Improvement of Sensitivity of Pooling Strategies for COVID-19

Group testing (or pool testing), for example, Dorfman’s method or grid method, has been validated for COVID-19 RT-PCR tests and implemented widely by most laboratories in many countries. These methods take advantages since they reduce resources, time, and overall costs required for a large number of samples. However, these methods could have more false negative cases and lower sensitivity. In order to maintain both accuracy and efficiency for different prevalence, we provide a novel pooling strategy based on the grid method with an extra pool set and an optimized rule inspired by the idea of error-correcting codes. The mathematical analysis shows that (i) the proposed method has the best sensitivity among all the methods we compared, if the false negative rate (FNR) of an individual test is in the range [1%, 20%] and the FNR of a pool test is closed to that of an individual test, and (ii) the proposed method is efficient when the prevalence is below 10%. Numerical simulations are also performed to confirm the theoretical derivations. In summary, the proposed method is shown to be felicitous under the above conditions in the epidemic.

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False Negative Mitigation in Group Testing for COVID-19 Screening

Frontiers in Medicine ◽

10.3389/fmed.2021.661277 ◽

2021 ◽

Vol 8 ◽

Author(s):

Amir Reza Alizad Rahvar ◽

Safar Vafadar ◽

Mehdi Totonchi ◽

Mehdi Sadeghi

Keyword(s):

Viral Load ◽

False Negative ◽

Group Testing ◽

False Negative Rate ◽

Screening Strategy ◽

Web Interface ◽

Rt Pcr ◽

Promising Candidate ◽

Testing Method ◽

High False Negative Rate

After lifting the COVID-19 lockdown restrictions and opening businesses, screening is essential to prevent the spread of the virus. Group testing could be a promising candidate for screening to save time and resources. However, due to the high false-negative rate (FNR) of the RT-PCR diagnostic test, we should be cautious about using group testing because a group's false-negative result identifies all the individuals in a group as uninfected. Repeating the test is the best solution to reduce the FNR, and repeats should be integrated with the group-testing method to increase the sensitivity of the test. The simplest way is to replicate the test twice for each group (the 2Rgt method). In this paper, we present a new method for group testing (the groupMix method), which integrates two repeats in the test. Then we introduce the 2-stage sequential version of both the groupMix and the 2Rgt methods. We compare these methods analytically regarding the sensitivity and the average number of tests. The tradeoff between the sensitivity and the average number of tests should be considered when choosing the best method for the screening strategy. We applied the groupMix method to screening 263 people and identified 2 infected individuals by performing 98 tests. This method achieved a 63% saving in the number of tests compared to individual testing. Our experimental results show that in COVID-19 screening, the viral load can be low, and the group size should not be more than 6; otherwise, the FNR increases significantly. A web interface of the groupMix method is publicly available for laboratories to implement this method.

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Evaluation of COVID-19 RT-qPCR Test in Multi sample Pools

Clinical Infectious Diseases ◽

10.1093/cid/ciaa531 ◽

2020 ◽

Vol 71 (16) ◽

pp. 2073-2078 ◽

Cited By ~ 76

Author(s):

Idan Yelin ◽

Noga Aharony ◽

Einat Shaer Tamar ◽

Amir Argoetti ◽

Esther Messer ◽

...

Keyword(s):

Quantitative Polymerase Chain Reaction ◽

False Negative ◽

Rna Extraction ◽

False Negative Rate ◽

Positive Sample ◽

Recent Emergence ◽

Single Positive ◽

Polymerase Chain ◽

Pool Test ◽

Current Screening

Abstract Background The recent emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) led to a current pandemic of unprecedented scale. Although diagnostic tests are fundamental to the ability to detect and respond, overwhelmed healthcare systems are already experiencing shortages of reagents associated with this test, calling for a lean immediately applicable protocol. Methods RNA extracts of positive samples were tested for the presence of SARS-CoV-2 using reverse transcription quantitative polymerase chain reaction, alone or in pools of different sizes (2-, 4-, 8-, 16-, 32-, and 64-sample pools) with negative samples. Transport media of additional 3 positive samples were also tested when mixed with transport media of negative samples in pools of 8. Results A single positive sample can be detected in pools of up to 32 samples, using the standard kits and protocols, with an estimated false negative rate of 10%. Detection of positive samples diluted in even up to 64 samples may also be attainable, although this may require additional amplification cycles. Single positive samples can be detected when pooling either after or prior to RNA extraction. Conclusions As it uses the standard protocols, reagents, and equipment, this pooling method can be applied immediately in current clinical testing laboratories. We hope that such implementation of a pool test for coronavirus disease 2019 would allow expanding current screening capacities, thereby enabling the expansion of detection in the community, as well as in close organic groups, such as hospital departments, army units, or factory shifts.

