Pooled nucleic acid testing to identify antiretroviral treatment failure during HIV infection in Seoul, South Korea

binGroup2: Statistical Tools for Infection Identification via Group Testing

Group testing is the process of testing items as an amalgamation, rather than separately, to determine the binary status for each item. Its use was especially important during the COVID-19 pandemic through testing specimens for SARS-CoV-2. The adoption of group testing for this and many other applications is because members of a negative testing group can be declared negative with potentially only one test. This subsequently leads to significant increases in laboratory testing capacity. Whenever a group testing algorithm is put into practice, it is critical for laboratories to understand the algorithm's operating characteristics, such as the expected number of tests. Our paper presents the binGroup2 package that provides the statistical tools for this purpose. This R package is the first to address the identification aspect of group testing for a wide variety of algorithms. We illustrate its use through COVID-19 and chlamydia/gonorrhea applications of group testing.

Prediction-driven pooled testing methods: Application to HIV treatment monitoring in Rakai, Uganda

Chronic medical conditions often necessitate regular testing for proper treatment. Regular testing of all afflicted individuals may not be feasible due to limited resources, as is true with HIV monitoring in resource-limited settings. Pooled testing methods have been developed in order 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, which could improve performance. We propose and evaluate four prediction-driven pooled testing methods that incorporate covariate information to improve pooled testing performance. We then 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 with 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. The best performing method using our motivating data from Rakai was a prediction-driven hybrid method, maintaining sensitivity over 96% and efficiency over 75% in likely scenarios. If these methods perform similarly in the field, they may contribute to improving mortality and reducing transmission in resource-limited settings. Although we evaluate our proposed pooling methods in the HIV treatment setting, they can be applied to any setting that necessitates testing of a quantitative biomarker for a threshold-based decision.

Pooled testing: A tool to increase efficiency of infant HIV diagnosis and virological monitoring

Background

Pooled testing, or pooling, has been used for decades to efficiently diagnose relatively rare conditions, such as infection in blood donors. Programmes for the prevention of mother-to-child transmission of HIV and for antiretroviral therapy (ART) are being rolled out in much of Africa and are largely successful. This increases the need for early infant diagnosis (EID) of HIV using qualitative nucleic acid testing and for virological monitoring of patients on ART using viral load testing. While numbers of patients needing testing are increasing, infant HIV infections and ART failures are becoming rarer, opening an opportunity for pooled testing approaches.

Aim

This review highlights the need for universal EID and viral load coverage as well as the challenges faced. We introduce the concept of pooled testing and highlight some important considerations before giving an overview of studies exploring pooled testing for EID and virological monitoring.

Results

For ART monitoring, pooling has been shown to be accurate and efficient; for EID it has not been tried although modelling shows it to be promising. The final part attempts to place pooling into the context of current mother-to-child transmission of HIV and ART programmes and their expected trajectories over the next years.

Conclusion

Several points warrant consideration: pre-selection to exclude samples with an elevated pre-test probability of positivity from pooled testing, the use of dried blood or plasma spots, and choosing a pooling strategy that is both practically feasible and economical. Finally, novel ideas are suggested to make pooling even more attractive.

The objective function controversy for group testing: Much ado about nothing?

Group testing is an indispensable tool for laboratories when testing high volumes of clinical specimens for infectious diseases. An important decision that needs to be made prior to implementation is determining what group sizes to use. In best practice, an objective function is chosen and then minimized to determine an optimal set of these group sizes, known as the optimal testing configuration (OTC). There are a few options for objective functions, and they differ based on how the expected number of tests, assay characteristics, and testing constraints are taken into account. These varied options have led to a recent controversy in the literature regarding which of two different objective functions is better. In our paper, we examine these objective functions over a number of realistic situations for infectious disease testing. We show that this controversy may be much ado about nothing because the OTCs and corresponding results (eg, number of tests and accuracy) are largely the same for standard testing algorithms in a wide variety of situations.

Determination of Varying Group Sizes for Pooling Procedure

Pooling is an attractive strategy in screening infected specimens, especially for rare diseases. An essential step of performing the pooled test is to determine the group size. Sometimes, equal group size is not appropriate due to population heterogeneity. In this case, varying group sizes are preferred and could be determined while individual information is available. In this study, we propose a sequential procedure to determine varying group sizes through fully utilizing available information. This procedure is data driven. Simulations show that it has good performance in estimating parameters.

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

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.

