 2023;31:1418–36. Fischer D, Thies F, Awad O, Brat C, Meybohm P, Baer PC, Müller MM, Urbschat A, Maier TJ, Zacharowski K, Roos J. Red Blood Cell-Derived Microparticles Exert No Cancer Promoting Effects on Colorectal Cancer Cells In Vitro. Int J Mol Sci 2022, 23. Thangaraju K, Neerukonda SN, Katneni U, Buehler PW. Extracellular Vesicles from Red Blood Cells and Their Evolving Roles in Health, Coagulopathy and Therapy. Int J Mol Sci 2020, 22. Yang L, Huang S, Zhang Z, Liu Z, Zhang L. Roles and Applications of Red Blood Cell-Derived Extracellular Vesicles in Health and Diseases. Int J Mol Sci 2022, 23. Zhu L, Chen M, Huang B, Zhang T, Chen K, Lian H, Liu M, Zhao K, Pang Y, Zhang J, et al. Genomic Analysis Uncovers Immune Microenvironment Characteristics and Drug Sensitivity of Ferroptosis in Breast Cancer Brain Metastasis. Front Genet. 2021;12:819632. 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8199768","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":555372998,"identity":"3ab3be50-52ba-44e8-9bc1-9b232027132d","order_by":0,"name":"Haoyu Wang","email":"","orcid":"","institution":"Clinical Medical College of Dali University","correspondingAuthor":false,"prefix":"","firstName":"Haoyu","middleName":"","lastName":"Wang","suffix":""},{"id":555372999,"identity":"65f5d5fc-ed4d-4d18-9810-97dde51dae91","order_by":1,"name":"Wenying Zhou","email":"","orcid":"","institution":"Clinical Medical College of Dali 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07:13:52","extension":"html","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":145840,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8199768/v1/2a48b81f76e58f36d8ea9267.html"},{"id":97656842,"identity":"42e99aab-af6a-41e6-85ec-f66fd085c405","added_by":"auto","created_at":"2025-12-08 07:13:52","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":228083,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of Red Blood Cell Distribution Width (RDW) between Non-Jaundice and Jaundice Patients across Different Etiological Groups.\u003c/p\u003e\n\u003cp\u003e(A) RDW-CV (Red Blood Cell Distribution Width - Coefficient of Variation) levels across different etiological groups.\u003c/p\u003e\n\u003cp\u003e(B) RDW-SD (Red Blood Cell Distribution Width - Standard Deviation) levels across different etiological groups.\u003c/p\u003e\n\u003cp\u003ePre-hepatic: Pre-hepatic causes; Hepatic: Hepatic causes; Post-hepatic (Benign): Benign post-hepatic causes; Other Post-hepatic cancer: Other malignant post-hepatic causes (excluding pancreatic cancer). P-values indicated in the figure result from statistical comparisons between non-jaundice and jaundice groups within each etiological subgroup.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8199768/v1/f7e8de9d94917821905eba54.png"},{"id":97656845,"identity":"532d7656-e8dc-450f-8168-05375db096c3","added_by":"auto","created_at":"2025-12-08 07:13:52","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":173057,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of RDW parameters and total bilirubin levels between non-jaundice and jaundice groups in pancreatic cancer.\u003c/p\u003e\n\u003cp\u003e(A) Red blood cell distribution width-standard deviation (RDW-SD) levels.\u003c/p\u003e\n\u003cp\u003e(B) Red blood cell distribution width-coefficient of variation (RDW-CV) levels.\u003c/p\u003e\n\u003cp\u003e(C) Total bilirubin levels presented on a logarithmic scale (μmol/L).\u003c/p\u003e\n\u003cp\u003eData are presented as individual data points with central lines representing the median (or mean). P-values were derived from statistical comparisons between the \"No\" and \"Yes\" jaundice groups for each parameter.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8199768/v1/9575b17ad380ec63f64396a9.png"},{"id":97656851,"identity":"a349caf8-66a7-4bc9-bca6-a4f1057b07e7","added_by":"auto","created_at":"2025-12-08 07:13:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":153598,"visible":true,"origin":"","legend":"\u003cp\u003eRed cell distribution width (RDW) and jaundice were used to group individuals for survival analysis.\u003c/p\u003e\n\u003cp\u003ePatients with pancreatic cancer in the non-jaundice with normal RDW group; Non-jaundice + High RDW: Patients with pancreatic cancer who have both high RDW and non-jaundice; Jaundice + Normal RDW: Patients with pancreatic cancer who have both normal RDW and jaundice; Jaundice + High RDW: Patients with pancreatic cancer who have both jaundice and high RDW.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8199768/v1/e96d361ceb568a3077f0ae8e.png"},{"id":97674771,"identity":"0099203d-5c21-44da-9b73-5b9422b949e0","added_by":"auto","created_at":"2025-12-08 09:44:13","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":50006,"visible":true,"origin":"","legend":"\u003cp\u003eStrong correlation between total bilirubin and red cell distribution width (RDW) and diagnostic performance of bilirubin for predicting elevated RDW.\u003c/p\u003e\n\u003cp\u003e(A) Correlation between total bilirubin (TBil) and RDW-CV with proposed clinical thresholds (TBIL = 17.1 µmol/L, RDW-CV = 15.4%). Pearson correlation coefficient r = 0.764, P = 3.92e-15.\u003c/p\u003e\n\u003cp\u003e(B) Correlation between total bilirubin and RDW-SD with proposed clinical thresholds (TBIL = 17.1 µmol/L, RDW-SD = 46 fL). Pearson r = 0.735, P = 1.27e-13.\u003c/p\u003e\n\u003cp\u003e(C) Receiver operating characteristic (ROC) curve analysis evaluating the diagnostic ability of total bilirubin to predict an elevated RDW-CV (≥15.4%). The area under the curve (AUC) is 0.926 (95% CI: 0.858-0.993).\u003c/p\u003e\n\u003cp\u003e(D) ROC curve analysis evaluating the diagnostic ability of total bilirubin to predict an elevated RDW-SD (≥46 fL). The AUC is 0.923 (95% CI: 0.859-0.986).\u003c/p\u003e\n\u003cp\u003eAUC, area under the curve; CI, confidence interval.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8199768/v1/f1827581db2ee35a184ed5e1.png"},{"id":98431450,"identity":"39d31618-617b-4d0e-8f99-53eadf06ed58","added_by":"auto","created_at":"2025-12-17 16:47:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1887457,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8199768/v1/423e44df-ec23-466b-9d6e-560db8421b06.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Impact of Jaundice-Induced Elevated Red Blood Cell Distribution Width on Survival Outcomes in Pancreatic Cancer Patients: A Retrospective Cohort Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePancreatic cancer is one of the most lethal malignancies worldwide, with a 5-year survival rate of less than 15%[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Driven by its rising incidence, it currently ranks second among causes of cancer-related death, surpassed only by lung cancer[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The extremely high mortality is largely attributable to difficulties in early detection and the limited availability of effective therapies. Clinically, obstructive jaundice is the predominant presenting symptom in approximately 90% of patients with pancreatic head cancer and develops in roughly 70% of all patients with pancreatic cancer as the tumor progresses. Patients with jaundice are consistently observed to have markedly shorter survival. Consequently, effective management of jaundice and accurate assessment of its prognostic implications have become critical for improving patient outcomes.\u003c/p\u003e\u003cp\u003eRed cell distribution width (RDW) is a routine hematological index that reflects heterogeneity in red blood cell volume and has traditionally been used to classify different types of anemia[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Recent evidence indicates that elevated RDW is closely associated with disturbances in nutritional metabolism, systemic inflammation, and poor prognosis across a range of diseases and cancers, and has been regarded as an \u0026ldquo;integrative\u0026rdquo; indicator of systemic pathological derangements[\u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. For instance, an RDW cutoff of 14.8% has been reported to distinguish benign from malignant causes of biliary obstruction, suggesting that increased RDW may serve as an auxiliary marker for etiological differentiation[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In hepatobiliary diseases, jaundice can drive abnormal increases in RDW by disrupting erythropoietic homeostasis through mechanisms such as intestinal microbiota imbalance and cholestatic liver injury[\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These findings suggest that RDW may represent a key link between jaundice and clinical prognosis.\u003c/p\u003e\u003cp\u003eIn pancreatic cancer, the hemoglobin-to-RDW ratio has been associated with survival outcomes, with lower ratios predicting worse survival[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, the underlying mechanisms remain unclear. It is unknown whether jaundice-induced elevations in RDW merely reflect overall disease severity or actively contribute to adverse prognosis.\u003c/p\u003e\u003cp\u003eBased on these considerations, we propose the following central hypothesis: in pancreatic cancer, jaundice triggers systemic inflammatory responses, gut microbiota dysbiosis, and cholestatic liver injury, ultimately leading to disordered erythropoiesis and increased RDW. Elevated RDW, in turn, impairs antitumor immunity and exacerbates nutritional and metabolic disturbances (hypoalbuminemia), thereby creating a vicious cycle that accelerates disease progression and shortens survival.