Frequency and Outcomes of Abnormal Imaging in Patients With Cirrhosis Enrolled in a Hepatocellular Carcinoma Surveillance Program

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148235/1/lt25398_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148235/2/lt25398.pd

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

<div><p>Objective</p><p>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.</p><p>Design</p><p>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.</p><p>Results</p><p>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).</p><p>Conclusion</p><p>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.</p></div

Misclassification table for composite liver-related clinical outcomes- UMHS cohort.

<p>Misclassification table for composite liver-related clinical outcomes- UMHS cohort.</p

Cumulative incidence of outcomes in UMHS cohort.

<p>Cumulative incidence of outcomes in UMHS cohort.</p

Baseline characteristics of patients by outcome- UMHS cohort.

<p>Baseline characteristics of patients by outcome- UMHS cohort.</p

Risk prediction data assessment and timeframe.

<p>Risk prediction data assessment and timeframe.</p