Intimal CD31-positive relative surfaces are associated with the dysfunction of autologous arteriovenous fistulas in patients receiving dialysis

Intimal CD31-positive relative surfaces are associated with the dysfunction of autologous arteriovenous fistulas in patients receiving dialysis | 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 Intimal CD31-positive relative surfaces are associated with the dysfunction of autologous arteriovenous fistulas in patients receiving dialysis Jie Li, Yulu Wu, Xiaohe Wang, Yuanyuan Zhang, Guocun Hou, Di Wang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8787435/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Mar, 2026 Read the published version in International Urology and Nephrology → Version 1 posted You are reading this latest preprint version Abstract Objective: This study aimed to evaluate the association between specific histological features of the cephalic vein, assessed intraoperatively, for subsequent dysfunction of primary radiocephalic arteriovenous fistulas (RCAVFs). Methods: In a retrospective cohort analysis, 170 patients undergoing first-time RCAVF creation were included. A segment of the cephalic vein had been harvested during surgery and was subjected to standardized histopathological analysis. Key parameters were quantified using digital morphometry and immunohistochemistry: medial microvascular density (CD31-positive relative surface area), intimal thickness, and medial collagen fiber orientation via second-harmonic generation microscopy. AVF failure was defined as the inability to use the fistula successfully for dialysis within 12 months postoperatively. Univariate and multivariate logistic regression, ROC analysis, and restricted cubic spline models were employed to identify and characterize predictors. Results: During the follow-up, 40 patients (23.5%) experienced AVF failure. In multivariate analysis, only a larger medial CD31-positive relative surface area remained a significant independent predictor of failure (odds ratio = 1.48 per 0.1% increase, 95% CI 1.15–1.90, P < 0.01). Intimal thickness showed a significant but weaker linear association. The CD31-positive area provided the highest individual predictive accuracy (AUC = 0.78), and a model combining it with intimal thickness achieved an AUC of 0.82. Nonlinear analysis revealed a threshold effect for CD31, with risk plateauing beyond a density of approximately 1.7%. Collagen fiber orientation was not associated with outcomes. Conclusions: Intraoperative medial microvascular density of the cephalic vein is a robust, independent histopathological maker associated with AVF failure. This finding underscores the importance of the pre-existing biological state of the venous wall, offering a novel biomarker that may refine risk stratification and shift focus toward biological mechanisms in vascular access management. Arteriovenous fistula Hemodialysis Intimal thickness CD31-positive relative surface Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Autologous arteriovenous fistula (AVF) is the preferred and gold-standard vascular access for hemodialysis in patients with end-stage renal disease (ESRD) due to its superior long-term patency and lower complication rates compared to other modalities [ 1 ]. However, a critical and unresolved challenge is the high rate of AVF maturation failure, which occurs in 20% to 60% of newly created fistulas and directly compromises dialysis adequacy, increases patient morbidity, and escalates healthcare costs [2–4]. Historically, research efforts have focused on identifying clinical, demographic, and hemodynamic predictors of AVF failure, such as advanced age, diabetes, small vessel diameter, and low blood flow [ 5 – 7 ]. At the pathological level, neointimal hyperplasia leading to venous stenosis has long been considered the dominant mechanism underlying AVF dysfunction [ 8 – 10 ]. Nonetheless, prediction models based primarily on these conventional factors exhibit limited discriminative capacity [ 2 ]. Furthermore, a growing body of evidence challenges the primacy of intimal hyperplasia as the principal cause of failure [ 11 ]. Recent studies suggest that AVF failure may be driven by a broader, pre-existing maladaptive state of the venous wall, characterized by a persistent pro-inflammatory and pro-fibrotic microenvironment, rather than by luminal narrowing alone [12]. This evolving perspective, however, is not yet fully substantiated by direct evidence. The role of the baseline biological state of the venous wall in determining AVF outcomes requires further validation through systematic histopathological analysis. In particular, the medial microvasculature (vasa vasorum) is crucial for vascular wall homeostasis, and its pathological expansion is a recognized response to wall stress, hypoxia, and inflammation [ 13 – 17 ]. We hypothesize that the density of microvessels within the tunica media of the cephalic vein, quantified in intraoperative specimens, may serve as an integrative histological biomarker of this adverse vascular wall milieu and could provide independent prognostic value beyond traditional anatomical measurements such as intimal thickness. Therefore, this retrospective cohort study was designed to systematically evaluate the association between specific histopathological features of the cephalic vein and subsequent AVF dysfunction. We employed a comprehensive histomorphometric approach, combining digital morphometry for intimal thickness, immunohistochemistry (CD31) for medial microvascular density, and second-harmonic generation (SHG) microscopy for collagen architecture analysis [ 18 ]. Our primary objective was to determine whether medial microvascular density was an independent predictor of AVF failure, thereby offering novel insights into the biological determinants of vascular access success. METHODS Study Design and Participants This retrospective cohort study aimed to assess whether specific histological features of the cephalic vein were associated with subsequent dysfunction of newly created radiocephalic arteriovenous fistula (RCAVF). The study protocol was approved by the Institutional Review Boards of Suzhou Hospital Affiliated to Medical School of Nanjing University (Approval NO. : IRB2021116), and was conducted in accordance with the Declaration of Helsinki. For this retrospective analysis, the requirement for written informed consent was waived by the ethics committee, as the study involved the analysis of anonymized existing data and archived tissue specimens collected during standard surgical care. Between January 2020 and November 2023, we retrospectively identified 195 consecutive patients with ESRD who had undergone their first permanent vascular access creation. \] Inclusion criteria were:1) Diagnosis of stage 5 chronic kidney disease (CKD) requiring maintenance renal replacement therapy; 2) Creation of a first-time, unilateral RCAVF; 3) Age ≥ 18 years; 4) Availability of intraoperative cephalic vein tissue specimen and complete clinical follow-up data. Exclusion criteria were:1) Contraindications to distal AVF creation, including insufficient arterial inflow (positive Allen’s test) or significant ipsilateral central venous stenosis/occlusion; 2) Previous permanent vascular access in the target limb; 3) Active severe systemic infection, acute coronary syndrome, cardiogenic shock, or active malignancy within 30 days prior to surgery;4) Life expectancy of less than 3 months; 5) AVF failure occurring within the first two weeks after surgery, as these early events are likely due to technical rather than biological factors. All enrolled patients had undergone standardized preoperative vascular mapping and surgical procedures. Clinical and outcome data were obtained through retrospective review of electronic medical records, dialysis logs, and vascular access clinic records to cover a 12-month postoperative period. For the final analysis, patients who were lost to follow-up or had incomplete key data (e.g., missing histology samples or incomplete primary outcome assessment) were excluded. Following these criteria, 16 patients lost to follow-up and 9 patients with incomplete data were excluded. Consequently, 170 patients with complete 12-month follow-up data constituted the final study cohort for analysis. Clinical Data Collection and Outcome Definition Data Collection Demographic characteristics, comorbidities, primary renal disease etiology, medication history, and preoperative laboratory parameters (including hemoglobin, serum albumin, corrected calcium, phosphate, and intact parathyroid hormone) were retrospectively extracted from electronic medical records. Standardized preoperative vascular mapping via Doppler ultrasound data were reviewed to document arterial and venous diameters, as well as flow volume in the brachial artery. Outcome Definition and Follow-up : The primary outcome of this study was AVF failure, which was pragmatically defined as the composite endpoint of events leading to the permanent inability to use the created AVF for hemodialysis within 12 months postoperatively. This was determined through chart review and included:1) Primary non-maturation: The AVF never attained suitability for cannulation. 2) Early thrombosis: Thrombosis occurring after the initial postoperative period (beyond 2 weeks) and before successful use. 3) Functional failure: Abandonment of a matured AVF due to complications (e.g., persistent stenosis, inadequate flow, aneurysm) that prevented sustained successful dialysis. AVF maturation and function were assessed based on retrospective data. AVF success was defined as the documented ability to perform three consecutive hemodialysis sessions with a two-needle cannulation achieving a blood flow rate ≥ 500 mL/min for the prescribed duration, without recourse to secondary interventions to promote or salvage functionality [ 19 ]. Follow-up evaluations, as documented in the records of the dialysis center or vascular access clinic, were reviewed. These included physical examination (thrill, bruit) and standardized duplex ultrasonography reports at scheduled intervals, which provided measurements of vein diameter, depth, and blood flow volume for outcome ascertainment. Surgical Procedure and Tissue Acquisition All patients underwent creation of a primary RCAVF under regional anesthesia. The procedures were performed by one of two dedicated vascular surgeons, each with over five years of experience in hemodialysis access surgery, following an identical, standardized protocol. Tissue Acquisition A 5–10 mm segment of the cephalic vein had been routinely harvested from its transected distal end immediately prior to performing the anastomosis. The specimen was promptly rinsed in sterile saline to remove intraluminal blood, and then immersion-fixed in 10% neutral buffered formalin at 4°C for 24 hours. Following fixation, tissues were processed through a graded ethanol series, embedded in paraffin, and stored for subsequent histopathological analysis. Histopathological and Immunohistochemical Analysis Histopathological evaluation was performed on paraffin-embedded cephalic vein specimens obtained during surgery. Consecutive sections of 4–5 µm thickness were cut using a Leica RM2245 rotary microtome for subsequent staining and imaging. Staining and Imaging . Three protocols were employed: (1) Hematoxylin and eosin (H&E) staining for general morphological assessment; (2) Immunohistochemistry (IHC) for CD31 (rabbit monoclonal anti-CD31, Clone SP216, 1:200 dilution; Wuhan Sanying) with HIER antigen retrieval (pH 9.0) to identify endothelial cells; and (3) Second-harmonic generation (SHG) microscopy (Zeiss LSM 880, 880 nm excitation) for