Prediction of Poor Outcome in Patients with Acute Liver Failure-Systematic Review of Prediction Models

Abstract

Introduction: Acute liver failure is a rare disease with high mortality and liver transplantation is the only life saving therapy. Accurate prognosis of ALF is crucial for proper intervention. Aim: To identify and characterize newly developed prognostic models of mortality for ALF patients, assess study quality, identify important variables and provide recommendations for the development of improved models in the future. Methods: The online databases MEDLINE® (1950-2012) and EMBASE® (1980-2012) were searched for English-language articles that reported original data from clinical trials or observational studies on prognostic models in ALF patients. Studies were included if they developed a new model or modified existing prognostic models. The studies were evaluated based on an existing framework for scoring the methodological and reporting quality of prognostic models. Results: Twenty studies were included, of which 18 reported on newly developed models, 1 on modification of the Kings College Criteria (KCC) and 1 on the Model for End-Stage Liver Disease (MELD). Ten studies compared the newly developed models to previously existing models (e.g. KCC); they all reported that the new models were superior. In the 12-point methodological quality score, only one study scored full points. On the 38-point reporting score, no study scored full points. There was a general lack of reporting on missing values. In addition, none of the studies used performance measures for calibration and accuracy (e.g. Hosmer-Lemeshow statistics, Brier score), and only 5 studies used the AUC as a measure of discrimination. Conclusions: There are many studies on prognostic models for ALF but they show methodological and reporting limitations. Future studies could be improved by better reporting and handling of missing data, the inclusion of model calibration aspects, use of absolute risk measures, explicit considerations for variable selection, the use of a more extensive set of reference models and more thorough validation. © 2012 Wlodzimirow et al.