A novel microRNA-based prognostic model outperforms standard prognostic models in patients with acetaminophen-induced acute liver failure

Abstract

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.

Keywords: Regeneration; biomarker; cell-death; outcome prediction.

Fig. 1.. Heat map and 2-way hierarchical clustering of miRNA expression in both outcome groups at both time-points.

Clustering was performed on all samples and on the top 16 miRNAs with the highest SD using dCq values (miRNA were excluded if not detected in greater than 100 samples). dCq, delta quantification cycle; miR/miRNA, microRNA.

Fig. 2.. The early time-point miRNA-based model.

(A) miRNA included within the model. (B) Model performance (n = 182; AUC 0.78 [95% CI 0.71–0.84; p <0.0001*]; pseudo r2 = 0.2213; HL statistic 12.67 [p = 0.12]). (C) MetaCore^™ pathway analysis including all miRNA within the model. (D) MetaCore^™ pathway analysis including miRNA within both time-point models (miR-149 and −191) (E) MetaCore^™ pathway analysis including miRNA only within the early time-point model (miR-20a, −27a, −150). (E) Comparisons with other outcome prediction models with and without threshold values using the DeLong method. Statistical significance set as per Benjamini-Hochberg procedure with a false discovery rate of 0.05 (*p <0.026). ALFSGPI, Acute Liver Failure Study Group prognostic index; HL, Hosmer-Lemeshow; KCC, King’s College criteria; MELD, model for end-stage liver disease; miR/miRNA, microRNA; OR, odds ratio.

Fig. 3.. The late time-point miRNA-based model.

(A) miRNA included within the model. (B) Model performance (n = 175; AUC 0.83 [95% CI 0.76–0.89; p <0.0001*]; pseudo r2 = 0.2767, HL statistic 11.54 [p = 0.17]). (C) MetaCore^™ pathway analysis including miRNA within the model. (D) Comparisons with other outcome prediction models with and without threshold values using the DeLong method. Statistical significance set as per Benjamini-Hochberg procedure with a false discovery rate of 0.05 (*p <0.026). ALFSGPI, Acute Liver Failure Study Group prognostic index; HL, Hosmer-Lemeshow; KCC, King’s College criteria; MELD, model for end-stage liver disease; miR/miRNA, microRNA; OR, odds ratio.

Fig. 4.. Comparing the performances of the early and late time-point models.

(A) Performance of the late time-point model at the early time-point (n = 167; AUC 0.54; 95% CI 0.45–0.63; p = 0.40). (B) Performance of the early time-point model at the late time-point (n = 165; AUC 0.65; 95% CI 0.56–0.73; p = 0.001*). (C,D) Combined model performance in patients with paired samples (n = 165; AUC 0.87 [95% CI 0.82–0.93; p <0.0001*]; pseudo r2 = 0.4153, HL statistic 14.37 [p = 0.07]). (E) Comparison of both models’ performances in patients with paired samples using the DeLong method. Statistical significance set as per Benjamini-Hochberg procedure with a false discovery rate of 0.05 (*p <0.026). HL, Hosmer-Lemeshow; OR, odds ratio.

Fig. 5.. Combining the models with clinical parameters.

(A) Early time-point model adjusted for MELD and vasopressor use. (B) Late time-point model adjusted for MELD and vasopressor use. (C) Early time-point model performance (n = 177; AUC 0.83 [95% CI 0.78–0.89; p <0.0001*], pseudo r2 = 0.3396, HL statistic 6.83 [p = 0.56]). (D) Late time-point model performance (n = 149; AUC 0.91 [95% CI 0.86–0.96; p <0.0001*), pseudo r2 = 0.5290, HL statistic = 6.74 (p = 0.57)). (E,F) Comparing the early (E) and late (F) time-point models with clinical parameters integrated (with and without threshold values) to other commonly used outcome prediction models using the DeLong method. Statistical significance set as per Benjamini-Hochberg procedure with a false discovery rate of 0.05 (*p <0.026). ALFSGPI, Acute Liver Failure Study Group prognostic index; HL, Hosmer-Lemeshow; KCC, King’s College criteria; MELD, model for end-stage liver disease; OR, odds ratio.