Clinical prediction models for hepatitis B virus-related acute-on-chronic liver failure: A technical report

3Citations
Citations of this article
7Readers
Mendeley users who have this article in their library.

Abstract

Background and Aims: It is critical but challenging to predict the prognosis of hepatitis B virus-related acute-on-chronic liver failure (HBV-ACLF). This study systematically summarized and evaluated the quality and performance of available clinical prediction models (CPMs). Methods: A keyword search of articles on HBV-ACLF CPMs published in PubMed from January 1995 to April 2020 was performed. Both the quality and performance of the CPMs were as-sessed. Results: Fifty-two CPMs were identified, of which 31 were HBV-ACLF specific. The modeling data were most-ly derived from retrospective (83.87%) and single-center (96.77%) cohorts, with sample sizes ranging from 46 to 1,202. Three-month mortality was the most common end-point. The Asian Pacific Association for the Study of the Liver consensus (51.92%) and Chinese Medical Association liver failure guidelines (40.38%) were commonly used for HBV-ACLF diagnosis. Serum bilirubin (67.74%), the international normalized ratio (54.84%), and hepatic encephalopathy (51.61%) were the most frequent variables used in models. Model discrimination was commonly evaluated (88.46%), but model calibration was seldom performed. The model for end-stage liver disease score was the most widely used (84.62%); however, varying performance was reported among the studies. Conclusions: Substantial limitations lie in the quality of HBV-ACLF-specific CPMs. Disease severity of study populations may impact model performance. The clinical utility of CPMs in predicting short-term prognosis of HBV-ACLF remains to be undefined.

Cite

CITATION STYLE

APA

Yu, X., Lu, Y., Sun, S., Tu, H., Xu, X., Gong, K., … Sheng, J. (2021). Clinical prediction models for hepatitis B virus-related acute-on-chronic liver failure: A technical report. Journal of Clinical and Translational Hepatology, 9(6), 838–849. https://doi.org/10.14218/JCTH.2021.00005

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free