Division of Nephrology, Department of Internal Medicine, Taichung Veterans General Hospital, Taichung City 40705, Taiwan.
Department of Life Sciences, Tunghai University, Taichung City 40724, Taiwan.
Biosensors (Basel). 2021 Aug 26;11(9):297. doi: 10.3390/bios11090297.
Vascular Access (VA) is often referred to as the "Achilles heel" for a Hemodialysis (HD)-dependent patient. Both the patent and sufficient VA provide adequacy for performing dialysis and reducing dialysis-related complications, while on the contrary, insufficient VA is the main reason for recurrent hospitalizations, high morbidity, and high mortality in HD patients. A non-invasive Vascular Wall Motion (VWM) monitoring system, made up of a pulse radar sensor and Support Vector Machine (SVM) classification algorithm, has been developed to detect access flow dysfunction in Arteriovenous Fistula (AVF). The harmonic ratios derived from the Fast Fourier Transform (FFT) spectrum-based signal processing technique were employed as the input features for the SVM classifier. The result of a pilot clinical trial showed that a more accurate prediction of AVF flow dysfunction could be achieved by the VWM monitor as compared with the Ultrasound Dilution (UD) flow monitor. Receiver Operating Characteristic (ROC) curve analysis showed that the SVM classification algorithm achieved a detection specificity of 100% at detection thresholds in the range from 500 to 750 mL/min and a maximum sensitivity of 95.2% at a detection threshold of 750 mL/min.
血管通路（VA）通常被称为血液透析（HD）依赖患者的“阿喀琉斯之踵”。通畅且充足的 VA 为进行透析和减少透析相关并发症提供了保障，而相反，不足的 VA 是 HD 患者反复住院、高发病率和高死亡率的主要原因。我们开发了一种非侵入性的血管壁运动（VWM）监测系统，由脉冲雷达传感器和支持向量机（SVM）分类算法组成，用于检测动静脉瘘（AVF）中的通路流量功能障碍。基于快速傅里叶变换（FFT）频谱的信号处理技术得出的谐波比被用作 SVM 分类器的输入特征。一项试点临床试验的结果表明，与超声稀释（UD）流量监测器相比，VWM 监测器可以更准确地预测 AVF 流量功能障碍。接收者操作特征（ROC）曲线分析表明，SVM 分类算法在检测阈值为 500 至 750 mL/min 范围内的检测特异性达到 100%，在检测阈值为 750 mL/min 时的最大灵敏度达到 95.2%。
