Sina Score as a New Machine Learning-Derived Online Prediction Model of Mortality for Cirrhotic Patients Awaiting Liver Transplantation: A Prospective Cohort Study.

Cirrhosis is responsible for a large proportion of mortality worldwide. Despite having multiple scoring systems, organ allocation for end-stage liver disease remains a major problem. Since anthropometric indices play important roles in predicting the prognosis of patients with cirrhosis, these variables were used in establishment of a novel scoring system. In order to evaluate a machine learning approach for predicting the probability of three-month mortality in cirrhotic patients awaiting liver transplantation, the clinical and anthropometric information of 64 patients referred to Abu-Ali-Sina Transplantation Center were collected and followed for three months. A LASSO logistic regression model was used to devise and validate a new machine learning approach and compare it to the Model for End-Stage Liver Disease (MELD) regarding the three-month mortality of cirrhotic patients. Hand grip, skeletal muscle mass index, average mean arterial pressure, serum sodium, and total bilirubin were assessed with this new machine learning approach to predict the prognosis of patients with cirrhosis, which we named the Sina score. Sixty-four patients were enrolled, with a mean age of 46.50 ± 12.871 years. Like the MELD score, the Sina score is a precise prognostic tool for predicting the three-month mortality probability in cirrhotic patients [area under the curve (AUC) = 0.753 and = 0.005 vs. AUC = 0.607 and = 0.238]. Our machine learning approach, the Sina score, was shown to be a precise prognostic tool, like the MELD, for the prediction of the three-month mortality probability of cirrhotic patients awaiting liver transplantation. The Sina score, given that its level of precision is on par with the MELD, can be recommended for the prediction of three-month mortality in cirrhotic patients awaiting liver transplantation.