An artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair:a retrospective observational study


Background Lifelong surveillance after endovascular repair (EVAR) of abdominal aortic aneurysms (AAA) is considered mandatory to detect potentially life-threatening endograft complications. A minority of patients require reintervention but cannot be predictively identified by existing methods. This study aimed to improve the prediction of endograft complications and mortality, through the application of machine-learning techniques. Methods Patients undergoing EVAR at 2 centres were studied from 2004-2010. Pre-operative aneurysm morphology was quantified and endograft complications were recorded up to 5 years following surgery. An artificial neural networks (ANN) approach was used to predict whether patients would be at low- or high-risk of endograft complications (aortic/limb) or mortality. Centre 1 data were used for training and centre 2 data for validation. ANN performance was assessed by Kaplan-Meier analysis to compare the incidence of aortic complications, limb complications, and mortality; in patients predicted to be low-risk, versus those predicted to be high-risk. Results 761 patients aged 75 +/- 7 years underwent EVAR. Mean follow-up was 36+/- 20 months. An ANN was created from morphological features including angulation/length/areas/diameters/ volume/tortuosity of the aneurysm neck/sac/iliac segments. ANN models predicted endograft complications and mortality with excellent discrimination between a low-risk and high-risk group. In external validation, the 5-year rates of freedom from aortic complications, limb complications and mortality were 95.9% vs 67.9%; 99.3% vs 92.0%; and 87.9% vs 79.3% respectively (p0.001) Conclusion This study presents ANN models that stratify the 5-year risk of endograft complications or mortality using routinely available pre-operative data.

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Divisions: College of Engineering & Physical Sciences > School of Informatics and Digital Engineering > Electrical and Electronic Engineering
College of Engineering & Physical Sciences > School of Engineering and Technology > Mechanical, Biomedical & Design
College of Engineering & Physical Sciences
Additional Information: © 2015 Karthikesalingam et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Data Availability: The data cannot be made available outside of the English National Health Service due to existing data agreements covering patients treated in the authors' healthcare system and local/national data governance policies. Data are available upon request from the corresponding author.
Uncontrolled Keywords: Agricultural and Biological Sciences(all),Biochemistry, Genetics and Molecular Biology(all),Medicine(all)
Publication ISSN: 1932-6203
Full Text Link: http://journals ... al.pone.0129024
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2015-07-15
Authors: Karthikesalingam, Alan
Attallah, Omneya
Ma, Xianghong (ORCID Profile 0000-0003-4957-2942)
Bahia, Sandeep Singh
Thompson, Luke
Vidal-Diez, Alberto
Choke, Edward C.
Bown, Matt J.
Sayers, Robert D.
Thompson, Matt M.
Holt, Peter J.



Version: Published Version

License: Creative Commons Attribution

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