label-free visualization of fibrillar collagen architecture. All stained slides were digitized as whole-slide images using a Hamamatsu NanoZoomer S60 scanner at a CAP-accredited core facility (Jiangsu Kaiji Biotechnology Co., Ltd.). Digital Morphometry and Quantitative Analysis. All quantitative analyses were performed on digitized whole-slide images. Vessel Wall Morphometry On H&E-stained sections, the contours of the internal elastic lamina (IEL) and external elastic lamina (EEL) were delineated by pathologists. At ten equidistant points around the vessel circumference, the intimal thickness (perpendicular distance from the IEL to the luminal surface) and medial thickness (distance from the IEL to the EEL) were systematically measured [ 20 ]. The arithmetic mean of these ten measurements was calculated for each parameter (Fig. 1 A). Quantification of Medial Microvascular Density : On CD31-immunostained sections, the boundary of the medial layer was first manually annotated. Subsequently, CD31-positive microvessels (vasa vasorum) within the defined medial area were automatically identified and segmented using a machine-learning classifier implemented via the Trainable Weka Segmentation plugin in ImageJ software. The medial CD31-positive relative surface area was then calculated as: (Total CD31-positive pixel area / Total medial area) × 100%. Collagen Organization Based on SHG images, the predominant orientation of medial collagen fibers was qualitatively assessed. Collagen anisotropy was additionally quantified using software-derived orientation indices. Blinded Evaluation and Quality Control. Two board-certified pathologists (each with > 10 years of experience), blinded to all clinical data, independently performed the quantitative and qualitative analyses. To formally assess inter-rater reliability, both pathologists independently delineated the IEL and performed key morphometric measurements on a randomly selected subset of 20 slides. The intraclass correlation coefficient (ICC) for mean intimal, medial thickness and medial CD31-positive relative surface area calculated from this subset exceeded 0.90. Consequently, a consensus-based workflow was adopted for the full cohort: one pathologist performed all initial measurements, which were then reviewed and verified by the second; any discrepancies were resolved through joint re-evaluation. Statistical Analysis All statistical analyses were performed using SPSS software (version 26.0; IBM Corp.) and R programming language (version 4.3.1; R Foundation). Continuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed data are presented as mean ± standard deviation and were compared between the AVF failure and non-failure groups using independent samples t-tests. Non-normally distributed data are presented as median (interquartile range) and were compared using the Mann-Whitney U test. Categorical variables are presented as frequency (percentage) and were compared using the chi-square test or Fisher’s exact test, as appropriate. Missing data were minimal (< 2% for any variable) and handled by complete-case analysis. To identify independent predictors of AVF failure while ensuring model robustness and preventing overfitting, variable selection for the multivariate logistic regression model was strictly governed by the 10 events-per-variable (EPV) rule. Accordingly, the four variables with the smallest P-values in the univariate analyses were entered into the final binary logistic regression model. The discriminatory performance of the final multivariate model was evaluated using receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC) was calculated to quantify the model's ability to differentiate between patients with and without AVF failure. The optimal probability cutoff for classification was determined using Youden’s index. To explore potential nonlinear relationships between continuous predictors and the log-odds of AVF failure, restricted cubic spline analyses were implemented with four knots placed at the 5th, 35th, 65th, and 95th percentiles of each variable’s distribution, using the rms package in R. All statistical tests were two-sided, and a P value < 0.05 was considered statistically significant. RESULTS 1. Study Population and Baseline Characteristics The final cohort comprised 170 patients with end-stage renal disease undergoing primary radiocephalic AVF creation. The majority (n = 146, 85.9%) were on maintenance hemodialysis via a temporary catheter at surgery, while 24 (14.1%) were pre-dialysis. Over the 12-month postoperative perio-, 40 patients (23.5%) experienced AVF failure, and 130 (76.5%) maintained functional patency. Baseline characteristics are detailed in Table II. The mean age was 55.1 ± 13.7 years. Patients in the failure group were significantly older than those in the non-failure group (59.3 ± 10.9 vs. 53.8 ± 14.3 years, P = 0.03). No significant differences were observed in sex, etiology of chronic kidney disease, or most comorbidities (P > 0.05). 2. Patterns and Etiology of AVF Failure The timing and presumed primary etiology of the 40 failure events are summarized in Table I. Failures occurred across the postoperative period: 6 (15.0%) early (15–30 days, predominantly thrombosis), 28 (70.0%) intermediate (> 30 days to 6 months, primarily stenosis), and 6 (15.0%) late (> 6 months to 1 year, manifesting as puncture-site aneurysms). Table Ⅰ Timing and Presumed Etiology of Adverse Events Leading to AVF Failure(n = 40) Time of Failure Presumed Primary Cause Number of Cases (%) Adverse Event Type Average Time to Event (Days) Early (≤ 30 days) Technical/Thrombotic 6 (15.0%) outflow tract thrombosis (4) 26.2 outflow tract stenosis (2) 28.5 Intermediate (> 30 days to 6 months) Neointimal Hyperplasia 28 (70.0%) outflow tract stenosis (28) 162.7 Late (> 6 months to 1 year) Hemodynamic/Structural 6(15.0%) Puncture site aneurysm (6) 320.5 Note : outflow tract refers to the venous segment distal to the anastomosis, while the inflow tract includes the donor artery, the anastomosis itself, and the adjacent vein segment within 2 cm proximal to the anastomosis. 3. Histological and Morphometric Findings SHG microscopy visualized the native collagen architecture within the venous wall (Fig. 2 ). Qualitative assessment of the predominant collagen fiber orientation (classified as parallel, perpendicular, mixed, or random) revealed no significant association with AVF failure rates ( P = 0.48). 4.Predictors of AVF Failure 4.1 Univariate and Multivariate Analyses Univariate analysis identified several factors associated with AVF failure, including older age, vascular calcification, lower hemoglobin, greater intimal thickness, and larger medial CD31-positive area and relative surface area (all P < 0.05; Table II). Table II. Univariate Analysis of Factors Associated with AVF Failure (n = 170) Variables Total sample (n = 170) AVF failure group (n = 40) AVF non-failure group (n = 130) P value 1. Demographics Sex n (%) 0.70 Male 112(100) 25(22.3) 87(77.7) Female 58(100) 15(25.9) 43(74.1) Age [years, M (SD)] 55.1 ± 13.7 59.3 ± 10.9 53.8 ± 14.3 0.03 2. Basic Disease Characteristics Chronic kidney disease etiology n (%) 0.11 Chronic glomerulonephritis 93(100) 19 (20.4) 74 (56.9) Interstitial nephritis 12 (100) 6 (50) 6 (50) Hypertensive nephropathy 24 (100) 4 (16.7) 20 (83.3) Diabetic nephropathy 41(100) 11 (26.8) 30 (73.2) Vascular calcification n (%) 0.04 Yes 29(100) 11 (37.9) 18 (62.1) No 141(100) 29(20.6) 101(79.4) 3.Laboratory tests Hemoglobin g/L 88.6 ± 18.9 94.0 ± 20.0 87.0 ± 18.3 0.04 Ca mmol/L 2.03 ± 0.28 2.07 ± 0.25 2.02 ± 0.28 0.28 serum phosphorus level mmol/L 1.65 ± 0.68 1.60 ± 0.75 1.66 ± 0.66 0.62 PTH pg/mL 259 ± 205 244 ± 55 263 ± 188 0.60 CRP mg/L 18.3 ± 38.7 16.8 ± 24.0 18.8 ± 42.2 0.77 4.Preoperative vascular ultrasound indicators Artery diameter cm 0.24 ± 0.07 0.24 ± 0.09 0.24 ± 0.06 0.80 Vein diameter cm 0.26 ± 0.08 0.24 ± 0.08 0.26 ± 0.08 0.06 PSV cm/s 71.8 ± 20.1 76.0 ± 19.4 70.5 ± 20.2 0.13 5.Vascular pathological indicators Intimal thickness um 71.2 ± 95.3 105.6 ± 132.7 60.5 ± 76. 8 0.02 Medial thickness um 246 ± 87.0 236 ± 86.8 249 ± 87.2 0.43 CD31-positive area um 2 8450 ± 9320 10850 ± 10100 7650 ± 8850 0.02 CD31-positive relative surface (%) 0.88 ± 0.79 1.35 ± 0.88 0.75 ± 0.71 < 0.01 Pattern of medial collagen fiber orientation 0.48 Parallel to lumen 95 (100) 21 (22.1) 74 (77.9) Perpendicular to lumen 10 (100) 4 (40.0) 6 (60) Mixed 24 (100) 4 (16.7) 20 (83.3) Random 41 (100) 11 (26.8) 30 (73.2) Note: a P < 0.05, b P < 0.01 M (SD) =mean (standard deviation); M (IQR)=median (interquartile range). Based on univariate results, four key variables—age, intimal thickness, medial CD31-positive area, and medial CD31-positive relative surface—were entered into a multivariate logistic regression model, adhering to the 10 events-per-variable rules. After adjustment, a larger medial CD31-positive relative surface area remained the strongest independent predictor (odds ratio [OR] = 1.48 per 0.1% increase; 95% CI 1.15–1.90; P < 0.01). Intimal thickness retained borderline significance (OR = 1.03 per 10 µm, 95% CI 1.00–1.06; P = 0.03), while age and medial CD31-positive area did not show independent associations (Table III). Table Ⅲ. Multivariate Logistic Regression Analysis of Factors Associated with AVF Failure(n = 170). Dependent variables P value OR 95%CI Age, years 0.12 0.85 0.69–0.74 Intimal thickness 10um 0.03 1.03 1.00-1.06 CD31-positive relative surface, % 0.01 1.48 1.15–1.90 CD31-positive area um2 0.27 1.24 0.85–1.80 4.2 Predictive Performance and Diagnostic Cut-offs ROC curve analysis evaluated the discriminative ability of the significant histological parameters and their composite model (Fig. 3 ). As detailed in Table IV, the composite risk score (integrating intimal thickness and CD31-positive relative surface area) demonstrated the highest predictive accuracy (AUC = 0.82, 95% CI 0.74–0.89). The CD31-positive relative surface area was the strongest individual predictor (AUC = 0.78, 95% CI 0.70–0.86), with an optimal cutoff of > 0.55% yielding 80% sensitivity and 75% specificity. Intimal thickness showed moderate discriminative capacity (AUC = 0.68, 95% CI 0.58–0.78). (Fig. 3 ) Table Ⅳ. Diagnostic performance of vascular parameters for predicting AVF failure in study cohort (N = 170). Variables AUC (95%CI) Optimal Cut-off Sensitivity (%) Specificity (%) Youden's Index PPV (%) NPV (%) Intimal thickness um 0.68 (0.58–0.78) 45.2 65 69 0.34 38.8 86.4 CD31-positive relative surface 0.78 (0.70–0.86) 0.55% 80 75 0.55 49.2 92.4 Composite risk score* 0.82 (0.74–0.89) 0.82 52 72 0.23 36.2 83.0 Note: AVF=arteriovenous fistulas; AUC=Area under the curve; 95%CI = 95% confidence interval. *Combined model integrating intimal thickness and CD31-positive relative surface through multivariable logistic regression. 