\u003c/p\u003e\u003cp\u003eTo test this hypothesis, we constructed a retrospective cohort that included patients with jaundice of diverse etiologies. First, using a systematic stratified-analytic approach, we delineated the spectrum of associations between jaundice and RDW at a pan-disease level. We then focused specifically on pancreatic cancer to quantify the independent impact of the \u0026ldquo;jaundice\u0026ndash;RDW\u0026rdquo; interaction phenotype on survival prognosis and to evaluate its value for risk stratification.\u003c/p\u003e\u003cp\u003eThe significance of this study lies in its establishment of a comprehensive evidence chain linking the clinical phenotype (jaundice), a readily obtainable biomarker (RDW), and prognostic outcomes across multiple disease categories. This work not only provides new insights into the prognostic role of jaundice but also offers clinicians a scientifically grounded and clinically feasible risk assessment tool. Ultimately, this research may facilitate more precise prognostic evaluation and individualized treatment planning in pancreatic cancer.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e2.1 Study Design and Population\u003c/h2\u003e\n \u003cp\u003eThis single-center retrospective cohort study included hospitalized patients treated at the First Affiliated Hospital of Dali University between February 2015 and October 2025. Follow-up was completed on October 30, 2025.(Ethics Number:DFY20250901001)\u003c/p\u003e\n \u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e2.1.1 Inclusion Criteria\u003c/h2\u003e\n \u003cp\u003e1. Pancreatic cancer: Diagnosis confirmed by pathological biopsy or by contrast-enhanced CT/MRI combined with tumor markers (CA19-9\u0026thinsp;\u0026ge;\u0026thinsp;39 U/mL), in accordance with the 2025 edition of the Guidelines for Integrated Diagnosis and Treatment of Pancreatic Cancer issued by the Chinese Cancer Association (CACA).\u003c/p\u003e\n \u003cp\u003e2. Jaundice: Patients were classified as having prehepatic (e.g., hemolytic anemia), hepatic (e.g., viral hepatitis), posthepatic non-malignant (e.g., choledocholithiasis), or posthepatic malignant jaundice based on medical history and imaging (abdominal ultrasound, CT). Jaundice was confirmed by liver function tests showing total bilirubin (TBil)\u0026thinsp;\u0026gt;\u0026thinsp;17.1 \u0026micro;mol/L.\u003c/p\u003e\n \u003cp\u003e3. Complete clinical data: Availability of tumor markers (CA19-9, CEA, CA125), liver function tests (TBil, albumin), baseline complete blood counts (RDW-CV, RDW-SD), and follow-up survival information.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e2.1.2 Exclusion Criteria\u003c/h2\u003e\n \u003cp\u003e1. Mixed etiologies: Jaundice attributable to two or more distinct causes (e.g., concomitant biliary obstruction and chronic liver disease), or cases in which the cause of jaundice was unclear.\u003c/p\u003e\n \u003cp\u003e2. Hematologic diseases affecting RDW: Acute leukemia, myelodysplastic syndromes, and other primary hematologic disorders known to cause marked abnormalities in RDW.\u003c/p\u003e\n \u003cp\u003e3. Recent medical interventions: Ongoing chemotherapy or radiotherapy (cancer patients were analyzed separately); use of erythropoietin within 1 month before admission; blood transfusion within 3 months before admission.\u003c/p\u003e\n \u003cp\u003e4. Incomplete data: Absence of TBil or baseline RDW measurements, or missing key clinical information required for etiological classification.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e2.1.3 Grouping Strategy\u003c/h2\u003e\n \u003cdiv id=\"Sec7\"\u003e\n \u003ch2\u003e2.1.3.1 Overall Cohort Stratification\u003c/h2\u003e\n \u003cp\u003eThe entire cohort was categorized into five etiological groups:\u003c/p\u003e\n \u003cp\u003e1. Preh\u003c/p\u003e\n \u003cp\u003eepatic disease group: Patients with hemolytic disorders (e.g., autoimmune hemolytic anemia, paroxysmal nocturnal hemoglobinuria) as the primary diagnosis.\u003c/p\u003e\n \u003cp\u003e2. Hepatic disease group: Patients with primary hepatic injury (e.g., cirrhosis, drug-induced liver injury, viral hepatitis).\u003c/p\u003e\n \u003cp\u003e3. Posthepatic non-malignant group: Patients with benign biliary obstruction (e.g., gallstones).\u003c/p\u003e\n \u003cp\u003e4. Other posthepatic cancer group: Patients with malignant biliary obstruction not caused by pancreatic cancer (e.g., cholangiocarcinoma, ampullary carcinoma).\u003c/p\u003e\n \u003cp\u003e5. Pancre\u003c/p\u003e\n \u003cp\u003eatic cancer group: Patients with pathologically confirmed pancreatic cancer or those diagnosed by contrast-enhanced CT/MRI plus elevated tumor markers (CA19-9\u0026thinsp;\u0026ge;\u0026thinsp;39 U/mL).\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003e2.1.3.2. Subgroup Stratified Analysis\u003c/h2\u003e\n \u003cp\u003eTo investigate the relationship between jaundice and RDW, patients in each etiological category were further stratified as follows:\u003c/p\u003e\n \u003cp\u003eJaundice Status: Total bilirubin (TBil)\u0026thinsp;\u0026gt;\u0026thinsp;17.1\u0026micro;mol/L is used to define the jaundice subgroup.\u003c/p\u003e\n \u003cp\u003eRDW Status: The high RDW category was characterized as patients with RDW-CV\u0026thinsp;\u0026gt;\u0026thinsp;15.4% or RDW-SD\u0026thinsp;\u0026gt;\u0026thinsp;46 fL.\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec9\"\u003e\n \u003ch2\u003e2.1.3.3. Interaction Stratification in the Pancreatic Cancer Cohort\u003c/h2\u003e\n \u003cp\u003eTo explore the prognostic effect of the interaction between jaundice and RDW in pancreatic cancer, patients in the pancreatic cancer group were divided into four subgroups:\u003c/p\u003e\n \u003cp\u003eGroup 1: Non-jaundiced\u0026thinsp;+\u0026thinsp;Normal RDW (TBil\u0026thinsp;\u0026le;\u0026thinsp;17.1 \u0026micro;mol/L and RDW-CV\u0026thinsp;\u0026le;\u0026thinsp;15.4%, RDW-SD\u0026thinsp;\u0026le;\u0026thinsp;46 fL)\u003c/p\u003e\n \u003cp\u003eGroup 2: Non-jaundiced\u0026thinsp;+\u0026thinsp;High RDW (TBil\u0026thinsp;\u0026le;\u0026thinsp;17.1 \u0026micro;mol/L and either RDW-CV\u0026thinsp;\u0026gt;\u0026thinsp;15.4% or RDW-SD\u0026thinsp;\u0026gt;\u0026thinsp;46 fL)\u003c/p\u003e\n \u003cp\u003eGroup 3: Jaundice\u0026thinsp;+\u0026thinsp;Normal RDW (TBil\u0026thinsp;\u0026gt;\u0026thinsp;17.1 \u0026micro;mol/L and RDW-CV\u0026thinsp;\u0026le;\u0026thinsp;15.4%, RDW-SD\u0026thinsp;\u0026le;\u0026thinsp;46 fL)\u003c/p\u003e\n \u003cp\u003eGroup4: Jaundice\u0026thinsp;+\u0026thinsp;High RDW (TBil\u0026thinsp;\u0026gt;\u0026thinsp;17.1 \u0026micro;mol/L and (RDW-CV\u0026thinsp;\u0026gt;\u0026thinsp;15.4% or RDW-SD\u0026thinsp;\u0026gt;\u0026thinsp;46 fL))\u003c/p\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\"\u003e\n \u003ch2\u003e2.2 Data Collection\u003c/h2\u003e\n \u003cp\u003eData were extracted from the hospital\u0026rsquo;s electronic medical record (EMR) system and included:\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAge, sex, and comorbidities (hypertension, diabetes).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eLaboratory indicators\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eFirst complete blood count after admission (RDW-CV, RDW-SD, mean corpuscular volume [MCV], C-reactive protein [CRP], neutrophil-to-lymphocyte ratio [NLR], hemoglobin), liver function tests (TBil, albumin, globulin), and tumor markers (CA19-9, CEA, CA125).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eImaging and pathology\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eTumor staging for pancreatic cancer (AJCC 8th edition TNM staging) and etiological diagnosis of jaundice (e.g., choledocholithiasis, cholangiocarcinoma).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSurvival outcomes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOverall survival (OS), defined as the time from hospital admission or disease diagnosis to death from any cause or last follow-up.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\"\u003e\n \u003ch2\u003e2.3 Statistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were performed using R software (version 4.5.1). A two-sided P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDescriptive statistics\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCategorical variables were summarized as frequency (percentage, n/%), and continuous variables as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBetween-group comparisons\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eOne-way analysis of variance (ANOVA) was used for continuous variables that met assumptions of normality and homogeneity of variance; Welch\u0026rsquo;s t-test was applied when variances were unequal. Pearson\u0026rsquo;s chi-square test was used for categorical variables.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCorrelation analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003ePearson correlation coefficients were calculated to assess linear relationships between RDW-CV, RDW-SD, TBil, albumin, and hemoglobin.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSurvival analysis\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eKaplan\u0026ndash;Meier curves were constructed to compare survival among the four \u0026ldquo;jaundice\u0026ndash;RDW\u0026rdquo; groups, and differences were evaluated using the log-rank test. Cox proportional hazards regression models (univariate and multivariate) were used to estimate the independent effects of RDW and jaundice on survival in pancreatic cancer, adjusting for age, sex, tumor stage, and albumin.