Abstract
Background
Autologous radio-cephalic arteriovenous fistulas (RC-AVFs) represent the first option for hemodialysis in China. However, they exhibit a high rate of failure to mature.
Methods
A total of 196 first-time RC-AVFs were included. We analyzed preoperative clinical and ultrasonic characteristics and bioelectrical impedance to screen risk factors using univariate and multivariate logistic regression. Subsequently, we constructed a nomogram and employed bootstrap resampling for internal validation. Additionally, we developed a risk score equation using a simplified Framingham heart study point system. Finally, we used a restricted cubic spline diagram to determine the clinical significance of the model variables.
Results
Seventy-six (38.8%) RC-AVFs failed to mature within 6 months. We identified arterial diameter (AD), total cholesterol (CHO) levels, lean tissue index (LTI), and a history of coronary artery disease (CAD) (p < 0.005) as independent impact factors through univariate and multivariate logistic regression. The area under the receiver operating characteristic curve was 0.79 (95% confidence interval [CI]: 0.72–0.85), and the bootstrap-corrected concordance index was 0.73 (95 % CI: 0.713–0.763). Based on the risk scoring system (0–22 points), patients were categorized into low (0–10), medium (11–14), and high-risk (15–22) groups. Finally, a restricted cubic spline diagram illustrated a significant increase in adverse event risk with an AD ≤ 2 mm, CHO levels ≥ 3.8 mmol/L, and LTI ≤ 14 kg/m2.
Conclusion
The risk prediction model incorporating LTI, CHO levels, AD, and a history of CAD showed good predictive performance for RC-AVF outcomes in patients with chronic kidney disease.
Keywords: End-stage renal disease, radial-cephalic arteriovenous fistula, risk prediction model, 6-month outcome, failure to mature
Introduction
Chronic kidney disease (CKD) is a significant contributor to the burden of noncommunicable diseases. It intricately interacts with other organs and systems, leading to physical deterioration, shortened life expectancy, and heightened mortality risk. In most countries, hemodialysis (HD) is the main renal replacement therapy for patients with end-stage CKD [1]. According to the Chinese National Renal Data System, the number of patients on HD in China surged to 916,647 by the end of December 2023, with a rapid annual growth rate. Vascular access (VA) is the lifeline of patients on HD. Radial-cephalic arteriovenous fistula (RC-AVF) remains the primary VA in China owing to lower extracorporeal blood flow, younger patients, and lower diabetic nephropathy [2,3]. However, challenges associated with failure to mature, wherein a newly created fistula fails to mature for use, have reshaped the fistula-first approach [4]. Several studies report that 40–60% of RC-AVFs fail to mature, underscoring the urgent need for timely intervention [5].
After the establishment of AVF, the vein experiences increased blood flow, leading to enlargement of its lumen and thickening of its walls until it reaches a stage suitable for puncture, known as maturation. Only a mature AVF can be used for HD [6]. Fistula maturation failure necessitates interventions to aid maturation or requires reoperation, invariably leading to prolonged use of central vein catheters [7], escalating medical costs, and increasing patient discomfort. Therefore, it is imperative to identify predictors and establish a predictive model for RC-AVF maturation failure. Currently, various models have been published to predict AVF maturity [8–11]. Unfortunately, many of these studies have not consistently predicted vascular access outcomes across diverse patient populations. This inconsistency may stem from variations in study populations, types of AVF, and substantial heterogeneity of outcome definitions.
Body composition plays a critical role in the clinical management of CKD, particularly in determining the necessity and timing of AVF placement. The lean tissue index (LTI), a simple quantitative measure of nutritional status and muscle mass [12], has been reported to predict AVF maturation [13]. This study explored the relationship between LTI and other clinical parameters associated with RC-AVF failure to mature within 6 months in patients undergoing hemodialysis. Our study aimed to develop a novel predictive model for RC-AVF maturation and facilitate early interventions for RC-AVF at high risk of maturation.
