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Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network.
J Vasc Surg. 2015 Jul; 62(1):8-15.JV

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.

Authors+Show Affiliations

Department of Surgery, Vanderbilt University Medical Center, Nashville, Tenn. Electronic address: eric.s.wise@vanderbilt.edu.Department of Surgery, Vanderbilt University Medical Center, Nashville, Tenn; Department of Biomedical Engineering, Vanderbilt University, Nashville, Tenn.Department of Surgery, Vanderbilt University Medical Center, Nashville, Tenn; Department of Surgery, Division of Vascular Surgery, VA Tennessee Valley Healthcare System, Nashville, Tenn.

Pub Type(s)

Comparative Study
Journal Article
Research Support, N.I.H., Extramural

Language

eng

PubMed ID

25953014

Citation

Wise, Eric S., et al. "Prediction of In-hospital Mortality After Ruptured Abdominal Aortic Aneurysm Repair Using an Artificial Neural Network." Journal of Vascular Surgery, vol. 62, no. 1, 2015, pp. 8-15.
Wise ES, Hocking KM, Brophy CM. Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network. J Vasc Surg. 2015;62(1):8-15.
Wise, E. S., Hocking, K. M., & Brophy, C. M. (2015). Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network. Journal of Vascular Surgery, 62(1), 8-15. https://doi.org/10.1016/j.jvs.2015.02.038
Wise ES, Hocking KM, Brophy CM. Prediction of In-hospital Mortality After Ruptured Abdominal Aortic Aneurysm Repair Using an Artificial Neural Network. J Vasc Surg. 2015;62(1):8-15. PubMed PMID: 25953014.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Prediction of in-hospital mortality after ruptured abdominal aortic aneurysm repair using an artificial neural network. AU - Wise,Eric S, AU - Hocking,Kyle M, AU - Brophy,Colleen M, Y1 - 2015/05/05/ PY - 2014/12/22/received PY - 2015/02/23/accepted PY - 2015/5/9/entrez PY - 2015/5/9/pubmed PY - 2015/9/9/medline SP - 8 EP - 15 JF - Journal of vascular surgery JO - J Vasc Surg VL - 62 IS - 1 N2 - 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. SN - 1097-6809 UR - https://www.unboundmedicine.com/medline/citation/25953014/Prediction_of_in_hospital_mortality_after_ruptured_abdominal_aortic_aneurysm_repair_using_an_artificial_neural_network_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0741-5214(15)00289-X DB - PRIME DP - Unbound Medicine ER -