4. Exploration of Nonlinear Associations Restricted cubic spline analyses, adjusted for age and sex, characterized the dose-response relationships between continuous predictors and AVF failure risk. Intimal thickness exhibited a statistically significant, approximately linear positive association ( P for overall < 0.01; P for nonlinearity = 0.19) (Fig. 4 A). In contrast, a distinct nonlinear pattern was observed for the medial CD31-positive relative surface area ( P for nonlinearity = 0.03), with risk increasing steeply until plateauing at approximately 1.7% (Fig. 4 B). DISCUSSION The high failure rate of AVF remains a major clinical challenge. This study demonstrates that increased medial microvascular density (CD31-positive relative surface area) in the cephalic vein at the time of surgery is an independent histopathological predictor of subsequent AVF failure, offering a novel perspective for assessing venous wall health. CD31, an endothelial cell marker, exhibits elevated expression within the tunica media, reflecting hyperplasia of the vasa vasorum. This microvascular network, located in the adventitia and media, supplies oxygen and nutrients to the vessel wall itself [ 13 , 14 ]. Neovascularization is a well-established compensatory response to wall stress, hypoxia, and inflammation [ 15 – 17 ]. Our findings align with the emerging view that AVF failure is driven primarily by a maladaptive inflammatory-fibrotic microenvironment rather than by intimal hyperplasia per se [ 21 , 22 ]. CD31-positive microvessels may thus serve as a histological signature of this pathological remodeling process. The observed nonlinear threshold effect further suggests a critical point of pathological microvascular activation, beyond which the risk of failure plateaus, potentially indicating an irreversible stage of venous wall injury. Univariate analysis indicated an association between greater intimal thickness and failure; however, after adjustment for medial CD31-positive area, this association was markedly attenuated. This implies that intimal thickening may not be an independent driver but rather a concomitant manifestation of the underlying pathological microenvironment characterized by increased microvascular density [ 21 ]. This observation challenges the traditional lumen-centric view and underscores the need to look “beyond the lumen” toward vascular wall biology[ 11 ]. In this context, intimal thickening is more likely a downstream consequence of signaling from an inflamed and hypoxic medial layer, rather than the initiating event of failure. The nonlinear relationship between medial CD31-positive area and failure risk stratifies patients into two distinct phases. Phase I (Escalating Risk): The venous segment experiences hypoxia and endothelial dysfunction, triggering compensatory neovascularization. This process simultaneously exacerbates wall inflammation and remodeling, leading to a progressive increase in failure risk. Phase II (Risk Plateau): Once neovascularization reaches a certain threshold, wall perfusion is temporarily improved and mechanical stress is mitigated, resulting in stabilization of failure risk. This biphasic pattern resembles hypoxia-driven angiogenesis observed in arterial diseases [ 15 – 17 ] but highlights its unique manifestation in the venous wall under AVF-specific hemodynamics. The present study provides a quantifiable intraoperative histological marker for real-time assessment of venous wall quality. If validated, measurement of medial microvascular density could be used for risk stratification and intensified surveillance of high-risk patients [ 22 ]. The threshold effect suggests a potential “point of no return” in venous wall injury; if identified intraoperatively, it could guide decisions such as selecting an alternative anastomotic site or initiating early intervention. Future studies should: 1) validate this biomarker in larger, multicenter cohorts; 2) employ spatial transcriptomics to molecularly characterize the microenvironment associated with high CD31-positive areas [ 23 – 25 ]; and 3) explore in preclinical models whether modulating this microenvironment can improve AVF outcomes [ 26 , 27 ]. Our conclusions must be interpreted within the context of several limitations. First, the sample size, while adequate for the primary analysis, may limit the generalizability of some secondary findings. Second, histological assessment was performed on a single venous segment, which may not capture the heterogeneity of the entire outflow tract. Third, the retrospective observational design establishes association but not causation. Fourth, while we adjusted for key confounders, residual confounding from unmeasured factors is possible. Finally, the quantitative histomorphometric approach used, though rigorous, requires specialized expertise and equipment, and its standardization across different pathology laboratories needs further evaluation before widespread clinical adoption. CONCLUSION In conclusion, this retrospective study identifies increased medial microvascular density (CD31-positive relative surface area) in the cephalic vein at surgery as an independent histopathological factor associated with higher risk of subsequent AVF failure. This finding supports the relevance of the venous wall's biological state in AVF outcomes and provides a specific histological marker for risk assessment. It contributes to the growing evidence that understanding AVF pathophysiology requires integration of biological markers alongside traditional anatomical measurements. Declarations ACKNOWLEDGMENTS Competing interests: The authors have no conflicts of interest to declare that are relevant to the content of this article. Ethics approval: This study involving human participants was approved by the Ethics Committee of Suzhou Hospital, Affiliated Hospital of Medical School of Nanjing University (Approval number: IRB2021116). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Funding: The research leading to these results received funding from Suzhou Gusu Health Talent Plan (No.GSWS2022129)；Suzhou science and Education Strengthening Health Project (No.MSXM2024078)；Science and Technology Program of Suzhou (SKYD2023092, SKYD2023093 and SYWD2024236). Data availability ： The data supporting the conclusions of this study can be obtained by contacting the email address: [email protected] . Due to the fact that the imaging data and follow-up data in this study involve the personal privacy information of research participants, in accordance with relevant regulations on sensitive data protection, third parties must clearly state the purpose of data use to the research team and provide relevant supporting documents of written consent from the research participants before applying to use the aforementioned data. The research team will provide data access support in accordance with compliant procedures after verifying that the relevant conditions are met, ensuring that data use complies with ethical requirements and personal privacy protection regulations. Author Contributions ： Jie Li and Goucun Hou contributed to the study conception and design. Data collection was performed by Yulu Wu, Ping Dong, Jiaying Wang and Di Wang; statistical analysis was conducted by Xiaohe Wang, Yuanyuan Zhang; and data analysis and interpretation were carried out by Yulu Wu, Lei Shen, and Jie Li. The first draft of the manuscript was written by Jie Li and Yulu Wu, and all authors provided critical revisions on previous versions of the manuscript. All authors critically revised the manuscript. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work. References Aitken E, Anijeet H, Ashby D, Barrow W, Calder F, Dowds B, et al. UK Kidney Association Clinical Practice Guideline on vascular access for haemodialysis. BMC Nephrol. 2025;26(1):461. https://doi.org/10.1186/s12882-025-03967-9 Dember LM, Imrey PB, Beck GJ, Cheung AK, Himmelfarb J, Huber TS, et al. Hemodialysis Fistula Maturation Study Group. Objectives and design of the hemodialysis fistula maturation study. Am J Kidney Dis. 2014;63(1):104-12. https://doi.org/10.1053/j.ajkd.2013.08.019 Lok CE, Huber TS, Orchanian-Cheff A, Rajan DK. Arteriovenous Access for Hemodialysis: A Review. JAMA. 2024;331(15):1307-1317. https://doi.org/10.1001/jama.2024.1726 Shenoy S, Allon M, Beathard G, Brouwer-Maier D, Dember LM, Glickman M, et al. Clinical Trial End Points for Hemodialysis Vascular Access: Background, Rationale, and Definitions. Clin J Am Soc Nephrol. 2018;13(3): CJN.13321216. https://doi.org/10.2215/CJN.13321216 Woodside KJ, Repeck KJ, Mukhopadhyay P, Schaubel DE, Shahinian VB, Saran R, et al.Arteriovenous Vascular Access-Related Procedural Burden Among Incident Hemodialysis Patients in the United States. Am J Kidney Dis. 2021;78(3):369-379.e1. https://doi.org/10.1053/j.ajkd.2021.02.328 Long J, Chen H, Huang Q, Chen X, Ellis RJ, Zanoli L, et al. Analysis of risk factors for late arteriovenous fistula failure and patency rates after angioplasty in hemodialysis patients: a retrospective cohort study. Transl Androl Urol. 2024;13(2):209-217.https://doi.org/10.21037/tau-23-383 Venkat Ramanan S, Prabhu RA, Rao IR, et al. Outcomes and predictors of failure of arteriovenous fistulae for hemodialysis. Int Urol Nephrol. 2022;54(1):185-192.https://doi.org/10.1007/s11255-021-03071-7 Wei S, Liu N, Fu Y, Sun M. Novel insights into modifiable risk factors for arteriovenous fistula failure and the importance of CKD lipid profile: A meta-analysis. J Vasc Access. 2024;25(5):1416-1431.https://doi.org/10.1177/11297298231187275 Yan R, Song A, Zhang C. The Pathological Mechanisms and Therapeutic Molecular Targets in Arteriovenous Fistula Dysfunction. Int J Mol Sci. 2024;25(17):9519.https://doi.org/10.3390/ijms25179519 Martinez L, Rojas MG, Tabbara M, Pereira-Simon S, Santos Falcon N, Rauf M A, et al. The Transcriptomics of the Human Vein Transformation After Arteriovenous Fistula Anastomosis Uncovers Layer-Specific Remodeling and Hallmarks of Maturation Failure. Kidney Int Rep. 2023;8 (4):837-850.https://doi.org/10.1016/j.ekir.2023.01.022 Vazquez-Padron RI, Duque JC, Tabbara M, Salman LH, Martinez L. Intimal Hyperplasia and Arteriovenous Fistula Failure: Looking Beyond Size Differences. Kidney360. 2021;2(8):1360-1372.https://doi.org/10.34067/KID.0002022021 Laboyrie SL, De Vries M R, Bijkerk R, & Rotmans JI. (2023). Building a scaffold for arteriovenous fistula maturation: unravelling the role of the extracellular matrix. Int J Mol Sci, 2023;24(13).https://doi.org/10.3390/ijms241310608 Kaller R, Russu E, Arbănași EM, Mureșan AV, Jakab M, Ciucanu CC, et al. Intimal CD31-Positive Relative Surfaces Are Associated with Systemic Inflammatory Markers and Maturation of Arteriovenous Fistula in Dialysis Patients. J Clin Med. 2023;12(13):4419. https://doi.org/10.3390/jcm12134419 Phillippi JA. On vasa vasorum: A history of advances in understanding the vessels of vessels. Sci Adv. 2022;8(16):eabl6364.https://doi.org/10.1126/sciadv.abl6364 Moreno PR, Purushothaman KR, Fuster V, et al. Plaque neovascularization is increased in ruptured atherosclerotic lesions of human aorta: implications for plaque vulnerability. Circulation. 2004;110(14):2032-2038. https://doi.org/10.1161/01.CIR.0000143233.87854.23 Kuwahara F, Kai H, Tokuda K, Shibata R, Kusaba K, Tahara N, et al. Hypoxia-inducible factor-1alpha/vascular endothelial growth factor pathway for adventitial vasa vasorum formation in hypertensive rat aorta. Hypertension. 2002;39(1):46-50. https://doi.org/10.1161/hy1201.097200 López-Cano C, Rius F, Sánchez E, Gaeta AM, Betriu À, Fernández E, et al. The influence of sleep apnea syndrome and intermittent hypoxia in carotid adventitial vasa vasorum. PLoS One. 2019;14(2):e0211742.https://doi.org/10.1371/journal.pone.0211742 Baria E, Nesi G, Santi R, Maio V, Massi D, Pratesi C, et al. Improved label-free diagnostics and pathological assessment of atherosclerotic plaques through nonlinear microscopy. J Biophotonics. 2018 v;11(11):e201800106.https://doi.org/10.1002/jbio.201800106 Lee T, Mokrzycki M, Moist L, Maya I, Vazquez M, Lok CE; North American Vascular Access Consortium. Standardized definitions for hemodialysis vascular access. Semin Dial. 2011;24(5):515-24.https://doi.org/10.1111/j.1525-139X.2011.00969.x Hijazi A, Bifulco C, Baldin P, Galon J. Digital Pathology for Better Clinical Practice. Cancers (Basel). 2024;16(9):1686.https://doi.org/10.3390/cancers16091686 Shiu YT, Litovsky SH, Cheung AK, Pike DB, Tey JCS, Zhang Y, et al. Preoperative Vascular Medial Fibrosis and Arteriovenous Fistula Development. Clin J Am Soc Nephrol. 