Artificial neural network is superior to MELD in predicting mortality of patients with end-stage liver disease

Background: Despite its accuracy, the model for end-stage liver disease (MELD), currently adopted to determine the prognosis of patients with liver cirrhosis, guide referral to transplant programmes and prioritise the allocation of donor organs, fails to predict mortality in a considerable proportion of patients. Aims: To evaluate the possibility to better predict 3-month liver disease-related mortality of patients awaiting liver transplantation using an artificial neural network (ANN). Patients and methods: The ANN was constructed using data from 251 consecutive people with cirrhosis listed for liver transplantation at the Liver Transplant Unit, Bologna, Italy. The ANN was trained to predict 3-month survival on 188 patients, tested on the remaining 63 (internal validation group) unknown by the system and finally on 137 patients listed for liver transplantation at the King's College Hospital, London, UK (external cohort). Predictions of survival obtained with ANN and MELD on the same datasets were compared using areas under receiver-operating characteristic (ROC) curves (AUC). Results: The ANN performed significantly better than MELD both in the internal validation group (AUC = 0.95 v 0.85; p = 0.032) and in the external cohort (AUC = 0.96 v 0.86; p = 0.044). Conclusions: The ANN measured the mortality risk of patients with cirrhosis more accurately than MELD and could better prioritise liver transplant candidates, thus reducing mortality in the waiting list.