Methods
This retrospective study enrolled patients with CKD who underwent RC-AVF surgery at the First Affiliated Hospital of Zhengzhou University between June 2021 and January 2023. The study adhered to the principles of the Declaration of Helsinki (revised in 2013) and was approved by the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (approval number: 2021-KY-0832). All patients were informed about the purpose of the clinical study and signed an informed consent form.
Inclusion criteria were as follows: (1) patients aged ≥18 years; (2) patients diagnosed with CKD stage 4 and 5; (3) fistula creation between the radial artery and cephalic vein.
Exclusion criteria were: (1) patients who previously underwent AVF or AVG creation; (2) life expectancy ≤ 6 months; (3) psychiatric illness impeding cooperation with the investigation; (4) limb defects or metal stents in the body preventing bioelectrical impedance examination; (5) incomplete data; (6) and refusal to participate in the study (Figure 1).
Figure 1.
Inclusion and exclusion criteria for enrolled patients.
RC-AVF surgery involved a preoperative assessment to determine fistula type and location. RC-AVF was created using a standard surgical procedure under local anesthesia. End-to-side anastomosis between the radial artery and cephalic vein was performed using a continuous 7-0 prolene vascular suture. The anastomotic diameter was approximately 0.6–0.7 cm. Postoperatively, no antiplatelet or anticoagulant drugs were administered.
Data collection
Data on patient-related factors were collected at baseline prior to AVF creation from electronic medical records. This included demographic data (age and sex), clinical information (diabetes mellitus, coronary artery disease (CAD), tumor, preoperative dialysis time, and clinical diagnosis), and relevant blood investigations at the time of AVF creation. CAD was defined as a history of myocardial infarction, coronary artery stenosis detected via angiography, or prior coronary angioplasty, stent implantation, or cardiac bypass surgery. Blood investigations included routine blood tests, liver and kidney function tests, electrolytes, C-reactive protein, and B-type natriuretic peptide precursor. For patients dependent on HD, pre-dialysis blood samples were obtained, and serum calcium levels were corrected for albumin.
The radial artery was palpated at the wrist before examination with ultrasound Doppler to mark the direction and exact position of the vessel. Experienced physicians performed vessel mapping using a Philips HD11 Doppler ultrasound with a 10-MHz probe. M-mode was utilized to precisely measure the systolic internal diameter of the radial artery. The systolic cross-sectional area of the radial artery was measured using B-mode imaging, and the time-averaged velocity (TAV) was calculated directly from the Doppler spectral waveform. Blood flow volume was calculated based on the TAV and cross-sectional area. To ensure consistency, all ultrasound examinations were conducted by a single experienced operator. LTI (lean tissue mass/height2] was derived from impedance data based on a three-compartment model using a Body Composition Analyzer (BC-420MA; Tanita, Tokyo, Japan).
AVF survivance and definition
Maturation was defined as the absence of alternative access usage for over a month. Failure to mature was defined as loss of patency, failure to meet the maturation definition, or necessitating assistant procedures to achieve maturation 6 months after AVF creation. The vascular access team initially evaluated maturation 1 month after AVF creation, primarily using ultrasonography to assess blood flow, depth from the skin surface, and venous diameter. If appropriate, RC-AVF was punctured, and subsequently maturation was assessed based on the above criteria in the following month. Those who were not suitable for puncture were evaluated again in 3 and 6 months.
Statistical analysis
R software (version 4.2.1) was used for statistical analysis in this study. Quantitative data with Gaussian distribution were expressed as means ± standard deviation, and non-normally distributed data were expressed as median and interquartile range. Count data were reported as numbers and percentages. Two-sample t-tests (for normally distributed data) and Wilcoxon rank-sum tests (for non-normal data) were used to compare quantitative variables between the two groups. The χ2 test was used to compare categorical variables between the two groups. Factors associated with 6-month RC-AVF maturation failure were identified using univariate and multivariate logistic regression analyses. Variables demonstrating significant differences (p < 0.05) in univariate logistic regression were incorporated into the multivariate logistic regression analysis. The backward stepwise method was used to screen the model variables. Subsequently, a nomogram for 6-month RC-AVF maturation failure was constructed. The bootstrap method was used for 500 iterations to validate the nomogram internally through repeated sampling. The model’s discriminative ability was evaluated using Harrell’s concordance index and the area under the receiver operating characteristic curve (AUC). Additionally, a calibration diagram was employed to analyze accuracy. Statistical significance was set at p < 0.05. Based on the Framingham heart study, a simple point system was adopted to assess the likelihood of RC-AVF immaturity more precisely [14]. Finally, a restricted cubic spline diagram (RCS) was utilized to interpret the clinical significance of surveillance variables.
