Optimizing screening for acute human immunodeficiency virus infection with pooled nucleic acid amplification tests

Abstract

Recent studies have shown the public health importance of identifying individuals with acute human immunodeficiency virus infection (AHI); however, the cost of nucleic acid amplification testing (NAAT) makes individual testing of at-risk individuals prohibitively expensive in many settings. Pooled NAAT (or group testing) can improve efficiency and test performance of testing for AHI, but optimizing the pooling algorithm can be difficult. We developed simple, flexible biostatistical models of specimen pooling with NAAT for the identification of AHI cases; these models incorporate group testing theory, operating characteristics of biological assays, and a model of viral dynamics during AHI. Pooling algorithm sensitivity, efficiency (test kits used per individual specimen evaluated), and positive predictive value (PPV) were modeled and compared for three simple pooling algorithms: two-stage minipools (D2), three-stage hierarchical pools (D3), and square arrays with master pools (A2m). We confirmed the results by stochastic simulation and produced reference tables and a Web calculator to facilitate pooling by investigators without specific biostatistical expertise. All three pooling strategies demonstrated improved efficiency and PPV for AHI case detection compared to individual NAAT. D3 and A2m algorithms generally provided better efficiency and PPV than D2; additionally, A2m generally exhibited better PPV than D3. Used selectively and carefully, the simple models developed here can guide the selection of a pooling algorithm for the detection of AHI cases in a wide variety of settings.

FIG. 1.

Schematic diagrams of three pooled testing strategies considered: D2, D3, and A2m. Positive pools are in gray; positive specimens are in black. In D2 (a), a positive master pool is broken down into individual specimens. One or more of these specimens may be positive. In D3 (b), a positive master pool is broken down into multiple subpools, all of which are tested. Positive subpools are broken into individual specimens, and one or more of these specimens may be positive. In A2m (c), if a master pool is positive, row and column pools are tested, some of which may be positive. Specimens at the intersection of positive row and column pools are tested individually; some or all of these may be positive.

FIG. 2.

Model of PAS as a function of master pool size (N) for hypothetical NAATs with different lower limits of detection. The model assumes use of a third-generation ELISA and a rate of exponential increase of HIV viral load during early AHI of 0.52 log₁₀ copies/ml/day.

FIG. 3.

Optimal pooling efficiency for a range of prevalences assuming a MAPS of 100, by pooling strategy. Efficiency is measured as tests per screened specimen. The figure shows the “entropy value” for efficiency, the theoretical best efficiency possible with any pooling algorithm.

FIG. 4.

Pooling PPV for pooling configuration that achieves best possible efficiency, with a maximum acceptable pooling size (MAPS) of 100, for a range of prevalences by pooling strategy. These PPVs correspond exactly to the efficiency graphs in Fig. 3. Also shown is the PPV for individual testing.