AUTHOR=Yang Ruiyuan , Li Kexin , Zou Cailun , Wee Aileen , Liu Jimin , Liu Liwei , Li Min , Wu Ting , Wang Yu , Ma Zikun , Wang Yan , Liu Jingyi , Huang Ang , Sun Ying , Chang Binxia , Liang Qingsheng , Jia Jidong , Zou Zhengsheng , Zhao Xinyan 

TITLE=Alanine Aminotransferase and Bilirubin Dynamic Evolution Pattern as a Novel Model for the Prediction of Acute Liver Failure in Drug-Induced Liver Injury

JOURNAL=Frontiers in Pharmacology

VOLUME=Volume 13 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2022.934467

DOI=10.3389/fphar.2022.934467

ISSN=1663-9812

ABSTRACT=Aims: To develop, optimize and validate a novel model using alanine aminotransferase (ALT) and total bilirubin (TB) dynamic evolution patterns in predicting acute liver failure (ALF) in drug-induced liver injury (DILI) patients.
Methods: The demographic, clinical data, liver biopsy and outcomes of DILI patients were collected from 2 hospitals. According to the dynamic evolution of ALT and TB after DILI onset, the enrolled patients were divided into ALT-mono-peak, TB-mono-peak, double-overlap-peak and double-separate-peak (DSP) patterns and compared. Logistic regression was used to develop this predictive model in both discovery and validation cohort.
Results: The proportion of ALF was significantly higher in patients with DSP pattern than that in the ALT-mono-peak pattern and DOP pattern ( 10.0% vs 0.0% vs 1.8%，P<0.05). The AUROC of the DSP pattern model was 0.720 (95% CI: 0.682-0.756) in discovery cohort and 0.828 (95% CI: 0.788-0.864) in validation cohort in predicting ALF, being further improved by combining with INR and alkaline phosphatase (ALP) (AUROC in discovery cohort: 0.899; validation cohort: 0.958). Histopathologically, patients with DSP pattern exhibited predominantly cholestatic hepatitis pattern (75.0%, P<0.05) with higher degree of necrosis (29.2%, P=0.084).
Conclusions: DILI patients with DSP pattern are more likely to progress to ALF. The predictive potency of the model for ALF can be improved by incorporating INR and ALP. This novel model allows for better identification of high-risk DILI patients, enabling timely measures to be instituted for better outcome.