Evaluations of modes of pooling specimens for COVID-19 screened by quantitative PCR and droplet digital PCR.

Though pooling samples for SARS-CoV-2 detection has effectively met the need for rapid diagnostic and screening tests, many factors can influence the sensitivity of a pooled test. In this study, we conducted a simulation experiment to evaluate modes of pooling specimens and aimed at formulating an optimal pooling strategy. We focussed on the type of swab, their solvent adsorption ability, pool size, pooling volume, and different factors affecting the quality of preserving RNA by different virus solutions. Both quantitative PCR and digital PCR were used to evaluate the sampling performance. In addition, we determined the detection limit by sampling which is simulated from the virus of different titers and evaluated the effect of sample-storage conditions by determining the viral load after storage. We found that flocked swabs were better than fibre swabs. The RNA-preserving ability of the non-inactivating virus solution was slightly better than that of the inactivating virus solution. The optimal pooling strategy was a pool size of 10 samples in a total volume of 9 mL. Storing the collected samples at 4 °C or 25 °C for up to 48 h had little effect on the detection sensitivity. Further, we observed that our optimal pooling strategy performed equally well as the single-tube test did. In clinical applications, we recommend adopting this pooling strategy for low-risk populations to improve screening efficiency and shape future strategies for detecting and managing other respiratory pathogens, thus contributing to preparedness for future public health challenges.

Pooled Pharyngeal, Rectal, and Urine Specimens for the Point-of-Care Detection of Chlamydia trachomatis and Neisseria gonorrhoeae by Lay Providers in Key Population-Led Health Services in Thailand.

Routine testing for Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) in people with heightened risk is lacking in Thailand. This study aimed to assess the performance of the Cepheid Xpert CT/NG assay, conducted by key population (KP) lay providers, for CT and NG detection on single-site and pooled specimens from the pharynx, rectum, and urine. Between August and October 2019, 188 men who have sex with men and 11 transgender women were enrolled. Participants collected urine specimens while trained KP lay providers obtained pharyngeal and rectal swabs. Compared to single-site testing with the Abbott RealTime CT/NG assay by medical technologists, the Xpert assay missed one pharyngeal NG infection out of 199 single-site specimens, giving a 93.3% sensitivity for pharyngeal NG and one missed pharyngeal NG infection out of fifty pooled specimens, giving an 88.9% sensitivity for pharyngeal NG. There was no discrepancy between the two assays for CT detection. The Cohen's Kappa coefficient of pooled specimen testing by the Xpert was 0.93 for NG and 1 for CT when compared to single-site testing by Abbott. Implementing pooled specimen testing by KP lay providers can be a cost-saving strategy to enhance the uptake of CT/NG services for populations facing increased risk.

Evaluation of an Automated High-Throughput Liquid-Based RNA Extraction Platform on Pooled Nasopharyngeal or Saliva Specimens for SARS-CoV-2 RT-PCR.

SARS-CoV-2 RT-PCR with pooled specimens has been implemented during the COVID-19 pandemic as a cost- and manpower-saving strategy for large-scale testing. However, there is a paucity of data on the efficiency of different nucleic acid extraction platforms on pooled specimens. This study compared a novel automated high-throughput liquid-based RNA extraction (LRE) platform (PHASIFY™) with a widely used magnetic bead-based total nucleic acid extraction (MBTE) platform (NucliSENS® easyMAG®). A total of 60 pools of nasopharyngeal swab and 60 pools of posterior oropharyngeal saliva specimens, each consisting of 1 SARS-CoV-2 positive and 9 SARS-CoV-2 negative specimens, were included for the comparison. Real-time RT-PCR targeting the SARS-CoV-2 RdRp/Hel gene was performed, and GAPDH RT-PCR was used to detect RT-PCR inhibitors. No significant differences were observed in the Ct values and overall RT-PCR positive rates between LRE and MBTE platforms (92.5% (111/120] vs. 90% (108/120]), but there was a slightly higher positive rate for LRE (88.3% (53/60]) than MBTE (81.7% (49/60]) among pooled saliva. The automated LRE method is comparable to a standard MBTE method for the detection of SAR-CoV-2 in pooled specimens, providing a suitable alternative automated extraction platform. Furthermore, LRE may be better suited for pooled saliva specimens due to more efficient removal of RT-PCR inhibitors.

Performance of Nucleic Acid Amplification Tests for Detection of Severe Acute Respiratory Syndrome Coronavirus 2 in Prospectively Pooled Specimens.

