Pooling of SARS-CoV-2 samples to increase molecular testing throughput.

The demand for SARS-CoV-2 testing has exceeded available resources, prompting the need for alternative testing strategies. It was already known that molecular testing for SARS-CoV-2 requires significant reagents and time. That uncertainty drove the search for methods to increase testing capacity without compromising diagnostic accuracy. Prior research has shown that pooling samples can reduce reagent use and increase throughput in low-prevalence settings. However, no prior work had resolved the sensitivity and specificity trade-offs of pooling in SARS-CoV-2 RT-PCR testing. This gap motivated the evaluation of pooling as a practical solution for high-throughput testing. The challenge lies in balancing increased throughput with the risk of missing low-level infections. This paper's contribution is to quantify the analytical impact of sample pooling on diagnostic accuracy.

The aim of this study was to assess the feasibility of 4-way pooling of SARS-CoV-2 samples for high-throughput RT-PCR testing. The specific problem addressed is the need to increase testing capacity while maintaining diagnostic accuracy. The motivation stems from the shortage of reagents and the high demand for SARS-CoV-2 testing. The study sought to determine whether pooling could reliably detect infections without significant loss of sensitivity. The focus was on evaluating the sensitivity, specificity, and reproducibility of pooled testing. The authors aimed to quantify the CT threshold loss associated with pooling. They also wanted to confirm that pooling does not introduce false negatives in low-positive samples. Their goal was to provide evidence for implementing pooling in clinical settings.

The study involved automated liquid handling to pool individual SARS-CoV-2 samples in a 1:4 ratio. Pooled samples were processed using an emergency use authorized CDC-based RT-PCR test. Positive samples were diluted to simulate low viral loads and compared to pooled CT values. Thirty-two distinct positive samples were combined with negative specimens to assess sensitivity. Reproducibility was tested by repeating low-positive samples. Specificity was evaluated by assaying 32 pools of negative specimens. Theoretical and empirical CT thresholds were compared to estimate sensitivity loss. The study used a controlled laboratory setting to minimize confounding variables.

Four-way pooling resulted in a 1.7 and 2.0 CT loss for N1 and N2 targets, respectively. Pooling correctly identified SARS-CoV-2 in 94% of tested samples. Two low-positive samples with CT > 35 were missed by pooling but detected in 75% and 37.5% of repeat tests. All negative samples remained negative when pooled. The CT loss was consistent with theoretical expectations for pooled testing. The study found no false positives in pooled negative samples. Reproducibility was lower for samples with high CT values. The results suggest that pooling is specific but may miss very low viral loads.

The authors propose that 1:4 pooling is specific and allows for increased testing throughput. They state that pooling conserves reagents and increases capacity without introducing false positives. The CT loss observed is consistent with theoretical predictions for pooled testing. They suggest that pooling is suitable for low-prevalence settings where sensitivity loss is acceptable. The study does not claim that pooling is optimal for all testing scenarios. The authors emphasize that pooling should be used judiciously based on local prevalence. They do not assert that pooling is a replacement for individual testing in high-risk populations. Their findings support the use of pooling to address resource constraints in testing.