Online interpretable dynamic prediction models for clinically significant posthepatectomy liver failure based on machine learning algorithms: a retrospective cohort study.

ABSTRACT:

Background

Posthepatectomy liver failure (PHLF) is the leading cause of mortality in patients undergoing hepatectomy. However, practical models for accurately predicting the risk of PHLF are lacking. This study aimed to develop precise prediction models for clinically significant PHLF.

Methods

A total of 226 patients undergoing hepatectomy at a single center were recruited. The study outcome was clinically significant PHLF. Five preoperative and postoperative machine learning (ML) models were developed and compared with four clinical scores, namely, the MELD, FIB-4, ALBI, and APRI scores. The robustness of the developed ML models was internally validated using fivefold cross-validation (CV) by calculating the average of the evaluation metrics and was externally validated on

SUBMITTER: Jin Y 

PROVIDER: S-EPMC11573074 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature