Improved HIV-1 Viral Load Monitoring Capacity Using Pooled Testing With Marker-Assisted Deconvolution

Abstract

Objective: Improve pooled viral load (VL) testing to increase HIV treatment monitoring capacity, particularly relevant for resource-limited settings.

Design: We developed marker-assisted mini-pooling with algorithm (mMPA), a new VL pooling deconvolution strategy that uses information from low-cost, routinely collected clinical markers to determine an efficient order of sequential individual VL testing and dictates when the sequential testing can be stopped.

Methods: We simulated the use of pooled testing to ascertain virological failure status on 918 participants from 3 studies conducted at the Academic Model Providing Access to Healthcare in Eldoret, Kenya, and estimated the number of assays needed when using mMPA and other pooling methods. We also evaluated the impact of practical factors, such as specific markers used, prevalence of virological failure, pool size, VL measurement error, and assay detection cutoffs on mMPA, other pooling methods, and single testing.

Results: Using CD4 count as a marker to assist deconvolution, mMPA significantly reduces the number of VL assays by 52% [confidence interval (CI): 48% to 57%], 40% (CI: 38% to 42%), and 19% (CI: 15% to 22%) compared with individual testing, simple mini-pooling, and mini-pooling with algorithm, respectively. mMPA has higher sensitivity and negative/positive predictive values than mini-pooling with algorithm, and comparable high specificity. Further improvement is achieved with additional clinical markers, such as age and time on therapy, with or without CD4 values. mMPA performance depends on prevalence of virological failure and pool size but is insensitive to VL measurement error and VL assay detection cutoffs.

Conclusions: mMPA can substantially increase the capacity of VL monitoring.

Figure 1. Schematic overview of MP, MPA, and mMPA using a numerical example of pooling K = 3 individual samples with VLs of 300, 2500, and 50 copies/mL respectively

The figure provides an example of VL pooling of samples from K = 3 patients, with individual VLs of 300, 2500 and 50. With a threshold of C = 1000, only the subject with VL=2500 has virologic failure. Deconvolution is based on the three methods used in this paper: (a) mini pool (MP); (b) mini-pool + algorithm (MPA); and (c) marker-assisted mini-pool + algorithm (mMPA). Each method incorporates two stages, the first incorporating VL testing of the pooled sample and the second incorporating deconvolution, outlined by text boxes. In the first stage, common for all three methods, the pool is “positive” because its VL of 950 is greater than the pool threshold (C/K) = 333, prompting the second stage. Using MP (a), all three individuals are then tested, for a total of 4 VL assays. Using MPA (b), sequential random testing of individual samples and a deconvolution algorithm determine the need for additional testing. In this example a total of 3 VL assays are needed. Using mMPA (c), with a risk score indicates that the second subject has the highest likelihood of VL failure and is thus tested first, only two VL assays are needed. { } denotes testing on the pool, and [ ] testing on an individual sample.

Figure 2. Impact of virological failure prevalence and pool size on pooling deconvolution methods

The figure demonstrates the impact of pool sizes K (X axis; from 3–10) and viral failure prevalence ((a)=18%; (b)=16%; (c)=14%; and (d)=11%) on the ATR (y axis) according to individual testing (IND; circles), MP (squares), MPA (plus signs) and mMPA (triangles). CD4 is used as a risk score for mMPA. The gray bounds indicate point-wise 95% confidence intervals obtained using the bootstrap method (with 500 resamples) where the intervals are the 2.5 and 97.5 percentiles of the bootstrap distributions.

Figure 3. Impact of VL assay measurement error on diagnostic accuracy of pooling deconvolution methods

The figure demonstrates the impact of VL assay measurement errors on individual testing (IND; circles), MP (triangles), MPA (squares) and mMPA (plus signs). CD4 is used as a risk score for mMPA. The impact of measurement error on pooling deconvolution is quantified in terms of (a) sensitivity, (b) specificity, (c) PPV, and (d) NPV. The measurement error is assumed to have a log normal distribution with a zero mean and a standard deviation of 0.12 on the log10 scale. PPV = positive predictive value. NPV = negative predictive value.