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePredictive performance\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eROC curves were generated to evaluate the ability of TBil to predict high RDW in pancreatic cancer, and the area under the curve (AUC), sensitivity, and specificity were calculated. An AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.9 was considered to indicate excellent predictive performance.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Baseline Characteristics of the Overall Cohort\u003c/h2\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes baseline characteristics for the 565 enrolled patients. Age, sex distribution, RDW indices, and TBil levels differed significantly across etiological groups (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\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\u003eBaseline Patient Characteristics by Etiology and Cancer Status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePre-hepatic\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;81\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHepatic\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;180\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePost-hepatic Non-cancer\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;194\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eOther Post-hepatic cancer\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePancreatic Cancer\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;73\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eP-value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e58.0\u0026thinsp;\u0026plusmn;\u0026thinsp;16.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;19.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e61.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e64.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e63.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e291.0 (51.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22.0 (27.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e118.0 (65.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e90.0\u003c/p\u003e\u003cp\u003e(46.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e18.0\u003c/p\u003e\u003cp\u003e(48.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e43.0\u003c/p\u003e\u003cp\u003e(58.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e274.0 (48.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59.0 (72.8%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e62.0\u003c/p\u003e\u003cp\u003e(34.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e104.0\u003c/p\u003e\u003cp\u003e(53.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e19.0\u003c/p\u003e\u003cp\u003e(51.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.0\u003c/p\u003e\u003cp\u003e(41.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW-CV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;3.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e20.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW-SD (fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e52.6\u0026thinsp;\u0026plusmn;\u0026thinsp;15.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e74.0\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e53.4\u0026thinsp;\u0026plusmn;\u0026thinsp;10.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTBil (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e72.9\u0026thinsp;\u0026plusmn;\u0026thinsp;89.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e54.6\u0026thinsp;\u0026plusmn;\u0026thinsp;41.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e46.0\u0026thinsp;\u0026plusmn;\u0026thinsp;48.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e62.6\u0026thinsp;\u0026plusmn;\u0026thinsp;63.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e187.9\u0026thinsp;\u0026plusmn;\u0026thinsp;159.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e128.6\u0026thinsp;\u0026plusmn;\u0026thinsp;136.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eMean\u0026plusmn;SD or n(%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e\u003csup\u003eb\u003c/sup\u003eOne-way analysis of means; Pearson\u0026rsquo;s Chi-squared test\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe prehepatic group had the highest RDW values (RDW-CV: 20.0% \u0026plusmn; 5.4%; RDW-SD: 74.0\u0026thinsp;\u0026plusmn;\u0026thinsp;24.3 fL), whereas the posthepatic non-malignant group had the lowest (RDW-CV: 13.8% \u0026plusmn; 1.8%; RDW-SD: 45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3 fL). All posthepatic malignant groups (including pancreatic cancer and other posthepatic malignancies) exhibited significantly higher RDW levels than the posthepatic benign group, whereas RDW levels did not differ significantly between the malignant categories. Total bilirubin levels were significantly lower in all benign etiology groups (prehepatic, hepatic, and posthepatic non-malignant) than in the other posthepatic cancer group (187.9\u0026thinsp;\u0026plusmn;\u0026thinsp;159.3 \u0026micro;mol/L) and the pancreatic cancer group (128.6\u0026thinsp;\u0026plusmn;\u0026thinsp;136.5 \u0026micro;mol/L).\u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates RDW differences between jaundiced and non-jaundiced patients across etiological groups. In the posthepatic malignant group, RDW differed significantly between patients with and without jaundice (Group A, P\u0026thinsp;=\u0026thinsp;0.0015). Statistically significant differences were also observed in the hepatic etiology group (P\u0026thinsp;=\u0026thinsp;0.0237), whereas no significant differences were found in the prehepatic and posthepatic benign groups. In Group B, significant differences in RDW were observed between hepatic and posthepatic malignant etiologies (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and P\u0026thinsp;=\u0026thinsp;0.0051, respectively), and within-group differences reached significance in the prehepatic group (P\u0026thinsp;=\u0026thinsp;0.0155). The posthepatic benign group was the only group without notable RDW differences.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn summary, markedly elevated RDW and hyperbilirubinemia were closely associated with malignant obstructive jaundice (particularly pancreatic cancer and other posthepatic malignancies), suggesting that RDW may play an important role in the pathophysiology of these conditions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Comparison of RDW and Bilirubin Levels in Post-Hepatic Lesions\u003c/h2\u003e\u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, RDW-CV (15.2% \u0026plusmn; 2.7%), RDW-SD (49.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1 fL), and TBil (148.6\u0026thinsp;\u0026plusmn;\u0026thinsp;146.6 \u0026micro;mol/L) were significantly higher in the posthepatic cancer group (n\u0026thinsp;=\u0026thinsp;110) than in the posthepatic non-malignant group (n\u0026thinsp;=\u0026thinsp;194) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Age and sex distribution did not differ significantly between these two groups.\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\u003eComparison Between Post-hepatic Non-cancer and Post-hepatic Cancers\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\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-cancer\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;194\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCancer\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;110\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge(years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e62.1\u0026thinsp;\u0026plusmn;\u0026thinsp;14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e61.2\u0026thinsp;\u0026plusmn;\u0026thinsp;16.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.080\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.162\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e151.0 (49.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90.0 (46.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e61.0 (55.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153.0 (50.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e104.0 (53.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.0 (44.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW-CV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.8\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW-SD (fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e46.5\u0026thinsp;\u0026plusmn;\u0026thinsp;6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e45.0\u0026thinsp;\u0026plusmn;\u0026thinsp;5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e49.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Bilirubin (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93.7\u0026thinsp;\u0026plusmn;\u0026thinsp;109.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e62.6\u0026thinsp;\u0026plusmn;\u0026thinsp;63.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e148.6\u0026thinsp;\u0026plusmn;\u0026thinsp;146.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eMean\u0026plusmn;SD;n(%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eb\u003c/sup\u003eWelch Two-Sample t-test; Pearson's Chi-squared test\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison Between Other Post-hepatic Cancers and Pancreatic Cancer\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\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOverall\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOther Post-hepatic Cancers\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePancreatic Cancer\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;73\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e63.