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Sample Pooling as a Strategy of SARS-COV-2 Nucleic Acid Screening Increases the False-negative Rate

10.1101/2020.05.18.20106138 ◽

2020 ◽

Cited By ~ 2

Author(s):

Yichuan Gan ◽

Lingyan Du ◽

Oluwasijibomi Damola Faleti ◽

Jing Huang ◽

Gang Xiao ◽

...

Keyword(s):

Viral Load ◽

Nucleic Acid ◽

Large Scale ◽

False Negative ◽

False Negative Rate ◽

High Viral Load ◽

Careful Consideration ◽

The World ◽

Sample Pooling ◽

Pooling Strategy

SummaryBackgroundIdentification of less costly and accurate methods for monitoring novel coronavirus disease 2019 (CoViD-19) transmission has attracted much interest in recent times. Here, we evaluated a pooling method to determine if this could improve screening efficiency and reduce costs while maintaining accuracy in Guangzhou, China.MethodsWe evaluated 8097 throat swap samples collected from individuals who came for a health check-up or fever clinic in The Third Affiliated Hospital, Southern Medical University between March 4, 2020 and April 26, 2020. Samples were screened for CoViD-19 infection using the WHO-approved quantitative reverse transcription PCR (RT-qPCR) primers. The positive samples were classified into two groups (high or low) based on viral load in accordance with the CT value of COVID-19 RT-qPCR results. Each positive RNA samples were mixed with COVID-19 negative RNA or ddH2O to form RNA pools.FindingsSamples with high viral load could be detected in pool negative samples (up to 1/1000 dilution fold). In contrast, the detection of RNA sample from positive patients with low viral load in a pool was difficult and not repeatable.InterpretationOur results show that the COVID-19 viral load significantly influences in pooling efficacy. COVID-19 has distinct viral load profile which depends on the timeline of infection. Thus, application of pooling for infection surveillance may lead to false negatives and hamper infection control efforts.FundingNational Natural Science Foundation of China; Hong Kong Scholars Program, Natural Science Foundation of Guangdong Province; Science and Technology Program of Guangzhou, China.Research in contextEvidence before this studySince it emergence in late 2019, CoViD-19 has dramatically increased the burden healthcare system worldwide. A research letter titled “Sample Pooling as a Strategy to Detect Community Transmission of SARS-CoV-2” which was recently published in JAMA journal proposed that sample pooling could be used for SARS-COV-2 community surveillance. Currently, the need for large-scale testing increases the number of 2019-nCOV nucleic acid analysis required for proper policy-making especially as work and normal school resumes. As far as we know, there are many countries and regions in the world, who are beginning to try this strategy for nucleic acid screening of SARS-CoV-2.Added value of this studyWe carried out a study using pooled samples formed from SARS-COV-2 negative samples and positive samples with high or low viral and assessed detection rate for the positive samples. We found that positive sample with high viral load could be detected in pools in a wide range of dilution folds (ranging from1/2 to 1/50). On the contrary, the sample with low viral load could only be detected in RNA “pools” at very low dilution ratio, and the repeatability was unsatisfactory. Our results show the application of the “pooling” strategy for large-scale community surveillance requires careful consideration and depends on the viral load of the positive samples.Implications of all the available evidenceAlthough the number of newly diagnosed cases has been reducing in some parts of the world, the possibility of a second wave of infection has made quick and efficient data gathering essential for policy-making, isolation and treatment of patients. Fast and efficient nucleic acid detection methods are encouraged, but sample pooling as a strategy of SARS-COV-2 nucleic acid screening increased the false-negative rate, especially those with asymptomatic infections have lower viral load. Therefore, the application of the “pooling” strategy for large-scale community surveillance requires careful consideration by policy makers.

