An Algorithm to Optimize Viral Load Testing in HIV-Positive Patients With Suspected First-Line Antiretroviral Therapy Failure in Cambodia

Abstract

To develop an algorithm for optimal use of viral load testing in patients with suspected first-line antiretroviral treatment (ART) failure. Data from a cohort of patients on first-line ART in Cambodia were analyzed in a cross-sectional way to detect markers for treatment failure. Markers with an adjusted likelihood ratio <0.67 or >1.5 were retained to calculate a predictor score. The accuracy of a 2-step algorithm based on this score followed by targeted viral load testing was compared with World Health Organization criteria for suspected treatment failure. One thousand eight hundred three viral load measurements of 764 patients were available for analysis. Prior ART exposure, CD4 count below baseline, 25% and 50% drop from peak CD4 count, hemoglobin drop of > or =1 g/dL, CD4 count <100 cells per microliter after 12 months of treatment, new onset of papular pruritic eruption, and visual analog scale <95% were included in the predictor score. A score >or=2 had the best combination of sensitivity and specificity and required confirmatory viral load testing for only 9% of patients. World Health Organization criteria had a similar sensitivity but a lower specificity and required viral load testing for 24.9% of patients. An algorithm combining a predictor score with targeted viral load testing in patients with an intermediate probability of failure optimizes the use of scarce resources.