Results
Baseline clinical characteristics
In total, 196 RC-AVFs were included in this study. The baseline clinical characteristics of all patients are presented in Table 1. These patients had an average age of 50 ± 13 years, and 67.9% were male. The leading causes of CKD were chronic glomerulonephritis (100 patients, 51%) and diabetic kidney disease (73 patients, 37.2%), other causes including polycystic kidney disease (5 patients, 2.7%), lupus nephritis (4 patients, 2.0%), multiple myeloma (4 patients, 2.0%), hypertensive renal damage (4 patients, 2.0%), ANCA-associated vasculitis (2 patients, 1.1%), obesity-related glomerulopathy (1 patient, 0.5%), congenital renal dysplasia (1 patient, 0.5%), obstructive nephropathy (1 patient, 0.5%), and anti-GBM disease (1 patient, 0.5%). One hundred and thirty-nine (70.9%) patients had a history of central venous catheterization for dialysis before RC-AVF surgery. Seventy-six patients (38.8%) had RC-AVF maturation failure. In the RC-AVF maturation group, there were 91 male and 29 female patients, with an average age of 48 ± 13 years. In contrast, the RC-AVF failure to mature group comprised 42 male and 34 female patients, with an average age of 53 ± 13 years. In the failure to mature group, 47 AVFs successfully matured after percutaneous transluminal angioplasty, 7 AVFs were treated with surgical reconstruction due to thrombosis, 2 AVFs converted to arteriovenous grafts, and 8 AVFs converted to central venous catheters. The remaining 12 AVFs had inadequate blood flow without treatments. Among the 12 deceased patients, causes of death included cardiogenic shock (2 patients), myocardial infarction (2 patients), malignant arrhythmia and ventricular fibrillation (1 patient), cerebral hemorrhage (2 patients), diabetes-related complications (1 patient), and car accident (1 patient). During the follow-up period, which coincided with the coronavirus disease pandemic, three patients died from severe pneumonia caused by severe acute respiratory syndrome coronavirus-2 infection. Cardiac events were the leading cause of death among these patients.
Table 1.
Baseline of clinical characteristics.
| Variables | Total (n = 196) | Maturation group (n = 120) | Failure to mature group (n = 76) | p |
|---|---|---|---|---|
| Age (years) | 50 ± 13 | 48 ± 13 | 53 ± 13 | 0.005 |
| Gender(male) | 133 (67.9%) | 91 (75.8%) | 42 (55.3%) | 0.003 |
| Doppler ultrasound indexes | ||||
| Arterial diameter (mm) | 2.0 (1.8–2.3) | 2.1 (1.8–2.5) | 1.9 (1.7–2.1) | <0.001 |
| Venous diameter (mm) | 2.7 (2.3–3.1) | 2.8 (2.3–3.3) | 2.6 (2.2–3.0) | 0.027 |
