Effect of perivascular adipose tissue on patency and maturation rate of arteriovenous fistula in hemodialysis patients
摘要
This prospective observational study aimed to examine the effects of the volume of perivascular adipose tissue (PVAT) and the fat attenuation index (FAI) on maturation and primary patency following the formation of an initial arteriovenous fistula (AVF).
MethodsThis study enrolled 106 patients who underwent first-time upper limb AVF creation. PVAT volume and FAI were quantified using non-contrast computed tomography (CT) within 2 weeks postoperatively. Patients were monitored for 12 months to evaluate primary patency and maturation. Statistical analyses included receiver operating characteristic (ROC) curve analysis to determine optimal cutoff values for group stratification, Kaplan–Meier survival analysis with the Log-rank tests, and multivariable Cox proportional hazards regression to identify independent risk factors.
ResultsA total of 33 patients (31.1%) exhibited adverse events within 1 year. ROC analysis determined the optimal cutoffs for group stratification of 4.32 cm3 for PVAT volume and –75.8 Hounsfield Units for FAI. According to Kaplan–Meier analysis, patients with lower PVAT volume and higher FAI had significantly worse primary patency (Log-rank P < 0.05). Lower PVAT volume (hazard ratio [HR]: 1.15, 95% confidence interval (CI) 1.03–1.28, P = 0.02) and higher FAI (HR: 1.06, 95% CI 1.01–1.12, P = 0.03) were identified as independent risk factors for adverse events in independent multivariable Cox models that were adjusted for age and AVF configuration. However, FAI was not significantly associated with the AVF maturation rate in survival analysis.
ConclusionsIn this prospective cohort, lower PVAT volume and higher FAI were independently associated with an increased risk of adverse events and decreased primary patency following AVF formation. According to these findings, AVF outcomes may be influenced by the perivascular adipose microenvironment, and PVAT volume and FAI may be useful imaging biomarkers for risk assessment.