Predicting outcome on admission and post-admission for acetaminophen-induced acute liver failure using classification and regression tree models.

ABSTRACT:

Background/aim

Assessing prognosis for acetaminophen-induced acute liver failure (APAP-ALF) patients often presents significant challenges. King's College (KCC) has been validated on hospital admission, but little has been published on later phases of illness. We aimed to improve determinations of prognosis both at the time of and following admission for APAP-ALF using Classification and Regression Tree (CART) models.

Methods

CART models were applied to US ALFSG registry data to predict 21-day death or liver transplant early (on admission) and post-admission (days 3-7) for 803 APAP-ALF patients enrolled 01/1998-09/2013. Accuracy in prediction of outcome (AC), sensitivity (SN), specificity (SP), and area under receiver-operating curve (AUROC) were compared between 3 models: K

SUBMITTER: Speiser JL 

PROVIDER: S-EPMC4401567 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature