Ruptured Abdominal Aortic Aneurysm: a Novel Method of Outcome Prediction Using Neural Network Technology

Background: reported survival following emergency surgery for ruptured abdominal aortic aneurysm (RAAA) varies widely between institutions. This is largely attributable to differences in case mix. The aim of this study was to identify and evaluate a set of prognostic variables that would accurately predict outcome for individual patients from perioperative indices. Methods: perioperative factors associated with subsequent mortality at our institution were identified by retrospective review of 102 consecutive operations for RAAA over a 7-year period (January 1990 to January 1997). Logistic regression analysis was used to select the most significant variables associated with subsequent mortality. These were used to construct, train, and validate a neural network designed to predict survival from surgery in individual cases on a prospective basis. Results: the 30-day mortality rate was 53%. Multivariate analysis identified four highly significant independent predictors of mortality; preoperative hypotension, intraperitoneal rupture, preoperative coagulopathy, and preoperative cardiac arrest. Using these inputs, the neural network correctly predicted outcome in 82.5% of individual cases. Conclusion: a neural network based on just four perioperative variables can accurately predict outcome of RAAA. Prognostic variables should be reported in studies as a measure of the effect of case mix on survival data. Neural networks have potential to aid decision-making relating to outcome for individual cases.