Assessment of multiplex digital droplet RT-PCR as a diagnostic tool for SARS-CoV-2 detection in nasopharyngeal swabs and saliva samples

Background Reverse transcription-quantitative PCR on nasopharyngeal swabs is currently the reference COVID-19 diagnosis method but exhibits imperfect sensitivity. Methods We developed a multiplex reverse transcription-digital droplet PCR (RT-ddPCR) assay, targeting six SARS-CoV-2 genomic regions, and evaluated it on nasopharyngeal swabs and saliva samples collected from 130 COVID-19 positive or negative ambulatory individuals, who presented symptoms suggestive of mild or moderate SARS-CoV2 infection. Results For the nasopharyngeal swab samples, the results obtained using the 6-plex RT-ddPCR and RT-qPCR assays were all concordant. The 6-plex RT-ddPCR assay was more sensitive than RT-qPCR (85% versus 62%) on saliva samples from patients with positive nasopharyngeal swabs. Conclusion Multiplex RT-ddPCR represents an alternative and complementary tool for the diagnosis of COVID-19, in particular to control RT-qPCR ambiguous results. It can also be applied to saliva for repetitive sampling and testing individuals for whom nasopharyngeal swabbing is not possible.

Adapting Lot Quality Assurance Sampling to accommodate imperfect tests: application to COVID-19 serosurveillance in Haiti

Background: Lot Quality Assurance Sampling (LQAS), a tool used for monitoring health indicators in low resource settings resulting in “high” or “low” classifications, assumes that determination of the trait of interest is perfect. This is often not true for diagnostic tests, with imperfect sensitivity and specificity. Here, we develop Lot Quality Assurance Sampling for Imperfect Tests (LQAS-IMP) to address this issue and apply it to a COVID-19 serosurveillance study in Haiti.Results: As part of the standard LQAS procedure, the user specifies allowable classification errors for the system, which is defined by a sample size and decision rule. We show that when an imperfect diagnostic test is used, the classification errors are larger than specified. We derive a modified procedure, LQAS-IMP, that accounts for the sensitivity and specificity of a diagnostic test to yield correct classification errors. We apply our methods to create a sampling scheme at Zanmi Lasante health facilities in Haiti to assess the prior circulation of COVID-19 among healthcare workers (HCWs) using a limited number of antibody tests. Conclusions: The LQAS-IMP procedure accounts for imperfect sensitivity and specificity in system design; if the accuracy of a test is known, the use of LQAS-IMP extends LQAS to applications for indicators that are based on laboratory tests, such as COVID-19 antibodies.

Adapting Lot Quality Assurance Sampling to accommodate imperfect tests: application to COVID-19 serosurveillance in Haiti

Background: Lot Quality Assurance Sampling (LQAS), a tool used for monitoring health indicators in low resource settings resulting in "high" or "low" classifications, assumes that determination of the trait of interest is perfect. This is often not true for diagnostic tests, with imperfect sensitivity and specificity. Here, we develop Lot Quality Assurance Sampling for Imperfect Tests (LQAS-IMP) to address this issue and apply it to a COVID-19 serosurveillance study in Haiti. Development: As part of the standard LQAS procedure, the user specifies allowable classification errors for the system, which is defined by a sample size and decision rule. We show that when an imperfect diagnostic test is used, the classification errors are larger than specified. We derive a modified procedure, LQAS-IMP, that accounts for the sensitivity and specificity of a diagnostic test to yield correct classification errors. Application: At Zanmi Lasante health facilities in Haiti, the goal was to assess the prior circulation of COVID-19 among healthcare workers (HCWs) using a limited number of antibody tests. As the COVID-19 antibody tests were known to have imperfect diagnostic accuracy, we used the LQAS-IMP procedure to define valid systems for sampling at eleven hospitals in Haiti. Conclusions: The LQAS-IMP procedure accounts for imperfect sensitivity and specificity in system design; if the accuracy of a test is known, the use of LQAS-IMP extends LQAS to applications for indicators that are based on laboratory tests, such as COVID-19 antibodies.

Validation of OCO-2 error analysis using simulated retrievals

Characterization of errors and sensitivity in remotely sensed observations of greenhouse gases is necessary for their use in estimating regional-scale fluxes. We analyze 15 orbits of the simulated Orbiting Carbon Observatory-2 (OCO-2) with the Atmospheric Carbon Observations from Space (ACOS) retrieval, which utilizes an optimal estimation approach, to compare predicted versus actual errors in the retrieved CO2 state. We find that the nonlinearity in the retrieval system results in XCO2 errors of ∼0.9 ppm. The predicted measurement error (resulting from radiance measurement error), about 0.2 ppm, is accurate, and an upper bound on the smoothing error (resulting from imperfect sensitivity) is not more than 0.3 ppm greater than predicted. However, the predicted XCO2 interferent error (resulting from jointly retrieved parameters) is a factor of 4 larger than predicted. This results from some interferent parameter errors that are larger than predicted, as well as some interferent parameter errors that are more strongly correlated with XCO2 error than predicted by linear error estimation. Variations in the magnitude of CO2 Jacobians at different retrieved states, which vary similarly for the upper and lower partial columns, could explain the higher interferent errors. A related finding is that the error correlation within the CO2 profiles is less negative than predicted and that reducing the magnitude of the negative correlation between the upper and lower partial columns from −0.9 to −0.5 results in agreement between the predicted and actual XCO2 error. We additionally study how the postprocessing bias correction affects errors. The bias-corrected results found in the operational OCO-2 Lite product consist of linear modification of XCO2 based on specific retrieved values, such as the CO2 grad del (δ∇CO2), (“grad del” is a measure of the change in the profile shape versus the prior) and dP (the retrieved surface pressure minus the prior). We find similar linear relationships between XCO2 error and dP or δ∇CO2 but see a very complex pattern of errors throughout the entire state vector. Possibilities for mitigating biases are proposed, though additional study is needed.

