Predicting risk of rupture and rupture-preventing re-interventions utilising repeated measures on aneurysm sac diameter following endovascular abdominal aortic aneurysm repair

Abstract

Background Clinical and imaging surveillance practices following endovascular aneurysm repair (EVAR) for intact abdominal aortic aneurysm (AAA) vary considerably and compliance with recommended lifelong surveillance is poor. The aim of this study was to develop a dynamic prognostic model to enable stratification of patients at risk of future secondary aortic rupture or the need for intervention to prevent rupture (rupture‐preventing reintervention) to enable the development of personalized surveillance intervals. Methods Baseline data and repeat measurements of postoperative aneurysm sac diameter from the EVAR‐1 and EVAR‐2 trials were used to develop the model, with external validation in a cohort from a single‐centre vascular database. Longitudinal mixed‐effects models were fitted to trajectories of sac diameter, and model‐predicted sac diameter and rate of growth were used in prognostic Cox proportional hazards models. Results Some 785 patients from the EVAR trials were included, of whom 155 (19·7 per cent) experienced at least one rupture or required a rupture‐preventing reintervention during follow‐up. An increased risk was associated with preoperative AAA size, rate of sac growth and the number of previously detected complications. A prognostic model using predicted sac growth alone had good discrimination at 2 years (C‐index 0·68), 3 years (C‐index 0·72) and 5 years (C‐index 0·75) after operation and had excellent external validation (C‐index 0·76–0·79). More than 5 years after operation, growth rates above 1 mm/year had a sensitivity of over 80 per cent and specificity over 50 per cent in identifying events occurring within 2 years. Conclusion Secondary sac growth is an important predictor of rupture or rupture‐preventing reintervention to enable the development of personalized surveillance intervals. A dynamic prognostic model has the potential to tailor surveillance by identifying a large proportion of patients who may require less intensive follow‐up.