A simple four-factor preoperative recipient scoring model for prediction of 90-day mortality after adult liver Transplantation:A retrospective cohort study. (September 2020) - Kong, LingXiang; Lv, Tao; Jiang, Li; Yang, Jian; Yang, Jiayin
Abstract: 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. Highlights: We developed a simple scoring system for liver transplantation prognosis. Four-factor model was used for quick prediction of short-term survival. The scoring model is useful when donor information is not available in advance.