2016;11(9):1615-1623.https://doi.org/10.2215/CJN.00180116 Duque JC, Martinez L, Tabbara M, et al. Vascularization of the arteriovenous fistula wall and association with maturation outcomes. J Vasc Access. 2020;21(2):161-168.https://doi.org/10.1177/1129729819864562 Davie NJ, Crossno JT Jr, Frid MG, Hofmeister SE, Reeves JT, Hyde DM, et al. Hypoxia-induced pulmonary artery adventitial remodeling and neovascularization: contribution of progenitor cells. Am J Physiol Lung Cell Mol Physiol. 2004;286(4):L668-L678.https://doi.org/10.1152/ajplung.00108.2003 Billaud M, Hill JC, Richards TD, Gleason TG, Phillippi JA. Medial Hypoxia and Adventitial Vasa Vasorum Remodeling in Human Ascending Aortic Aneurysm. Front Cardiovasc Med. 2018;5:124.https://doi.org/10.3389/fcvm.2018.00124 Chumachenko PV, Postnov AY, Ivanova AG, Afanasieva OI, Afanasiev MA, Ekta MB, et al. Thoracic Aortic Aneurysm and Factors Affecting Aortic Dissection. J Pers Med. 2020;10(4):153.https://doi.org/10.3390/jpm10040153 Gössl M, Versari D, Hildebrandt HA, Bajanowski T, Sangiorgi G, Erbel R, et al. Segmental heterogeneity of vasa vasorum neovascularization in human coronary atherosclerosis. JACC Cardiovasc Imaging. 2010;3(1):32-40.https://doi.org/10.1016/j.jcmg.2009.09.021 Virmani R, Kolodgie FD, Burke AP, Finn AV, Gold HK, Tulenko TN, et al. Atherosclerotic plaque progression and vulnerability to rupture: angiogenesis as a source of intraplaque hemorrhage. Arterioscler Thromb Vasc Biol. 2005;25(10):2054-2061.https://doi.org/10.1161/01.ATV.0000178991.71605.18 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 26 Mar, 2026 Read the published version in International Urology and Nephrology → 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-8787435","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591649886,"identity":"bb32bf58-e4ee-485d-9d65-b35f21fddcbf","order_by":0,"name":"Jie Li","email":"","orcid":"","institution":"Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Li","suffix":""},{"id":591649887,"identity":"b7e4f4db-861d-4e60-af0d-8c8277c60fd3","order_by":1,"name":"Yulu Wu","email":"","orcid":"","institution":"Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Yulu","middleName":"","lastName":"Wu","suffix":""},{"id":591649888,"identity":"342bd52c-e799-41fc-8b75-fa1ce525652f","order_by":2,"name":"Xiaohe Wang","email":"","orcid":"","institution":"Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Xiaohe","middleName":"","lastName":"Wang","suffix":""},{"id":591649889,"identity":"e59b14eb-8e2a-4d34-bac4-c86274738448","order_by":3,"name":"Yuanyuan Zhang","email":"","orcid":"","institution":"Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Yuanyuan","middleName":"","lastName":"Zhang","suffix":""},{"id":591649890,"identity":"d6209789-5d17-49c1-b49c-fdfd54f0f3c4","order_by":4,"name":"Guocun Hou","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIiWNgGAWjYBACAziLvbHh8J8KCTl54rXwHD74gOeMhbFhA9FaJNKSDXjbKhIZDhDQYs7eY/yap8IucTtDjpmE5DyJBMYG5oePbuDRYtlzxsya50xy4s6GM2YShtsk8tgZ2IyNc/A57EaOmTFv24HEDQd7zCQSt0kUMzbwsEkTp+Uwj5nEwTkSiQ0HCGsxfgzWcowt2bCxgRgtZ46VMc45k2y84QzzwccMxySMDZsJ+eV48+YPbyrsZDfcf9hwmKGmTk6evfnhY3xagIBNApXPjF85WMkHwmpGwSgYBaNgRAMAbExPnorNnJsAAAAASUVORK5CYII=","orcid":"","institution":"Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University","correspondingAuthor":true,"prefix":"","firstName":"Guocun","middleName":"","lastName":"Hou","suffix":""},{"id":591649891,"identity":"6dd53304-d69a-4949-98c8-70ece257d64d","order_by":5,"name":"Di Wang","email":"","orcid":"","institution":"Suzhou Research Center of Medical School, Suzhou Hospital, Affiliated Hospital of Medical School, Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wang","suffix":""},{"id":591649892,"identity":"1fbc175d-2097-4570-b34d-c8b11a00c74f","order_by":6,"name":"Lei Shen","email":"","orcid":"","institution":"The First Affiliated Hospital of Soochow University","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Shen","suffix":""}],"badges":[],"createdAt":"2026-02-04 14:08:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8787435/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8787435/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11255-026-05111-6","type":"published","date":"2026-03-26T16:10:17+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":102757836,"identity":"98a4e3d9-c9e0-46f3-a8d3-ef62b36636b8","added_by":"auto","created_at":"2026-02-16 10:04:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":766367,"visible":true,"origin":"","legend":"\u003cp\u003eQuantitative assessment of venous tunica media prior to AVF surgery.\u003c/p\u003e\n\u003cp\u003eNote:\u003c/p\u003e\n\u003cp\u003e(A) Schematic representation of the morphometric measurement protocol on an H\u0026amp;E-stained section.\u003c/p\u003e\n\u003cp\u003e(B) The CD31-labeled vascular cross-section includes the intima, media, and adventitia.\u003c/p\u003e\n\u003cp\u003e(C)machine learning classifiers were trained through iterative threshold optimization (30 training iterations, 0.1 learning rate) to discriminate CD31+ microvessels.\u003c/p\u003e\n\u003cp\u003e(D) ImageJ automatically identifies CD31-stained vasa vasorum vessels within the tunica media and quantifies their area.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8787435/v1/1e2928292446391dea9b3408.png"},{"id":102757834,"identity":"2b08de44-cb35-4d8e-b055-cc67b335d92a","added_by":"auto","created_at":"2026-02-16 10:04:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":793630,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSecond-harmonic generation (SHG) microscopy reveals anisotropic collagen organization in arteriovenous fistula (AVF) venous walls.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(1A-6A) Transection showing medial collagen fibers (green signal) exhibiting circumferential alignment parallel to the vascular lumen.\u003c/p\u003e\n\u003cp\u003e(1A-6B) Longitudinal sections showing collagen fibers arranged in railroad track pattern relative to the lumen of the vein.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8787435/v1/e462fbf1841173e808f24118.png"},{"id":102757837,"identity":"5ac0e456-2fe8-43f7-b949-253f090a9fe5","added_by":"auto","created_at":"2026-02-16 10:04:59","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49474,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eReceiver operating characteristic (ROC) curve analysis for AVF failure prediction.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIntimal thickness (AUC=0.68, cut-off value=45.24um, P\u0026lt;0.01); CD31-positive relative surface (AUC=0.78, cut-off value=0.55%, P\u0026lt;0.01), composite risk score (AUC=0.82, cut-off value=0.52, P\u0026lt;0.01).\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8787435/v1/1c36ff8729277ba60a31d5ba.png"},{"id":102757835,"identity":"d3d43aeb-3c45-4102-b9e8-cd712b6879eb","added_by":"auto","created_at":"2026-02-16 10:04:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":133686,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAssociation of histological parameters with AVF failure risk analyzed by restricted cubic spline.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Intimal thickness showed a near-linear positive association with the log-odds of AVF failure (P for overall association \u0026lt; 0.01; P for nonlinearity = 0.19),\u003c/p\u003e\n\u003cp\u003e(B) A nonlinear association was observed between CD31-positive relative surface and AVF failure risk (P for nonlinearity = 0.03). The risk increased approximately linearly up to a threshold of 1.7% (dashed vertical line), beyond which it plateaued.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8787435/v1/0e34840f70c6573b3637c97d.png"},{"id":105755776,"identity":"f2574061-b52d-44d0-af2c-88166680be93","added_by":"auto","created_at":"2026-03-30 16:30:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3031242,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8787435/v1/e1032964-dd1c-4294-b77f-8950e6525a7f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intimal CD31-positive relative surfaces are associated with the dysfunction of autologous arteriovenous fistulas in patients receiving dialysis","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eAutologous arteriovenous fistula (AVF) is the preferred and gold-standard vascular access for hemodialysis in patients with end-stage renal disease (ESRD) due to its superior long-term patency and lower complication rates compared to other modalities [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. However, a critical and unresolved challenge is the high rate of AVF maturation failure, which occurs in 20% to 60% of newly created fistulas and directly compromises dialysis adequacy, increases patient morbidity, and escalates healthcare costs [2\u0026ndash;4].\u003c/p\u003e \u003cp\u003eHistorically, research efforts have focused on identifying clinical, demographic, and hemodynamic predictors of AVF failure, such as advanced age, diabetes, small vessel diameter, and low blood flow [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. At the pathological level, neointimal hyperplasia leading to venous stenosis has long been considered the dominant mechanism underlying AVF dysfunction [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR7\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Nonetheless, prediction models based primarily on these conventional factors exhibit limited discriminative capacity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Furthermore, a growing body of evidence challenges the primacy of intimal hyperplasia as the principal cause of failure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Recent studies suggest that AVF failure may be driven by a broader, pre-existing maladaptive state of the venous wall, characterized by a persistent pro-inflammatory and pro-fibrotic microenvironment, rather than by luminal narrowing alone [12].\u003c/p\u003e \u003cp\u003eThis evolving perspective, however, is not yet fully substantiated by direct evidence. The role of the baseline biological state of the venous wall in determining AVF outcomes requires further validation through systematic histopathological analysis. In particular, the medial microvasculature (vasa vasorum) is crucial for vascular wall homeostasis, and its pathological expansion is a recognized response to wall stress, hypoxia, and inflammation [\u003cspan additionalcitationids=\"CR14 CR15 CR16\" citationid=\"CR12\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. We hypothesize that the density of microvessels within the tunica media of the cephalic vein, quantified in intraoperative specimens, may serve as an integrative histological biomarker of this adverse vascular wall milieu and could provide independent prognostic value beyond traditional anatomical measurements such as intimal thickness.\u003c/p\u003e \u003cp\u003eTherefore, this retrospective cohort study was designed to systematically evaluate the association between specific histopathological features of the cephalic vein and subsequent AVF dysfunction. We employed a comprehensive histomorphometric approach, combining digital morphometry for intimal thickness, immunohistochemistry (CD31) for medial microvascular density, and second-harmonic generation (SHG) microscopy for collagen architecture analysis [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our primary objective was to determine whether medial microvascular density was an independent predictor of AVF failure, thereby offering novel insights into the biological determinants of vascular access success.