Pooled nucleic acid amplification tests for severe acute respiratory syndrome coronavirus 2 could increase availability of testing at decreased cost. However, the effect of dilution on analytical sensitivity through sample pooling has not been well characterized. We tested 1,648 prospectively pooled specimens by using 3 nucleic acid amplification tests for severe acute respiratory syndrome coronavirus 2: a laboratory-developed real-time reverse transcription PCR targeting the envelope gene, and 2 commercially available Panther System assays targeting open reading frame 1ab. Positive percent agreement (PPA) of pooled versus individual testing ranged from 71.7% to 82.6% for pools of 8 and from 82.9% to 100.0% for pools of 4. We developed and validated an independent stochastic simulation model to estimate effects of dilution on PPA and efficiency of a 2-stage pooled real-time reverse transcription PCR testing algorithm. PPA was dependent on the proportion of tests with positive results, cycle threshold distribution, and assay limit of detection.

Estimating relative risk of a log-transformed exposure measured in pools.

Pooling biospecimens prior to performing laboratory assays is a useful tool to reduce costs, achieve minimum volume requirements and mitigate assay measurement error. When estimating the risk of a continuous, pooled exposure on a binary outcome, specialized statistical techniques are required. Current methods include a regression calibration approach, where the expectation of the individual-level exposure is calculated by adjusting the observed pooled measurement with additional covariate data. While this method employs a linear regression calibration model, we propose an alternative model that can accommodate log-linear relationships between the exposure and predictive covariates. The proposed model permits direct estimation of the relative risk associated with a log-transformation of an exposure measured in pools. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Case-control data analysis for randomly pooled biomarkers.

Pooled study designs, where individual biospecimens are combined prior to measurement via a laboratory assay, can reduce lab costs while maintaining statistical efficiency. Analysis of the resulting pooled measurements, however, often requires specialized techniques. Existing methods can effectively estimate the relation between a binary outcome and a continuous pooled exposure when pools are matched on disease status. When pools are of mixed disease status, however, the existing methods may not be applicable. By exploiting characteristics of the gamma distribution, we propose a flexible method for estimating odds ratios from pooled measurements of mixed and matched status. We use simulation studies to compare consistency and efficiency of risk effect estimates from our proposed methods to existing methods. We then demonstrate the efficacy of our method applied to an analysis of pregnancy outcomes and pooled cytokine concentrations. Our proposed approach contributes to the toolkit of available methods for analyzing odds ratios of a pooled exposure, without restricting pools to be matched on a specific outcome.

Positing, fitting, and selecting regression models for pooled biomarker data.

Pooling biospecimens prior to performing lab assays can help reduce lab costs, preserve specimens, and reduce information loss when subject to a limit of detection. Because many biomarkers measured in epidemiological studies are positive and right-skewed, proper analysis of pooled specimens requires special methods. In this paper, we develop and compare parametric regression models for skewed outcome data subject to pooling, including a novel parameterization of the gamma distribution that takes full advantage of the gamma summation property. We also develop a Monte Carlo approximation of Akaike's Information Criterion applied to pooled data in order to guide model selection. Simulation studies and analysis of motivating data from the Collaborative Perinatal Project suggest that using Akaike's Information Criterion to select the best parametric model can help ensure valid inference and promote estimate precision.

Semiparametric regression models for a right-skewed outcome subject to pooling.

Pooling specimens prior to performing laboratory assays has various benefits. Pooling can help to reduce cost, preserve irreplaceable specimens, meet minimal volume requirements for certain lab tests, and even reduce information loss when a limit of detection is present. Regardless of the motivation for pooling, appropriate analytical techniques must be applied in order to obtain valid inference from composite specimens. When biomarkers are treated as the outcome in a regression model, techniques applicable to individually measured specimens may not be valid when measurements are taken from pooled specimens, particularly when the biomarker is positive and right skewed. In this paper, we propose a novel semiparametric estimation method based on an adaptation of the quasi-likelihood approach that can be applied to a right-skewed outcome subject to pooling. We use simulation studies to compare this method with an existing estimation technique that provides valid estimates only when pools are formed from specimens with identical predictor values. Simulation results and analysis of a motivating example demonstrate that, when appropriate estimation techniques are applied to strategically formed pools, valid and efficient estimation of the regression coefficients can be achieved.

Regression for skewed biomarker outcomes subject to pooling.

Epidemiological studies involving biomarkers are often hindered by prohibitively expensive laboratory tests. Strategically pooling specimens prior to performing these lab assays has been shown to effectively reduce cost with minimal information loss in a logistic regression setting. When the goal is to perform regression with a continuous biomarker as the outcome, regression analysis of pooled specimens may not be straightforward, particularly if the outcome is right-skewed. In such cases, we demonstrate that a slight modification of a standard multiple linear regression model for poolwise data can provide valid and precise coefficient estimates when pools are formed by combining biospecimens from subjects with identical covariate values. When these x-homogeneous pools cannot be formed, we propose a Monte Carlo expectation maximization (MCEM) algorithm to compute maximum likelihood estimates (MLEs). Simulation studies demonstrate that these analytical methods provide essentially unbiased estimates of coefficient parameters as well as their standard errors when appropriate assumptions are met. Furthermore, we show how one can utilize the fully observed covariate data to inform the pooling strategy, yielding a high level of statistical efficiency at a fraction of the total lab cost.