Clinical characteristics and prognosis of non-APAP drug-induced acute liver failure: a large multicenter cohort study

Alpha-fetoprotein and carbohydrate antigen 19-9 as prognostic biomarkers in acute liver failure: A retrospective study

ObjectiveTo investigate the clinical significance of alpha-fetoprotein and carbohydrate antigen 19-9 as potential predictors of outcomes in patients with acute liver failure.MethodsWe conducted a retrospective analysis of 208 patients with acute liver failure admitted to the intensive care unit between 2009 and 2023. Serum alpha-fetoprotein and carbohydrate antigen 19-9 levels were measured on Days 1 and 3, and their prognostic value was evaluated using logistic regression and receiver operating characteristic curve analyses. Patients were stratified by etiologies to assess biomarker performance across different causes of acute liver failure.ResultsNonsurvivors had significantly lower alpha-fetoprotein levels and higher carbohydrate antigen 19-9 levels than survivors on Days 1 and 3 (all p < 0.05). Alpha-fetoprotein levels increased over time in both groups, whereas carbohydrate antigen 19-9 levels increased in nonsurvivors and decreased in survivors. The combination of carbohydrate antigen 19-9 with the Model for End-Stage Liver Disease score significantly improved prognostic accuracy, with an area under the curve value of 0.828, compared with 0.784 for alpha-fetoprotein combined with Model for End-Stage Liver Disease score. Etiology-specific analysis revealed that carbohydrate antigen 19-9 showed the best predictive performance in acetaminophen-induced acute liver failure (area under the curve value = 0.885), whereas alpha-fetoprotein showed better predictive performance in viral hepatitis-associated acute liver failure (area under the curve value = 0.880).ConclusionsAlpha-fetoprotein is a protective prognostic factor, whereas carbohydrate antigen 19-9 enhances outcome prediction, particularly when combined with Model for End-Stage Liver Disease score. Etiology-specific biomarker performance supports tailored prognostic approaches in the management of acute liver failure.

Development and Validation of a New Model Including Inflammation Indexes for the Long-Term Prognosis of Hepatitis B-Related Acute-On-Chronic Liver Failure

Acute-on-chronic liver failure (ACLF) is a severe condition characterized by a systemic inflammatory response and associated with high mortality. Currently, there is no reliable prediction model for long-term prognosis in ACLF. This study aimed to develop and validate a prognostic model incorporating inflammation indexes to predict the long-term outcome of patients with hepatitis B virus-related ACLF (HBV-ACLF). A retrospective analysis of clinical data from HBV-ACLF patients (n = 986) treated at the Third Affiliated Hospital of Sun Yat-sen University between January 2014 and December 2018 was conducted. Patients were randomly divided into training (n = 690) and validation (n = 296) cohorts. The Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression analyses were used to identify independent risk factors for long-term mortality. The following variables were identified as independent predictors of long-term mortality: age, cirrhosis, hepatic encephalopathy, total bilirubin (TBIL), international normalized ratio (INR), monocyte-to-lymphocyte ratio (MLR), and neutrophil-to-platelet ratio (NPR). A novel nomogram was established by assigning weights to each variable. The C-index of the nomogram was 0.777 (95% confidence interval [CI]: 0.752-0.802). In the training set, the area under the curve (AUC) for predicting mortality at 1, 3, and 12 months was 0.841 (95% CI: 0.807-0.875), 0.827 (95% CI: 0.796-0.859), and 0.829 (95% CI: 0.798-0.859), respectively. The nomogram demonstrated superior predictive performance for 12-month survival compared to the model for end-stage liver disease (MELD) score (0.767, 95% CI: 0.730-0.804, p < 0.001) and the clinical overt sepsis in acute liver failure clinical practice Guidelines-ACLF II score (0.807, 95% CI: 0.774-0.840, p = 0.028). Finally, calibration curves and decision curve analysis (DCA) confirmed the clinical utility of the nomogram. The novel inflammation-based scoring system, incorporating MLR and NPR, effectively predicts long-term mortality in HBV-ACLF patients.

Establishing a predictive nomogram for 21‑day transplant-free survival in drug-induced liver failure

BACKGROUND

The high prevalence of drug-induced liver failure (DILF) have drawn great attention from clinicians.

AIM

To further delineate the clinical features of DILF and develop an easily applicable nomogram, based on readily-discernable clinical data, to predict transplant-free survival (TFS) at different time points.

METHODS

202 DILF patients were enrolled between January 2016 and December 2022, and were followed up from DILF diagnosis to death, liver transplantation, or 91 days afterward, whichever came first. The primary endpoint, though, was 21-day TFS. Clinical data was collected from all patients, and independent risk factors associated with death/liver transplantation was identified using both uni- and multi-variate Cox regression analyses.

RESULTS

Independent risk factors incorporated into the predictive nomogram are neutrophils (HR = 1.148, 95% CI = 1.048-1.257), prothrombin time (HR = 1.048, 95% CI = 1.017-1.080), albumin (HR = 0.880, 95% CI = 0.823-0.941), acute kidney injury (HR = 2.487, 95% CI = 1.134-5.452), and hepatic encephalopathy (HR = 3.378, 95% CI = 1.744-6.543). The resulting nomogram was highly predictive, with an area under the curve of 0.947 for 21-day TFS.

CONCLUSIONS

Compared to existing models, such as the Model for End-Stage Liver Disease score, the predictive nomogram is more accurate, only requires easily-measurable clinical and laboratory metrics, as well as being able to directly calculate TFS at various time points.

Chinese guideline for the diagnosis and treatment of drug-induced liver injury: an update

Drug-induced liver injury (DILI) is an important adverse drug reaction that can lead to acute liver failure or even death in severe cases. Currently, the diagnosis of DILI still follows the strategy of exclusion. Therefore, a detailed history taking and a thorough and careful exclusion of other potential causes of liver injury is the key to correct diagnosis. This guideline was developed based on evidence-based medicine provided by the latest research advances and aims to provide professional guidance to clinicians on how to identify suspected DILI timely and standardize the diagnosis and management in clinical practice. Based on the clinical settings in China, the guideline also specifically focused on DILI in chronic liver disease, drug-induced viral hepatitis reactivation, common causing agents of DILI (herbal and dietary supplements, anti-tuberculosis drugs, and antineoplastic drugs), and signal of DILI in clinical trials and its assessment.