Development and validation of a machine learning model to predict prognosis in liver failure patients treated with non-bioartificial liver support system.

ABSTRACT:

Background and objectives

The prognosis of liver failure treated with non-bioartificial liver support systems is poor. Detecting its risk factors and developing relevant prognostic models still represent the top priority to lower its death risk.

Methods

All 215 patients with liver failure treated with non-bioartificial liver support system were retrospectively analyzed. Potential prognostic factors were investigated, and the Nomogram and the Random Survival Forests (RSF) models were constructed, respectively. Notably, we evaluated the performance of models and calculated the risk scores to divide patients into low-risk and high-risk groups.

Results

In the training set, multifactorial Cox regression analysis showed that etiology, hepatic encephalopathy, total bilirubin

SUBMITTER: Shi S 

PROVIDER: S-EPMC10965676 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature