Xu X, Zhuang Y, Zeng J, Cai F, He T, Wu J, Chen C, Zou Z, Zhang X, Lv G. Value of a quantitative model of axillary venous blood flow spectrum for the detection of central venous stenosis in patients undergoing hemodialysis via radiocephalic arteriovenous fistula.
ANNALS OF TRANSLATIONAL MEDICINE 2022;
10:77. [PMID:
35282095 PMCID:
PMC8848436 DOI:
10.21037/atm-22-160]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 01/13/2022] [Indexed: 11/10/2022]
Abstract
Background
Central venous stenosis (CVS) of radiocephalic arteriovenous fistula (RCAVF) affects RCAVF function and longevity. Ultrasound screening for CVS is limited by acoustic window. Herein, we analyzed the quantitative axillary venous (AxV) spectrum in hemodialysis patients via RCAVF, and constructed central venous stenosis index (CVSI) model based on the spectrum parameters to early detect resting asymptomatic CVS.
Methods
From August 2017 to May 2021, stage 5 chronic kidney disease (CKD) patients dialysed via RCAVF at the First Affiliated Hospital of Fujian Medical University were included in this study. No CVS-related symptoms were found and the pulsation at the arteriovenous anastomosis was normal. However, the patients had the sensation of swelling in the ipsilateral upper limb during dialysis; the venous pressure advanced upon the completion of dialysis; or both (n=52). The inclusion criteria were as follows: (I) Ultrasound (US) showed that the temporal phases of the AxV spectrum were “normal”; and (II) CVS was confirmed by digital subtraction angiography (DSA). The exclusion criteria were as follows: (I) stent placement; (II) multiple stenosis; and (III) placement of central venous catheter. A total of 37 patients participated in the analysis. Eighteen patients were included in the CVS group, and 19 cases without CVS were included in the control group. Independent sample t-test was used to screen each parameter of the AxV spectrum, and a CVSI model was constructed by principal component analysis (PCA). The receiver operating characteristic curve (ROC) was applied to analyze the diagnostic value of CVSI.
Results
According to the independent sample t-test, 9 parameters were found to have statistical significance (all P<0.05); they were analyzed by PCA, and the CVSI model was constructed. The ROC showed that CVSI had diagnostic value for CVS. When the cut-off value of CVSI was 7.13, the maximum value of the Youden index was 0.842, with a sensitivity of 100% and a specificity of 84.2%.
Conclusions
The CVSI helps to early detect resting asymptomatic CVS and dramatically increases the detection rate of CVS.
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