Grootes_et_al-2018-British_Journal_of_Surgery.pdf (375.63 kB)
Predicting risk of rupture and rupture-preventing re-intervention utilising repeated measures on aneurysm sac diameter following endovascular abdominal aortic aneurysm repair
journal contributionposted on 2018-07-30, 14:14 authored by Isabelle Grootes, Jessica K. Barrett, Pinar Ulug, Fiona Rohlffs, Sani Laukontaus, Riikka Tulamo, Maarit Venermo, Roger Greenhalgh, Michael J. Sweeting
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. This study developed a dynamic prognostic model to enable stratification of patients at risk of future secondary rupture or rupture preventing re-intervention (RPR) to enable the development of personalised surveillance intervals. Method: Baseline data and repeat measurements of post-operative aneurysm sac diameter from the EVAR-1 and EVAR-2 trials were used to develop the model with external validation in a cohort from Helsinki. 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: 785 patients from the EVAR trials were included of which 155 (20%) suffered at least one rupture or RPR during follow-up. An increased risk was associated with pre-operative AAA size, rate of sac growth, and the number of previously detected complications. A prognostic model using only predicted sac growth had good discrimination at 2-years (C-index = 0.68), 3- years (C-index= 0.72) and 5-years (C-index= 0.75) post-operation and had excellent external validation (C-indices 0.76 to 0.79). After 5-years post-operation, growth rates above 1mm/year had a sensitivity of over 80% and specificity over 50% in identifying events occurring within 2 years. Conclusion: Secondary sac growth is an important predictor of rupture or RPR. A dynamic prognostic model has the potential to tailorsurveillance by identifying a large proportion of patients who may require less intensive follow-up.
This study was funded by the UK National Institute for Health Research (NIHR) Health Technology Assessment programme (project number 11/36/46) and Camelia Botnar Arterial Research Foundation. We are grateful to Matti Laine for providing data on AAA patients who underwent elective EVAR from the Helsinki University Hospital. Additional support for this project for work done at the University of Cambridge came from the UK Medical Research Council (MR/L003120/1), the British Heart Foundation (RG/13/13/30194), and the NIHR (Cambridge Biomedical Research Centre). JKB was supported by the UK Medical Research Council (grants MR/K014811/1, MR/L501566/1 and unit programme MC_UU_00002/5).
CitationBritish Journal of Surgery, 2018, 105(10), pp. 1294-1304
Author affiliation/Organisation/COLLEGE OF LIFE SCIENCES/School of Medicine/Department of Health Sciences
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