Why the study?
Arteriovenous shunt stenosis causes turbulent and pulsatile flow, requiring quantitative hemodynamic analysis using Doppler ultrasound to rapidly screen the degree of stenosis in hemodialysis patients.
Does hemodynamic quantitative analysis using Doppler ultrasound improve the screening accuracy for the degree of stenosis in patients with arteriovenous shunt stenosis undergoing hemodialysis compared to machine learning methods?
Does hemodynamic quantitative analysis using Doppler ultrasound improve the screening accuracy for the degree of stenosis in patients with arteriovenous shunt stenosis undergoing hemodialysis compared to machine learning methods?
Noninvasive hemodynamic analysis with Doppler ultrasound and parallel decision trees provides an efficient and accurate method for screening arteriovenous shunt stenosis in hemodialysis patients.
May enhance AV shunt stenosis screening accuracy in hemodialysis; leaves open prospective validation versus machine learning.
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.
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Wu et al. (2019) studied this question.
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