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 which improves predictive ability via 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
One-hundred and twenty-five of 332 total patients from a single-institution from 1998–2013 who had attempted rAAA repair were reviewed for preoperative factors associated with in-hospital mortality. One-hundred and eight patients received an open operation, and 17 patients received endovascular repair. Five variables were found significant upon multivariate analysis (P < .05), and four of these five: preoperative shock, loss of consciousness, cardiac arrest and age were modeled via multiple logistic regression and an ANN. These predictive models were compared against the Glasgow Aneurysm Score (GAS). All models were assessed by generation of receiver operating characteristic curves and Actual vs. Predicted outcomes plots, with area under the curve (AUC) and Pearson r2 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, though 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 considered a risk factor). Algorithms derived from multiple logistic regression, ANN and GAS models generated AUC values of .85 ± .04, .88 ± .04 (training-set) and .77 ± .06, and Pearson r2 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. Though still requiring external validation, our model is available for demonstration at https://redcap.vanderbilt.edu/surveys/?s=NN97NM7DTK.