Combination of Gene Expression Signature and Model for End-Stage Liver Disease Score Predicts Survival of Patients With Severe Alcoholic Hepatitis

Gastroenterology. 2018 Mar;154(4):965-975. doi: 10.1053/j.gastro.2017.10.048. Epub 2017 Nov 20.

Abstract

Background & aims: Patients with severe alcoholic hepatitis (AH) have a high risk of death within 90 days. Corticosteroids, which can cause severe adverse events, are the only treatment that increases short-term survival. It is a challenge to predict outcomes of patients with severe AH. Therefore, we developed a scoring system to predict patient survival, integrating baseline molecular and clinical variables.

Methods: We obtained fixed liver biopsy samples from 71 consecutive patients diagnosed with severe AH and treated with corticosteroids from July 2006 through December 2013 in Brussels, Belgium (derivation cohort). Gene expression patterns were analyzed by microarrays and clinical data were collected for 180 days. We identified gene expression signatures and clinical data that are associated with survival without liver transplantation at 90 and 180 days after initiation of corticosteroid therapy. Findings were validated using liver biopsies from 48 consecutive patients with severe AH treated with corticosteroids, collected from March 2010 through February 2015 at hospitals in Belgium and Switzerland (validation cohort 1) and in liver biopsies from 20 patients (9 received corticosteroid treatment), collected from January 2012 through May 2015 in the United States (validation cohort 2).

Results: We integrated data on expression patterns of 123 genes and the model for end-stage liver disease (MELD) scores to assign patients to groups with poor survival (29% survived 90 days and 26% survived 180 days) and good survival (76% survived 90 days and 65% survived 180 days) (P < .001) in the derivation cohort. We named this assignment system the gene signature-MELD (gs-MELD) score. In validation cohort 1, the gs-MELD score discriminated patients with poor survival (43% survived 90 days) from those with good survival (96% survived 90 days) (P < .001). The gs-MELD score also discriminated between patients with a poor survival at 180 days (34% survived) and a good survival at 180 days (84% survived) (P < .001). The time-dependent area under the receiver operator characteristic curve for the score was 0.86 (95% confidence interval 0.73-0.99) for survival at 90 days, and 0.83 (95% confidence interval 0.71-0.96) for survival at 180 days. This score outperformed other clinical models to predict survival of patients with severe AH in validation cohort 1. In validation cohort 2, the gs-MELD discriminated patients with a poor survival at 90 days (12% survived) from those with a good survival at 90 days (100%) (P < .001).

Conclusions: We integrated data on baseline liver gene expression pattern and the MELD score to create the gs-MELD scoring system, which identifies patients with severe AH, treated or not with corticosteroids, most and least likely to survive for 90 and 180 days.

Keywords: Cirrhosis; Ethanol; MELD; Transcription.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Validation Study

MeSH terms

  • Adrenal Cortex Hormones / therapeutic use
  • Adult
  • Area Under Curve
  • Belgium
  • Biopsy
  • Decision Support Techniques*
  • Female
  • Gene Expression Profiling / methods*
  • Genetic Markers
  • Genetic Predisposition to Disease
  • Hepatitis, Alcoholic / diagnosis*
  • Hepatitis, Alcoholic / drug therapy
  • Hepatitis, Alcoholic / genetics*
  • Hepatitis, Alcoholic / mortality
  • Humans
  • Kaplan-Meier Estimate
  • Male
  • Middle Aged
  • Oligonucleotide Array Sequence Analysis
  • Phenotype
  • Predictive Value of Tests
  • Proportional Hazards Models
  • ROC Curve
  • Reproducibility of Results
  • Risk Assessment
  • Risk Factors
  • Severity of Illness Index
  • Time Factors
  • Transcriptome*
  • Treatment Outcome

Substances

  • Adrenal Cortex Hormones
  • Genetic Markers