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e64.0\u0026thinsp;\u0026plusmn;\u0026thinsp;7.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e63.7\u0026thinsp;\u0026plusmn;\u0026thinsp;9.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\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\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.413\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61.0 (55.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.0 (48.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.0 (58.9%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.0 (44.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e19.0 (51.4%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e30.0 (41.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW-CV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.2\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15.6\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.300\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW-SD (fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49.1\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e49.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.418\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Bilirubin (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e148.6\u0026thinsp;\u0026plusmn;\u0026thinsp;146.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e187.9\u0026thinsp;\u0026plusmn;\u0026thinsp;159.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128.6\u0026thinsp;\u0026plusmn;\u0026thinsp;136.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.058\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eMean \u0026plusmn; SD; n (%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eb\u003c/sup\u003eWelch Two-Sample t-test; Pearson's Chi-squared test\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWithin the posthepatic cancer group, RDW indices and TBil levels did not differ significantly between pancreatic cancer (n\u0026thinsp;=\u0026thinsp;73) and other posthepatic malignancies (n\u0026thinsp;=\u0026thinsp;37) (all P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Correlation between RDW and Clinical Indicators in the Pancreatic Cancer Subgroup\u003c/h2\u003e\u003cp\u003eIn patients with pancreatic cancer, RDW was strongly positively correlated with TBil (RDW-CV: r\u0026thinsp;=\u0026thinsp;0.764, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; RDW-SD: r\u0026thinsp;=\u0026thinsp;0.735, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001). RDW showed weak negative correlations with hemoglobin and albumin (both P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and no significant correlations with tumor markers such as CEA, CA19-9, or CA125, or with classical inflammatory indices (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eCorrelation Analysis Between RDW and Clinical Parameters in Pancreatic Cancer Pearson Correlation Coefficients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eClinical Parameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eRDW-CV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eRDW-SD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eClinical Significance\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003er value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP value\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003er value\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\u003eTBil (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.764\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.735\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStrong correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA19-9 (U/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.469\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA (ng/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.172\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.283\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA125 (U/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.215\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNeutrophil-Lymphocyte Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.476\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatelet-Lymphocyte Ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.214\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.069\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eC-reactive Protein (mg/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.144\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.328\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.177\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.393\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWeak correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-0.397\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWeak correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMean Corpuscular Volume (fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-0.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.798\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.349\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo correlation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e Correlation strength: |r| \u0026ge; 0.7 (Strong), 0.5 \u0026le; |r| \u0026lt; 0.7 (Moderate), 0.3 \u0026le;|r| \u0026lt; 0.5 (Weak), |r| \u0026lt; 0.3 (No correlation)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Comparison of RDW and Clinical Parameters Across Jaundice Levels in pancreatic cancer\u003c/h2\u003e\u003cp\u003eIn subgroup analyses based on TBil (threshold 17.1 \u0026micro;mol/L), patients with jaundice (n\u0026thinsp;=\u0026thinsp;44) had significantly higher RDW-CV, RDW-SD, and TBil levels than those without jaundice (n\u0026thinsp;=\u0026thinsp;28) (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while albumin levels were significantly lower in the jaundiced group (P\u0026thinsp;=\u0026thinsp;0.006). Hemoglobin and CA19-9 did not differ significantly between the two groups (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eComparison of RDW and Clinical Parameters by TBil Level (Threshold: 17.1 \u0026micro;mol/L)\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\u003eOverall\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;72\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTBil\u0026thinsp;\u0026lt;\u0026thinsp;17.1\u0026micro;mol/L\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;28\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTBil\u0026thinsp;\u0026ge;\u0026thinsp;17.1\u0026micro;mol/L\u003c/p\u003e\u003cp\u003eN\u0026thinsp;=\u0026thinsp;44\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP-value\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW-CV (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13.4\u0026thinsp;\u0026plusmn;\u0026thinsp;1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e16.0\u0026thinsp;\u0026plusmn;\u0026thinsp;2.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW-SD (fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e48.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e42.7\u0026thinsp;\u0026plusmn;\u0026thinsp;4.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e52.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Bilirubin (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e125.6\u0026thinsp;\u0026plusmn;\u0026thinsp;134.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e198.5\u0026thinsp;\u0026plusmn;\u0026thinsp;126.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA19-9 (U/mL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e345.2\u0026thinsp;\u0026plusmn;\u0026thinsp;393.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e259.6\u0026thinsp;\u0026plusmn;\u0026thinsp;370.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e399.7\u0026thinsp;\u0026plusmn;\u0026thinsp;402.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAlbumin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37.9\u0026thinsp;\u0026plusmn;\u0026thinsp;5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40.0\u0026thinsp;\u0026plusmn;\u0026thinsp;3.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.6\u0026thinsp;\u0026plusmn;\u0026thinsp;6.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.006\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin (g/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e130.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e133.2\u0026thinsp;\u0026plusmn;\u0026thinsp;20.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e128.2\u0026thinsp;\u0026plusmn;\u0026thinsp;19.