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False-Negative Mitigation in Group Testing for COVID-19 Screening

10.1101/2020.07.31.20154070 ◽

2020 ◽

Author(s):

Amir Reza Alizad-Rahvar ◽

Safar Vafadar ◽

Mehdi Totonchi ◽

Mehdi Sadeghi

Keyword(s):

False Negative ◽

Group Testing ◽

False Negative Rate ◽

Screening Strategy ◽

Web Interface ◽

Rt Pcr ◽

Clinical Genetic ◽

Promising Candidate ◽

Testing Method ◽

High False Negative Rate

After lifting the COVID-19 lockdown restrictions and opening businesses, screening is essential to prevent the spread of the virus. Group testing could be a promising candidate for screening to save time and resources. However, due to the high false-negative rate (FNR) of the RT-PCR diagnostic test, we should be cautious about using group testing because a group's false-negative result identifies all the individuals in a group as uninfected. Repeating the test is the best solution to reduce the FNR, and repeats should be integrated with the group-testing method to increase the sensitivity of the test. The simplest way is to replicate the test twice for each group (the 2Rgt method). In this paper, we present a new method for group testing (the groupMix method), which integrates two repeats in the test. Then we introduce the adaptive version of both the groupMix and the 2Rgt methods. We compare these methods analytically regarding the sensitivity and the average number of tests. The tradeoff between the sensitivity and the average number of tests should be considered when choosing the best method for the screening strategy. We applied the non-adaptive groupMix method to screening 263 people and identified 2 infected individuals by performing 98 tests. This method achieved a 63% saving in the number of tests compared to individual testing. This method is currently applied to COVID-19 screening in the Clinical Genetic Laboratory at the Royan Institute, Tehran, Iran. Our experimental results show that in COVID-19 screening, the viral load can be low, and the group size should not be more than 6; otherwise, the FNR increases significantly. A web interface of the non-adaptive groupMix method is publicly available for laboratories to implement this method.

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Evaluation of COVID-19 RT-qPCR test in multi-sample pools

10.1101/2020.03.26.20039438 ◽

2020 ◽

Cited By ~ 47

Author(s):

Idan Yelin ◽

Noga Aharony ◽

Einat Shaer Tamar ◽

Amir Argoetti ◽

Esther Messer ◽

...

Keyword(s):

Diagnostic Tests ◽

False Negative ◽

False Negative Rate ◽

Positive Sample ◽

Testing Laboratories ◽

Recent Emergence ◽

Single Positive ◽

Pool Test ◽

Current Screening ◽

A Current

AbstractThe recent emergence of SARS-CoV-2 lead to a current pandemic of unprecedented levels. Though diagnostic tests are fundamental to the ability to detect and respond, many health systems are already experiencing shortages of reagents associated with this test. Here, testing a pooling approach for the standard RT-qPCR test, we find that a single positive sample can be detected even in pools of up to 32 samples, with an estimated false negative rate of 10%. Detection of positive samples diluted in even up to 64 samples may also be attainable, though may require additional amplification cycles. As it uses the standard protocols, reagents and equipment, this pooling method can be applied immediately in current clinical testing laboratories. We hope that such implementation of a pool test for COVID-19 would allow expanding current screening capacities thereby enabling the expansion of detection in the community, as well as in close integral groups, such as hospital departments, army units, or factory shifts.

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FAST: a Flexible, Accurate and Speedy Test for COVID-19

10.1101/2020.06.04.20122473 ◽

2020 ◽

Cited By ~ 1

Author(s):

Linjiajie Fang ◽

Bing-Yi Jing ◽

Shen Ling ◽

Qing Yang

Keyword(s):

Close Contact ◽

False Negative ◽

Group Testing ◽

False Negative Rate ◽

Rt Pcr ◽

False Negatives ◽

Improve Efficiency ◽

Negative Rate ◽

Pcr Test

AbstractAs the COVID-19 pandemic continues worldwide, there is an urgent need to detect infected patients as quickly and accurately as possible. Group testing proposed by Technion [1][2] could improve efficiency greatly. However, the false negative rate (FNR) would be doubled. Using USA as an example, group testing would have over 70,000 false negatives, compared to 35,000 false negatives by individual testing.In this paper, we propose a Flexible, Accurate and Speedy Test (FAST), which is faster and more accurate than any existing tests. FAST first forms small close contact subgroups, e.g. families and friends. It then pools subgroups to form larger groups before RT-PCR test is done. FAST needs a similar number of tests to Technion’s method, but sharply reduces the FNR to a negligible level. For example, FAST brings down the number of false negatives in USA to just 2000, and it is seven times faster than individual testing.