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Design and Participants\u003c/h2\u003e\n \u003cp\u003eThis retrospective cohort study aimed to assess whether specific histological features of the cephalic vein were associated with subsequent dysfunction of newly created radiocephalic arteriovenous fistula (RCAVF). The study protocol was approved by the Institutional Review Boards of Suzhou Hospital Affiliated to Medical School of Nanjing University (Approval NO. : IRB2021116), and was conducted in accordance with the Declaration of Helsinki. For this retrospective analysis, the requirement for written informed consent was waived by the ethics committee, as the study involved the analysis of anonymized existing data and archived tissue specimens collected during standard surgical care.\u003c/p\u003e\n \u003cp\u003eBetween January 2020 and November 2023, we retrospectively identified 195 consecutive patients with ESRD who had undergone their first permanent vascular access creation.\u003c/p\u003e\n \u003cp\u003e\\] Inclusion criteria were:1) Diagnosis of stage 5 chronic kidney disease (CKD) requiring maintenance renal replacement therapy; 2) Creation of a first-time, unilateral RCAVF; 3) Age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; 4) Availability of intraoperative cephalic vein tissue specimen and complete clinical follow-up data. Exclusion criteria were:1) Contraindications to distal AVF creation, including insufficient arterial inflow (positive Allen\u0026rsquo;s test) or significant ipsilateral central venous stenosis/occlusion; 2) Previous permanent vascular access in the target limb; 3) Active severe systemic infection, acute coronary syndrome, cardiogenic shock, or active malignancy within 30 days prior to surgery;4) Life expectancy of less than 3 months; 5) AVF failure occurring within the first two weeks after surgery, as these early events are likely due to technical rather than biological factors.\u003c/p\u003e\n \u003cp\u003eAll enrolled patients had undergone standardized preoperative vascular mapping and surgical procedures. Clinical and outcome data were obtained through retrospective review of electronic medical records, dialysis logs, and vascular access clinic records to cover a 12-month postoperative period. For the final analysis, patients who were lost to follow-up or had incomplete key data (e.g., missing histology samples or incomplete primary outcome assessment) were excluded.\u003c/p\u003e\n \u003cp\u003eFollowing these criteria, 16 patients lost to follow-up and 9 patients with incomplete data were excluded. Consequently, 170 patients with complete 12-month follow-up data constituted the final study cohort for analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eClinical Data Collection and Outcome Definition\u003c/h3\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDemographic characteristics, comorbidities, primary renal disease etiology, medication history, and preoperative laboratory parameters (including hemoglobin, serum albumin, corrected calcium, phosphate, and intact parathyroid hormone) were retrospectively extracted from electronic medical records. Standardized preoperative vascular mapping via Doppler ultrasound data were reviewed to document arterial and venous diameters, as well as flow volume in the brachial artery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOutcome Definition and Follow-up\u003c/strong\u003e: The primary outcome of this study was AVF failure, which was pragmatically defined as the composite endpoint of events leading to the permanent inability to use the created AVF for hemodialysis within 12 months postoperatively. This was determined through chart review and included:1) Primary non-maturation: The AVF never attained suitability for cannulation. 2) Early thrombosis: Thrombosis occurring after the initial postoperative period (beyond 2 weeks) and before successful use. 3) Functional failure: Abandonment of a matured AVF due to complications (e.g., persistent stenosis, inadequate flow, aneurysm) that prevented sustained successful dialysis.\u003c/p\u003e\n\u003cp\u003eAVF maturation and function were assessed based on retrospective data. AVF success was defined as the documented ability to perform three consecutive hemodialysis sessions with a two-needle cannulation achieving a blood flow rate\u0026thinsp;\u0026ge;\u0026thinsp;500 mL/min for the prescribed duration, without recourse to secondary interventions to promote or salvage functionality [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eFollow-up evaluations, as documented in the records of the dialysis center or vascular access clinic, were reviewed. These included physical examination (thrill, bruit) and standardized duplex ultrasonography reports at scheduled intervals, which provided measurements of vein diameter, depth, and blood flow volume for outcome ascertainment.\u003c/p\u003e\n\u003ch3\u003eSurgical Procedure and Tissue Acquisition\u003c/h3\u003e\n\u003cp\u003eAll patients underwent creation of a primary RCAVF under regional anesthesia. The procedures were performed by one of two dedicated vascular surgeons, each with over five years of experience in hemodialysis access surgery, following an identical, standardized protocol.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTissue Acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 5\u0026ndash;10 mm segment of the cephalic vein had been routinely harvested from its transected distal end immediately prior to performing the anastomosis. The specimen was promptly rinsed in sterile saline to remove intraluminal blood, and then immersion-fixed in 10% neutral buffered formalin at 4\u0026deg;C for 24 hours. Following fixation, tissues were processed through a graded ethanol series, embedded in paraffin, and stored for subsequent histopathological analysis.\u003c/p\u003e\n\u003ch3\u003eHistopathological and Immunohistochemical Analysis\u003c/h3\u003e\n\u003cp\u003eHistopathological evaluation was performed on paraffin-embedded cephalic vein specimens obtained during surgery. Consecutive sections of 4\u0026ndash;5 \u0026micro;m thickness were cut using a Leica RM2245 rotary microtome for subsequent staining and imaging.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStaining and Imaging\u003c/strong\u003e. Three protocols were employed: (1) Hematoxylin and eosin (H\u0026amp;E) staining for general morphological assessment; (2) Immunohistochemistry (IHC) for CD31 (rabbit monoclonal anti-CD31, Clone SP216, 1:200 dilution; Wuhan Sanying) with HIER antigen retrieval (pH 9.0) to identify endothelial cells; and (3) Second-harmonic generation (SHG) microscopy (Zeiss LSM 880, 880 nm excitation) for label-free visualization of fibrillar collagen architecture. All stained slides were digitized as whole-slide images using a Hamamatsu NanoZoomer S60 scanner at a CAP-accredited core facility (Jiangsu Kaiji Biotechnology Co., Ltd.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDigital Morphometry and Quantitative Analysis.\u003c/strong\u003e All quantitative analyses were performed on digitized whole-slide images.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVessel Wall Morphometry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn H\u0026amp;E-stained sections, the contours of the internal elastic lamina (IEL) and external elastic lamina (EEL) were delineated by pathologists. At ten equidistant points around the vessel circumference, the intimal thickness (perpendicular distance from the IEL to the luminal surface) and medial thickness (distance from the IEL to the EEL) were systematically measured [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e]. The arithmetic mean of these ten measurements was calculated for each parameter (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification of Medial Microvascular Density\u003c/strong\u003e: On CD31-immunostained sections, the boundary of the medial layer was first manually annotated. Subsequently, CD31-positive microvessels (vasa vasorum) within the defined medial area were automatically identified and segmented using a machine-learning classifier implemented via the Trainable Weka Segmentation plugin in ImageJ software. The medial CD31-positive relative surface area was then calculated as: (Total CD31-positive pixel area / Total medial area) \u0026times; 100%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCollagen Organization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on SHG images, the predominant orientation of medial collagen fibers was qualitatively assessed. Collagen anisotropy was additionally quantified using software-derived orientation indices.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBlinded Evaluation and Quality Control.\u003c/strong\u003e Two board-certified pathologists (each with \u0026gt;\u0026thinsp;10 years of experience), blinded to all clinical data, independently performed the quantitative and qualitative analyses. To formally assess inter-rater reliability, both pathologists independently delineated the IEL and performed key morphometric measurements on a randomly selected subset of 20 slides. The intraclass correlation coefficient (ICC) for mean intimal, medial thickness and medial CD31-positive relative surface area calculated from this subset exceeded 0.90. Consequently, a consensus-based workflow was adopted for the full cohort: one pathologist performed all initial measurements, which were then reviewed and verified by the second; any discrepancies were resolved through joint re-evaluation.\u003c/p\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses were performed using SPSS software (version 26.0; IBM Corp.) and R programming language (version 4.3.1; R Foundation). Continuous variables were assessed for normality using the Shapiro-Wilk test. Normally distributed data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation and were compared between the AVF failure and non-failure groups using independent samples t-tests. Non-normally distributed data are presented as median (interquartile range) and were compared using the Mann-Whitney U test. Categorical variables are presented as frequency (percentage) and were compared using the chi-square test or Fisher\u0026rsquo;s exact test, as appropriate. Missing data were minimal (\u0026lt;\u0026thinsp;2% for any variable) and handled by complete-case analysis.\u003c/p\u003e\n \u003cp\u003eTo identify independent predictors of AVF failure while ensuring model robustness and preventing overfitting, variable selection for the multivariate logistic regression model was strictly governed by the 10 events-per-variable (EPV) rule. Accordingly, the four variables with the smallest P-values in the univariate analyses were entered into the final binary logistic regression model.\u003c/p\u003e\n \u003cp\u003eThe discriminatory performance of the final multivariate model was evaluated using receiver operating characteristic (ROC) curve analysis. The area under the ROC curve (AUC) was calculated to quantify the model\u0026apos;s ability to differentiate between patients with and without AVF failure. The optimal probability cutoff for classification was determined using Youden\u0026rsquo;s index.\u003c/p\u003e\n \u003cp\u003eTo explore potential nonlinear relationships between continuous predictors and the log-odds of AVF failure, restricted cubic spline analyses were implemented with four knots placed at the 5th, 35th, 65th, and 95th percentiles of each variable\u0026rsquo;s distribution, using the rms package in R.\u003c/p\u003e\n \u003cp\u003eAll statistical tests were two-sided, and a P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003e1. Study Population and Baseline Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final cohort comprised 170 patients with end-stage renal disease undergoing primary radiocephalic AVF creation. The majority (n\u0026thinsp;=\u0026thinsp;146, 85.9%) were on maintenance hemodialysis via a temporary catheter at surgery, while 24 (14.1%) were pre-dialysis. Over the 12-month postoperative perio-, 40 patients (23.5%) experienced AVF failure, and 130 (76.5%) maintained functional patency.