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eMean\u0026plusmn;SD\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003csup\u003eb\u003c/sup\u003eWelch Two-Sample t-test\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Survival Analysis in the Pancreatic Cancer Subgroup Stratified by \"Jaundice-RDW\"\u003c/h2\u003e\u003cp\u003eQuadrant survival analysis based on jaundice and RDW status revealed that only the \u0026ldquo;jaundice with high RDW\u0026rdquo; group (n\u0026thinsp;=\u0026thinsp;35) had a significantly higher risk of death compared with the reference group (\u0026ldquo;no jaundice and normal RDW\u0026rdquo;, n\u0026thinsp;=\u0026thinsp;24, median survival 18.0 months) (HR\u0026thinsp;=\u0026thinsp;2.65, 95% CI: 1.22\u0026ndash;5.75, P\u0026thinsp;=\u0026thinsp;0.014; log-rank P\u0026thinsp;=\u0026thinsp;0.028) and exhibited the shortest median survival (11.0 months). Survival did not differ significantly between the reference group and the other two groups (jaundice\u0026thinsp;+\u0026thinsp;normal RDW, non-jaundice\u0026thinsp;+\u0026thinsp;high RDW) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e,Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSurvival Analysis by Four-Group Classification Based on Jaundice and RDW Status\u003csup\u003ea\u003c/sup\u003e Group 1 (Non-jaundice\u0026thinsp;+\u0026thinsp;Normal RDW) as Reference\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian Survival (months)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e95% CI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHR vs Group 1 (95%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\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\u003eGroup 1: No jaundice\u0026thinsp;+\u0026thinsp;Normal RDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(15.3-NA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1 (Reference)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 2: Non-jaundice\u0026thinsp;+\u0026thinsp;High RDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNR\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.44(0.90-79.04)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.062\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 3: Jaundice\u0026thinsp;+\u0026thinsp;Normal RDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e16.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(9.4\u0026ndash;NA)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.43 (0.50\u0026ndash;4.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.504\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup 4: Jaundice\u0026thinsp;+\u0026thinsp;High RDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e(7.8\u0026ndash;17.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.65 (1.22\u0026ndash;5.75)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.014\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eJaundice defined as total bilirubin (TBil)\u0026thinsp;\u0026gt;\u0026thinsp;17.1 \u0026micro;mol/L; High red cell distribution width (RDW) defined as RDW-CV\u0026thinsp;\u0026gt;\u0026thinsp;15.4% or RDW-SD\u0026thinsp;\u0026gt;\u0026thinsp;46 fL\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003eNR: Not Reached (median survival not reached during follow-up)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.6 Independent Prognostic Factors in Pancreatic Cancer (Cox Regression)\u003c/h2\u003e\u003cp\u003eCox regression identified RDW-CV (HR\u0026thinsp;=\u0026thinsp;1.169, P\u0026thinsp;=\u0026thinsp;0.012) and the albumin-to-globulin (A/G) ratio (HR\u0026thinsp;=\u0026thinsp;3.332, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) as significant prognostic risk factors for pancreatic cancer. In multivariate analysis, RDW-CV (HR\u0026thinsp;=\u0026thinsp;1.728, P\u0026thinsp;=\u0026thinsp;0.008) and A/G ratio (HR\u0026thinsp;=\u0026thinsp;3.377, P\u0026thinsp;=\u0026thinsp;0.007) remained independent prognostic indicators (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eUnivariate and multivariate Cox regression analysis of prognostic factors in pancreatic cancer patients (n\u0026thinsp;=\u0026thinsp;73)\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\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eClinical parameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eUnivariate analysis\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eMultivariate analysis\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHR(95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHR (95% CI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eP\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.662 (0.341\u0026ndash;1.286)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge (years)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.504 (0.784\u0026ndash;2.885)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.220\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor site\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.524 (0.248\u0026ndash;1.106)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM staging\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.811 (0.368\u0026ndash;1.785)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMCV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.182 (0.656\u0026ndash;7.257)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA19-9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.736 (0.377\u0026ndash;1.436)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.368\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCA125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.067 (0.563\u0026ndash;2.022)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCEA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.765 (0.269\u0026ndash;2.176)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.616\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGlobulin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.483 (0.065\u0026ndash;3.564)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.475\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHemoglobin\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.269 (0.638\u0026ndash;2.521)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.497\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCRP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.12 (0.544\u0026ndash;2.308)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.758\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNLR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.681 (0.891\u0026ndash;3.17)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal Bilirubin (\u0026micro;mol/L)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.002 (1\u0026ndash;1.004)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.060\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.999 (0.995\u0026ndash;1.003)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.578\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW-CV(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.159 (1.033\u0026ndash;1.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.729 (1.151\u0026ndash;2.597)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRDW-SD (fL)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.031 (0.994\u0026ndash;1.068)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.858 (0.765\u0026ndash;0.962)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA/G ratio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3.332 (1.734\u0026ndash;6.403)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;0.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.377 (1.396\u0026ndash;8.167)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.007\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: HR: Hazard Ratio; CI: Confidence Interval. Reference groups: Gender (Female), Tumor location (Head), TNM stage (I-II), all biomarkers (Normal).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.7 Confounding Effects of Tumor Staging in Pancreatic Cancer\u003c/h2\u003e\u003cp\u003eThe distribution of TNM stages (I\u0026ndash;IV) did not differ significantly among the four \u0026ldquo;jaundice\u0026ndash;RDW\u0026rdquo; groups (χ\u0026sup2; = 11.31, P\u0026thinsp;=\u0026thinsp;0.255), indicating that tumor stage was unlikely to be a major confounder in the observed survival differences (Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eDistribution of Tumor Stages by Jaundice and RDW Groups\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGroup\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal n\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStage I\u003c/p\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStage II\u003c/p\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eStage III\u003c/p\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eStage IV\u003c/p\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003cp\u003en(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOverall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e73\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15(20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e31(42.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8(11%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15 (20.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4 (5.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-jaundice\u0026thinsp;+\u0026thinsp;Normal RDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8 (33.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (12.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7 (29.2%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3 (12.5%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNon-jaundice\u0026thinsp;+\u0026thinsp;High RDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1 (25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2(50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1(25%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0(0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJaundice\u0026thinsp;+\u0026thinsp;Normal RDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (30%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5 (50%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1(10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1 (10%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0(0%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJaundice\u0026thinsp;+\u0026thinsp;High RDW\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e9 (25.