Chronic medical conditions often necessitate regular testing for proper treatment. Regular testing of all afflicted individuals may not be feasible due to limited resources. Pooled testing methods have been developed to allow regular testing for all while reducing resource burden. However, the most commonly-used methods do not make use of covariate information predictive of treatment failure. We propose and evaluate four prediction-driven pooled testing methods that incorporate covariate information to improve pooled testing performance and compare these methods in the HIV treatment management setting to current methods with respect to testing efficiency, sensitivity and number of testing rounds using simulated data and data collected in Rakai, Uganda. Results show that the prediction-driven methods increase efficiency by up to 20% compared to current methods while maintaining equivalent sensitivity and reducing number of testing rounds by up to 70%. When predictions were incorrect, the performance of prediction-based matrix methods remained robust. These methods can be applied to any setting that necessitates testing of a quantitative biomarker for a threshold-based decision.