\u003c/p\u003e\n\u003cp\u003eBaseline characteristics are detailed in Table II. The mean age was 55.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7 years. Patients in the failure group were significantly older than those in the non-failure group (59.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9 vs. 53.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3 years, P\u0026thinsp;=\u0026thinsp;0.03). No significant differences were observed in sex, etiology of chronic kidney disease, or most comorbidities (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Patterns and Etiology of AVF Failure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eThe timing and presumed primary etiology of the 40 failure events are summarized in Table I. Failures occurred across the postoperative period: 6 (15.0%) early (15\u0026ndash;30 days, predominantly thrombosis), 28 (70.0%) intermediate (\u0026gt;\u0026thinsp;30 days to 6 months, primarily stenosis), and 6 (15.0%) late (\u0026gt;\u0026thinsp;6 months to 1 year, manifesting as puncture-site aneurysms).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable Ⅰ Timing and Presumed Etiology of Adverse Events Leading to AVF Failure(n\u0026thinsp;=\u0026thinsp;40)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Taba\" border=\"1\"\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime of Failure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePresumed Primary Cause\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber of Cases (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAdverse Event Type\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage Time to Event (Days)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEarly (\u0026le;\u0026thinsp;30 days)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTechnical/Thrombotic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6 (15.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eoutflow tract thrombosis (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eoutflow tract stenosis (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e28.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermediate (\u0026gt;\u0026thinsp;30 days to 6 months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeointimal Hyperplasia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e28 (70.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eoutflow tract stenosis (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e162.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLate (\u0026gt;\u0026thinsp;6 months to 1 year)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemodynamic/Structural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e6(15.0%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePuncture site aneurysm (6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e320.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e: outflow tract refers to the venous segment distal to the anastomosis, while the inflow tract includes the donor artery, the anastomosis itself, and the adjacent vein segment within 2 cm proximal to the anastomosis.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e3. Histological and Morphometric Findings\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSHG microscopy visualized the native collagen architecture within the venous wall (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). Qualitative assessment of the predominant collagen fiber orientation (classified as parallel, perpendicular, mixed, or random) revealed no significant association with AVF failure rates (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.48).\u003c/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.Predictors of AVF Failure\u003c/strong\u003e\u003c/p\u003e\u003cspan\u003e\n \u003cp\u003e\u003cstrong\u003e4.1 Univariate and Multivariate Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003c/span\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate analysis identified several factors associated with AVF failure, including older age, vascular calcification, lower hemoglobin, greater intimal thickness, and larger medial CD31-positive area and relative surface area (all P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Table II).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable II. Univariate Analysis of Factors Associated with AVF Failure (n\u0026thinsp;=\u0026thinsp;170)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabb\" border=\"1\"\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal sample (n\u0026thinsp;=\u0026thinsp;170)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAVF failure group (n\u0026thinsp;=\u0026thinsp;40)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAVF non-failure group (n\u0026thinsp;=\u0026thinsp;130)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e1. Demographics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25(22.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87(77.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(25.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(74.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge [years, M (SD)]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e55.1\u0026thinsp;\u0026plusmn;\u0026thinsp;13.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59.3\u0026thinsp;\u0026plusmn;\u0026thinsp;10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e53.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2. Basic Disease Characteristics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eChronic kidney disease etiology n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic glomerulonephritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e93(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (20.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (56.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterstitial nephritis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertensive nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetic nephropathy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eVascular calcification n (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (37.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (62.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e141(100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(20.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e101(79.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.Laboratory tests\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin g/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88.6\u0026thinsp;\u0026plusmn;\u0026thinsp;18.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e94.0\u0026thinsp;\u0026plusmn;\u0026thinsp;20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87.0\u0026thinsp;\u0026plusmn;\u0026thinsp;18.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCa mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.03\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eserum phosphorus level mmol/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.65\u0026thinsp;\u0026plusmn;\u0026thinsp;0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60\u0026thinsp;\u0026plusmn;\u0026thinsp;0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.66\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePTH pg/mL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e259\u0026thinsp;\u0026plusmn;\u0026thinsp;205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e244\u0026thinsp;\u0026plusmn;\u0026thinsp;55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e263\u0026thinsp;\u0026plusmn;\u0026thinsp;188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCRP mg/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.3\u0026thinsp;\u0026plusmn;\u0026thinsp;38.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.8\u0026thinsp;\u0026plusmn;\u0026thinsp;24.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.8\u0026thinsp;\u0026plusmn;\u0026thinsp;42.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.Preoperative vascular ultrasound indicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eArtery diameter cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVein diameter cm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePSV cm/s\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.8\u0026thinsp;\u0026plusmn;\u0026thinsp;20.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e76.0\u0026thinsp;\u0026plusmn;\u0026thinsp;19.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70.5\u0026thinsp;\u0026plusmn;\u0026thinsp;20.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.Vascular pathological indicators\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntimal thickness um\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e71.2\u0026thinsp;\u0026plusmn;\u0026thinsp;95.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105.6\u0026thinsp;\u0026plusmn;\u0026thinsp;132.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60.5\u0026thinsp;\u0026plusmn;\u0026thinsp;76. 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedial thickness um\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e246\u0026thinsp;\u0026plusmn;\u0026thinsp;87.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e236\u0026thinsp;\u0026plusmn;\u0026thinsp;86.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e249\u0026thinsp;\u0026plusmn;\u0026thinsp;87.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD31-positive area um\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8450\u0026thinsp;\u0026plusmn;\u0026thinsp;9320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10850\u0026thinsp;\u0026plusmn;\u0026thinsp;10100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7650\u0026thinsp;\u0026plusmn;\u0026thinsp;8850\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD31-positive relative surface (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePattern of medial collagen fiber orientation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParallel to lumen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21 (22.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e74 (77.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerpendicular to lumen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (40.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20 (83.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRandom\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 (73.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: a P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, b P\u0026thinsp;\u0026lt;\u0026thinsp;0.01 M (SD) =mean (standard deviation); M (IQR)=median (interquartile range).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eBased on univariate results, four key variables\u0026mdash;age, intimal thickness, medial CD31-positive area, and medial CD31-positive relative surface\u0026mdash;were entered into a multivariate logistic regression model, adhering to the 10 events-per-variable rules. After adjustment, a larger medial CD31-positive relative surface area remained the strongest independent predictor (odds ratio [OR]\u0026thinsp;=\u0026thinsp;1.48 per 0.1% increase; 95% CI 1.15\u0026ndash;1.90; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Intimal thickness retained borderline significance (OR\u0026thinsp;=\u0026thinsp;1.03 per 10 \u0026micro;m, 95% CI 1.00\u0026ndash;1.06; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03), while age and medial CD31-positive area did not show independent associations (Table III).