| Laboratory examinations | ||||
| WBC (×109 g/L) | 5.7 (4.6–7.0) | 5.7 (4.6–6.8) | 5.8 (4.6–7.1) | 0.896 |
| Hemoglobin (g/L) | 90.1 (75.9-105) | 89.1 (72.8–103.8) | 91 (79–109) | 0.432 |
| Platelet (×109 g/L) | 182 (140–219) | 180 (139–214) | 186 (142–223) | 0.44 |
| Calcium (mmol/L) | 2.2 (2.0–2.3) | 2.15 (2.0–2.2) | 2.2 (2.0–2.3) | 0.526 |
| Phosphorus (mmol/L) | 1.8 (1.5–2.3) | 1.9 (1.5–2.3) | 1.8 (1.4–2.2) | 0.449 |
| Albumin (g/L) | 36.4 ± 6.1 | 36.9 ± 5.8 | 35.5 ± 6.5 | 0.128 |
| Globlin (g/L) | 28.8 ± 5.3 | 28.6 ± 5.2 | 29.2 ± 5.4 | 0.323 |
| Cholesterol (mmol/L) | 3.9 (3.0–4.6) | 3.7 (3.0–4.4) | 4.1 (3.4–5.2) | 0.023 |
| LDL (mmol/L) | 2.1 (1.6–2.8) | 2.2 (1.5–2.6) | 2.1 (1.7–3.2) | 0.102 |
| CRP (mg/L) | 2.9 (1.4–9.7) | 3.2 (1.5–10.1) | 2.5 (1.2–9.3) | 0.3 |
| BMI (kg/m2) | 23.4 (20.9–26.1) | 23.6 (21.1–25.8) | 23.6 (20.4–26.4) | 0.701 |
| LTI (kg/m2) | 14.1 (11.8–16.6) | 15.3 ± 2.9 | 12.2 (10.8–14.4) | <0.001 |
| Comorbidity | ||||
| DM | 75 (38.3%) | 41 (34.2%) | 34 (44.7%) | 0.139 |
| Coronary artery disease | 42 (21.4%) | 18 (15.0%) | 24 (31.6%) | 0.006 |
| Cancer | 5 (2.6%) | 3 (2.5%) | 2 (2.6%) | 0.955 |
| History of CCVC | 139 (70.9%) | 83 (69.2%) | 56 (73.7%) | 0.5 |
| Clinical diagnosisnous | ||||
| Chronic glomerulonephritis | 100 (51%) | 67 (56%) | 33 (43%) | 0.1 |
| DKD | 73 (37.2%) | 39 (33%) | 34 (45%) | 0.1 |
| Others | 23 (11.8%) | 14 (11%) | 9 (12%) | >0.5 |
Table note: LDL, low-density lipoprotein; DKD, diabetic kidney disease; CRP, C-reactive protein; BMI, body mass index; DM, diabetes mellitus; LTI, lean tissue index; WBC, white blood cell; CCVC, cervical central venous catheter.
Univariate logistic regression analysis of risk factors for RC-AVF maturation failure
Univariate logistic regression analysis revealed significant differences in age, sex, arterial diameter (AD), venous diameter, total cholesterol (CHO) levels, LTI, and a history of CAD between the two groups (p < 0.05, Table 2). Older age (odds ratio [OR] = 1.033, 95% CI: 1.009–1.058, p = 0.006), female sex (OR = 2.54, 95% CI: 1.372–4.702, p = 0.003), high CHO levels (OR = 1.305, 95% CI: 1.034–1.647, p = 0.025), and a history of CAD (OR = 2.615, 95% CI: 1.303–5.248, p = 0.007) were identified as risk factors for RC-AVF maturation failure. Conversely, a larger AD (OR = 0.219, 95% CI: 0.095–0.502, p < 0.001), larger venous diameter (OR = 0.576, 95% CI: 0.352–0.944, p = 0.028), and higher LTI (OR = 0.707, 95% CI: 0.624–0.802, p < 0.001) were protective factors for RC-AVF maturation failure.
Table 2.
Univariate logistic regression analysis of risk factors for AVF maturation failure.
