Artificial intelligence to predict overall survivals of patients with cirrhosis and outcomes of variceal bleeding.

Background And Aim: The ability to predict survival in cirrhosis is essential to management. Artificial intelligence models are promising alternatives to current scores and staging systems. The objective of this study was to test the feasibility of such a model to predict the short- and long-term survival of patients with different stages of cirrhosis.

Materials And Methods: Clinical, laboratory, and survival data of patients with cirrhosis were collected retrospectively. A machine learning model was designed using feature selection. The model's prediction performance was compared with the Model for End-stage Liver Disease-serum sodium (MELD-Na) and the Child-Turcotte-Pugh (CTP) scores using area under the curve (AUC) analysis.

Results: The study population consisted of 124 cirrhotic patients. The AUC of the CTP score for 1-, 3-, and 12-month overall survival was 0.75 (CI:0.61-0.88), 0.77 (0.65-0.88), and 0.69 (CI:0.60-0.79), respectively. The AUC of the MELD-Na scores for the same time points was 0.7 (CI:0.62-0.86), 0.73 (CI:0.63-0.83), and 0.68 (CI:0.59-0.78). The machine learning model mean AUC for the entire study population was 0.87 (±0.082) for 1 month, 0.85 (±0.077) for 3 months, and 0.76 (±0.076) for 12 months. The model predicted 1-, 3-, and 12-month survival with an AUC of 0.91 (±0.03), 0.88 (±0.10), and 0.91 (±0.06), respectively, in patients with variceal bleeding.

Conclusion: To the best of our knowledge, this is the first study to test a machine learning model in this context. The model outperformed the MELD-Na and CTP scores in the prediction of short- and long-term survival and also successfully predicted high risk variceal bleeding.