Predicting Mortality in Patients with Cirrhosis of Liver with Application of Neural Network Technology

Background: Prediction of mortality from cirrhosis is important in planning optimal timing of liver transplantation and other interventions. We evaluated the role of the Artificial Neural Network (ANN), which uses non-linear statistics for pattern recognition in predicting one-year liver disease-related mortality using information available during initial clinical evaluation.

Methods: The ANN was constructed using software with data from a training set (n = 46) selected at random from a cohort of adult cirrhotics (n = 92). After training, validation was performed in the remaining patients (n = 46) whose outcome in terms of one-year mortality was unknown to the network. The performance of ANN was compared to those of a logistic regression model (LRM) and Child-Pugh's score (CPS). Death (related to cirrhosis/its complications) within one year of inclusion was the outcome variable. The ANN was also tested in an external validation sample (EVS, n = 62) from another hospital.

Results: Patients in the EVS were younger (mean age, 41 vs 45 years), infrequently of alcoholic etiology (5% vs 49%), had less severe disease (mean CPS 6.6 vs 10.8), and had lower one-year mortality (13 vs 46%). In the internal validation sample, ANN's accuracy was 91%, sensitivity 90% and specificity 92% in prediction of one-year mortality; area under the receiver-operating characteristic (ROC) curve was 0.94. The performance of the LRM (accuracy 74%) and the CPS (accuracy 55%) was significantly worse than ANN (P < 0.05, McNemar's test). Despite differences in the characteristics of the two groups, the ANN performed fairly well in the EVS (accuracy of 90%, area under curve 0.85).

Conclusions: ANN can accurately predict one-year mortality in cirrhosis and is superior to CPS and LRM.