Cost-effective HIV-1 virological monitoring in resource-limited settings using a modified commercially available qPCR RNA assay

Virological monitoring through plasma viral load (PVL) quantification is essential for clinical management of HIV patients undergoing antiretroviral treatment (ART), and for detecting treatment failure. Quantitative PCR (qPCR)-based tests are the gold standard for measuring PVL. Largely because of their high cost, however, implementation of these tests in low- and middle-income countries fails to cover the testing demand. In this study, we aimed at reducing the running cost of the commercially available Abbott RealTime™ HIV-1 assay by minimizing the reagent consumption. To this end, a modified version of the assay was obtained by reducing the assay's reagents volume to about a half, and validated using a panel of 104 plasma samples. Compared to the standard version, the modified Abbott assay allowed for a 50% reduction in running costs. At the same time, it showed a 100% concordance in identifying samples with detectable viral load, strong correlation (Pearson's r=0.983, P<0.0001), and a high agreement between PVL values (mean percent difference between PVL values±standard deviation=0.76±3.18%). In detecting viral failure (PVL>1000copiesmL^-1), the modified assay showed a sensitivity of 94.6%, a specificity of 93.8%, and a negative and positive predictive values of 93.8% and 94.6%, respectively. The modified assay therefore reliably quantifies PVL, predicts viral failure, and allows for a ca. 50% reduction in the assay's running costs. It may thus be implemented as an ART monitoring tool in resource-limited settings and for research purposes.

Pooled Nucleic Acid Testing to Detect Antiretroviral Treatment Failure in HIV-Infected Patients in Mozambique

BACKGROUND

In resource-limited settings, viral load monitoring of HIV-infected patients receiving antiretroviral therapy (ART) is not readily available because of high costs. Here, we compared the accuracy and costs of quantitative and qualitative pooled methods with standard viral load testing.

METHODS

Blood was collected prospectively from 461 patients receiving first-line ART in Mozambique who had not been evaluated previously with viral load testing. Screening for virologic failure of ART was performed quantitatively (ie, standard viral loads) and qualitatively [one and 2 rounds of polymerase chain reaction (PCR)]. Individual samples and minipools of 5 samples were then analyzed using both methods. The relative efficiency, accuracy, and costs of each method were calculated based on viral load thresholds for ART failure.

RESULTS

Standard viral load testing of individual samples revealed a high rate of ART failure (19%-23%) across all virologic failure thresholds, and the majority of the patients (93%) with viral loads >1500 copies per milliliter had genotypic resistance to drugs in their ART regimen. Pooled quantitative screening and deconvolution testing had positive and negative predictive values exceeding 95% with cost savings of $11,250 compared with quantitative testing of each sample individually. Pooled qualitative screening and deconvolution testing had a higher cost savings of $30,147 for 1 PCR round and $25,535 for 2 PCR rounds compared with quantitative testing each sample individually. Both pooled qualitative PCR methods had positive and negative predictive values ≥90%, but the pooled 1-round PCR method had a sensitivity of 64%.

CONCLUSIONS

Given the high rate of undiagnosed ART failure and drug resistance in this cohort, it is clear that virologic monitoring is urgently needed in this population. Here, we compared alternative methods of virologic monitoring with standard viral load testing of individual samples and found these methods to be cost saving and accurate. The test characteristics of each method will likely need to be considered for each local population before it is adopted.

Pooling Bio-Specimens in the Presence of Measurement Error and Non-Linearity in Dose-Response: Simulation Study in the Context of a Birth Cohort Investigating Risk Factors for Autism Spectrum Disorders

We sought to determine the potential effects of pooling on power, false positive rate (FPR), and bias of the estimated associations between hypothetical environmental exposures and dichotomous autism spectrum disorders (ASD) status. Simulated birth cohorts in which ASD outcome was assumed to have been ascertained with uncertainty were created. We investigated the impact on the power of the analysis (using logistic regression) to detect true associations with exposure (X₁) and the FPR for a non-causal correlate of exposure (X₂, r = 0.7) for a dichotomized ASD measure when the pool size, sample size, degree of measurement error variance in exposure, strength of the true association, and shape of the exposure-response curve varied. We found that there was minimal change (bias) in the measures of association for the main effect (X₁). There is some loss of power but there is less chance of detecting a false positive result for pooled compared to individual level models. The number of pools had more effect on the power and FPR than the overall sample size. This study supports the use of pooling to reduce laboratory costs while maintaining statistical efficiency in scenarios similar to the simulated prospective risk-enriched ASD cohort.