Development of a prognostic model for pediatric acute liver failure in a Brazilian center

Abstract Objective: Pediatric acute liver failure (PALF) is a heterogeneous, rare, and severe condition, which outcome is survival due to liver spontaneous recovery or death. The patients who do not recover may be allocated to liver transplantation, which is the standard treatment. This study aimed to build a prognostic model to support the clinical decision to indicate liver transplantation for patients with PALF in a Brazilian center. Methods: The authors retrospectively analyzed the clinical variables of 120 patients in the liver transplantation program of the 'Children's Institute of the University of São Paulo, Brazil. The authors conducted a univariate analysis of variables associated with survival in PALF. Logistic multivariate analysis was performed to find a prognostic model for the outcome of patients with pediatric acute liver failure. Results: Risk factors were analyzed using univariate analysis. Two prognostic models were built using multiple logistic regression, which resulted in 2 models: model 1(INR/ALT) and model 2 (INR/Total bilirubin). Both models showed a high sensitivity (97.9%/96.9%), good positive predictive value (89.5%/90.4%), and accuracy (88.4%/88.5%), respectively. The receiver operating characteristic was calculated for both models, and the area under the curve was 0.87 for model 1 and 0.88 for model 2. The Hosmer-Lemeshow test showed that model 1 was good. Conclusion: The authors built a prognostic model for PALF using INR and ALT that can contribute to the clinical decision to allocate patients to liver transplantation.

Saved in:
Bibliographic Details
Main Authors: Colleti Junior,José, Tannuri,Ana Cristina Aoun, Tannuri,Uenis, Delgado,Artur Figueiredo, Carvalho,Werther Brunow de
Format: Digital revista
Language:English
Published: Sociedade Brasileira de Pediatria 2022
Online Access:http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0021-75572022000600607
Tags: Add Tag
No Tags, Be the first to tag this record!