A simple four-factor preoperative recipient scoring model for prediction of 90-day mortality after adult liver Transplantation:A retrospective cohort study.

BACKGROUND

Although numerous scoring models are available to predict the prognosis of patients undergoing liver transplantation (LT), the field lacks a simple model for quick prediction of the short-term survival of patients after LT in the event that the donor's information is not available in advance.

METHODS

A total of 1495 adult patients underwent LT in the present study. Three-quarters of recipients were randomly selected into the test set (n = 1121), while the remaining 25% formed the validation set (n = 374). Univariate and multivariate analysis and machine-learning techniques were applied to evaluate possible influencing factors. To further simplify the model, a weighted-scoring system was designed considering each influencing factor and its importance in an artificial neural network (ANN).

RESULTS

In the test set, multivariate analysis identified creatinine, age, and total bilirubin as independent risk factors, while albumin was an independent protective factor. Logistic regression analysis showed the C-statistic to be 0.650, while ANN indicated this to be 0.698. We simplified the model to obtain the final scoring model, for which the C-statistic was 0.636, and defined four risk grades. The 90-day mortality rates corresponding to the four risk levels were 6.2%, 11.8%, 24.0%, and 34.9%, respectively. In the validation set, the C-statistic value of the original model was 0.668 and that of the simplified model was 0.647.

CONCLUSION

We developed a simple scoring system for the preliminary prediction of the postoperative 90-day mortality of adult LT based on preoperative characteristics of LT recipients.