A multicenter randomized controlled trial indicates that paclitaxel-coated balloons provide no benefit for arteriovenous fistulas.

The role of paclitaxel-coated balloons has been established in the coronary and peripheral arterial circulations with recent interest in the use of paclitaxel-coated balloons to improve patency rates following angioplasty of arteriovenous fistulas. To assess the efficacy of paclitaxel-coated angioplasty balloons to prolong the survival time of target lesion primary patency in arteriovenous fistulas, we designed an investigator-led multi-center randomized controlled trial with follow up time variable for a minimum of one year. Patients with an arteriovenous fistula who were undergoing an angioplasty for a clinical indication were included but patients with one or more lesions outside the treatment segment were excluded. Following successful treatment with a high-pressure balloon, 212 patients were randomized. In the intervention arm, the second component was insertion of a paclitaxel-coated balloon. In the control arm, an identical procedure was followed, but using a standard balloon. The primary endpoint was time to loss of clinically driven target lesion primary patency. Primary analysis showed no significant evidence for a difference in time to end of target lesion primary patency between groups: hazard ratio 1.18 with a 95% confidence interval of 0.78 to 1.79. There were no significant differences for any secondary outcomes, including patency outcomes and adverse events. Thus, our study demonstrated no evidence that paclitaxel-coated balloons provide benefit, following standard care high-pressure balloon angioplasty, in the treatment of arteriovenous fistulas. Hence, in view of the benefit suggested by other trials, the role of paclitaxel-coated angioplasty balloons remains uncertain.

Deploying artificial intelligence software in an NHS trust: a how-to guide for clinicians.

Over the past 10 years, artificial intelligence (AI) has become one of the fastest-growing sectors in healthcare. There are now numerous new technologies designed to cut costs and improve diagnoses and treatment pathways. However, there is significant scepticism amongst National Health Service (NHS) staff regarding the usefulness of AI and it's cost to the NHS. This has likely resulted in underuse and slow adoption of software that may revolutionize our healthcare system and ensure its continued survival and effectiveness. Several governing bodies have put forward guidance on the safe and effective adoption of AI tools, but this rarely covers the reality of selecting and deploying new software. This article set out clear guidance on the practicalities and pitfalls of deploying digital solutions in healthcare, using the example of a deep learning algorithm designed to improve the accuracy of chest X-ray (CXR) interpretation in the emergency department.

Using Artificial Intelligence to Stratify Normal versus Abnormal Chest X-rays: External Validation of a Deep Learning Algorithm at East Kent Hospitals University NHS Foundation Trust.

Background: The chest radiograph (CXR) is the most frequently performed radiological examination worldwide. The increasing volume of CXRs performed in hospitals causes reporting backlogs and increased waiting times for patients, potentially compromising timely clinical intervention and patient safety. Implementing computer-aided detection (CAD) artificial intelligence (AI) algorithms capable of accurate and rapid CXR reporting could help address such limitations. A novel use for AI reporting is the classification of CXRs as 'abnormal' or 'normal'. This classification could help optimize resource allocation and aid radiologists in managing their time efficiently. Methods: qXR is a CE-marked computer-aided detection (CAD) software trained on over 4.4 million CXRs. In this retrospective cross-sectional pre-deployment study, we evaluated the performance of qXR in stratifying normal and abnormal CXRs. We analyzed 1040 CXRs from various referral sources, including general practices (GP), Accident and Emergency (A&E) departments, and inpatient (IP) and outpatient (OP) settings at East Kent Hospitals University NHS Foundation Trust. The ground truth for the CXRs was established by assessing the agreement between two senior radiologists. Results: The CAD software had a sensitivity of 99.7% and a specificity of 67.4%. The sub-group analysis showed no statistically significant difference in performance across healthcare settings, age, gender, and X-ray manufacturer. Conclusions: The study showed that qXR can accurately stratify CXRs as normal versus abnormal, potentially reducing reporting backlogs and resulting in early patient intervention, which may result in better patient outcomes.

The ethical adoption of artificial intelligence in radiology.

Artificial intelligence (AI) is rapidly transforming healthcare-with radiology at the pioneering forefront. To be trustfully adopted, AI needs to be lawful, ethical and robust. This article covers the different aspects of a safe and sustainable deployment of AI in radiology during: training, integration and regulation. For training, data must be appropriately valued, and deals with AI companies must be centralized. Companies must clearly define anonymization and consent, and patients must be well-informed about their data usage. Data fed into algorithms must be made AI-ready by refining, purification, digitization and centralization. Finally, data must represent various demographics. AI needs to be safely integrated with radiologists-in-the-loop: guiding forming concepts of AI solutions and supervising training and feedback. To be well-regulated, AI systems must be approved by a health authority and agreements must be made upon liability for errors, roles of supervised and unsupervised AI and fair workforce distribution (between AI and radiologists), with a renewal of policy at regular intervals. Any errors made must have a root-cause analysis, with outcomes fedback to companies to close the loop-thus enabling a dynamic best prediction system. In the distant future, AI may act autonomously with little human supervision. Ethical training and integration can ensure a "transparent" technology that will allow insight: helping us reflect on our current understanding of imaging interpretation and fill knowledge gaps, eventually moulding radiological practice. This article proposes recommendations for ethical practise that can guide a nationalized framework to build a sustainable and transparent system.

Paclitaxel-coated balloon fistuloplasty versus plain balloon fistuloplasty only to preserve the patency of arteriovenous fistulae used for haemodialysis (PAVE): study protocol for a randomised controlled trial.

BACKGROUND: The initial therapy for a stenosis in an arteriovenous fistula used for haemodialysis is radiological balloon dilatation or angioplasty. The benefit of angioplasty is often short-lived, intervention-free survival is reported to be 40-50 % at 1 year. Previous small studies and observational data suggest that paclitaxel-coated balloons may be of benefit in improving outcomes after fistuloplasty of stenotic arteriovenous fistulae. METHODS/DESIGN: We have designed a multicentre, double-blind randomised controlled trial to test the superiority of paclitaxel-coated balloons for preventing restenosis after fistuloplasty in patients with a native arteriovenous fistula. Two hundred and eleven patients will be followed up for a minimum of 1 year. Inclusion criteria include a clinical indication for a fistuloplasty, an access circuit that is free of synthetic graft material or stents, and a residual stenosis of 30 % or less after plain balloon fistuloplasty. Exclusion criteria include a synchronous venous lesion in the same access circuit, location of the stenosis central to the thoracic inlet or a thrombosed access circuit at the time of treatment. The primary endpoint is time to end of target lesion primary patency. This is defined as a clinically-driven radiological or surgical re-intervention at the treatment segment, thrombosis that includes the treatment segment, or abandonment of the access circuit due to an inability to re-treat the treatment segment. Secondary endpoints include angiographic late lumen loss, time to end of access circuit cumulative patency, the total number of interventions, and quality of life. The trial is funded by the National Institute for Health Research. DISCUSSION: We anticipate that this trial will provide rigorous data that will determine the efficacy of additional paclitaxel-coated balloon fistuloplasty versus plain balloon fistuloplasty only to preserve the patency of arteriovenous fistulae used for haemodialysis. TRIAL REGISTRATION: ISRCTN14284759 . Registered on 28 October 2015.