Author information

1. Institute of Applied Microbiology - iAMB, Aachen Biology and Biotechnology - ABBt, RWTH Aachen University, Aachen, Germany.
2. Systems Pharmacology & Medicine, Bayer AG, Leverkusen, Germany.
3. Division of Translational Medicine and Human Genetics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
4. Department of Genetics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
5. Department of Microbiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
6. Institute for Immunology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
7. Institute for Diabetes, Obesity, and Metabolism, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
8. Medical Department I, Frankfurt University Hospital, Leverkusen, Germany.
9. European Foundation for Study of Chronic Liver Failure, Barcelona, Spain.
10. Clinical Pharmacometrics, Bayer AG, Wuppertal, Germany.
11. Department of Medicine III, University Hospital Aachen, Aachen, Germany.
12. Institute for Systems Medicine, University Hospital RWTH Aachen, Aachen, Germany.

Abstract

Background & aims: Decompensation is a hallmark of disease progression in cirrhotic patients. Early detection of a phase transition from compensated cirrhosis to decompensation would enable targeted therapeutic interventions potentially extending life expectancy. This study aims to (a) identify the predictors of decompensation in a large, multicentric cohort of patients with compensated cirrhosis, (b) to build a reliable prognostic score for decompensation and (c) to evaluate the score in independent cohorts.

Methods: Decompensation was identified in electronic health records data from 6049 cirrhosis patients in the IBM Explorys database training cohort by diagnostic codes for variceal bleeding, encephalopathy, ascites, hepato-renal syndrome and/or jaundice. We identified predictors of clinical decompensation and developed a prognostic score using Cox regression analysis. The score was evaluated using the IBM Explorys database validation cohort (N = 17662), the Penn Medicine BioBank (N = 1326) and the UK Biobank (N = 317).

Results: The new Early Prediction of Decompensation (EPOD) score uses platelet count, albumin, and bilirubin concentration. It predicts decompensation during a 3-year follow-up in three validation cohorts with AUROCs of 0.69, 0.69 and 0.77, respectively, and outperforms the well-known MELD and Child-Pugh score in predicting decompensation. Furthermore, the EPOD score predicted the 3-year probability of decompensation.

Conclusions: The EPOD score provides a prediction tool for the risk of decompensation in patients with cirrhosis that outperforms well-known cirrhosis scores. Since EPOD is based on three blood parameters, only, it provides maximal clinical feasibility at minimal costs.