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable Ⅲ. Multivariate Logistic Regression Analysis of Factors Associated with AVF Failure(n\u0026thinsp;=\u0026thinsp;170).\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tabc\" border=\"1\"\u003e\n \u003ccolgroup cols=\"4\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDependent variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.69\u0026ndash;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntimal thickness 10um\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00-1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD31-positive relative surface, %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.15\u0026ndash;1.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD31-positive area um2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u0026ndash;1.80\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e4.2 Predictive Performance and Diagnostic Cut-offs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eROC curve analysis evaluated the discriminative ability of the significant histological parameters and their composite model (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). As detailed in Table IV, the composite risk score (integrating intimal thickness and CD31-positive relative surface area) demonstrated the highest predictive accuracy (AUC\u0026thinsp;=\u0026thinsp;0.82, 95% CI 0.74\u0026ndash;0.89). The CD31-positive relative surface area was the strongest individual predictor (AUC\u0026thinsp;=\u0026thinsp;0.78, 95% CI 0.70\u0026ndash;0.86), with an optimal cutoff of \u0026gt;\u0026thinsp;0.55% yielding 80% sensitivity and 75% specificity. Intimal thickness showed moderate discriminative capacity (AUC\u0026thinsp;=\u0026thinsp;0.68, 95% CI 0.58\u0026ndash;0.78). (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable Ⅳ. Diagnostic performance of vascular parameters for predicting AVF failure in study cohort (N\u0026thinsp;=\u0026thinsp;170).\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tabd\" border=\"1\"\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAUC\u003c/p\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOptimal Cut-off\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSensitivity\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSpecificity\u003c/p\u003e\n \u003cp\u003e(%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eYouden\u0026apos;s Index\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePPV (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNPV (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntimal thickness um\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68\u003c/p\u003e\n \u003cp\u003e(0.58\u0026ndash;0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCD31-positive relative surface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003cp\u003e(0.70\u0026ndash;0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e49.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e92.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComposite risk score*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003cp\u003e(0.74\u0026ndash;0.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote: AVF=arteriovenous fistulas; AUC=Area under the curve; 95%CI\u0026thinsp;=\u0026thinsp;95% confidence interval.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e*Combined model integrating intimal thickness and CD31-positive relative surface through multivariable logistic regression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4. Exploration of Nonlinear Associations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRestricted cubic spline analyses, adjusted for age and sex, characterized the dose-response relationships between continuous predictors and AVF failure risk. Intimal thickness exhibited a statistically significant, approximately linear positive association (\u003cem\u003eP\u003c/em\u003e for overall\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.19) (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). In contrast, a distinct nonlinear pattern was observed for the medial CD31-positive relative surface area (\u003cem\u003eP\u003c/em\u003e for nonlinearity\u0026thinsp;=\u0026thinsp;0.03), with risk increasing steeply until plateauing at approximately 1.7% (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n"},{"header":"DISCUSSION","content":"\u003cp\u003eThe high failure rate of AVF remains a major clinical challenge. This study demonstrates that increased medial microvascular density (CD31-positive relative surface area) in the cephalic vein at the time of surgery is an independent histopathological predictor of subsequent AVF failure, offering a novel perspective for assessing venous wall health.\u003c/p\u003e \u003cp\u003eCD31, an endothelial cell marker, exhibits elevated expression within the tunica media, reflecting hyperplasia of the vasa vasorum. This microvascular network, located in the adventitia and media, supplies oxygen and nutrients to the vessel wall itself [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Neovascularization is a well-established compensatory response to wall stress, hypoxia, and inflammation [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our findings align with the emerging view that AVF failure is driven primarily by a maladaptive inflammatory-fibrotic microenvironment rather than by intimal hyperplasia per se [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. CD31-positive microvessels may thus serve as a histological signature of this pathological remodeling process. The observed nonlinear threshold effect further suggests a critical point of pathological microvascular activation, beyond which the risk of failure plateaus, potentially indicating an irreversible stage of venous wall injury.\u003c/p\u003e \u003cp\u003eUnivariate analysis indicated an association between greater intimal thickness and failure; however, after adjustment for medial CD31-positive area, this association was markedly attenuated. This implies that intimal thickening may not be an independent driver but rather a concomitant manifestation of the underlying pathological microenvironment characterized by increased microvascular density [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This observation challenges the traditional lumen-centric view and underscores the need to look \u0026ldquo;beyond the lumen\u0026rdquo; toward vascular wall biology[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In this context, intimal thickening is more likely a downstream consequence of signaling from an inflamed and hypoxic medial layer, rather than the initiating event of failure.\u003c/p\u003e \u003cp\u003eThe nonlinear relationship between medial CD31-positive area and failure risk stratifies patients into two distinct phases. Phase I (Escalating Risk): The venous segment experiences hypoxia and endothelial dysfunction, triggering compensatory neovascularization. This process simultaneously exacerbates wall inflammation and remodeling, leading to a progressive increase in failure risk.\u003c/p\u003e \u003cp\u003ePhase II (Risk Plateau): Once neovascularization reaches a certain threshold, wall perfusion is temporarily improved and mechanical stress is mitigated, resulting in stabilization of failure risk.\u003c/p\u003e \u003cp\u003eThis biphasic pattern resembles hypoxia-driven angiogenesis observed in arterial diseases [\u003cspan additionalcitationids=\"CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e17\u003c/span\u003e] but highlights its unique manifestation in the venous wall under AVF-specific hemodynamics.\u003c/p\u003e \u003cp\u003eThe present study provides a quantifiable intraoperative histological marker for real-time assessment of venous wall quality. If validated, measurement of medial microvascular density could be used for risk stratification and intensified surveillance of high-risk patients [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The threshold effect suggests a potential \u0026ldquo;point of no return\u0026rdquo; in venous wall injury; if identified intraoperatively, it could guide decisions such as selecting an alternative anastomotic site or initiating early intervention. Future studies should: 1) validate this biomarker in larger, multicenter cohorts; 2) employ spatial transcriptomics to molecularly characterize the microenvironment associated with high CD31-positive areas [\u003cspan additionalcitationids=\"CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e25\u003c/span\u003e]; and 3) explore in preclinical models whether modulating this microenvironment can improve AVF outcomes [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur conclusions must be interpreted within the context of several limitations. First, the sample size, while adequate for the primary analysis, may limit the generalizability of some secondary findings. Second, histological assessment was performed on a single venous segment, which may not capture the heterogeneity of the entire outflow tract. Third, the retrospective observational design establishes association but not causation. Fourth, while we adjusted for key confounders, residual confounding from unmeasured factors is possible. Finally, the quantitative histomorphometric approach used, though rigorous, requires specialized expertise and equipment, and its standardization across different pathology laboratories needs further evaluation before widespread clinical adoption.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eIn conclusion, this retrospective study identifies increased medial microvascular density (CD31-positive relative surface area) in the cephalic vein at surgery as an independent histopathological factor associated with higher risk of subsequent AVF failure. This finding supports the relevance of the venous wall's biological state in AVF outcomes and provides a specific histological marker for risk assessment. It contributes to the growing evidence that understanding AVF pathophysiology requires integration of biological markers alongside traditional anatomical measurements.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cu\u003eACKNOWLEDGMENTS\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003eThis study involving human participants was approved by the Ethics Committee of Suzhou Hospital, Affiliated Hospital of Medical School of Nanjing University (Approval number: IRB2021116). All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe research leading to these results received funding from\u0026nbsp;Suzhou Gusu Health Talent Plan (No.GSWS2022129)；Suzhou science and Education Strengthening Health Project (No.MSXM2024078)；Science and Technology Program of Suzhou (SKYD2023092, SKYD2023093 and SYWD2024236).