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Prognostication of Ruptured Abdominal Aortic Aneurysm Repair Using an Artificial Neural Network
Eric S. Wise, M.D., Kyle Hocking, Ph.D., Colleen Brophy, M.D..
Vanderbilt University, Nashville, TN, USA.

Objective:
First responders attending to ruptured abdominal aortic aneurysm (rAAA) patients relay pertinent information to anticipating providers prior to hospital transfer. Early identification of characteristics portending a poor outcome may prevent unnecessary operations and provide improved prognostic information. Artificial neural networks (ANN) are advanced, continually adapting computational systems taught to identify complex non-linear relationships among variables correlated with an outcome, with superior discriminant ability. Using ANN methodology, this study modeled in-hospital mortality after rAAA repair as a function of independent pre-operative predictors. We then developed a simplified ANN in which only variables most easily obtained prior to patient arrival can be used to accurately predict in-hospital mortality.
Methods:
125 patients from 1998-2013 who had rAAA repair were reviewed for factors correlated with in-hospital mortality. Seven variables were significant in multivariate regression analysis (MRA); these variables were input into a computational ANN. Four of the pre-operative variables: age, shock, GCS <15 and cardiac arrest, chosen for their simplicity and ease of availability, were input into a second ANN (ANN-4, Figure 1A). MRA and ANN models were compared against the Glasgow Aneurysm Score (GAS). Models were assessed with a validation cohort, and by ROC-curve generation, with area under the curve (AUC) as the primary measure of each model’s discriminant ability.
Results:
Of the 125 patients, 53 (42%) did not survive to discharge. Seven pre-operative factors were significant (P<.05) independent predictors of mortality: age, elevated lactate, myocardial disease, acute or chronic renal failure, GCS<15, cardiac arrest and shock. After modeling these seven factors via MRA and ANN, the generated AUC’s were 0.8971±0.06 and 0.9971±0.004 (Figures 1B and 1C), respectively. In ANN-4, the AUC values were 0.8617±0.06 (Figure 1D) and 0.9259±0.05 in training and validation cohorts, respectively. Using an ANN output >0.5 for predicted death, ANN-4 discriminated with 86.5% sensitivity and 76.5% specificity. GAS fits the dataset with an AUC of 0.7666±0.06.
Conclusion:
ANN modeling represents a novel adjunct to the surgeon’s clinical judgment in rAAA patient management. Patients known to be elderly, in shock, requiring CPR and with GCS<15 may represent a cohort for whom attempted repair of rAAA carries little survival benefit.


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