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Cited by 96Aortic aneurysm repair treatmentsAortic Disease and Treatment ApproachesIntracranial Aneurysms: Treatment and Complications

Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network

Open access
Wise, Eric S., Hocking, Kyle M., Brophy, Colleen M.·2015·Journal of Vascular Surgery
DOI 10.1016/j.jvs.2015.02.038PMC PMC4484301
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Abstract

Objective Ruptured abdominal aortic aneurysm (rAAA) carries a high mortality rate, even with prompt transfer to a medical center. An artificial neural network (ANN) is a computational model that improves predictive ability through pattern recognition while continually adapting to new input data. The goal of this study was to effectively use ANN modeling to provide vascular surgeons a discriminant adjunct to assess the likelihood of in-hospital mortality on a pending rAAA admission using easily obtainable patient information from the field.Methods Of 332 total patients from a single institution from 1998 to 2013 who had attempted rAAA repair, 125 were reviewed for preoperative factors associated with in-hospital mortality; 108 patients received an open operation, and 17 patients received endovascular repair. Five variables were found significant on multivariate analysis (P < .05), and four of these five (preoperative shock, loss of consciousness, cardiac arrest, and age) were modeled by multiple logistic regression and an ANN. These predictive models were compared against the Glasgow Aneurysm Score. All models were assessed by generation of receiver operating characteristic curves and actual vs predicted outcomes plots, with area under the curve and Pearson r(2) value as the primary measures of discriminant ability.Results Of the 125 patients, 53 (42%) did not survive to discharge. Five preoperative factors were significant (P < .05) independent predictors of in-hospital mortality in multivariate analysis: advanced age, renal disease, loss of consciousness, cardiac arrest, and shock, although renal disease was excluded from the models. The sequential accumulation of zero to four of these risk factors progressively increased overall mortality rate, from 11% to 16% to 44% to 76% to 89% (age ≥ 70 years considered a risk factor). Algorithms derived from multiple logistic regression, ANN, and Glasgow Aneurysm Score models generated area under the curve values of 0.85 ± 0.04, 0.88 ± 0.04 (training set), and 0.77 ± 0.06 and Pearson r(2) values of .36, .52 and .17, respectively. The ANN model represented the most discriminant of the three.Conclusions An ANN-based predictive model may represent a simple, useful, and highly discriminant adjunct to the vascular surgeon in accurately identifying those patients who may carry a high mortality risk from attempted repair of rAAA, using only easily definable preoperative variables. Although still requiring external validation, our model is available for demonstration at https://redcap.vanderbilt.edu/surveys/?s=NN97NM7DTK.

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  • Healthcare systems have increasingly sought to use ML to assist in risk stratification, and these ML models may outperform statistical models in calibration and discrimination.

    “Healthcare systems have increasingly sought to use ML to assist in risk stratification, and these ML models may outperform statistical models in calibration and discrimination.”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [23]

  • ML models have been shown to better predict mortality than conventional logistic regression after liver cancer surgery, aortic aneurysm surgery, and cardiac surgery.

    “These ML models have been shown to better predict mortality than conventional logistic regression after liver cancer surgery, aortic aneurysm surgery, and cardiac surgery.”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [23]

  • Multiple ML models that risk-stratify patients with a disease or prepare patients for surgery have been recently developed and validated.

    “Multiple ML models that risk-stratify patients with a disease or prepare patients for surgery have been recently developed and validated (16, 20–25).”

    From Accessing Artificial Intelligence for Clinical Decision-Making (2021) · cites this paper as [23]

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Authors
Wise, E. S., Hocking, K. M., & Brophy, C. M.
Affiliations
  • Vanderbilt University Medical Center (US)
  • Vanderbilt University (US)
  • VA Tennessee Valley Healthcare System (US)
Corresponding author
Wise, Eric S.
ORCID
Venue
Journal of Vascular Surgery
Publisher
Elsevier BV
Published
2015-07
Volume
62
Issue
1
Pages
8-15
ISSN
0741-5214 (ISSN-L)
DOI
10.1016/j.jvs.2015.02.038
PMC
PMC4484301
OpenAlex
W1794427698
Cited by
95
References
40
Citations by year
2026: 10 · 2025: 12 · 2024: 16 · 2023: 10 · 2022: 9 · 2021: 5 · 2020: 7 · 2019: 11 · 2018: 3 · 2017: 6 · 2016: 4 · 2015: 2
FWCI
3.96
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Vascular Surgery (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
  • National Institutes of Health (UL1TR000445)
  • National Center for Advancing Translational Sciences
Topics
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Aortic aneurysm repair treatments
  • Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Aortic Disease and Treatment Approaches
  • Health Sciences › Medicine › Neurology › Intracranial Aneurysms: Treatment and Complications
Keywords
ruptured abdominal aortic aneurysm, in-hospital mortality prediction, artificial neural networks, Glasgow Aneurysm Score, preoperative risk factors, preoperative shock, loss of consciousness, cardiac arrest, old age, endovascular aneurysm repair, open aneurysm repair, risk stratification
MeSH terms
Age Factors, Aged, Aged, 80 and over, Algorithms, Aortic Rupture, Decision Support Techniques, Female, Heart Arrest, Humans, Male, Middle Aged, Predictive Value of Tests, Retrospective Studies, Risk Factors, ROC Curve, Shock, Tennessee, Time Factors, Unconsciousness, Multivariate Analysis, Logistic Models, Odds Ratio, Databases, Factual, Neural Networks, Computer, Treatment Outcome, Hospital Mortality, Aortic Aneurysm, Abdominal, Risk Assessment, Area Under Curve, Blood Vessel Prosthesis Implantation, Endovascular Procedures

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