Arteriovenous Shunt Stenosis Research Articles

Study of Osteopontin as a Marker of Arteriovenous Shunt Stenosis in Hemodialysis Patients.

Although arteriovenous fistula (AVF) is the recommended access for hemodialysis (HD), it carries a high risk for stenosis. Since osteopontin (OPN) is implicated in the process of vascular calcification in HD patients, OPN may be a marker for AVF stenosis. The present study evaluated OPN as a potential marker of AVF stenosis in HD patients. Diagnosing a stenotic lesion was made by combining B mode with color and pulse wave Doppler imaging. Criteria for diagnosis of stenotic AVF included 50% reduction in diameter in B mode in combination with a 2-3-fold increase of peak systolic velocity compared with the unaffected segment. The present study included 60 HD patients with stenotic AVF and 60 patients with functional AVF. Comparison between the two groups revealed that patients in the former group had significantly higher serum OPN levels [median (IQR): 17.1 (12.1-30.4) vs 5.8 (5.0-10.0) ng/mL, p<0.001]. All patients were classified into those with low (< median) and with high (≥ median) OPN levels. Comparison between these groups revealed that the former group had a significantly lower frequency of stenotic AVF (31.7 vs 68.3%, p<0.001) and a longer time to AVF stenosis [mean (95% CI): 68.4 (54.7-82.1) vs 46.5 (39.6-53.4) months, p=0.001]. OPN levels in HD patients may be useful markers for predicting and detecting AVF stenosis.

Arteriovenous shunt stenosis assessment based on empirical mode decomposition and 1D-convolutional neural network: Clinical trial stage

Patients suffering from end-stage renal disease usually receive dialysis therapy. The arteriovenous (AV) shunt is a vital vascular access for achieving sufficient blood flow in the vein during hemodialysis and can be either an arteriovenous fistula (AVF) or an arteriovenous graft (AVG). However, the lumens of dialysis accesses are frequently narrowed by thrombosis, resulting in stenosis at the venous anastomosis site or progression of inflow stenosis at the arterial anastomosis site. A narrowed vascular wall will produce abnormal physical stress and cause turbulent flow and high blood pressure. Hence, murmur sounds will occur around the stenotic site. The auscultation method is a noninvasive technique to detect these sounds as phonoangiograph (PAG) signals. The empirical mode decomposition (EMD) method is used to decompose intrinsic fast and slow oscillation components from PAG signals. In this study, the slow oscillation component containing key spectral energy distributions (< 100, 100–300 Hz, and > 300 Hz) will be used to identify the normal and abnormal conditions for further stenosis level assessment. After extracting key frequency-based features using EMD, 1D convolutional and pooling processes, feature patterns can be extracted from spectral patterns, which can distinguish distinct feature patterns and reduce the dimensions of feature patterns and the number of feature datasets for training the classifier. Next, a convolutional neural network-based classifier is used to assess the stenosis levels at a near venous anastomosis site. Its model can solve nonlinear mapping applications and nonlinear separable classifications, including the normal condition, AVG stenosis, and AVF stenosis. The experimental tests with cross-validation will indicate that the proposed method provides a promising result in clinical trials, including mean recall (%), mean precision (%), mean accuracy (%), and a mean F1 score of 95.35 %, 89.49 %, 86.82 %, and 0.9232, respectively, by offering an automatic procedure for AV shunt stenosis assessment without the need for manual feature extraction and classification.

Hemodynamic Quantitative Analysis Based on Doppler Ultrasound for Arteriovenous Shunt Stenosis Screening

Clinically, arteriovenous shunt (AVS) stenosis results in turbulent and pulsatile flow because of high resistance and pressure within a narrowed space inside a stenotic access. Palpation and ultrasound methods are primarily used (first-line examination) to rapidly screen the risk of the degree of stenosis (DOS). Therefore, quantitative hemodynamic analysis involving Doppler ultrasound is performed in patients suffering from AVS stenosis and undergoing long-term hemodialysis. Doppler ultrasound with a center frequency of 7.5 MHz can provide substantial resolution and sensitivity to the measurement of blood flow velocity within a range of depth of 20.0-30.0 mm and a scan diameter of 10.0 mm. A hemodynamic method is used to analyze blood flow through a hemodialysis access in terms of dimensionless numbers. In this study, velocities were measured using Doppler ultrasound at three specific sites in vessels, namely, arterial anastomosis, loop, and venous anastomosis sites. Dimensionless numbers, such as supracritical Reynolds numbers, critical peak Reynolds numbers, and resistive indices, are determined in accordance with parallel conditional expression-based rules to create decision trees for the rapid screening of the DOS at the abovementioned specific sites. For the enrolled subjects, results demonstrate that noninvasive hemodynamic analysis with Doppler ultrasound measurements and parallel decision trees has potential for the efficient screening of the DOS in patients suffering from AVS stenosis and undergoing long-term hemodialysis. Experimental results also indicate that the hit and true-positive rates of the proposed screening method in clinical indication are higher than those of the machine learning method.

