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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...

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Autores principales: Karthikesalingam, Alan, Attallah, Omneya, Ma, Xianghong, Bahia, Sandeep Singh, Thompson, Luke, Vidal-Diez, Alberto, Choke, Edward C., Bown, Matt J., Sayers, Robert D., Thompson, Matt M., Holt, Peter J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503678/
https://www.ncbi.nlm.nih.gov/pubmed/26176943
http://dx.doi.org/10.1371/journal.pone.0129024
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author Karthikesalingam, Alan
Attallah, Omneya
Ma, Xianghong
Bahia, Sandeep Singh
Thompson, Luke
Vidal-Diez, Alberto
Choke, Edward C.
Bown, Matt J.
Sayers, Robert D.
Thompson, Matt M.
Holt, Peter J.
author_facet Karthikesalingam, Alan
Attallah, Omneya
Ma, Xianghong
Bahia, Sandeep Singh
Thompson, Luke
Vidal-Diez, Alberto
Choke, Edward C.
Bown, Matt J.
Sayers, Robert D.
Thompson, Matt M.
Holt, Peter J.
author_sort Karthikesalingam, Alan
collection PubMed
description 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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spelling pubmed-45036782015-07-17 An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study Karthikesalingam, Alan Attallah, Omneya Ma, Xianghong Bahia, Sandeep Singh Thompson, Luke Vidal-Diez, Alberto Choke, Edward C. Bown, Matt J. Sayers, Robert D. Thompson, Matt M. Holt, Peter J. PLoS One Research Article 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. Public Library of Science 2015-07-15 /pmc/articles/PMC4503678/ /pubmed/26176943 http://dx.doi.org/10.1371/journal.pone.0129024 Text en © 2015 Karthikesalingam et al http://creativecommons.org/licenses/by/4.0/ 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 properly credited.
spellingShingle Research Article
Karthikesalingam, Alan
Attallah, Omneya
Ma, Xianghong
Bahia, Sandeep Singh
Thompson, Luke
Vidal-Diez, Alberto
Choke, Edward C.
Bown, Matt J.
Sayers, Robert D.
Thompson, Matt M.
Holt, Peter J.
An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study
title An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study
title_full An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study
title_fullStr An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study
title_full_unstemmed An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study
title_short An Artificial Neural Network Stratifies the Risks of Reintervention and Mortality after Endovascular Aneurysm Repair; a Retrospective Observational study
title_sort artificial neural network stratifies the risks of reintervention and mortality after endovascular aneurysm repair; a retrospective observational study
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4503678/
https://www.ncbi.nlm.nih.gov/pubmed/26176943
http://dx.doi.org/10.1371/journal.pone.0129024
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