Using liver stiffness to predict and monitor the risk of decompensation and mortality in patients with alcohol-related liver disease

Katrine Holtz Thorhauge, Georg Semmler, Stine Johansen, Katrine Prier Lindvig, Maria Kjærgaard, Johanne Kragh Hansen, Nikolaj Torp, Camilla Dalby Hansen, Peter Andersen, Benedikt Silvester Hofer, Wenyi Gu, Mads Israelsen, Mattias Mandorfer, Thomas Reiberger, Jonel Trebicka, Maja Thiele*, Aleksander Krag

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

Abstract

Background & Aims: Liver stiffness measurement (LSM) is recommended for disease prognostication and monitoring. We evaluated if LSM, using transient elastography, and LSM changes predict decompensation and mortality in patients with alcohol-related liver disease (ALD). Methods: We performed an observational cohort study of compensated patients at risk of ALD from Denmark and Austria. We evaluated the risk of decompensation and all-cause mortality, stratified for compensated advanced chronic liver disease (cACLD: baseline LSM ≥10 kPa) and LSM changes after a median of 2 years. In patients with cACLD, we defined LSM changes as (A) LSM increase ≥20% (“cACLD increasers”) and (B) follow-up LSM <10 kPa or <20 kPa with LSM decrease ≥20% (“cACLD decreasers”). In patients without cACLD, we defined follow-up LSM ≥10 kPa as an LSM increase (“No cACLD increasers”). The remaining patients were considered LSM stable. Results: We followed 536 patients for 3,008 patient-years, median age 57 years (IQR 49–63), baseline LSM 8.1 kPa (IQR 4.9-21.7). 371 patients (69%) had follow-up LSM after a median of 25 months (IQR 17–38), 41 subsequently decompensated and 55 died. Of 125 with cACLD at baseline, 14% were “cACLD increasers” and 43% “cACLD decreasers”, while 13% of patients without cACLD were “No cACLD increasers” (n = 33/246). Baseline LSM, follow-up LSM and LSM changes accurately predicted decompensation (C-index: baseline LSM 0.85; follow-up LSM 0.89; LSM changes 0.85) and mortality (C-index: baseline LSM 0.74; follow-up LSM 0.74; LSM changes 0.70). When compared to “cACLD decreasers”, “cACLD increasers” had significantly lower decompensation-free survival and higher risks of decompensation (subdistribution hazard ratio 4.39, p = 0.004) and mortality (hazard ratio 3.22, p = 0.01). Conclusion: LSM by transient elastography predicts decompensation and all-cause mortality in patients with compensated ALD both at diagnosis and when used for monitoring. Impact and implications: Patients at risk of alcohol-related liver disease (ALD) are at significant risk of progressive disease and adverse outcomes. Monitoring is essential for optimal disease surveillance and patient guidance, but non-invasive monitoring tools are lacking. In this study we demonstrate that liver stiffness measurement (LSM), using transient elastography, and LSM changes after a median of 2 years, can predict decompensation and all-cause mortality in patients at risk of ALD with and without compensated advanced chronic liver disease. These findings are in line with results from non-alcoholic fatty liver disease, hepatitis C and primary sclerosing cholangitis, and support the clinical utility of LSM, using transient elastography, for disease prognostication and monitoring in chronic liver diseases including ALD, as recommended by the Baveno VII.

Original languageEnglish
JournalJournal of Hepatology
Volume81
Issue number1
Pages (from-to)23-32
ISSN0168-8278
DOIs
Publication statusPublished - Jul 2024

Keywords

  • ALD
  • Baveno VII
  • Fibroscan
  • cACLD
  • transient elastography
  • Predictive Value of Tests
  • Prognosis
  • Elasticity Imaging Techniques/methods
  • Humans
  • Middle Aged
  • Male
  • Liver Diseases, Alcoholic/mortality
  • Denmark/epidemiology
  • Austria/epidemiology
  • Female
  • Liver/diagnostic imaging
  • Cohort Studies

Fingerprint

Dive into the research topics of 'Using liver stiffness to predict and monitor the risk of decompensation and mortality in patients with alcohol-related liver disease'. Together they form a unique fingerprint.

Cite this