Validation of OCO-2 error analysis using simulated retrievals

Characterization of errors and sensitivity in remotely sensed observations of greenhouse gases is necessary for their use in estimating regional-scale fluxes. We analyze 15 orbits of simulated OCO-2 with the Atmospheric Carbon Observations from Space (ACOS) retrieval, which utilizes an optimal estimation approach, to compare predicted versus actual errors in the retrieved CO2 state. We find that the non-linearity in the retrieval system results in XCO2 errors of ~0.9 ppm. The predicted measurement error (resulting from radiance measurement error), about 0.2 ppm, is accurate, and an upper bound on the smoothing error (resulting from imperfect sensitivity) is not more than 0.3 ppm greater than predicted. However, the predicted XCO2 interferent error (resulting from jointly retrieved parameters) is a factor of 4 larger than predicted. This results from some interferent parameter errors larger than predicted, as well as some interferent parameter errors more strongly correlated with XCO2 error than predicted. Variations in the magnitude of CO2 Jacobians at different retrieved states, which vary similarly for the upper and lower partial columns, could explain the higher interferent errors. A related finding is that the error correlation within the CO2 profiles is less negative than predicted, and that reducing the magnitude of the negative correlation between the upper and lower partial columns from −0.9 to −0.5 results in agreement between the predicted and actual XCO2 error. We additionally study the post-processing bias correction affects errors. The bias corrected results found in the operational OCO-2 Lite product consists of linear modification of XCO2 based on specific retrieved values, such as the CO2_grad_delta (a measure of the change in the profile shape versus the prior) and dP (the retrieved surface pressure minus the prior). We find similar linear relationships between XCO2 error and dP or CO2_grad_delta, but see a very complex pattern of errors throughout the entire state vector. Possibilities for mitigating biases are proposed, though additional study is needed.

Bicentric Evaluation of Six Anti-Toxoplasma Immunoglobulin G (IgG) Automated Immunoassays and Comparison to the Toxo II IgG Western Blot

ABSTRACT A comparative study of the Toxoplasma IgGI and IgGII Access (Access I and II, respectively; Beckman Coulter Inc.), AxSYM Toxo IgG (AxSYM; Abbott Diagnostics), Vidas Toxo IgG (Vidas; bioMerieux, Marcy l'Etoile, France), Immulite Toxo IgG (Immulite; Siemens Healthcare Diagnostics Inc.), and Modular Toxo IgG (Modular; Roche Diagnostics, Basel, Switzerland) tests was done with 406 consecutive serum samples. The Toxo II IgG Western blot (LDBio, Lyon, France) was used as a reference technique in the case of intertechnique discordance. Of the 406 serum samples tested, the results for 35 were discordant by the different techniques. Using the 175 serum samples with positive results, we evaluated the standardization of the titrations obtained (in IU/ml); the medians (second quartiles) obtained were 9.1 IU/ml for the AxSYM test, 21 IU/ml for the Access I test, 25.7 IU/ml for the Access II test, 32 IU/ml for the Vidas test, 34.6 IU/ml for the Immulite test, and 248 IU/ml for the Modular test. For all the immunoassays tested, the following relative sensitivity and specificity values were found: 89.7 to 100% for the Access II test, 89.7 to 99.6% for the Immulite test, 90.2 to 99.6% for the AxSYM test, 91.4 to 99.6% for the Vidas test, 94.8 to 99.6% for the Access I test, and 98.3 to 98.7% for the Modular test. Among the 406 serum samples, we did not find any false-positive values by two different tests for the same serum sample. Except for the Modular test, which prioritized sensitivity, it appears that the positive cutoff values suggested by the pharmaceutical companies are very high (either for economical or for safety reasons). This led to imperfect sensitivity, a large number of unnecessary serological follow-ups of pregnant women, and difficulty in determining the serological status of immunosuppressed individuals.

Evaluation of Febrile Infant

In Reply.— We appreciate Dr Levin's contribution to our analysis for febrile infants and agree that including the imperfect sensitivity of the CSF cell count in identifying meningitis improves the model's completeness. However, taken in perspective, the addition does not substantially alter our findings. In the original analysis, All Sepsis Tests + IV Antibiotics prevented 78% of sequelae, and All Sepsis Tests + IM Ceftriaxone prevented 76% of sequelae.1 Rodewald's study2 reported that a CSF cell count ≥6 had a sensitivity of 98.4% in identifying bacterial meningitis.