Factors Affecting Timing of Antiretroviral Treatment Initiation Based on Monitoring CD4 Counts

Abstract

Objective To evaluate factors affecting antiretroviral therapy (ART) start time when triggered by a CD4 count < 350 cells/μl while monitoring counts over time. Measurement frequency, requirement for confirmatory counts, and precision and accuracy of CD4 enumeration technology were considered. Methods Using a model of CD4 count trajectories among seroconverters in the Multicenter AIDS Cohort Study, sequences of counts were simulated for a large hypothetical population monitored for 5 years from seroconversion. Time of first count < 350 cells/μl was defined as ART start time. The simulation was adapted to evaluate the effect of the above factors on these times. ART initiation was considered “very late” among patients whose underlying trajectory declined below 200 cells/μl during the period simulated if no previous observed count was < 350 cells/μl. Results For 12-, 6-, 4- and 3-monthly measurements, median start time was 48, 36, 32 and 30 months after seroconversion and proportion of patients starting ART very late was 11.5%, 1.6%, 0.2% and 0.1%. For 6-monthly measurements, requiring confirmation increased the median to 49 months and proportion to 8.9%. Changes in standard deviation of short-term variability in counts of 25% and measurement bias for a novel technology of ± 10% changed median time by ± 6 months with modest change in the proportion very late (range 0.5% to 3.2%). Conclusion 6-monthly measurements appear adequate in achieving low rates of very late ART whereas confirmation affects rates adversely. Studies comparing new versus standard measurement technologies should focus on ruling out modest bias, particularly proximal to important thresholds for treatment management.

Nonlinear mixed-effects models for HIV viral load trajectories before and after antiretroviral therapy interruption, incorporating left censoring

“…We compare different models based on observed/predicted plots, normal QQ-plots, and residual plots (see Appendix), as well as the simplicity of the model and its interpretation. We find that a √ z ij -transformation of CD4 provides satisfactory results since it produces similar results as the log-transformation and it is widely used in the analysis of datasets from the AIDS Clinical Trials Group network (ACTG) (see, e.g., Noubary and Hughes 2012).…”

Analysis of Bias and ART Enrollment for a Point-of-Care CD4/CD4% Analyzer

“…Recent reports have shown that random fluctuations in CD4 count correlate with fluctuations in total lymphocyte count [15]. Abnormally high or low lymphocyte counts caused by factors other than HIV can produce elevated or depressed CD4 counts that are not representative of between the PointCare NOW and FACSCalibur in classifying results as being highly likely to indicate initiating ART, highly likely to indicate not initiating ART, and requiring a near-term repeat measurement to rule out normal day-to-day fluctuations in patient CD4 count [9][10][11].…”