key: cord-0902370-ia19fb9d authors: Ayaz, Akif; Demir, Asli Guner Ozturk; Ozturk, Gurkan; Kocak, Mehmet title: A Pooled RT-PCR Testing Strategy for More Efficient Covid-19 Pandemic Management date: 2021-12-15 journal: Int J Infect Dis DOI: 10.1016/j.ijid.2021.12.328 sha: 07821b38120c52cef5ba320bf5712a51bdb59d74 doc_id: 902370 cord_uid: ia19fb9d Objectives RT-PCR testing is indispensable in Covid-19 pandemic management. With the spread of the pandemic with emerging variants, the RT-PCR capacity is overburdened and new strategies and capabilities need to be established. One option is the pooled RT-PCR testing. Design We conducted an experiment with various mixtures of Covid-19 samples known to be negative and positive, and investigated the impact of pool size and mixture level on final cycle threshold (CT)- measures. More specifically, 5, 10, and 20 Covid-19 negative samples are combined with 1, 2 or 3 low-CT or high-CT Covid-19 positive samples. Results We have shown that average input CT and number of high and low CT samples in the pool are the main drivers of the final CT assessment, making the detectability easier. Pool size was not significantly associated with Final CT albeit suggestive. Conclusions We conclude that pooled RT-PCR testing strategy does not reduce the sensitivity of RT-PCR and thus provides a practical way to expand the RT-PCR screening capacity in pandemic management. The pool size not being significnat, we recommend that the pool size of 20 would be a practical level reducing the time to obtain the results and the cost of RT-PCR testing needs. The Covid-19 outbreak emerged in Wuhan, China in December 2019, and has covered the world under its influence by rapidly becoming a pandemic infecting more than 4.5 million by mid-May 2020, killing more than 300,000 globally, and more than 90 million people tested, While United States is leading in terms of total cases and deaths, new epicenters of the pandemic such as Russia, Brasil, India are emerging and it is highly likely that other epicenters will emerge as well. As of the end of August 2021, a total of about 2.5 billion tests were conducted and more than 216 million positive cases were reported. When only the countries that report both the number of tests conducted and positive cases experienced were considered, we see that Covid-19 positivity rate is oscillating between 4% and 10% with a current mean of 6.4% (Supplementary Figure-1 ). In fighting with this pandemic, chest-computed tomography is one of the first-line testing modalities for patients who present to the healthcare system with symptoms especially respiratory symptoms (Li and Xia, 2019) . On the other hand, the World Health Organization (WHO) provided guidelines for Covid-19 genetic-based testing within Nucleic Acid Amplification Tests (NAAT) framework such as RT-PCR (Reverse Transcription-Polymerase Chain Reaction) (WHO, 2020). RT-PCR tests are conducted in designated laboratories with trained personnel and its accuracy is affected by the sample quality (Lippi et al., 2020) , whether or not the sampling is oropharyngeal and nasopharyngeal (Carver and Jones, 2020) , and RNA degradation . Antibody test-kits are also used mainly as supplemental tools to the RT-PCR approach to diagnose past infections using body fluids such as blood; however, these tests have less favorable diagnostic measures , and the timing of these tests is highly critical and the repeat tests are needed (Beeching et al., 2020) . In the COVID-19 diagnostics report published by the National University of Singapore, Saw Swee Hock School of Public Health, they describe many commercial and non-commercial COVID-19 diagnostic tests (He et al., 2020) . However, almost all of these tests are not presented with their corresponding Sensitivity and Specificity measures, or unrealistic characteristics such as 100% sensitivity and 100% specificity are reported (Yap et al., 2020) . Chan et al. (2020) reported that RT-PCR has 95% sensitivity. In this study, we present a more comprehensive pooled-sampling strategy of Covid-19 RT-PCR testing with carefully selected and independent Covid-19 positive samples with varying viral load levels, varying pool sizes and negative-positive mixing. In our pooled-sampling strategy, we employed the following steps: 1. Identified sets of 5, 10, and 20 Covid-19 samples which were tested as negative through RT-PCR testing. These sets formed our negative base. 2.5 µl were taken for RT-PCR reaction from nasopharyngeal and oropharyngeal swab samples taken into Bio-speedy vNAT transfer tube. According to the manufacturer's protocol, 5 µl of 2X Prime Script Mix and 2.5 µl of 2X Prime Script Mix were reacted with a total volume of 10 µl. RT-PCR studies were performed in the Biorad CFX96 device in accordance with the conditions in Supplementary Table-2. The recommended threshold level for CFX96 Touch™ instruments, 200 RFU, was used in the analysis. After examining the shape of the amplification curves, if a sample was given a Cq value by the instruments' software and the curve was sigmoidal, it was decided using the Cq value. Non-sigmoidal curves were considered negative. If a sample was given a Cq value but the curve was not sigmoidal, the result was recorded as negative. While sigmoidal curves with Cq-HEX (IC) ≤30 were included in the analysis, samples with nonsigmoidal curves or Cq-HEX>30 were repeated. We constructed Analysis of Covariance models where CT measures for the positive samples Our multivariable model suggested that that there is a linear association between the Baseline CT and Final CT, where each one-unit higher Baseline-CT results in one-unit higher Final CT on average. Number of high CT samples in the pool was also significantly associated with the resulting Final CT, suggesting that every additional high CT sample added to the pool decreased the Final-CT by one-unit controlling for Baseline CT ( Figure 1) ; similarly, every additional low CT sample added to the pool decreased the Final-CT by about 2.6 cycles on average controlling for Baseline CT (Figure 2 ). Although the interaction between Baseline CT