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17 (48.6%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2 (5.7%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6 (17.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1 (2.9%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eChi-square test:χ\u0026sup2;= 11.31, df\u0026thinsp;=\u0026thinsp;9, p\u0026thinsp;=\u0026thinsp;0.255\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e\u003cp\u003eRDW: Red cell distribution width; Normal RDW: RDW-CV\u0026thinsp;\u0026le;\u0026thinsp;15.4% or RDW-SD\u0026thinsp;\u0026le;\u0026thinsp;46; High RDW: RDW-CV\u0026thinsp;\u0026gt;\u0026thinsp;15.4% or RDW-SD\u0026thinsp;\u0026gt;\u0026thinsp;46\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"7\"\u003eJaundice: Total bilirubin\u0026thinsp;\u0026gt;\u0026thinsp;17.1 \u0026micro;mol/L\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.8 Total bilirubin demonstrated outstanding predictive efficacy for high RDW status in Pancreatic Cancer\u003c/h2\u003e\u003cp\u003eTBil showed excellent predictive performance for high RDW status, with AUCs of 0.926 (95% CI: 0.858\u0026ndash;0.993) for high RDW-CV and 0.923 (95% CI: 0.859\u0026ndash;0.986) for high RDW-SD, and a sensitivity of 0.92 in both analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eBy systematically analyzing clinical data from 565 patients with jaundice of different etiologies, this study constructed a coherent logical framework from a pan-disease phenomenon to a pancreatic cancer\u0026ndash;specific prognostic pattern. We demonstrated that distinct mechanisms of jaundice affect RDW in different etiological contexts and that jaundice-related RDW elevation has important prognostic implications in pancreatic cancer.\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.1 Pan-Disease Spectrum of RDW Associations Across Etiologies\u003c/h2\u003e\u003cp\u003eComparison of baseline RDW across etiologies revealed a characteristic gradient (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Prehepatic diseases showed the highest RDW-CV (20.0%), followed by posthepatic malignancies (RDW-CV: 15.2%) and hepatic diseases (RDW-CV: 15.7%), whereas the posthepatic benign group had the lowest RDW-CV (13.8%). This pattern suggests that the impact of different diseases on erythrocyte dynamics is heterogeneous.\u003c/p\u003e\u003cp\u003eWe further evaluated whether the effect of jaundice on RDW differed across etiologies by comparing RDW levels between jaundiced and non-jaundiced patients within each group. The jaundiced subgroups had significantly higher RDW than the non-jaundiced subgroups in hepatic, posthepatic benign, and posthepatic malignant conditions (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the largest differences observed in the hepatic (P\u0026thinsp;=\u0026thinsp;6 \u0026times; 10⁻⁵) and malignant posthepatic groups (P\u0026thinsp;=\u0026thinsp;0.0015). In contrast, RDW did not differ significantly between jaundiced and non-jaundiced patients in the prehepatic or posthepatic benign groups (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). These findings indicate that the effect of jaundice on RDW elevation depends on specific pathophysiological pathways and is not uniformly present across all etiologies.\u003c/p\u003e\u003cp\u003eOn this basis, the principal mechanisms underlying RDW elevation in different etiologies can be summarized as follows:\u003c/p\u003e\u003cp\u003ePrehepatic diseases: Predominant pattern of direct red blood cell destruction\u003c/p\u003e\u003cp\u003eIn prehepatic disorders, extensive hemolysis is the primary mechanism, leading to both increased RDW and elevated bilirubin. Prior studies have shown that RDW has early diagnostic value in hemolytic diseases, facilitating timely recognition of jaundice and hyperbilirubinemia[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This observation explains why RDW is markedly elevated in prehepatic disorders compared with other etiologies (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), yet differences between jaundiced and non-jaundiced subgroups within this category are not statistically significant. By the time bilirubin rises sufficiently to manifest clinically as jaundice, hemolysis has typically reached an advanced stage. The bone marrow responds by rapidly releasing large numbers of immature erythrocytes to compensate for peripheral loss, creating a highly heterogeneous population of red blood cells and further increasing RDW. Thus, elevated RDW in prehepatic jaundice reflects classical disruption of the red blood cell life cycle.\u003c/p\u003e\u003cp\u003eHepatic disorders: Combined inflammatory and metabolic dysregulation\u003c/p\u003e\u003cp\u003eIn hepatic diseases, multiple factors contribute to RDW elevation. Hepatocyte injury induces inflammatory responses that interfere with erythropoietin (EPO) metabolism[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Portal hypertension and hypersplenism further shorten the lifespan of red blood cells[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In this setting, hyperbilirubinemia acts as an important synergistic factor. Elevated bilirubin, particularly unconjugated bilirubin, exerts cytotoxic effects and exacerbates oxidative damage to red blood cell membranes, thereby reducing their survival[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Together with underlying hepatic pathology, these processes collectively drive RDW elevation.\u003c/p\u003e\u003cp\u003ePosthepatic malignant obstruction: Prominent inflammation\u0026ndash;malnutrition axis\u003c/p\u003e\u003cp\u003eAlthough RDW has been linked to various malignancies, the relationship between pancreatic cancer and RDW has not been extensively characterized. Our study focused on malignant obstructive jaundice caused by pancreatic cancer and demonstrated a particularly strong association between RDW and TBil. In pancreatic cancer, TBil showed excellent predictive performance for high RDW status (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.92), and RDW was strongly positively correlated with TBil (r\u0026thinsp;=\u0026thinsp;0.764, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This suggests that the magnitude of RDW abnormality in malignant obstruction is directly reflected in bilirubin levels. Additionally, RDW was negatively correlated with hemoglobin and albumin, supporting a vicious cycle of \u0026ldquo;biliary obstruction \u0026rarr; bile salt toxicity \u0026rarr; disruption of the intestinal barrier/endotoxemia \u0026rarr; systemic inflammatory activation \u0026rarr; malabsorption \u0026rarr; impaired erythropoiesis.\u0026rdquo; In this framework, jaundice is not merely a clinical manifestation but a fundamental pathophysiological driver of high RDW.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.2 Clinical Translation: Prognostic Value of the \u0026ldquo;Jaundice\u0026ndash;RDW\u0026rdquo; Interaction Model in Pancreatic Cancer\u003c/h2\u003e\u003cp\u003eThe survival analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) revealed a clinically meaningful hierarchical pattern. The \u0026ldquo;jaundice with high RDW\u0026rdquo; group had a markedly poor prognosis, with a median survival of only 11.0 months and a 2.65-fold increased risk of death compared with the \u0026ldquo;no jaundice and normal RDW\u0026rdquo; group (HR\u0026thinsp;=\u0026thinsp;2.65, 95% CI: 1.22\u0026ndash;5.75, P\u0026thinsp;=\u0026thinsp;0.014). After adjustment for confounders, multivariate Cox regression further confirmed RDW-CV as an independent prognostic factor (HR\u0026thinsp;=\u0026thinsp;1.728, P\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e\u003cp\u003eImportantly, the prognostic significance of the jaundice\u0026ndash;RDW interaction was not explained by differences in anatomical tumor burden. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e, the distribution of TNM stages did not differ significantly among the four \u0026ldquo;jaundice\u0026ndash;RDW\u0026rdquo; groups (P\u0026thinsp;=\u0026thinsp;0.255), whereas survival did. This finding has two major implications. First, it effectively rules out tumor stage as the primary driver of intergroup survival differences, lending strong support to the independent prognostic value of the \u0026ldquo;jaundice\u0026thinsp;+\u0026thinsp;high RDW\u0026rdquo; phenotype. Second, it highlights a clinically relevant reality beyond TNM staging: patients in the \u0026ldquo;jaundice with high RDW\u0026rdquo; group (n\u0026thinsp;=\u0026thinsp;35) were distributed across all stages (Stage I: 25.7%; Stage II: 48.6%; Stage IV: 17.1%), suggesting that once this phenotype develops, patients may enter a common adverse trajectory dominated by systemic pathophysiological disturbance, regardless of anatomical stage.\u003c/p\u003e\u003cp\u003eCollectively, these findings strongly support our central hypothesis that systemic alterations induced by biliary obstruction, as reflected by elevated RDW, may exert a stronger influence on prognosis than tumor growth alone in the course of pancreatic cancer. In this context, host-related factors play a pivotal role. Clinically, patients with the \u0026ldquo;jaundice\u0026thinsp;+\u0026thinsp;high RDW\u0026rdquo; phenotype may benefit from more aggressive management strategies, including early and effective biliary drainage, enhanced nutritional support, and timely intervention targeting systemic inflammation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.3 From Biomarker to Active Effector: Hypothesized Role of Red Blood Cell\u0026ndash;Derived Vesicles\u003c/h2\u003e\u003cp\u003eOur results raise a deeper scientific question that goes beyond the use of RDW as a prognostic marker: do elevated RDW values actively promote tumor progression through specific mechanisms? We put forward a novel hypothesis that red blood cell\u0026ndash;derived extracellular vesicles (REVs) may serve as critical intermediaries linking red blood cell heterogeneity with malignant progression.