Endovascular aortic aneurysm repair (EVAR) requires long-term surveillance to detect and treat postoperative complications. However, prediction models to optimise follow-up strategies are still lacking. The primary objective of this study is to develop predictive models of post-operative outcomes following elective EVAR using Artificial Intelligence (AI)-driven analysis. The secondary objective is to investigate morphological aortic changes following EVAR.

This international, multicentre, observational study will retrospectively include 500 patients who underwent elective EVAR. Primary outcomes are EVAR postoperative complications including deaths, re-interventions, endoleaks, limb occlusion and stent-graft migration occurring within 1 year and at mid-term follow-up (1 to 3 years). Secondary outcomes are aortic anatomical changes. Morphological changes following EVAR will be analysed and compared based on preoperative and postoperative CT angiography (CTA) images (within 1 to 12 months, and at the last follow-up) using the AI-based software PRAEVAorta 2 (Nurea). Deep learning algorithms will be applied to stratify the risk of postoperative outcomes into low or high-risk categories. The training and testing dataset will be respectively composed of 70% and 30% of the cohort.

The study protocol is designed to ensure that the sponsor and the investigators comply with the principles of the Declaration of Helsinki and the ICH E6 good clinical practice guideline. The study has been approved by the ethics committee of the University Hospital of Patras (Patras, Greece) under the number 492/05.12.2024. The results of the study will be presented at relevant national and international conferences and submitted for publication to peer-review journals.

To describe primary care providers’ (PCPs) experience and satisfaction with receiving risk communication documents on their patient’s breast cancer (BC) risk assessment and proposed screening action plan.

Descriptive cross-sectional study.

A survey was distributed to all 763 PCPs linked to 1642 women participating in the Personalized Risk Assessment for Prevention and Early Detection of Breast Cancer: Integration and Implementation (PERSPECTIVE I&I) research project in Quebec, approximately 1–4 months after the delivery of the risk communication documents. The recruitment phase took place from July 2021 to July 2022.

PCPs.

Descriptive analyses were conducted to report participants’ experiences and satisfaction with receiving risk communication. Responses to two open-ended questions were subjected to content analysis.

A total of 168 PCPs answered the survey, from which 73% reported being women and 74% having more than 15 years of practice. Only 38% were familiar with the risk-based BC screening approach prior to receiving their patient risk category. A majority (86%) agreed with the screening approach and would recommend it to their patients if implemented at the population level. A majority of PCPs also reported understanding the information provided (92%) and expressed agreement with the proposed BC screening action plan (89%). Some PCPs recommended simplifying the materials, acknowledging the potential increase in workload and emphasising the need for careful planning of professional training efforts.

PCPs expressed positive attitudes towards a risk-based BC screening approach and were generally satisfied with the information provided. This study suggests that, if introduced in Canada in a manner similar to the PERSPECTIVE I&I project, risk-based BC screening would likely be supported by most PCPs. However, they emphasised the importance of addressing concerns such as professional training and the potential impact on workload if the approach were to be implemented at the population level. Future qualitative studies are needed to further explore the training needs of PCPs and to develop strategies for integrating this approach with the high workloads faced by PCPs.