and number of High CT samples in the pool was not very strong (p=0.014), interaction between Baseline CT and number of Low CT samples in the pool was highly significant (p=0.0007) as visible in Figure- 2 as well. Pool size was not highly significant but suggestive, controlling for Baseline CT (0.065) and number of high and low CT samples in the pool. In addition,there was no significant interaction with Baseline CT, either (p=0.65), as shown in Figure 3 indicated by almost parallel prediction lines for different pool sizes. We have shown in this experiment that RT-PCR tests are highly sensitive regardless of how Table 1) . With the pooling, ıt is already expected that CT values will go up due to mixing with negative values. Interestingly, for some combinations especially with the mixtures of negatives with Low CT samples, final CT values went down, suggesting improved detectability as shown in Figure 4 . We present some of the specific cases with better detectability with pooling in Table-1. We have also looked into the impact of the variability of Baseline CT entering the pool on Final CT. When High CT cases are considered, there is no significant effect; however, when pools with at least two Low CT cases are included in the pool, there was a significant association of the CT variability with Final CT (Supplementary Figure 1) , where increased variability (i.e., more diverse Low CT positive cases included) results in better detectability (i.e., lower Final CT). To assess the reproducibility of CT measurements, we have conducted a separate experiment where just posivite samples were pooled with pool sizes of 2, 5, 10, and 20 positive samples separately for high CT samples and low CT samples (a total of 8 independent scenarios). From each pool, CT was measured with 5 repeats. One of the practical messages for these supplementary analysis was that the standard deviation of the CT values was averaging around 0.32 with a range from 0.115 to 0.56. This means that the CT measurements are quite stable with the repeat tests in a pooled strategy where it is not expected to change beyond about 0.6 cycle (based on 2 standard deviation error margin). Another interesting finding from our supplementary analysis was that for higher standard deviation of the input CTs in the high CT scenarios, the standard deviation of the resulting CTs decreased, while the opposite was true for the low CT pools (Supplementary Figure 2.) One weakness of our experimental design is that the individual and pooled samples were not processed on the same plate simultaneously; this is partly because we needed to make sure to describe each pool with negatives, high or low CT positive cases. As a future study, we are in the process of simultaneous processing of individual and pooled samples on the same plate in our testing center. Another possible weakness is that we were not able to test higher pool sizes such as 50 and 100 due to the logistical difficulties of establishing such pools as the necessity of keeping the amount of sampling materials to be taken from each sample uniform. We have seen again in the COVID19 pandemic that among the most important factors affecting the results was the way the nasopharyngeal/oropharyngeal samples were taken. Since the samples coming to our center are taken by different health personnel from many institutions, we can consider this as a negative factor. Since the outbreak of the pandemic, many changes have been observed in the SARS-CoV-2 genome. We do not know how this affects our results. In addition, it has been reported by the manufacturer that CT values may be affected in frozen and thawed samples. As the governments are forced to lift the Covid-19 restriction on workforce, transportation, and schools, pooled and pod-testing needs are increasing. Although such tests are typically done through rapid-test kits, there is no doubt that the pooled sample needs for RT-PCR testing will also increase with different current and potential future variants of the coronavirus. Such pooling approach will first lessen the burden on the testing labs and will result in cost-saving as well. As mentioned earlier, in every pool of 20 samples, we expect to have one positive sample based on the positivity rate profile globally, which may differ by region based on the RT-PCR testing strategies. It is clear that in any mass-testing strategy, such a positivity rate will go down as the net cast will catch more asymptomatic cases majority of whom will not be infected. Therefore, as the pandemic restrictions are being lifted and more and more testing is planned to be conducted, pooled sample strategy offers a solution protecting the lab staff from added burden and keeping the cost of increased testing programs at affordable levels to central governments as well as local administrations. In our analyses, the pool size has a suggestive but not a significant predictor of Final CT and it did not have a significant interaction with Base CT, either; in fact, its week association with Final CT was negative suggesting that increasing pool size resulted in slightly lower Final CT. If we assume a linear trend, increasing the pool size from 10 to 20 decreased Final CT by 0.6, slignly more than half a cycle on average, controlling for the other key factors in the model. In addition, as mentioned earlier, rare incidence of failing to detect positivity in a pool sample was experienced in all three pool sizes we experimented, namely, 5, 10, and 20. Therefore, we suggest that a pool size of 20 can be recommended as a pooled-sampling RT-PCR strategy for massive testing. We have shown that at various mixing of negative of positive Covid-19 samples, RT-PCR testing still has high sensitivity. With the increased need to test more and more individuals as the pandemic restrictions are lifted or at least relaxed, the added burden can be managed through carefully structured pooled-sampling with RT-PCR. Ethics approval: As we are using blinded Covid-19 tests with no Protected Health Information (PHI), ethics approval is not needed. 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