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Longitudinal Screening for Diabetic Retinopathy in a Nationwide Screening Program: Comparing Deep Learning and Human Graders

Journal of Diabetes Research ◽

10.1155/2020/8839376 ◽

2020 ◽

Vol 2020 ◽

pp. 1-8

Author(s):

Jirawut Limwattanayingyong ◽

Variya Nganthavee ◽

Kasem Seresirikachorn ◽

Tassapol Singalavanija ◽

Ngamphol Soonthornworasiri ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Deep Learning ◽

Screening Program ◽

False Negative ◽

False Negative Rate ◽

Lower Sensitivity ◽

Patients With Diabetes ◽

Fundus Photographs ◽

New Onset

Objective. To evaluate diabetic retinopathy (DR) screening via deep learning (DL) and trained human graders (HG) in a longitudinal cohort, as case spectrum shifts based on treatment referral and new-onset DR. Methods. We randomly selected patients with diabetes screened twice, two years apart within a nationwide screening program. The reference standard was established via adjudication by retina specialists. Each patient’s color fundus photographs were graded, and a patient was considered as having sight-threatening DR (STDR) if the worse eye had severe nonproliferative DR, proliferative DR, or diabetic macular edema. We compared DR screening via two modalities: DL and HG. For each modality, we simulated treatment referral by excluding patients with detected STDR from the second screening using that modality. Results. There were 5,738 patients (12.3% STDR) in the first screening. DL and HG captured different numbers of STDR cases, and after simulated referral and excluding ungradable cases, 4,148 and 4,263 patients remained in the second screening, respectively. The STDR prevalence at the second screening was 5.1% and 6.8% for DL- and HG-based screening, respectively. Along with the prevalence decrease, the sensitivity for both modalities decreased from the first to the second screening (DL: from 95% to 90%, p = 0.008 ; HG: from 74% to 57%, p < 0.001 ). At both the first and second screenings, the rate of false negatives for the DL was a fifth that of HG (0.5-0.6% vs. 2.9-3.2%). Conclusion. On 2-year longitudinal follow-up of a DR screening cohort, STDR prevalence decreased for both DL- and HG-based screening. Follow-up screenings in longitudinal DR screening can be more difficult and induce lower sensitivity for both DL and HG, though the false negative rate was substantially lower for DL. Our data may be useful for health-economics analyses of longitudinal screening settings.

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258. False Negative Rate of T2Candida Assay in Blood Culture Positive Candidemia

Open Forum Infectious Diseases ◽

10.1093/ofid/ofz360.333 ◽

2019 ◽

Vol 6 (Supplement_2) ◽

pp. S143-S144

Author(s):

Todd McCarty ◽

Sixto M Leal ◽

Rachael A Lee ◽

Cameron White ◽

Peter Pappas

Keyword(s):

Clinical Trial ◽

Blood Culture ◽

Antifungal Therapy ◽

Treatment Time ◽

Tertiary Care ◽

False Negative ◽

False Negative Rate ◽

Lower Sensitivity ◽

Candida Spp ◽

Negative Rate

Abstract Background The T2Candida (T2C) assay is an FDA-approved, non-culture-based rapid diagnostic that utilizes PCR and magnetic resonance technology to detect candidemia in a whole blood specimen. T2C can detect the 5 most common pathogenic species: C. albicans, C. tropicalis, C. parapsilosis, C. krusei, and C. glabrata. The sensitivity of T2C is reported to be 88–94%, varying by the species, based on the original clinical trial from 2015. Only 6 patients with candidemia were included in the study, so it was supplemented with samples spiked with known quantities of Candida spp. In this study, we sought to evaluate the sensitivity of T2C with routine usage in a tertiary-care academic hospital. Methods All patients with a blood culture (BC) positive for Candida spp. during the years 2016 through 2018 were identified. Repeat positive cultures of the same species within 30 days of the initial culture were excluded. We then reviewed the medical records of those patients with a T2C collected ±12 hours from the time of the BC collection. Data collection included demographics, time to antifungal therapy, time to culture reported positive, impact of false negative T2C on antifungal therapy, and 30-day mortality. Results There were 281 episodes of candidemia (designated as a positive blood culture) in the study period. Forty-four of these episodes had a T2C collected within the specified timeframe (Figure 1). Overall, there were 17 false-negative T2C, reflecting a sensitivity of 61% (27/44). Excluding species not detected by T2C, the sensitivity was 71% (21/38). Of the false-negative group, antifungal therapy was impacted in 8 patients: delayed initiation in 6 patients (1–4 days) and treatment interruption in 2 patients (1 dose each). Demographics, time to treatment, time to culture positivity, and 30-day mortality were similar in the two groups (Table 1). Conclusion In spite of the test being readily available and increasingly used, only 44/281 (16%) of patients with a positive BC had a T2C ordered concurrently. Our experience shows a much lower sensitivity than the clinical trial, in part due to species not detected by T2C. Considering only those organisms on the T2C panel, the false-negative rate was 29%. Impact on treatment was limited to half of the false-negative patients with no difference in mortality. Disclosures All authors: No reported disclosures.