| Parameters | Odds ratio | 95%CI | p |
|---|---|---|---|
| Age | 1.033 | 1.009-1.058 | 0.006 |
| Gender (female) | 2.54 | 1.372-4.702 | 0.003 |
| Arterial diameter | 0.219 | 0.095-0.502 | <0.001 |
| LTI | 0.707 | 0.624-0.802 | <0.001 |
| Serum total cholesterol | 1.305 | 1.034-1.647 | 0.025 |
| LDL | 1.249 | 0.955-1.634 | 0.105 |
| Coronary artery disease | 2.615 | 1.303-5.248 | 0.007 |
| Hemoglobin | 1.005 | 0.992-1.019 | 0.431 |
| Vein diameter | 0.576 | 0.352-0.944 | 0.028 |
| Albumin | 0.964 | 0.919-1.011 | 0.129 |
| BMI | 0.987 | 0.923-1.055 | 0.699 |
| WBC | 0.992 | 0.887-1.111 | 0.895 |
| Platelet | 1.002 | 0.998-1.006 | 0.438 |
| Calcium | 1.511 | 0.424-5.384 | 0.524 |
| Phosphorus | 0.833 | 0.519-1.335 | 0.448 |
| History of CCVC | 0.801 | 0.422-1.521 | 0.498 |
| Diabetes | 1.56 | 0.866-2.811 | 0.139 |
| CRP | 0.991 | 0.974-1.009 | 0.325 |
Table note: LDL, low-density lipoprotein; CRP, C-reactive protein; BMI, body mass index; LTI, lean tissue index; WBC, white blood cell; CCVC, cervical central venous catheter.
Multivariate logistic regression analysis and nomogram for RC-AVF maturation failure
Significant elements identified in the univariate logistic regression analysis were included in the multivariate logistic regression analysis. RC-AVF maturation failure within 6 months postoperatively was designated as the dependent variable (assignment: maturation = 0; maturation failure = 1), and influencing factors were further screened using the backward stepwise method. Serum CHO levels (OR = 1.307, 95% CI: 0.997–1.712) and a history of CAD (OR = 2.93, 95% CI: 1.325–6.480) emerged as independent risk factors for RC-AVF maturation failure (p < 0.05, Table 3). Conversely, a larger AD (OR = 0.36, 95% CI: 0.459–0.934) and higher LTI (OR = 0.737, 95% CI: 0.646–0.841) were found to be protective factors for AVF maturation failure (p < 0.05; Table 3). A predictive nomogram for RC-AVF failure to mature was developed based on four key clinical variables (Figure 2(A)). The nomogram had good discrimination ability, with a model AUC of 0.79 (95% CI: 0.72–0.85) (Figure 2(B)). Bootstrap was employed to extract 500 samples for internal validation, resulting in the concordance index of 0.73 (95 % CI: 0.713–0.763) for the verification set. The calibration curve demonstrated good agreement between predicted and observed risks (Figure 2(C)).
Table 3.
Multivariate logistic regression analysis of risk factors for AVF maturation failure.
| Risk factors | Regression coefficient | OR | 95%CI | p |
|---|---|---|---|---|
| Coronary artery disease | 1.075 | 2.93 | 1.325-6.480 | 0.008 |
| lean tissue index | −0.305 | 0.737 | 0.646-0.841 | <0.001 |
| Serum total cholesterol | 0.267 | 1.307 | 0.997-1.712 | 0.048 |
| Larger arterial diameter | −1.021 | 0.36 | 0.459-0.934 | 0.028 |
| Constant | 4.541 |
Figure 2.
The risk prediction model construction for RC-AVF maturation failure. (A) Nomogram of risk factors for 6-month RC-AVF failure to mature; (B) sensitivity; (C) Calibration curve.
Construction of risk scoring model and RCS
To enhance the clinical applicability of the predictive model, we devised a scoring equation based on the Framingham Heart Study simple point system. The detailed steps were as follows (Table 4). First, significant parameters (p < 0.05) from the multivariate logistic regression analysis were integrated into the scoring equation. ORs and 95% CIs were used to enhance interpretability. Second, reference values for each category were determined. Given that AD, serum CHO, and LTI were continuous variables, they were primarily discretized. AD was segmented into intervals of 0.5, serum CHO levels at intervals of 2, and LTI at intervals of 5. The lowest-risk group was the reference, with the median value used for each interval. The remaining categorical variables were assigned 0 and 1 as the reference values. Third, the distance between the median value of each interval and the reference group (Wij-WiREF) was calculated. Fourth, regression units for each risk factor category were further calculated using the regression coefficient (βi) multiplied by the numerical distance (Wij-WiREF). The constant (B) for the point system was determined as B = (−0.5) × (−1.0213) = 0.51065. Fifth, the score associated with each group was computed using the formula: Pointsij = βi(Wij −WiREF)/B. The points were rounded to the nearest integer. Finally, the probability of AVF maturation was calculated as follows: (Table 5). The total score of the model was 22, and patients were further categorized into low (0–10), medium (11–14), and high-risk (15–22) groups. Inter-group comparisons showed differences among the risk groups (Table 6).
