Adaptive Viral Load Monitoring Frequency to Facilitate Differentiated Care: A Modeling Study From Rakai, Uganda.

BACKGROUND: After scale-up of antiretroviral therapy (ART), routine annual viral load monitoring has been adopted by most countries, but reduced frequency of viral load monitoring may offer cost savings in resource-limited settings. We investigated if viral load monitoring frequency could be reduced while maintaining detection of treatment failure.
METHODS: The Rakai Health Sciences Program performed routine, biannual viral load monitoring on 2489 people living with human immunodeficiency virus (age ≥15 years). On the basis of these data, we built a 2-stage simulation model to compare different viral load monitoring schemes. We fit Weibull regression models for time to viral load >1000 copies/mL (treatment failure), and simulated data for 10 000 individuals over 5 years to compare 5 monitoring schemes to the current viral load testing every 6 months and every 12 months.
RESULTS: Among 7 monitoring schemes tested, monitoring every 6 months for all subjects had the fewest months of undetected failure but also had the highest number of viral load tests. Adaptive schemes using previous viral load measurements to inform future monitoring significantly decreased the number of viral load tests without markedly increasing the number of months of undetected failure. The best adaptive monitoring scheme resulted in a 67% reduction in viral load measurements, while increasing the months of undetected failure by <20%.
CONCLUSIONS: Adaptive viral load monitoring based on previous viral load measurements may be optimal for maintaining patient care while reducing costs, allowing more patients to be treated and monitored. Future empirical studies to evaluate differentiated monitoring are warranted.