Hemodialysis Arteriovenous Shunts Dysfunction Screening with a Fractional-order Feature and Non-cooperative Game Based Decision-making Model

s Early clots and thrombosis to the progress of stenosis leading to arteriovenous shunt dysfunction occurs at the venous anastomsis site or the outflow vein. To prevent vascular access complications, such as inflow or outflow stenosis, this study proposes a computerized decision support system combining feature extraction methods and a non-cooperative game (NCG)-based decision-making model to evaluate arteriovenous shunt stenosis in clinical usages. Feature extraction methods, including the Burg autoregressive (AR) method and the fractional-order self-synchronization error formulation, are used to estimate the characteristic frequencies and to quantify the differences between the reference data and the routine examination data, in terms of the degree of stenosis (DOS). For 42 long-term follow-up patients, a less parameterized NCG model is then used to identify the possible level of stenosis. A novel screening model might be further built on an embedded system, for portable medical screening applications.

Estimating Residual Stenosis for an Arteriovenous Shunt Using a Flexible Fuzzy Classifier

This article determines whether the diameter of a vascular lumen is clinically valuable for hemodialysis therapy. A novel method is proposed that uses a flexible fuzzy classifier (FFC) implementing a curve approximation function to estimate residual stenosis in an arteriovenous shunt (AVS). The auscultation method provides a noninvasive technique for monitoring the degree of AVS stenoses. The Burg autoregressive method is used to identify the characteristic spectra for changes in frequency and amplitude between frequencies of 25 and 800 Hz. Fractional order self-synchronization error formulation is then used to quantify the degree of stenosis between normal access and AVS stenosis in the frequency spectra. To estimate the diameter of a vascular lumen, the FFC is used to create a nonlinear prediction model. For the 42 long-term patients studied, the results of this examination show that the proposed prediction model estimates residual stenosis efficiently.

A Rule-Based Decision-Making Diagnosis System to Evaluate Arteriovenous Shunt Stenosis for Hemodialysis Treatment of Patients Using Fuzzy Petri Nets

This paper proposes a rule-based decision-making diagnosis system to evaluate arteriovenous shunt (AVS) stenosis for long-term hemodialysis treatment of patients using fuzzy petri nets (FPNs). AVS stenoses are often associated with blood sounds, resulting from turbulent flow over the narrowed blood vessel. Phonoangiography provides a noninvasive technique to monitor the sounds of the AVS. Since the power spectra changes in frequency and amplitude with the degree of AVS stenosis, it is difficult to make a human-made decision to judge the degree using a combination of those variances. The Burg autoregressive (AR) method is used to estimate the frequency spectra of a phonoangiographic signal and identify the characteristic frequencies. A rule-based decision-making method, FPNs, is designed as a decision-making system to evaluate the degree of stenosis (DOS) in routine examinations. For 42 long-term follow-up patients, the examination results show the proposed diagnosis system has greater efficiency in evaluating AVS stenosis.

Arteriovenous Shunt Stenosis Evaluation Using a Fractional-Order Fuzzy Petri Net Based Screening System for Long-Term Hemodialysis Patients

This paper proposes the evaluation of arteriovenous shunt (AVS) stenosis using a fractional-order Fuzzy Petri net based screening system for long-term hemodialysis treatment of patients. The screening system uses the Burg method, the fractional-order chaos system, and the Fuzzy Petri net (FPN) for early detection of AVS dysfunction. The Burg method is an autoregressive (AR) model that is used to estimate the frequency spectra of a phonoangiographic signal and to identify the spectral peaks in the region from 25 Hz to 800 Hz. In AVS, the frequency spectrum varies between normal blood flow and turbulent flow. The power spectra demonstrate changes in frequency and amplitude as the degree of stenosis changes. A screening system combining fractional-order chaos system and FPN is used to track the differences in the frequency spectra between the normal and stenosis access. The dynamic errors are indexes that can be used to evaluate the degree of AVS stenosis using a FPN. For 42 long-term follow-up patients, testing results show that the proposed screening system is more efficient in the evaluation of AVS stenosis.