Table 4.
Score for each risk factor.
| Risk factor | Categories | Reference value (Wij) | βi | βi(Wij–WiREF) | Pointsij = βi(Wij–WiREF)/B |
|---|---|---|---|---|---|
| Arterial diameter | 1.0-1.4 | 1.2 | −1.0213 | 2.5533 | 5 |
| 1.5-1.9 | 1.7 | 2.0426 | 4 | ||
| 2.0-2.4 | 2.2 | 1.532 | 3 | ||
| 2.5-2.9 | 2.7 | 1.0213 | 2 | ||
| 3.0-3.4 | 3.2 | 0.5107 | 1 | ||
| 3.5-3.9 | 3.7 = W1REF | 0 | 0 | ||
| Cholesterol | 1.0-2.9 | 1.95 = W2REF | 0.2674 | 0 | 0 |
| 3.0-4.9 | 3.95 | 0.5348 | 1 | ||
| 5.0-6.9 | 5.95 | 1.0696 | 2 | ||
| 7.0-8.9 | 7.95 | 1.6044 | 3 | ||
| Lean tissue index | 5.0-9.9 | 7.45 | −0.3049 | 6.098 | 12 |
| 10.0-14.9 | 12.45 | 4.5735 | 9 | ||
| 15.0-19.9 | 17.45 | 3.049 | 6 | ||
| 20.0-24.9 | 22.45 | 1.5245 | 3 | ||
| 25.0-29.9 | 27.45 = W3REF | 0 | 0 | ||
| Coronary artery disease | 1 | 1.075 | 2.1051 | 2 | |
| 0 = W4REF | 0 | 0 |
Table 5.
RC-AVF 6-month maturation failure risks.
| Point total | ∑βiXi | Estimate of risk |
|---|---|---|
| 0 | −7.086 | 0.0008 |
| 1 | −6.5754 | 0.0014 |
| 2 | −6.0648 | 0.0023 |
| 3 | −5.5541 | 0.0039 |
| 4 | −5.0435 | 0.0064 |
| 5 | −4.5328 | 0.0106 |
| 6 | −4.0222 | 0.0176 |
| 7 | −3.5115 | 0.029 |
| 8 | −3.001 | 0.0474 |
| 9 | −2.4902 | 0.0765 |
| 10 | −1.9796 | 0.1214 |
| 11 | −1.469 | 0.1871 |
| 12 | −0.9583 | 0.2772 |
| 13 | −0.4476 | 0.39 |
| 14 | 0.063 | 0.5157 |
| 15 | 0.5737 | 0.6396 |
| 16 | 1.0843 | 0.7473 |
| 17 | 1.595 | 0.8313 |
| 18 | 2.1056 | 0.8914 |
| 19 | 2.6163 | 0.9319 |
| 20 | 3.1269 | 0.958 |
| 21 | 3.6376 | 0.9744 |
| 22 | 4.1482 | 0.9845 |
Table 6.
Inter-group comparisons between risk groups.
| Characteristic | OR | 95% CI | p-value |
|---|---|---|---|
| Group | |||
| Low risk | 1 | reference | |
| Medium risk | 4.84 | (2.02–13.5) | <0.001 |
| High risk | 24.7 | (8.49–83.5) | <0.001 |
OR = Odds Ratio, CI = Confidence Interval.
The RCS depicted in Figure 3 illustrates its clinical significance. The reference points (hazard ratio/OR = 1) were 2.0 mm for AD, 3.8 mmol/L for CHO levels, and 14 kg/m2 for LTI. The results indicated that the risk of AVF maturation failure significantly increased with an AD ≤ 2 mm (Figure 3(A)), CHO levels ≥ 3.8 mmol/L (Figure 3(B)), and LTI ≤ 14 kg/m2 (Figure 3(C)).
Figure 3.
