NEURAL NETWORKS AS PREDICTORS OF OUTCOMES IN ALCOHOLIC PATIENTS WITH SEVERE LIVER-DISEASE

We developed and evaluated neural networks as predictors of outcomes i n alcoholic patients with severe liver disease using commonly availabl e clinical and laboratory values, Hospital charts of 144 patients were reviewed. Nine variables (five laboratory, four clinical) were record ed along with in-hospital death or survival, Data were organized into separate development and validation sets. Neural network predictions o f survival were compared with those of the Maddrey discriminant functi on and logistic regression models developed on the same data, Model pe rformance was evaluated by comparing areas under receiver-operating ch aracteristic (ROC) curves and the distributions of model scores, Survi vors had significantly different laboratory and clinical characteristi cs, the most important being a higher prothrombin time, lower bilirubi n, and lower incidence of encephalopathy, Neural network performance w as significantly better than that of the Maddrey score (ROC areas, 81. 5% vs, 73.8%; P = .04), The ROC rea for neural networks was similar to that of logistic regression (ROC area 78.2%; P = .3), but the neural networks were more successful in classifying patients into low- and hi gh-risk groups (P < .001), A neural network score with laboratory data from hospital-day 7 improved prognostic accuracy further to 84.3%, Af ter adjusting for baseline risk, the neural network change ill illness severity was still a significant predictor of mortality (P = .001), N eural networks using clinical and laboratory data showed a high progno stic accuracy for predicting mortality in alcoholic patients with seve re Liver disease.