• Alan Karthikesalingam, Omneya Attallah, Xianghong Ma, Sandeep Singh Bahia, Luke Thompson, Alberto Vidal-Diez, Edward C Choke, Matt J Bown, Robert D Sayers, Matt M Thompson, and Peter J Holt.
• Department of Outcomes Research, St George's Vascular Institute, London, SW17 0QT, United Kingdom.
• Plos One. 2015 Jan 1; 10 (7): e0129024.

BackgroundLifelong 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.MethodsPatients 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.Results761 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 (p<0.001).ConclusionThis 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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