Restricted cubic spline (RCS). RCS analysis of AD (A), CHO (B) and LTI (C) according to AVF maturation failure. The reference points (HR/OR =1) were 2.0 mm for arterial diameter, 3.8 mmol/L for cholesterol, and 14 kg/m2 for LTI; OR, odds ratio; CI, confidence interval; HR, hazard ratio. AD, arterial diameter; CHO, cholesterol; LTI, lean tissue index.
Discussion
According to the baseline data report from the China Dialysis Outcomes and Practice Patterns Study published in 2021, the primary cause of end-stage renal disease (ESRD) in China is chronic glomerulonephritis (45.9%), followed by diabetic kidney disease (19.9%) [15]. In our study, chronic glomerulonephritis was the leading cause (51%), followed by diabetic kidney disease (37.2%). Notably, the prevalence of diabetic kidney disease in our study is higher than the national data. This discrepancy may be attributed to differences in the study period, geographic region, and the relatively smaller sample.
Successful HD depends on well-functioning vascular access. Compared with catheters, AVFs offer longer survival times and lower complication rates [16]. The United States National Vascular Access Improvement Initiative launched the “fistula first” initiative in 2003 to promote AVF usage among patients undergoing HD in the United States. However, clinical practice has revealed that early unassisted patency rates of AVF are not consistently high, with approximately 30-70% experiencing dysmaturity, hindering their usability. Previous studies have identified several factors influencing AVF maturation, including inflammation, anastomotic artery type, venous insufficiency, malnutrition, race, CAD, peripheral vascular disease, and age [17–23]. Therefore, comprehensive preoperative assessments and postoperative monitoring are crucial during the early stages of AVF establishment. RC-AVF is widely used in China and other countries; however, its maturation rate remains relatively low. Currently, there is no simple and easy-to-use early prediction model for RC-AVF maturation. Consequently, four predictors, AD, CAD, CHO levels, and LTI, were identified through univariate and multivariate logistic regression, and a risk prediction model was established to improve the 6-month RC-AVF maturation rate.
Protein malnutrition is prevalent among patients undergoing HD, affecting approximately 25-50% [24]. The nutritional status of these patients is influenced by factors such as inflammation, uremic toxin accumulation, metabolic acidosis, mental depression, and other factors [25]. Malnutrition not only contributes to various adverse clinical outcomes of HD but also significantly impacts AVF maturation [26]. Multiple indicators, including body mass index, triceps skin fold thickness, and grip strength, are used to assess the nutritional status of patients undergoing HD. Muscle mass can also be used to determine nutritional status. The advent of body composition analysers allows for more accurate assessment of patients’ muscle tissue content [27]. In the past, nephrologists focused on predicting the patient’s water load using this apparatus, overlooking its role in assessing muscle content. The LTI accurately reflects muscle content in the results from body composition analyzers. Dai et al. identified low LTI as an independent risk factor for AVF dysfunction in patients undergoing HD [28], aligning with our study’s findings. Our study examined the relationship between LTI and 6-month RC-AVF failure to mature, revealing that lower LTI was associated with decreased RC-AVF maturation rates. The RCS diagram showed a marked increase in RC-AVF immaturity risk when LTI was ≤ 14 kg/m2. Low LTI reflects poor nutritional status and protein-energy wasting, potentially impairing AVF maturation. Additionally, Wang et al. clarified that the levels of interleukin-6 and tumour necrosis factor-α in patients with low LTI were significantly higher when compared with those in patients with normal LTI, and high inflammation was also an unfavorable factor for AVF [29].
Previous studies have indicated that venous diameter predicts AVF maturation. Luavao et al. evaluated 158 patients to determine factors influencing AVF maturation, identifying venous diameter as the sole independent predictor [30]. The cutoff point for venous diameter is currently inconsistent, such as 2, 2.5, 3 and 4 mm [31–33]. The likelihood of AVF failing to mature increased when the venous diameter fell below this threshold. However, our study found that venous diameter did not significantly impact AVF maturation. Further analysis revealed that only 2 out of 16 patients with a venous diameter < 2 mm experienced maturation failure of the RC-AVF within 6 months postoperatively. We speculate that this may be attributed to the small sample size, as the number of patients with venous diameters < 2 mm was insufficient to accurately reflect the success rate in this subgroup. Moreover, our study found that a larger venous diameter was a protective factor for RC-AVF maturation in univariate logistic regression. However, in multivariate logistic regression, the impact of venous diameter on RC-AVF maturation was not found to be significant. The influence of venous diameter on AVF maturation may have been masked by more dominant clinical factors.
