Adapted time-varying covariates Cox model for predicting future cirrhosis development performs well in a large hepatitis C cohort.

ABSTRACT:

Background

Patients with hepatitis C virus (HCV) frequently remain at risk for cirrhosis after sustained virologic response (SVR). Existing cirrhosis predictive models for HCV do not account for dynamic antiviral treatment status and are limited by fixed laboratory covariates and short follow up time. Advanced fibrosis assessment modalities, such as transient elastography, remain inaccessible in many settings. Improved cirrhosis predictive models are needed.

Methods

We developed a laboratory-based model to predict progression of liver disease after SVR. This prediction model used a time-varying covariates Cox model adapted to utilize longitudinal laboratory data and to account for antiretroviral treatment. Individuals were included if they had a history of detectable HCV RNA

SUBMITTER: Beste LA 

PROVIDER: S-EPMC8670121 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature