A new machine-learning-based prediction of survival in patients with end-stage liver disease

Abstract

Abstract Objectives The shortage of grafts for liver transplantation requires risk stratification and adequate allocation rules. This study aims to improve the model of end-stage liver disease (MELD) score for 90-day mortality prediction with the help of different machine-learning algorithms. Methods We retrospectively analyzed the clinical and laboratory data of 654 patients who were recruited during the evaluation process for liver transplantation at University Hospital Leipzig. After comparing 13 different machine-learning algorithms in a nested cross-validation setting and selecting the best performing one, we built a new model to predict 90-day mortality in patients with end-stage liver disease. Results Penalized regression algorithms yielded the highest prediction performance in our machine-learning algorithm benchmark. In favor of a simpler model, we chose the least absolute shrinkage and selection operator (lasso) regression. Beside the classical MELD international normalized ratio (INR) and bilirubin, the lasso regression selected cystatin C over creatinine, as well as IL-6, total protein, and cholinesterase. The new model offers improved discrimination and calibration over MELD and MELD with sodium (MELD-Na), MELD 3.0, or the MELD-Plus7 risk score. Conclusions We provide a new machine-learning-based model of end-stage liver disease that incorporates synthesis and inflammatory markers and may improve the classical MELD score for 90-day survival prediction.