Epl. Turton et al., Ruptured abdominal aortic aneurysm: a novel method of outcome prediction using neural network technology, EUR J VAS E, 19(2), 2000, pp. 184-189
Citations number
43
Categorie Soggetti
Surgery
Journal title
EUROPEAN JOURNAL OF VASCULAR AND ENDOVASCULAR SURGERY
Background: reported survival following emergency surgery for ruptured abdo
minal aortic aneurysm (RAAA) varies widely between institutions. This is la
rgely 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 oper
ations for RAAA over a 7-year period (January 1990 to January 1997). Logist
ic regression analysis was used to select the most significant variables as
sociated with subsequent mortality. These were used to construct, train, an
d validate a neural network designed to predict survival from surgery in in
dividual cases on a prospective basis.
Results: the 30-day mortality rate was 53%. Multivariate analysis identifie
d four highly significant independent predictors of mortality; preoperative
hypotension, intraperitoneal rupture, preoperative coagulopathy, and preop
erative cardiac arrest. Using these inputs, the neural network correctly pr
edicted 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 reporte
d in studies as a measure of the effect of case mix on survival data. Neura
l networks have potential to aid decision-making relating to outcome for in
dividual cases.