Analysis and Prediction of COVID-19 Patients’ False Negative Results for SARS-CoV-2 Detection with Pharyngeal Swab Specimen: A Retrospective Study

  1. Hui Xu
  2. Li Yan
  3. Chun (Martin) Qiu
  4. Bo Jiao
  5. Yanyan Chen
  6. Xi Tan
  7. Zhuo Chen
  8. Ling Ai
  9. Yaru Xiao
  10. Ailin Luo
  11. Shusheng Li

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

False negative results of SARS-CoV-2 nucleic acid detection pose threats to COVID-19 patients and medical workers alike.

Objective

To develop multivariate models to determine clinical characteristics that contribute to false negative results of SARS-CoV-2 nucleic acid detection, and use them to predict false negative results as well as time windows for testing positive.

Design

Retrospective Cohort Study (Ethics number of Tongji Hospital: No. IRBID: TJ-20200320)

Setting

A database of outpatients in Tongji Hospital (University Hospital) from 15 January 2020 to 19 February 2020.

Patients

1,324 outpatients with COVID-19

Measurements

Clinical information on CT imaging reports, blood routine tests, and clinic symptoms were collected. A multivariate logistic regression was used to explain and predict false negative testing results of SARS-CoV-2 detection. A multivariate accelerated failure model was used to analyze and predict delayed time windows for testing positive.

Results

Of the 1,324 outpatients who diagnosed of COVID-19, 633 patients tested positive in their first SARS-CoV-2 nucleic acid test (47.8%), with a mean age of 51 years (SD=14.9); the rest, which had a mean age of 47 years (SD=15.4), tested negative in the first test. “Ground glass opacity” in a CT imaging report was associated with a lower chance of false negatives (aOR, 0.56), and reduced the length of time window for testing positive by 26%. “Consolidation” was associated with a higher chance of false negatives (aOR, 1.57), and extended the length of time window for testing positive by 44%. In blood routine tests, basophils (aOR, 1.28) and eosinophils (aOR, 1.29) were associated with a higher chance of false negatives, and were found to extend the time window for testing positive by 23% and 41%, respectively. Age and gender also affected the significantly.

Limitation

Data were generated in a large single-center study.

Conclusion

Testing outcome and positive window of SARS-CoV-2 detection for COVID-19 patients were associated with CT imaging results, blood routine tests, and clinical symptoms. Taking into account relevant information in CT imaging reports, blood routine tests, and clinical symptoms helped reduce a false negative testing outcome. The predictive AFT model, what we believe to be one of the first statistical models for predicting time window of SARS-CoV-2 detection, could help clinicians improve the accuracy and efficiency of the diagnosis, and hence, optimizes the timing of nucleic acid detection and alleviates the shortage of nucleic acid detection kits around the world.

Primary Funding Source

None.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.03.26.20043042: (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

    No key resources detected.

    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: We detected the following sentences addressing limitations in the study:
    Several limitations should be considered when interpreting these findings. Firstly, this is a single center, and retrospective study. The results need more multiple, prospective research to verify it. Secondly, with the exception of the time window, there are other reasons related to false negative results. For example, it has been reported that the sensitivity of SARS-CoV-2 nucleic acid detection of sputum specimens is higher than that of pharyngeal swabs (7, 17), which may be related to the main invasion of SARS-CoV-2 on lower respiratory tract cells. However, a dry cough is the main manifestation of COVID-19 patients, imposing difficulty on obtaining sputum specimens. With an unsatisfactory liquefaction of sputum specimen, false negative nucleic acid results increase. In addition, the collection of sputum specimen from the lower respiratory tract is easy to cause spatter, which increases the risk of infection for the operator, so it is not recommended to be used in an outpatient clinic. In conclusion, we present what we believe to be one of the first statistical models for predicting nucleic acid test results for patients diagnosed with COVID-19. The predictive model of time window for testing positive could help clinicians identify patients at a higher risk and improve the rate of accurate diagnosis of COVID-19. The model can be extended to predict false negative of other tests for SARS-CoV-2. More external validation studies are now required to demonstrate predictions in...

    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.
    • 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.

    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 checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.03.26.20043042v1 on medRxiv