Predicting risk of rupture and rupture-preventing reinterventions following endovascular abdominal aortic aneurysm repair.

BRITISH JOURNAL OF SURGERY(2018)

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Abstract
BackgroundClinical 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. MethodsBaseline 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. ResultsSome 785 patients from the EVAR trials were included, of whom 155 (197 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 2years (C-index 068), 3years (C-index 072) and 5years (C-index 075) after operation and had excellent external validation (C-index 076-079). More than 5years after operation, growth rates above 1mm/year had a sensitivity of over 80 per cent and specificity over 50 per cent in identifying events occurring within 2years. ConclusionSecondary 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. Potential to tailor surveillance
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Key words
rupture-preventing
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