Comparative accuracy of prognostic models for short-term mortality in acute-on-chronic liver failure patients: CAP-ACLF

Abstract

Background: Multiple predictive models of mortality exist for acute-on-chronic liver failure (ACLF) patients that often create confusion during decision-making. We studied the natural history and evaluated the performance of prognostic models in ACLF patients.

Methods: Prospectively collected data of ACLF patients from APASL-ACLF Research Consortium (AARC) was analyzed for 30-day outcomes. The models evaluated at days 0, 4, and 7 of presentation for 30-day mortality were: AARC (model and score), CLIF-C (ACLF score, and OF score), NACSELD-ACLF (model and binary), SOFA, APACHE-II, MELD, MELD-Lactate, and CTP. Evaluation parameters were discrimination (c-indices), calibration [accuracy, sensitivity, specificity, and positive/negative predictive values (PPV/NPV)], Akaike/Bayesian Information Criteria (AIC/BIC), Nagelkerke-R², relative prediction errors, and odds ratios.

Results: Thirty-day survival of the cohort (n = 2864) was 64.9% and was lowest for final-AARC-grade-III (32.8%) ACLF. Performance parameters of all models were best at day 7 than at day 4 or day 0 (p < 0.05 for C-indices of all models except NACSELD-ACLF). On comparison, day-7 AARC model had the numerically highest c-index 0.872, best accuracy 84.0%, PPV 87.8%, R² 0.609 and lower prediction errors by 10-50%. Day-7 NACSELD-ACLF-binary was the simple model (minimum AIC/BIC 12/17) with the highest odds (8.859) and sensitivity (100%) but with a lower PPV (70%) for mortality. Patients with day-7 AARC score > 12 had the lowest 30-day survival (5.7%).

Conclusions: APASL-ACLF is often a progressive disease, and models assessed up to day 7 of presentation reliably predict 30-day mortality. Day-7 AARC model is a statistically robust tool for classifying risk of death and accurately predicting 30-day outcomes with relatively lower prediction errors. Day-7 AARC score > 12 may be used as a futility criterion in APASL-ACLF patients.

Keywords: AARC; APASL; CILF-C ACLF; Deaths; EASL; Liver failure; MELD; NACSELD; Natural history; Prediction.