SARS-CoV2 Testing: The Limit of Detection Matters
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Abstract
Resolving the COVID-19 pandemic requires diagnostic testing to determine which individuals are infected and which are not. The current gold standard is to perform RT-PCR on nasopharyngeal samples. Best-in-class assays demonstrate a limit of detection (LoD) of ~100 copies of viral RNA per milliliter of transport media. However, LoDs of currently approved assays vary over 10,000-fold. Assays with higher LoDs will miss more infected patients, resulting in more false negatives. However, the false-negative rate for a given LoD remains unknown. Here we address this question using over 27,500 test results for patients from across our healthcare network tested using the Abbott RealTime SARS-CoV-2 EUA. These results suggest that each 10-fold increase in LoD is expected to increase the false negative rate by 13%, missing an additional one in eight infected patients. The highest LoDs on the market will miss a majority of infected patients, with false negative rates as high as 70%. These results suggest that choice of assay has meaningful clinical and epidemiological consequences. The limit of detection matters.
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SciScore for 10.1101/2020.06.02.131144: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Note, the Ct determination on Abbott M2000rt platform is alternatively called the fractional cycle number (FCN) and is specifically one way of determining the cycle number at the maximum amplification efficiency inflection point, i.e, the maxRatio, of each amplification curve (6). Abbottsuggested: (Abbott, RRID:SCR_010477)We used Python (v3.6) and its NumPy, SciPy, Matplotlib, and Pandas libraries to plot linear regression and Theil-Sen slopes with 95% confidence intervals on repeat positives; a normalized cumulative distribution (histogram) of positive results (with reversed x-axis for … SciScore for 10.1101/2020.06.02.131144: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Note, the Ct determination on Abbott M2000rt platform is alternatively called the fractional cycle number (FCN) and is specifically one way of determining the cycle number at the maximum amplification efficiency inflection point, i.e, the maxRatio, of each amplification curve (6). Abbottsuggested: (Abbott, RRID:SCR_010477)We used Python (v3.6) and its NumPy, SciPy, Matplotlib, and Pandas libraries to plot linear regression and Theil-Sen slopes with 95% confidence intervals on repeat positives; a normalized cumulative distribution (histogram) of positive results (with reversed x-axis for ease of interpretation); binned histogram by 0.5 log10 units, and linear regression on log10-transformed data. Pythonsuggested: (IPython, RRID:SCR_001658)NumPysuggested: (NumPy, RRID:SCR_008633)SciPysuggested: (SciPy, RRID:SCR_008058)Matplotlibsuggested: (MatPlotLib, RRID:SCR_008624)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- No conflict of interest statement was detected. If there are no conflicts, we encourage authors to explicit state so.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.06.02.131144: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization We used basic principles of PCR and detailed measurements of PCR efficiency on 50 randomly chosen positive samples to convert from Ct values to viral load , in units of copies of viral RNA per mL of viral transport medium . Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Note , the Ct determination on Abbott M2000rt platform is alternatively called the fractional cycle number ( FCN ) and is specifically one way of determining the cycle number at the maximum amplification efficiency inflection point , i.e , the maxRatio , of … SciScore for 10.1101/2020.06.02.131144: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization We used basic principles of PCR and detailed measurements of PCR efficiency on 50 randomly chosen positive samples to convert from Ct values to viral load , in units of copies of viral RNA per mL of viral transport medium . Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources Note , the Ct determination on Abbott M2000rt platform is alternatively called the fractional cycle number ( FCN ) and is specifically one way of determining the cycle number at the maximum amplification efficiency inflection point , i.e , the maxRatio , of each amplification curve ( 6) . Abbottsuggested: (Abbott, SCR_010477)We used Python ( v3.6 ) and its NumPy , SciPy , Matplotlib , and Pandas libraries to plot linear regression and Theil-Sen slopes with 95 % confidence intervals on repeat positives; a normalized cumulative distribution ( histogram ) of positive results ( with reversed x-axis for ease of interpretation); binned histogram by 0.5 log10 units , and linear regression on log10transformed data . Pythonsuggested: (IPython, SCR_001658)<div style="margin-bottom:8px"> <div><b>NumPy</b></div> <div>suggested: (NumPy, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008633">SCR_008633</a>)</div> </div> <div style="margin-bottom:8px"> <div><b>SciPy</b></div> <div>suggested: (SciPy, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008058">SCR_008058</a>)</div> </div> <div style="margin-bottom:8px"> <div><b>Matplotlib</b></div> <div>suggested: (MatPlotLib, <a href="https://scicrunch.org/resources/Any/search?q=SCR_008624">SCR_008624</a>)</div> </div> </td></tr></table>Results from OddPub: We did not find a statement about open data. We also did not find a statement about open code. Researchers are encouraged to share open data when possible (see Nature blog).
About SciScore
SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, including references cited, please follow this link.
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