Venous remodeling plays a crucial role in AVF maturation [34]. Therefore, the influence of venous dilatation on AVF maturation cannot be disregarded. Adequate venous dilatation may mitigate the maturation risk associated with very small vein diameter [35]. This is also the purpose of establishing a risk-prediction model. Given the complexity of the AVF maturation process, it is essential to consider various factors comprehensively rather than relying solely on one clinical indicator.
Elevated cholesterol levels have been linked to increased incidence of peripheral vascular disease, whereas statins have shown potential for reducing major limb adverse events [36]. Increased low-density lipoprotein (LDL), triglycerides levels, and decreased high-density lipoprotein levels were associated with endothelial dysfunction and reduced nitric oxide bioavailability, leading to local vasomotor functional derangement [37]. Wei et al. demonstrated that increased CHO and LDL levels were associated with AVF failure [38]. Our study similarly indicated that a CHO level below 3.8 mmol/L was associated with a lower rate of 6-month RC-AVF failure to mature. However, in patients with ESRD, the “cholesterol paradox” exists, where lower CHO levels are associated with more cardiovascular events due to increased protein energy expenditure and inflammation [39]. Hence, the Kidney Disease: Improving Global Outcomes guidelines do not recommend the use of statins or a combination of statins and ezetimibe in patients who have not received statin therapy before HD. However, if patients have already started statin therapy before HD, then they should continue the treatment. This may be due to the higher risk of cardiovascular events in these patients. Therefore, for patients who have not received statin therapy prior to HD, localized drug delivery may be a strategy to improve RC-AVF maturation rates [40].
In our study, a history of central venous catheterization was not identified as an independent significant risk factor for AVF maturation in univariate logistic regression analysis. However, previous studies have linked catheterization to impaired AVF outcomes, with higher rates of central vein stenosis and lower AVF survival [41,42]. This may be due to chronic inflammation and endothelial damage, which contribute to venous stenosis over time. Prolonged catheter dwell time is a key risk factor for stenosis. In our study, the median catheterization duration was 6 months, suggesting that short-term catheterization may not cause significant structural changes in the vein, minimizing its impact on AVF maturation. Moreover, some studies suggest worse outcomes with ipsilateral catheterization [43], however, only eight patients in our study had this history. Given the small sample size, the statistical power may have been insufficient to detect a significant effect, which could have influenced our results.
Study limitations
This model has some limitations. Firstly, it was developed based on a retrospective analysis conducted at a single center with a relatively small sample size, which may have introduced potential confounding factors. Therefore, the findings require validation through large-scale, multicenter prospective studies to enhance their generalizability. Secondly, certain parameters, such as flow-mediated dilation and nitroglycerin-mediated dilation, were not included in the model. Incorporating these factors in future studies may provide a more comprehensive understanding of AVF maturation and improve predictive accuracy.
Conclusions
We constructed a risk prediction model that incorporated the LTI along with AD, history of CAD, and serum CHO levels, providing valuable insights for the creation of AVF.
Acknowledgments
Author Pei Wang and author Wan Zeng have given substantial contributions to the conception or the design of the manuscript, author Wan Zeng, author Beihao Zhang, author Xin Wang, author Ruimin Wang, author Yunxia Niu, author Xiaohong Yue and author Xianhui Liang to acquisition, analysis and interpretation of the data. All authors have participated to drafting the manuscript, author Pei Wang revised it critically. All authors read and approved the final version of the manuscript.
Funding Statement
This study was funded by the National Natural Science Foundation of China (No. 81873612).
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The data associated with the paper are not publicly available but are available from the corresponding author on reasonable request.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data associated with the paper are not publicly available but are available from the corresponding author on reasonable request.