Improvement of predictive models of risk of disease progression in chronic hepatitis C by incorporating longitudinal data.

ABSTRACT:

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Existing predictive models of risk of disease progression in chronic hepatitis C have limited accuracy. The aim of this study was to improve upon existing models by applying novel statistical methods that incorporate longitudinal data. Patients in the Hepatitis C Antiviral Long-term Treatment Against Cirrhosis trial were analyzed. Outcomes of interest were (1) fibrosis progression (increase of two or more Ishak stages) and (2) liver-related clinical outcomes (liver-related death, hepatic decompensation, hepatocellular carcinoma, liver transplant, or increase in Child-Turcotte-Pugh score to ≥7). Predictors included longitudinal clinical, laboratory, and histologic data. Models were constructed using logistic regression and two machine learning methods (random forest and b

SUBMITTER: Konerman MA 

PROVIDER: S-EPMC4480773 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature