Assessing risk of fibrosis progression and liver-related clinical outcomes among patients with both early stage and advanced chronic hepatitis C

Monica A. Konerman, Dongxia Lu, Yiwei Zhang, Mary Thomson, Ji Zhu, Aashesh Verma, Boang Liu, Nizar Talaat, Ulysses Balis, Peter D.R. Higgins, Anna S.F. Lok, Akbar K. Waljee

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Objective: Assessing risk of adverse outcomes among patients with chronic liver disease has been challenging due to non-linear disease progression. We previously developed accurate prediction models for fibrosis progression and clinical outcomes among patients with advanced chronic hepatitis C (CHC). The primary aim of this study was to validate fibrosis progression and clinical outcomes models among a heterogeneous patient cohort. Design: Adults with CHC with 3 years follow-up and without hepatic decompensation, hepatocellular carcinoma (HCC), liver transplant (LT), HBV or HIV co-infection at presentation were analyzed (N = 1007). Outcomes included: 1) fibrosis progression 2) hepatic decompensation 3) HCC and 4) LT-free survival. Predictors included longitudinal clinical and laboratory data. Machine learning methods were used to predict outcomes in 1 and 3 years. Results: The external cohort had a median age of 49.4 years (IQR 44.3–54.3); 61% were male, 80% white, and 79% had genotype 1. At presentation, 73% were treatment naïve and 31% had cirrhosis. Fibrosis progression occurred in 34% over a median of 4.9 years (IQR 3.2–7.6). Clinical outcomes occurred in 22% over a median of 4.4 years (IQR 3.2–7.6). Model performance for fibrosis progression was limited due to small sample size. The area under the receiver operating characteristic curve (AUROC) for 1 and 3-year risk of clinical outcomes was 0.78 (95% CI 0.73–0.83) and 0.76 (95% CI 0.69–0.81). Conclusion: Accurate assessments for risk of clinical outcomes can be obtained using routinely collected data across a heterogeneous cohort of patients with CHC. These methods can be applied to predict risk of progression in other chronic liver diseases.

Original languageEnglish (US)
Article numbere0187344
JournalPloS one
Volume12
Issue number11
DOIs
StatePublished - Nov 2017

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