Percutaneous Endovascular Therapy of Renal Artery Stenosis: Technical and Clinical Developments in the past Decade

Guide Catheter Extension Device Is Effective in Renal Angioplasty for Severely Calcified Lesions

BACKGROUND The GuideLiner catheter extension device is a monorail-type "Child" support catheter that facilitates coaxial alignment with the guide catheter and provides an appropriate back-up force. This device has been developed in the field of coronary intervention, and now is becoming widely applied in the field of endovascular treatment. However, there has been no report on the effectiveness of the guide catheter extension device in percutaneous transluminal renal angioplasty (PTRA). CASE REPORT We encountered a case of atherosclerotic subtotal occlusion at the ostium of the left renal artery. Due to the severely calcified orifice and weaker back-up force provided by a JR4 guide catheter, we could not pass any guidewires through the target lesion. Therefore, we introduced a guide catheter extension device, the GuideLiner catheter, through the guide catheter and achieved good guidewire maneuverability. We finally deployed 2 balloon-expandable stents and successfully performed all PTRA procedures. CONCLUSIONS The guide catheter extension device can be effective in PTRA for severely calcified subtotal occlusion.

Laparoscopy-Assisted Hybrid Technique for Renal Revascularization Using an Endovascular Graft: Report of Three Cases

Atherosclerotic renal artery stenosis (RAS) is relatively common and often associated with hypertension and progressive renal insufficiency. Technical improvements of interventional endovascular tools have led to a more widespread use of endoluminal renal artery revascularization. Sometimes an endovascular procedure for (RAS) may fail. We report 3 cases successfully treated with laparoscopic assisted aortorenal bypass using an endovascular graft.

NUMERICAL SIMULATION AND VALIDITY OF A NOVEL METHOD FOR THE PREDICTION OF ARTERY STENOSIS VIA INPUT IMPEDANCE AND SUPPORT VECTOR MACHINE

The early detection and intervention of artery stenosis is very important to reduce the mortality of cardiovascular disease. A novel method for predicting artery stenosis was proposed by using the input impedance of the systemic arterial tree and support vector machine (SVM). Based on the built transmission line model of a 55-segment systemic arterial tree, the input impedance of the arterial tree was calculated by using a recursive algorithm. A sample database of the input impedance was established by specifying the different positions and degrees of artery stenosis. A SVM prediction model was trained by using the sample database. 10-fold cross-validation was used to evaluate the performance of the SVM. The effects of stenosis position and degree on the accuracy of the prediction were discussed. The results showed that the mean specificity, sensitivity and overall accuracy of the SVM are 80.2%, 98.2% and 89.2%, respectively, for the 50% threshold of stenosis degree. Increasing the threshold of the stenosis degree from 10% to 90% increases the overall accuracy from 82.2% to 97.4%. Increasing the distance of the stenosis artery from the heart gradually decreases the overall accuracy from 97.1% to 58%. The deterioration of the stenosis degree to 90% increases the prediction accuracy of the SVM to more than 90% for the stenosis of peripheral artery. The simulation demonstrated theoretically the feasibility of the proposed method for predicting artery stenosis via the input impedance of the systemic arterial tree and SVM.