\u003c/p\u003e\u003cp\u003eSimilar to extracellular vesicles (EVs) originating from other cell types, REVs participate in intercellular communication and contribute to a variety of physiological and pathological processes[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Increasing evidence suggests that REVs released from circulating erythrocytes have diagnostic and therapeutic potential in multiple diseases[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. REVs have been implicated in thrombosis, hemostasis, infection, cancer, and inflammation[\u003cspan additionalcitationids=\"CR21 CR22\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. They can serve as promising nanocarriers for drug delivery, but may also act as therapeutic targets or biomarkers for diagnosis and prognosis[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. These observations indicate that REVs hold substantial potential for advancing precision medicine. In the context of obstructive jaundice, large-scale production of REVs may exacerbate tumor biological behavior through several potential mechanisms: (1) \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eImmune regulation\u003c/span\u003e: REVs may be phagocytosed by tumor-associated macrophages, promoting their polarization toward an immunosuppressive M2 phenotype. (2) \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eReprogramming of iron metabolism\u003c/span\u003e: Iron-driven oxidative stress has been shown to promote tumor growth and metastasis by remodeling the extracellular matrix, suppressing immune responses, and inducing genetic alterations[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. As a major source of extracellular iron, massive release of REVs may induce iron overload in tumor cells, leading to genomic instability and enhanced tumor progression. (3) \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eIntercellular communication\u003c/span\u003e: Through paracrine signaling, specific miRNAs carried by REVs (e.g., miR-451a) may alter the gene expression profiles of pancreatic cancer cells, thereby influencing their invasive and metastatic potential.\u003c/p\u003e\u003cp\u003eThe coexistence of marked survival differences and comparable stage distributions among the \u0026ldquo;jaundice\u0026ndash;RDW\u0026rdquo; groups provides an ideal clinical model for the REV hypothesis. We speculate that REV-mediated systemic effects may represent a distinct malignant driver that is only partially related to anatomical tumor stage. This could explain why patients with the \u0026ldquo;jaundice\u0026thinsp;+\u0026thinsp;high RDW\u0026rdquo; phenotype have similarly poor outcomes across stages: they may share a tumor-promoting microenvironment and systemic milieu shaped by carriers such as REVs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.4 Innovation and Limitations\u003c/h2\u003e\u003cp\u003eThis study has several innovative aspects. First, it systematically compares the mechanisms by which different etiologies of jaundice affect RDW across disease categories. Second, focusing on pancreatic cancer, it demonstrates that the interaction between jaundice and RDW independently influences prognosis regardless of tumor stage and establishes a coherent clinical evidence chain linking \u0026ldquo;obstructive jaundice \u0026rarr; elevated RDW \u0026rarr; poor prognosis.\u0026rdquo; Third, it prospectively proposes REVs as a potential mechanistic bridge connecting red blood cell heterogeneity to tumor progression.\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged. The retrospective design may introduce selection bias. The relatively small sample sizes in the non-jaundiced\u0026thinsp;+\u0026thinsp;high RDW group and the other posthepatic malignancy group may limit statistical power for certain comparisons. Direct measurements of intermediate factors such as REVs and specific inflammatory cytokines were not available. In addition, the impact of treatments such as biliary drainage and chemotherapy on RDW dynamics and prognosis was not fully evaluated.\u003c/p\u003e\u003cp\u003eFuture research should therefore focus on: (1) validating the prognostic value of the \u0026ldquo;jaundice\u0026ndash;RDW\u0026rdquo; stratification system in prospective, multicenter cohorts; (2) isolating and characterizing REVs from plasma of jaundiced patients using animal models and co-culture systems to directly test their biological functions and molecular mechanisms; and (3) dynamically monitoring changes in RDW and REVs during treatment to assess their potential as biomarkers for therapeutic response and recurrence surveillance.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThrough pan-disease comparisons spanning prehepatic (dominant red blood cell destruction), hepatic (combined inflammatory and metabolic disturbances), and posthepatic malignant obstruction (driven by severe inflammation and malnutrition) etiologies, this study systematically elucidates how diverse causes of jaundice affect RDW. Crucially, we show that, beyond tumor stage, the combination of jaundice and elevated RDW independently predicts prognosis in pancreatic cancer. In this context, RDW is elevated from a conventional adjunct index to a core biomarker representing host-related factors, with REVs proposed as a potential mechanistic link between red blood cell heterogeneity and tumor progression. This conceptual shift\u0026mdash;from clinical observation to mechanistic hypothesis\u0026mdash;broadens our understanding of pancreatic cancer pathophysiology and opens new avenues for developing prognostic models that transcend purely morphological staging and for designing treatment strategies that explicitly target host factors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eRDW\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eRed cell distribution width\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCT\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ecomputed tomography\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMRI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emagnetic resonance imaging\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eROC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eReceiver operating characteristic\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTBil\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTotal Bilirubin\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eMCV\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003emean corpuscular volume\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCRP\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eC-reactive protein\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNLR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eneutrophil-to-lymphocyte ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eANOVA\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOne-way analysis of variance\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOverall survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eAUC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eArea under the curve\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ehazard ratio\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eCI\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003econfidence interval\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eREVs\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ered blood cell\u0026ndash;derived extracellular vesicles\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cp\u003e This study was conducted in accordance with the Declaration of Helsinki and was approved by the Medical Ethics Committee of The First Affiliated Hospital of Dali University (Ethics Number: DFY20250901001).\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003cp\u003eNot applicable.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eData accessibility\u003c/h2\u003e\u003cp\u003eThe data and figures generated and analyzed during this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eConflict of interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003e This work is supported by Yunnan Provincial Foundation Joint Program for Local Undergraduate Universities, (202101BA070001-276)\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eH.W. and W.Z. contributed equally to this work. They were jointly responsible for the conception and design of the study, performed data analysis and interpretation, and drafted the initial manuscript. Z.F., H.D., and T.M. contributed substantially to patient data acquisition, curation, and validation. Y.T. provided critical supervision and assisted in project administration and manuscript revision. L.J., as the corresponding author, secured funding and ethical approval, provided overall scientific direction, supervised all stages of the research, and critically revised the manuscript for important intellectual content. All authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis study was funded in part by the Yunnan Provincial Foundation Joint Program for Local Undergraduate Universities (Grant No. 202101BA070001-276).The authors gratefully acknowledge the clinical and administrative staff of the First Affiliated Hospital of Dali University for their invaluable contributions to patient care and data curation. We extend our sincere appreciation to the Department of General Surgery for their collaborative support. We are deeply indebted to the patients and their families whose participation made this study possible.