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003eThe data supporting the conclusions of this study can be obtained by contacting the email address: [email protected]. Due to the fact that the imaging data and follow-up data in this study involve the personal privacy information of research participants, in accordance with relevant regulations on sensitive data protection, third parties must clearly state the purpose of data use to the research team and provide relevant supporting documents of written consent from the research participants before applying to use the aforementioned data. The research team will provide data access support in accordance with compliant procedures after verifying that the relevant conditions are met, ensuring that data use complies with ethical requirements and personal privacy protection regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003cstrong\u003e：\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJie Li and Goucun Hou contributed to the study conception and design. Data collection was performed by Yulu Wu, Ping Dong, Jiaying Wang and Di Wang; statistical analysis was conducted by Xiaohe Wang, Yuanyuan Zhang; and data analysis and interpretation were carried out by Yulu Wu, Lei Shen, and Jie Li. The first draft of the manuscript was written by Jie Li and Yulu Wu, and all authors provided critical revisions on previous versions of the manuscript. All authors critically revised the manuscript. All authors read and approved the final manuscript and agree to be accountable for all aspects of the work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAitken E, Anijeet H, Ashby D, Barrow W, Calder F, Dowds B, et al. UK Kidney Association Clinical Practice Guideline on vascular access for haemodialysis. BMC Nephrol. 2025;26(1):461. https://doi.org/10.1186/s12882-025-03967-9\u003c/li\u003e\n \u003cli\u003eDember LM, Imrey PB, Beck GJ, Cheung AK, Himmelfarb J, Huber TS, et al. Hemodialysis Fistula Maturation Study Group. Objectives and design of the hemodialysis fistula maturation study. Am J Kidney Dis. 2014;63(1):104-12. https://doi.org/10.1053/j.ajkd.2013.08.019\u003c/li\u003e\n \u003cli\u003eLok CE, Huber TS, Orchanian-Cheff A, Rajan DK. Arteriovenous Access for Hemodialysis: A Review. JAMA. 2024;331(15):1307-1317. https://doi.org/10.1001/jama.2024.1726\u003c/li\u003e\n \u003cli\u003eShenoy S, Allon M, Beathard G, Brouwer-Maier D, Dember LM, Glickman M, et al. Clinical Trial End Points for Hemodialysis Vascular Access: Background, Rationale, and Definitions. Clin J Am Soc Nephrol. 2018;13(3): CJN.13321216. https://doi.org/10.2215/CJN.13321216\u003c/li\u003e\n \u003cli\u003eWoodside KJ, Repeck KJ, Mukhopadhyay P, Schaubel DE, Shahinian VB, Saran R, et al.Arteriovenous Vascular Access-Related Procedural Burden Among Incident Hemodialysis Patients in the United States. Am J Kidney Dis. 2021;78(3):369-379.e1. https://doi.org/10.1053/j.ajkd.2021.02.328\u003c/li\u003e\n \u003cli\u003eLong J, Chen H, Huang Q, Chen X, Ellis RJ, Zanoli L, et al. Analysis of risk factors for late arteriovenous fistula failure and patency rates after angioplasty in hemodialysis patients: a retrospective cohort study. Transl Androl Urol. 2024;13(2):209-217.https://doi.org/10.21037/tau-23-383\u003c/li\u003e\n \u003cli\u003eVenkat Ramanan S, Prabhu RA, Rao IR, et al. Outcomes and predictors of failure of arteriovenous fistulae for hemodialysis. Int Urol Nephrol. 2022;54(1):185-192.https://doi.org/10.1007/s11255-021-03071-7\u003c/li\u003e\n \u003cli\u003eWei S, Liu N, Fu Y, Sun M. Novel insights into modifiable risk factors for arteriovenous fistula failure and the importance of CKD lipid profile: A meta-analysis. J Vasc Access. 2024;25(5):1416-1431.https://doi.org/10.1177/11297298231187275\u003c/li\u003e\n \u003cli\u003eYan R, Song A, Zhang C. The Pathological Mechanisms and Therapeutic Molecular Targets in Arteriovenous Fistula Dysfunction. Int J Mol Sci. 2024;25(17):9519.https://doi.org/10.3390/ijms25179519\u003c/li\u003e\n \u003cli\u003eMartinez L, Rojas MG, Tabbara M, Pereira-Simon S, Santos Falcon N, Rauf M A, et al. The Transcriptomics of the Human Vein Transformation After Arteriovenous Fistula Anastomosis Uncovers Layer-Specific Remodeling and Hallmarks of Maturation Failure. Kidney Int Rep. 2023;8 (4):837-850.https://doi.org/10.1016/j.ekir.2023.01.022\u003c/li\u003e\n \u003cli\u003eVazquez-Padron RI, Duque JC, Tabbara M, Salman LH, Martinez L. Intimal Hyperplasia and Arteriovenous Fistula Failure: Looking Beyond Size Differences. Kidney360. 2021;2(8):1360-1372.https://doi.org/10.34067/KID.0002022021\u003c/li\u003e\n \u003cli\u003eLaboyrie SL, De Vries M R, Bijkerk R, \u0026amp; Rotmans JI. (2023). Building a scaffold for arteriovenous fistula maturation: unravelling the role of the extracellular matrix. Int J Mol Sci, 2023;24(13).https://doi.org/10.3390/ijms241310608\u003c/li\u003e\n \u003cli\u003eKaller R, Russu E, Arbănași EM, Mureșan AV, Jakab M, Ciucanu CC, et al. Intimal CD31-Positive Relative Surfaces Are Associated with Systemic Inflammatory Markers and Maturation of Arteriovenous Fistula in Dialysis Patients. J Clin Med. 2023;12(13):4419. https://doi.org/10.3390/jcm12134419\u003c/li\u003e\n \u003cli\u003ePhillippi JA. On vasa vasorum: A history of advances in understanding the vessels of vessels. Sci Adv. 2022;8(16):eabl6364.https://doi.org/10.1126/sciadv.abl6364\u003c/li\u003e\n \u003cli\u003eMoreno PR, Purushothaman KR, Fuster V, et al. Plaque neovascularization is increased in ruptured atherosclerotic lesions of human aorta: implications for plaque vulnerability. Circulation. 2004;110(14):2032-2038. https://doi.org/10.1161/01.CIR.0000143233.87854.23\u003c/li\u003e\n \u003cli\u003eKuwahara F, Kai H, Tokuda K, Shibata R, Kusaba K, Tahara N, et al. Hypoxia-inducible factor-1alpha/vascular endothelial growth factor pathway for adventitial vasa vasorum formation in hypertensive rat aorta. Hypertension. 2002;39(1):46-50. https://doi.org/10.1161/hy1201.097200\u003c/li\u003e\n \u003cli\u003eL\u0026oacute;pez-Cano C, Rius F, S\u0026aacute;nchez E, Gaeta AM, Betriu \u0026Agrave;, Fern\u0026aacute;ndez E, et al. The influence of sleep apnea syndrome and intermittent hypoxia in carotid adventitial vasa vasorum. PLoS One. 2019;14(2):e0211742.https://doi.org/10.1371/journal.pone.0211742\u003c/li\u003e\n \u003cli\u003eBaria E, Nesi G, Santi R, Maio V, Massi D, Pratesi C, et al. Improved label-free diagnostics and pathological assessment of atherosclerotic plaques through nonlinear microscopy. J Biophotonics. 2018 v;11(11):e201800106.https://doi.org/10.1002/jbio.201800106\u003c/li\u003e\n \u003cli\u003eLee T, Mokrzycki M, Moist L, Maya I, Vazquez M, Lok CE; North American Vascular Access Consortium. Standardized definitions for hemodialysis vascular access. Semin Dial. 2011;24(5):515-24.https://doi.org/10.1111/j.1525-139X.2011.00969.x\u003c/li\u003e\n \u003cli\u003eHijazi A, Bifulco C, Baldin P, Galon J. Digital Pathology for Better Clinical Practice. Cancers (Basel). 2024;16(9):1686.https://doi.org/10.3390/cancers16091686\u003c/li\u003e\n \u003cli\u003eShiu YT, Litovsky SH, Cheung AK, Pike DB, Tey JCS, Zhang Y, et al. Preoperative Vascular Medial Fibrosis and Arteriovenous Fistula Development. Clin J Am Soc Nephrol. 2016;11(9):1615-1623.https://doi.org/10.2215/CJN.00180116\u003c/li\u003e\n \u003cli\u003eDuque JC, Martinez L, Tabbara M, et al. Vascularization of the arteriovenous fistula wall and association with maturation outcomes. J Vasc Access. 2020;21(2):161-168.https://doi.org/10.1177/1129729819864562\u003c/li\u003e\n \u003cli\u003eDavie NJ, Crossno JT Jr, Frid MG, Hofmeister SE, Reeves JT, Hyde DM, et al. Hypoxia-induced pulmonary artery adventitial remodeling and neovascularization: contribution of progenitor cells. Am J Physiol Lung Cell Mol Physiol. 2004;286(4):L668-L678.https://doi.org/10.1152/ajplung.00108.2003\u003c/li\u003e\n \u003cli\u003eBillaud M, Hill JC, Richards TD, Gleason TG, Phillippi JA. Medial Hypoxia and Adventitial Vasa Vasorum Remodeling in Human Ascending Aortic Aneurysm. Front Cardiovasc Med. 2018;5:124.https://doi.org/10.3389/fcvm.2018.00124\u003c/li\u003e\n \u003cli\u003eChumachenko PV, Postnov AY, Ivanova AG, Afanasieva OI, Afanasiev MA, Ekta MB, et al. Thoracic Aortic Aneurysm and Factors Affecting Aortic Dissection. J Pers Med. 2020;10(4):153.https://doi.org/10.3390/jpm10040153\u003c/li\u003e\n \u003cli\u003eG\u0026ouml;ssl M, Versari D, Hildebrandt HA, Bajanowski T, Sangiorgi G, Erbel R, et al. Segmental heterogeneity of vasa vasorum neovascularization in human coronary atherosclerosis. JACC Cardiovasc Imaging. 2010;3(1):32-40.https://doi.org/10.1016/j.jcmg.2009.09.021\u003c/li\u003e\n \u003cli\u003eVirmani R, Kolodgie FD, Burke AP, Finn AV, Gold HK, Tulenko TN, et al. Atherosclerotic plaque progression and vulnerability to rupture: angiogenesis as a source of intraplaque hemorrhage. Arterioscler Thromb Vasc Biol. 2005;25(10):2054-2061.https://doi.org/10.1161/01.ATV.0000178991.71605.18\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Arteriovenous fistula, Hemodialysis, Intimal thickness, CD31-positive relative surface","lastPublishedDoi":"10.21203/rs.3.rs-8787435/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8787435/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective: \u003c/strong\u003eThis study aimed to evaluate the association between specific histological features of the cephalic vein, assessed intraoperatively, for subsequent dysfunction of primary radiocephalic arteriovenous fistulas (RCAVFs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e In a retrospective cohort analysis, 170 patients undergoing first-time RCAVF creation were included. A segment of the cephalic vein had been harvested during surgery and was subjected to standardized histopathological analysis. Key parameters were quantified using digital morphometry and immunohistochemistry: medial microvascular density (CD31-positive relative surface area), intimal thickness, and medial collagen fiber orientation via second-harmonic generation microscopy. AVF failure was defined as the inability to use the fistula successfully for dialysis within 12 months postoperatively. Univariate and multivariate logistic regression, ROC analysis, and restricted cubic spline models were employed to identify and characterize predictors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDuring the follow-up, 40 patients (23.5%) experienced AVF failure. In multivariate analysis, only a larger medial CD31-positive relative surface area remained a significant independent predictor of failure (odds ratio = 1.48 per 0.1% increase, 95% CI 1.15–1.90, P \u0026lt; 0.01). Intimal thickness showed a significant but weaker linear association. The CD31-positive area provided the highest individual predictive accuracy (AUC = 0.78), and a model combining it with intimal thickness achieved an AUC of 0.82. Nonlinear analysis revealed a threshold effect for CD31, with risk plateauing beyond a density of approximately 1.7%. Collagen fiber orientation was not associated with outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e Intraoperative medial microvascular density of the cephalic vein is a robust, independent histopathological maker associated with AVF failure. This finding underscores the importance of the pre-existing biological state of the venous wall, offering a novel biomarker that may refine risk stratification and shift focus toward biological mechanisms in vascular access management.\u003c/p\u003e","manuscriptTitle":"Intimal CD31-positive relative surfaces are associated with the dysfunction of autologous arteriovenous fistulas in patients receiving dialysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 10:04:54","doi":"10.21203/rs.3.rs-8787435/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":"07a5dbe6-6232-4b17-8c82-359b963dafc4","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-30T16:25:19+00:00","versionOfRecord":{"articleIdentity":"rs-8787435","link":"https://doi.org/10.1007/s11255-026-05111-6","journal":{"identity":"international-urology-and-nephrology","isVorOnly":false,"title":"International Urology and Nephrology"},"publishedOn":"2026-03-26 16:10:17","publishedOnDateReadable":"March 26th, 2026"},"versionCreatedAt":"2026-02-16 10:04:54","video":"","vorDoi":"10.1007/s11255-026-05111-6","vorDoiUrl":"https://doi.org/10.1007/s11255-026-05111-6","workflowStages":[]},"version":"v1","identity":"rs-8787435","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8787435","identity":"rs-8787435","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}