False-negative results of initial RT-PCR assays for COVID-19: A systematic review
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Abstract
A false-negative case of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is defined as a person with suspected infection and an initial negative result by reverse transcription-polymerase chain reaction (RT-PCR) test, with a positive result on a subsequent test. False-negative cases have important implications for isolation and risk of transmission of infected people and for the management of coronavirus disease 2019 (COVID-19). We aimed to review and critically appraise evidence about the rate of RT-PCR false-negatives at initial testing for COVID-19.
Methods
We searched MEDLINE, EMBASE, LILACS, as well as COVID-19 repositories, including the EPPI-Centre living systematic map of evidence about COVID-19 and the Coronavirus Open Access Project living evidence database. Two authors independently screened and selected studies according to the eligibility criteria and collected data from the included studies. The risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. We calculated the proportion of false-negative test results using a multilevel mixed-effect logistic regression model. The certainty of the evidence about false-negative cases was rated using the GRADE approach for tests and strategies. All information in this article is current up to July 17, 2020.
Results
We included 34 studies enrolling 12,057 COVID-19 confirmed cases. All studies were affected by several risks of bias and applicability concerns. The pooled estimate of false-negative proportion was highly affected by unexplained heterogeneity (tau-squared = 1.39; 90% prediction interval from 0.02 to 0.54). The certainty of the evidence was judged as very low due to the risk of bias, indirectness, and inconsistency issues.
Conclusions
There is substantial and largely unexplained heterogeneity in the proportion of false-negative RT-PCR results. The collected evidence has several limitations, including risk of bias issues, high heterogeneity, and concerns about its applicability. Nonetheless, our findings reinforce the need for repeated testing in patients with suspicion of SARS-Cov-2 infection given that up to 54% of COVID-19 patients may have an initial false-negative RT-PCR (very low certainty of evidence).
Systematic review registration
Protocol available on the OSF website: https://tinyurl.com/vvbgqya .
Article activity feed
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SciScore for 10.1101/2020.04.16.20066787: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This variability is a limitation for the interpretation of the mean proportion of the FN results as a summary estimate. Kucirka et al. also detected similar uncertainties in their Bayesian modelling of false-negative rates of RT-PCR by time since exposure, based on information from seven studies and 1330 respiratory samples (68). As …
SciScore for 10.1101/2020.04.16.20066787: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
No key resources detected.
Results from OddPub: Thank you for sharing your data.
Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:This variability is a limitation for the interpretation of the mean proportion of the FN results as a summary estimate. Kucirka et al. also detected similar uncertainties in their Bayesian modelling of false-negative rates of RT-PCR by time since exposure, based on information from seven studies and 1330 respiratory samples (68). As an alternative, we chose to illustrate the impact of this heterogeneity by showing the number of false-negative cases expected in a cohort of 100 patients tested under three different prevalence of the disease scenarios. We based our calculations on the limits of the false-negative prediction interval. Using a prevalence of 50%, we found that up to 27 cases would be misdiagnosed and then they would not receive adequate clinical management. We emphasised that these numerical approaches should be interpreted with caution due to very low quality of evidence. Our systematic review faced other challenges in its development. First, our study was initially planned as a rapid review aiming to provide a quick response to our local clinicians at the beginning of the COVID-19 pandemic. Due to the permanent involvement of clinicians managing COVID-19 patients at this point, we were able to define a review question that responds to a clinical inquiry relevant to current clinical practice (69-71). However, due to the increasing number of publications potentially eligible to answer the review question, our approach evolved into a living-systematic review with re...
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:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- No funding statement was detected.
- No protocol registration statement was detected.
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SciScore for 10.1101/2020.04.16.20066787: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable There were 577 men versus 213 women included ( Table 1) . Table 2: Resources
Software and Algorithms Sentences Resources We searched on MEDLINE , EMBASE , LILACS , the WHO database of COVID-19 publications , the EPPI-Centre living systematic map of evidence about COVID-19 , and the living systematic review developed by the University of Bern ( ISPM) . MEDLINEsuggested: (MEDLINE, SCR_002185)<div style="margin-bottom:8px"> <div><b>EMBASE</b></div> <div>suggested: …SciScore for 10.1101/2020.04.16.20066787: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable There were 577 men versus 213 women included ( Table 1) . Table 2: Resources
Software and Algorithms Sentences Resources We searched on MEDLINE , EMBASE , LILACS , the WHO database of COVID-19 publications , the EPPI-Centre living systematic map of evidence about COVID-19 , and the living systematic review developed by the University of Bern ( ISPM) . MEDLINEsuggested: (MEDLINE, SCR_002185)<div style="margin-bottom:8px"> <div><b>EMBASE</b></div> <div>suggested: (EMBASE, <a href="https://scicrunch.org/resources/Any/search?q=SCR_001650">SCR_001650</a>)</div> </div> </td></tr><tr><td style="min-width:100px;vertical-align:top;border-bottom:1px solid lightgray">We calculated the false-negative proportion with the corresponding 95 % CI using a multilevel mixed-effect logistic regression model using STATA 16®</td><td style="min-width:100px;border-bottom:1px solid lightgray"> <div style="margin-bottom:8px"> <div><b>STATA</b></div> <div>suggested: (Stata, <a href="https://scicrunch.org/resources/Any/search?q=SCR_012763">SCR_012763</a>)</div> </div> </td></tr></table>Results from OddPub: Thank you for sharing your code.
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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