Improve the prediction of liver transplant mortality based on pre-transplant factors: A multi-center study from China

Aims To designed a new model using pre-transplant data to predict post-transplant mortality for Chinese population and compared its performance to that of existing models. Methods In this multicenter study, 544 recipients of liver transplants for non-tumor indications were enrolled in the training group and 276 patients in the validation group. The new Simplified Mortality Prediction Scores (SMOPS) model was compared to the MELD and four existing models using the C-statistic. Results SMOPS model used 6 independent pre-transplantation risk factors screened from the training group (chronic liver failure/organ failure scores, fever > 37.6 ℃, ABO blood-type compatibility, arterial lactate level, leukocyte count and re-transplantation). The SMOPS accurately predicted patients' 30-day, 90-day and 365-day mortality following liver transplantation, and its' scores were more accurate than those of the other models. The SMOPS generated four levels of risk: low risk (<10 points), moderate risk (11–20 points), high risk (21–25 points) and futile risk (≥26 points). The survival within all risk levels was not different between MELD=40 and MELD<40. The survival within moderate-, high- or extreme-risk ALF was not different between ALF and non-ALF. Conclusion The SMOPS model uses pre-transplant risk factors to stratify post-transplant survival and is superior to current models for Chinese population, and has the potential to contribute to improvements in organ-allocation policies.