Key result
An artificial neural network using pre-operative data successfully stratified patients, with 5-year freedom from aortic complications of 95.9% in low-risk versus 67.9% in high-risk groups.
Why the study?
Does an artificial neural network risk stratification model predict endograft complications and mortality in patients undergoing EVAR for non-ruptured infrarenal AAA?
Observational (n=761)
Yes
Does an artificial neural network risk stratification model predict endograft complications and mortality in patients undergoing EVAR for non-ruptured infrarenal AAA?
Absolute Event Rate: 95.9% vs 67.9%
p-value: p=<0.001
An artificial neural network using routinely available pre-operative morphological and clinical data can accurately stratify the 5-year risk of endograft complications and mortality after EVAR.
May inform EVAR patient counseling and surveillance; leaves open prospective validation before clinical adoption.
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 (p<0.001). 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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Karthikesalingam et al. (2015) conducted an observational in Non-ruptured infrarenal abdominal aortic aneurysms (n=761). Artificial Neural Network (ANN) low-risk prediction vs. ANN high-risk prediction was evaluated on 5-year freedom from aortic complications (p=<0.001). An artificial neural network using pre-operative data successfully stratified patients, with 5-year freedom from aortic complications of 95.9% in low-risk versus 67.9% in high-risk groups.
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