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Two Stage Hierarchical Group Testing Strategy to Increase SARS-CoV-2 Testing Capacity at an Institution of Higher Education: A Retrospective Analysis

10.1101/2021.04.14.21255494 ◽

2021 ◽

Author(s):

Troy J Ganz ◽

Markus Leslloyd Waithe-Alleyne ◽

Deirdre Slate ◽

Rachel Donner ◽

Kevin Hines ◽

...

Keyword(s):

Higher Education ◽

Limit Of Detection ◽

False Negative ◽

Rna Extraction ◽

Group Testing ◽

False Negative Rate ◽

Diagnostic Methods ◽

Nucleic Acid Amplification ◽

Configuration Model ◽

Analytical Sensitivities

Population testing for severe acute respiratory syndrome 2 (SAR-CoV-2) is necessary owing to the possibility of viral transmission from asymptomatic cases, yet scarcity of reagents and equipment has added to the cost prohibitive implementation of screening campaigns at institutions of higher education. The high analytical sensitivities of leading nucleic acid amplification diagnostic methods allow for group testing to increase testing capacity. A feasibility study was performed using an optimized testing configuration model for pooling three, five, and ten samples. Following the standard RNA extraction and purification workflow for quantitative reverse transcriptase polymerase chain reaction (RT-qPCR) method using Thermo Fisher TaqPath COVID-19 multiplex primers and probes for the ORF1ab, N, and S genes, matrix and dilution effects were assessed using pooled negative samples as the diluent. Probit analysis produced a limit of detection of 16075 (ORF1ab), 1308 (N), and 1180182 (S) genomic copy equivalents per milliliter. Trials comparing neat to 1:5 dilution for 34 weak-to-strongly positive samples demonstrated average threshold cycle (CT) shifts of 2.31+/-1.16 (ORF1ab), 2.23+/-1.12 (N), and 2.79+/-1.40 (S). Notwithstanding observed S gene dropouts, the false negative rate was unaffected. As the ratio of asymptomatic positive to symptomatic positive SARS-CoV-2 infected individuals was approximately 4:1 and the average prevalence was 0.16% since we started testing in August 2020, pooled testing was identified as a viable, cost-effective option for monitoring the Northeastern University community.

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Clinically Meaningful Change

Methodology ◽

10.1027/1614-2241/a000168 ◽

2019 ◽

Vol 15 (3) ◽

pp. 97-105

Author(s):

Rodrigo Ferrer ◽

Antonio Pardo

Keyword(s):

Effect Size ◽

False Negative ◽

False Negative Rate ◽

Point Of View ◽

Skewed Distribution ◽

Effect Sizes ◽

False Negatives ◽

Large Size ◽

Before And After ◽

Post Test

Abstract. In a recent paper, Ferrer and Pardo (2014) tested several distribution-based methods designed to assess when test scores obtained before and after an intervention reflect a statistically reliable change. However, we still do not know how these methods perform from the point of view of false negatives. For this purpose, we have simulated change scenarios (different effect sizes in a pre-post-test design) with distributions of different shapes and with different sample sizes. For each simulated scenario, we generated 1,000 samples. In each sample, we recorded the false-negative rate of the five distribution-based methods with the best performance from the point of view of the false positives. Our results have revealed unacceptable rates of false negatives even with effects of very large size, starting from 31.8% in an optimistic scenario (effect size of 2.0 and a normal distribution) to 99.9% in the worst scenario (effect size of 0.2 and a highly skewed distribution). Therefore, our results suggest that the widely used distribution-based methods must be applied with caution in a clinical context, because they need huge effect sizes to detect a true change. However, we made some considerations regarding the effect size and the cut-off points commonly used which allow us to be more precise in our estimates.

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