Background & Aims: Acute on chronic liver failure (ACLF) causes high short-term mortality in patients with previously stable chronic liver disease. To date there are no models to predict which patients are likely to develop ACLF, and existing models to predict ACLF mortality are based on limited cohorts. We sought to create novel risk prediction scores using a large cohort of patients with cirrhosis. Methods: We performed a retrospective cohort study of 74 790 patients with incident cirrhosis in the Veterans Health Administration database using randomized 70% derivation/30% validation sets. ACLF events were identified per the European ACLF criteria. Multivariable logistic regression was used to derive prediction models for developing ACLF at 3, 6 and 12 months, and ACLF mortality at 28 and 90 days. Mortality models were compared to model for end-stage liver disease (MELD), MELD-sodium and the Chronic Liver Failure Consortium (CLIF-C) ACLF score. Results: Models for the developing ACLF had very good discrimination (concordance [C] statistics 0.83-0.87) at all timepoints. Models for ACLF mortality also had good discrimination at 28 and 90 days (C-statistics 0.79-0.82), and were superior to MELD, MELD-sodium and the CLIF-C ACLF score. The calibration of the novel models was excellent at all timepoints. Conclusion: We have obtained highly-predictive models for developing ACLF, as well as for ACLF short-term mortality in a diverse United States cohort. These may be used to identify outpatients at significant risk of ACLF, which may prompt closer follow-up or early transplant referral, and facilitate decision making for patients with diagnosed ACLF, including escalation of care, expedited transplant evaluation or palliation.
- chronic liver disease
- European Association for the Study of the Liver (EASL)
- prediction modeling
ASJC Scopus subject areas