Predictors of progression through the cascade of care to a cure for hepatitis C patients using decision trees and random forests

Abstract

Background: This study uses machine learning techniques to identify sociodemographic and clinical predictors of progression through the hepatitis C (HCV) cascade of care for patients in the 1945-1965 birth cohort in the Southern United States.

Methods: We compared sociodemographic and clinical variables between groups of patients for three care outcomes: linkage to care, initiation of antiviral treatment, and virologic cure. A decision tree model and random forest model were built for each outcome.

Results: Patients were primarily male, African American/Black or Caucasian/White, non-Hispanic or Latino, and insured. The average age at first HCV screening was 60 years old, and common medical diagnoses included chronic kidney disease, fibrosis and/or cirrhosis, transplanted liver, diabetes mellitus, and liver cell carcinoma. Variables used in predicting linkage to care included age at first HCV screening, insurance at first HCV screening, race, fibrosis and/or cirrhosis, other liver disease, ascites, and transplanted liver. Variables used in predicting initiation of antiviral treatment included insurance at first HCV screening, gender, other liver cancer, steatosis, and liver cell carcinoma. Variables used in predicting virologic cure included insurance at first HCV screening, transplanted liver, and ethnicity.

Conclusion: These patients have a high hepatic health burden, likely reflecting complications of untreated HCV and highlighting the urgency to cure HCV in this birth cohort. We found differences in HCV care outcomes based on sociodemographic and clinical variables. More work is needed to understand the mechanisms of these differences in care outcomes and to improve HCV care.

Keywords: Decision tree analysis; Linkage to care; Random forest modeling; Treatment initiation; Virologic cure.