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Updated: Nov 9, 2025
Generation of a Rat Model of Acute Liver Failure by Combining 70% Partial Hepatectomy and Acetaminophen
Published on: November 27, 2019
A novel microRNA-based prognostic model outperforms standard prognostic models in patients with acetaminophen-induced
Oliver D Tavabie1, Constantine J Karvellas2, Siamak Salehi1
1Institute of Liver Studies, King's College Hospital, London, UK.
Background & Aims:
Acetaminophen (APAP)-induced acute liver failure (ALF) remains the most common cause of ALF in the Western world. Conventional prognostic models, utilising markers of liver injury and organ failure, lack sensitivity for mortality prediction. We previously identified a microRNA signature that is associated with successful regeneration post-auxiliary liver transplant and with recovery from APAP-ALF. Herein, we aimed to use this microRNA signature to develop outcome prediction models for APAP-ALF.
Methods:
We undertook a nested, case-control study using serum samples from 194 patients with APAP-ALF enrolled in the US ALF Study Group registry (1998-2014) at early (day 1-2) and late (day 3-5) time-points. A microRNA qPCR panel of 22 microRNAs was utilised to assess microRNA expression at both time-points. Multiple logistic regression was used to develop models which were compared to conventional prognostic models using the DeLong method.
Results:
Individual microRNAs confer limited prognostic value when utilised in isolation. However, incorporating them within microRNA-based outcome prediction models increases their clinical utility. Our early time-point model (AUC = 0.78, 95% CI 0.71-0.84) contained a microRNA signature associated with liver regeneration and our late time-point model (AUC = 0.83, 95% CI 0.76-0.89) contained a microRNA signature associated with cell-death. Both models were enhanced when combined with model for end-stage liver disease (MELD) score and vasopressor use and both outperformed the King's College criteria. The early time-point model combined with clinical parameters outperformed the ALF Study Group prognostic index and the MELD score.
Conclusions:
Our findings demonstrate that a regeneration-linked microRNA signature combined with readily available clinical parameters can outperform existing prognostic models for ALF in identifying patients with poor prognosis who may benefit from transplantation.
Lay Summary:
While acute liver failure can be reversible, some patients will die without a liver transplant. We show that blood test markers that measure the potential for liver recovery may help improve identification of patients unlikely to survive acute liver failure who may benefit from a liver transplant.
Insights
New microRNA signatures can predict outcomes in acetaminophen-induced acute liver failure (APAP-ALF). These blood tests improve identification of patients needing liver transplantation, outperforming current models.
Area of Science:
- Hepatology
- Molecular Biology
- Biomarker Discovery
Background:
- Acetaminophen (APAP)-induced acute liver failure (ALF) is a leading cause of ALF in Western countries.
- Existing prognostic models for APAP-ALF lack sufficient sensitivity for accurate mortality prediction.
- Previous research identified a microRNA signature linked to liver regeneration and recovery from APAP-ALF.
Purpose of the Study:
- To develop novel outcome prediction models for APAP-ALF using a previously identified microRNA signature.
- To assess the clinical utility of these microRNA-based models in predicting patient prognosis.
- To compare the performance of microRNA models against conventional prognostic tools.
Main Methods:
- A nested, case-control study involving 194 APAP-ALF patients from the US ALF Study Group registry.
- Serum samples were analyzed for microRNA expression at early (days 1-2) and late (days 3-5) time-points using a 22-microRNA qPCR panel.
- Multiple logistic regression was employed to build predictive models, which were then compared to existing models using the DeLong method.
Main Results:
- Individual microRNAs had limited prognostic value, but models incorporating them showed increased clinical utility.
- An early time-point model (AUC=0.78) and a late time-point model (AUC=0.83) demonstrated significant predictive power.
- Models were enhanced when combined with Model for End-Stage Liver Disease (MELD) score and vasopressor use, outperforming King's College criteria and ALF Study Group prognostic index.
Conclusions:
- A regeneration-linked microRNA signature, combined with clinical parameters, can improve prognostic accuracy in APAP-ALF.
- These novel models outperform existing prognostic tools in identifying patients with poor prognosis.
- This approach aids in identifying patients who may benefit from timely liver transplantation.