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data and figures generated and analyzed during this study are available from the corresponding authors upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eStoop TF, Javed AA, Oba A, Koerkamp BG, Seufferlein T, Wilmink JW, Besselink MG. Pancreatic cancer. Lancet. 2025;405:1182\u0026ndash;202.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWenzel P, Mogler C, G\u0026ouml;rg\u0026uuml;l\u0026uuml; K, Alg\u0026uuml;l H. Pancreatic Cancer: Current Concepts, Trends, and Future Directions. Turk J Gastroenterol. 2024;36:69\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBessman JD, Gilmer PR Jr., Gardner FH. Improved classification of anemias by MCV and RDW. Am J Clin Pathol. 1983;80:322\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarc\u0026iacute;a-Escobar A, L\u0026aacute;zaro-Garc\u0026iacute;a R, Goicolea-Ruig\u0026oacute;mez J, Gonz\u0026aacute;lez-Casal D, Fontenla-Cerezuela A, Soto N, Gonz\u0026aacute;lez-Panizo J, Datino T, Pizarro G, Moreno R, Cabrera J. Red Blood Cell Distribution Width is a Biomarker of Red Cell Dysfunction Associated with High Systemic Inflammation and a Prognostic Marker in Heart Failure and Cardiovascular Disease: A Potential Predictor of Atrial Fibrillation Recurrence. High Blood Press Cardiovasc Prev. 2024;31:437\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi N, Zhou H, Tang Q. Red Blood Cell Distribution Width: A Novel Predictive Indicator for Cardiovascular and Cerebrovascular Diseases. Dis Markers. 2017;2017:7089493.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCetinkaya E, Senol K, Saylam B, Tez M. Red cell distribution width to platelet ratio: new and promising prognostic marker in acute pancreatitis. World J Gastroenterol. 2014;20:14450\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao W, Shao Y, Wang N, Jiang Z, Yu S, Wang J. Pretreatment red blood cell distribution width may be a potential biomarker of prognosis in urologic cancer: a systematic review and meta-analysis. Biomark Med. 2022;16:1289\u0026ndash;300.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBeyazit Y, Kekilli M, Ibis M, Kurt M, Sayilir A, Onal IK, Purnak T, Oztas E, Tas A, Yesil Y, Arhan M. Can red cell distribution width help to discriminate benign from malignant biliary obstruction? A retrospective single center analysis. Hepatogastroenterology. 2012;59:1469\u0026ndash;73.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTan M, Liu B, You R, Huang Q, Lin L, Cai D, Yang R, Li D, Huang H. 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BMC Gastroenterol. 2024;24:288.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLin H, Luo P, Liu C, Lin X, Que C, Zhong W. The application value of mean red blood cell volume and red blood cell volume distribution width combined with total serum bilirubin in the early screening of neonatal hemolytic disease. BMC Pediatr. 2023;23:19.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHe M, Wei W, Zhang Y, Xiang Z, Peng D, Kasimumali A, Rong S. Gut microbial metabolites SCFAs and chronic kidney disease. J Transl Med. 2024;22:172.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi B, You Z, Xiong XZ, Zhou Y, Wu SJ, Zhou RX, Lu J, Cheng NS. Elevated red blood cell distribution width predicts poor prognosis in hilar cholangiocarcinoma. Oncotarget. 2017;8:109468\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKoschade SE, Moser LM, Sokolovskiy A, Michael FA, Serve H, Brandts CH, Finkelmeier F, Zeuzem S, Trebicka J, Ferstl P, Ballo O. Bone Marrow Assessment in Liver Cirrhosis Patients with Otherwise Unexplained Peripheral Blood Cytopenia. J Clin Med 2023, 12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZelenka J, Len\u0026iacute;cek M, Muchov\u0026aacute; L, Jirsa M, Kudla M, Balaz P, Zadinov\u0026aacute; M, Ostrow JD, Wong RJ, V\u0026iacute;tek L. Highly sensitive method for quantitative determination of bilirubin in biological fluids and tissues. J Chromatogr B Analyt Technol Biomed Life Sci. 2008;867:37\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGeorgatzakou HT, Fortis SP, Papageorgiou EG, Antonelou MH, Kriebardis AG. Blood Cell-Derived Microvesicles in Hematological Diseases and beyond. Biomolecules 2022, 12.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMa SR, Xia HF, Gong P, Yu ZL. Red Blood Cell-Derived Extracellular Vesicles: An Overview of Current Research Progress, Challenges, and Opportunities. Biomedicines 2023, 11.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCai N, Zhan X, Zhang Q, Di H, Chen C, Hu Y, Yan X. Red Blood Cell-Derived Small Extracellular Vesicles Inhibit Influenza Virus through Surface-Displayed Sialic Acids. Angew Chem Int Ed Engl. 2025;64:e202413946.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePeng B, Yang Y, Wu Z, Tan R, Pham TT, Yeo EYM, Pirisinu M, Jayasinghe MK, Pham TC, Liang K, et al. Red blood cell extracellular vesicles deliver therapeutic siRNAs to skeletal muscles for treatment of cancer cachexia. Mol Ther. 2023;31:1418\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFischer D, Thies F, Awad O, Brat C, Meybohm P, Baer PC, M\u0026uuml;ller MM, Urbschat A, Maier TJ, Zacharowski K, Roos J. Red Blood Cell-Derived Microparticles Exert No Cancer Promoting Effects on Colorectal Cancer Cells In Vitro. Int J Mol Sci 2022, 23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThangaraju K, Neerukonda SN, Katneni U, Buehler PW. Extracellular Vesicles from Red Blood Cells and Their Evolving Roles in Health, Coagulopathy and Therapy. Int J Mol Sci 2020, 22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang L, Huang S, Zhang Z, Liu Z, Zhang L. Roles and Applications of Red Blood Cell-Derived Extracellular Vesicles in Health and Diseases. Int J Mol Sci 2022, 23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhu L, Chen M, Huang B, Zhang T, Chen K, Lian H, Liu M, Zhao K, Pang Y, Zhang J, et al. 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Front Genet. 2021;12:819632.\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":"Pancreatic cancer, Jaundice, Red cell distribution width (RDW), Survival prognosis, Risk stratification","lastPublishedDoi":"10.21203/rs.3.rs-8199768/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8199768/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eTo investigate the clinical significance of jaundice-related elevation in red cell distribution width (RDW) for predicting survival outcomes in patients with pancreatic cancer.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe established a multi-etiology cohort and stratified patients according to the underlying cause of disease and RDW status. Correlations between RDW and markers of inflammation, nutritional status, and bilirubin were examined. The independent prognostic impact of the \u0026ldquo;jaundice\u0026ndash;RDW\u0026rdquo; phenotype on pancreatic cancer was evaluated using Kaplan\u0026ndash;Meier survival curves and Cox proportional hazards models. Receiver operating characteristic (ROC) analyses were further performed to assess the ability of bilirubin to predict high RDW.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eRDW levels showed distinct distributions across different causes of jaundice; malignant obstructive jaundice, including pancreatic cancer, was associated with markedly higher RDW. In patients with pancreatic cancer, RDW was strongly positively correlated with total bilirubin (r\u0026thinsp;\u0026gt;\u0026thinsp;0.73). Across tumor stages, patients with the \u0026ldquo;jaundice with high RDW\u0026rdquo; phenotype had the shortest median survival (11.0 months) and a substantially increased risk of death (HR\u0026thinsp;=\u0026thinsp;2.65). Bilirubin demonstrated excellent discriminatory performance for high RDW status (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.92).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eIn pancreatic cancer, the combination of jaundice and high RDW constitutes a simple and practical tool for risk stratification. Patients presenting with both jaundice and elevated RDW should be prioritized in clinical management, and more intensive, comprehensive therapeutic strategies should be considered to improve outcomes. This study provides readily applicable clinical parameters for precision prognostic assessment in pancreatic cancer.\u003c/p\u003e","manuscriptTitle":"Impact of Jaundice-Induced Elevated Red Blood Cell Distribution Width on Survival Outcomes in Pancreatic Cancer Patients: A Retrospective Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-08 07:13:47","doi":"10.21203/rs.3.rs-8199768/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":"b1d9da86-75ce-42a7-8314-fe1df2655995","owner":[],"postedDate":"December 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-14T03:53:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-08 07:13:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8199768","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8199768","identity":"rs-8199768","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}