Prediction of in-hospital mortality after XXXX repair using an artificial neural network.

@entity1 @entity8 @entity1755 @entity909 @entity439 @entity1167
OBJECTIVE: @entity1755 ( @entity1755 ) 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 @entity1755 admission using easily obtainable @entity1 information from the field. METHODS: Of 332 total @entity1 from a single institution from 1998 to 2013 who had attempted @entity1755 repair, 125 were reviewed for preoperative factors associated with in-hospital mortality; 108 @entity1 received an open operation, and 17 @entity1 received endovascular repair. Five variables were found significant on multivariate analysis (P  < .05), and four of these five (preoperative shock, @entity909 , @entity1167 , and age) were modeled by multiple logistic regression and an ANN. These predictive models were compared against the Glasgow @entity439 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 @entity1 , 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, @entity8 , @entity909 , @entity1167 , and shock, although @entity8 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 @entity439 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 @entity1 who may carry a high mortality risk from attempted repair of @entity1755 , 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.
