背景:由于患者生存的感知优势,建立了主动脉瘤疾病的血管内修复术,减少术后并发症,和更短的住院时间。高空间和对比度分辨率的3DCT血管造影图像用于计划程序并告知设备选择和制造,但是在标准护理中,手术是使用2DX射线透视的图像引导进行的,同时注射肾毒性造影剂以可视化血管。这项研究旨在评估对患者的益处,从业者,以及新型图像融合医疗设备(CydarEV)的健康服务,这使得这种高分辨率的3D信息可以在手术时提供给操作者。
方法:该试验是一个多中心,开放标签,340名患者的双臂随机对照临床试验,随机1:1接受血管内动脉瘤修复的标准治疗或使用CydarEV的治疗,包括云计算的CE标记的医疗设备,增强智能,计算机视觉。主要结果是程序时间,次要结果是程序效率,技术有效性,患者结果,和成本效益。临床诊断为AAA或TAAA适合血管内修复并能够提供书面知情同意书的患者将被邀请参加。
结论:该试验是第一个评估血管内主动脉手术中先进图像融合技术的随机对照试验,可以很好地评估该技术对患者预后和NHS成本的影响。
背景:ISRCTN13832085。12月2021年3月3日。
BACKGROUND: Endovascular repair of aortic aneurysmal disease is established due to perceived advantages in patient survival, reduced postoperative complications, and shorter hospital lengths of stay. High spatial and contrast resolution 3D CT angiography images are used to plan the procedures and inform device selection and manufacture, but in standard care, the surgery is performed using image-guidance from 2D X-ray fluoroscopy with injection of nephrotoxic contrast material to visualise the blood vessels. This
study aims to assess the benefit to patients, practitioners, and the health service of a novel image fusion medical device (Cydar EV), which allows this high-resolution 3D information to be available to operators at the time of surgery.
METHODS: The
trial is a multi-centre, open label, two-armed randomised controlled clinical
trial of 340 patient, randomised 1:1 to either standard treatment in endovascular aneurysm repair or treatment using Cydar EV, a CE-marked medical device comprising of cloud computing, augmented intelligence, and computer vision. The primary outcome is procedural time, with secondary outcomes of procedural efficiency, technical effectiveness, patient outcomes, and cost-effectiveness. Patients with a clinical diagnosis of AAA or TAAA suitable for endovascular repair and able to provide written informed consent will be invited to participate.
CONCLUSIONS: This
trial is the first randomised controlled
trial evaluating advanced image fusion technology in endovascular aortic surgery and is well placed to evaluate the effect of this technology on patient outcomes and cost to the NHS.
BACKGROUND: ISRCTN13832085. Dec. 3, 2021.