Evaluation of the Performance of Algorithms That Use Serial Hepatitis C RNA Tests to Predict Treatment Initiation and Sustained Virological Response Among Patients Infected With Hepatitis C Virus

The structure of electronic medical record data prevents easy population-level monitoring of hepatitis C virus (HCV) treatment uptake and cure. Using an HCV registry from a public hospital system in Atlanta, Georgia, we developed multiple algorithms that use serial HCV RNA test results as proxy measures for initiation of direct-acting antiviral (DAA) treatment and sustained virological response (SVR). We calculated sensitivity and positive predictive values (PPVs) by comparing the algorithms with the DAA initiation and SVR results from the registry. From December 2013 to August 2016, 1,807 persons actively infected with HCV were identified in the registry. Of those, 698 initiated DAA treatment on the basis of medical record abstraction; of 442 patients with treatment start and/or end dates, 314 had documented SVR. Treatment algorithm 2 (a detectable HCV RNA result followed by 2 sequential HCV RNA test results) and treatment algorithm 5 (a detectable HCV RNA result followed by 2 sequential HCV RNA test results >6 weeks apart) had the highest sensitivity (87% and 85%, respectively) and PPV (80% and 82%, respectively) combinations. Four SVR algorithms relied on fulfilling treatment algorithm definitions and having an undetectable HCV RNA test result ≥12 weeks after the last HCV RNA result; sensitivity for all 4 algorithms was 79%, and PPV was 92%-93%. Algorithms using serial quantitative HCV RNA results can serve as proxy measures for evaluating population-level DAA treatment and SVR outcomes.