ArticleHepatology international2023
Comparison of the prognostic models for mortality in idiosyncratic drug-induced liver injury.
Article in Hepatology international, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Chinese guideline for the diagnosis and treatment of drug-induced liver injury: an update.Hepatology international · 2024Guideline
- Multicenter Evaluation of Large Language Models Versus Hepatologists for Prognostic Prediction in Drug-Induced Liver Injury.Liver international : official journal of the International Association for the Study of the Liver · 2026Article
- Aspartate aminotransferase and model for end-stage liver disease reliably predict mortality in drug-induced liver injury.Scientific reports · 2026Article
- Time to use the right classification to predict the severity of checkpoint inhibitor-induced liver injury, as assessed for causality using the updated RUCAM.Alimentary pharmacology & therapeutics · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
backgroundSeveral models have been proposed to predict acute liver failure/death in patients with drug-induced liver injury (DILI), but the predictive performances of them have not been systematically compared. We aim to compare the current models for their predictive potency of mortality at DILI onset.
methodsDILI patients hospitalized at both Beijing Friendship Hospital and the Fifth Medical Center of PLA General Hospital were categorized into death/liver transplantation (LT) or survival without LT group. Predictive potency of 28-day, 90-day, 6-month and 12-month death/LT outcomes of Hy's Law, nHy's Law, Robles-Diaz Model, drug-induced liver toxicity (DrILTox ALF) Score, Model for End-stage Liver Disease (MELD) Score, and Ghabril Model was compared by Delong method.
resultsA total of 6.3% (83/1314) patients died or received LT within 12 months after DILI onset. The area under receiver operating characteristic of Hy's Law, nHy's Law, and Robles-Diaz Model was all lower than 0.750 for the prediction of within 12 months' mortality. DrILTox ALF Score, MELD Score and Ghabril Model showed better predictive potency of 28-day [0.896 (0.878-0.912), 0.934 (0.919-0.947), 0.935 (0.921-0.948), respectively], 90-day [0.883 (0.864-0.899), 0.951 (0.938-0.962), 0.952 (0.939-0.963), respectively], 6-month [0.820 (0.799-0.841), 0.905 (0.888-0.921) and 0.908 (0.891-0.923), respectively] and 12-month [0.801 (0.779-0.823), 0.882 (0.863-0.899) and 0.885 (0.866-0.902), respectively] mortality.
conclusionDespite the difference of clinical characteristics and implicated-drug categories between China and industrialized countries, we demonstrate that MELD Score and Ghabril Model have the best predictive performance in the prediction of mortality within 12 months after DILI onset.
Indexed as
Identifiers
36327052What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.