TY - JOUR
T1 - A novel microRNA-based prognostic model outperforms standard prognostic models in patients with acetaminophen-induced acute liver failure
AU - Tavabie, Oliver D.
AU - Karvellas, Constantine J.
AU - Salehi, Siamak
AU - Speiser, Jaime L.
AU - Rose, Christopher F.
AU - Menon, Krishna
AU - Prachalias, Andreas
AU - Heneghan, Michael A.
AU - Agarwal, Kosh
AU - Lee, William M.
AU - McPhail, Mark J. W.
AU - Aluvihare, Varuna R.
N1 - Funding Information:
This study was supported by the Roche Organ Transplant Research Foundation and the National Institute of Diabetes and Digestive and Kidney Disease . The sponsors did not influence any aspect of the study or composition of this manuscript.
Publisher Copyright:
© 2021 European Association for the Study of the Liver
PY - 2021/8
Y1 - 2021/8
N2 - 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.
AB - 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.
KW - Regeneration
KW - cell-death
KW - outcome prediction
KW - biomarker
UR - http://www.scopus.com/inward/record.url?scp=85104947837&partnerID=8YFLogxK
U2 - 10.1016/j.jhep.2021.03.013
DO - 10.1016/j.jhep.2021.03.013
M3 - Article
SN - 0168-8278
VL - 75
SP - 424
EP - 434
JO - Journal of Hepatology
JF - Journal of Hepatology
IS - 2
ER -