Abstract
ABSTRACT
Standard durations of direct acting antivirals (DAAs; 8–12 weeks) can be a barrier to HCV treatment initiation and completion among marginalized populations. This study developed and internally validated a machine learning model to predict short‐duration (4–6 weeks) DAA response using baseline clinical factors with potential to improve treatment uptake, cost‐effectiveness and health system efficiency. Baseline data from several short‐duration DAA clinical trials and treatment discontinuations from real‐world cohort studies were used. Multiple machine learning models were evaluated. Nested cross‐validation was employed to optimize model hyperparameters and assess performance. Clinical utility was evaluated using Area Under Receiver Operator Characteristics (AUROC), Area Under Precision Recall Curve (AUPRC) and Matthews Correlation Coefficient (MCC). Threshold optimisation strategies were applied to balance model accuracy and DAA costs. Statistical analyses were conducted to estimate HCV RNA cutoffs predictive of treatment failure. Of 264 participants receiving short‐duration DAAs (median 42 days; IQR 28–42), 94 (36%) experienced treatment failure. Predictors of failure included shorter durations, higher HCV RNA, higher AST–ALT ratio, genotype 3 and DAA class. The Elastic Net (regularized logistic regression) model demonstrated strong performance (AUROC: 83%; AUPRC: 73%). The Youden Index threshold balanced sensitivity (81%) and specificity (76%) with MCC of 0.56. A cost‐optimized threshold, prioritizing retreatment minimization, achieved high sensitivity (98%) but reduced specificity (51%). HCV RNA cutoffs predictive of treatment failure were higher for protease+NS5A than NS5A + NS5B inhibitor regimens. Predictive models using baseline clinical data can identify individuals likely to respond to short‐duration DAAs. Such models, if externally validated in larger and more diverse datasets, could facilitate HCV elimination efforts by improving treatment uptake, particularly for people who inject drugs, people experiencing homelessness or incarceration.