Chest CT versus RT-PCR for the detection of COVID-19: systematic review and meta-analysis of comparative studies

  1. Mohammad Karam
  2. Sulaiman Althuwaikh
  3. Mohammad Alazemi
  4. Ahmad Abul
  5. Amrit Hayre
  6. Abdulmalik Alsaif
  7. Gavin Barlow

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Abstract

To compare the performance of chest computed tomography (CT) scan versus reverse transcription polymerase chain reaction (RT-PCR) as the reference standard in the initial diagnostic assessment of coronavirus disease 2019 (COVID-19) patients.

Design

A systematic review and meta-analysis were performed as per the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. A search of electronic information was conducted using the following databases: MEDLINE, EMBASE, EMCARE, CINAHL and the Cochrane Central Register of Controlled Trials.

Setting

Studies that compared the diagnostic performance within the same patient cohort of chest CT scan versus RT-PCR in COVID-19 suspected patients.

Participants

Thirteen non-randomised studies enrolling 4092 patients were identified.

Main outcome measures

Sensitivity, specificity and accuracy were primary outcome measures. Secondary outcomes included other test performance characteristics and discrepant findings between both investigations.

Results

Chest CT had a median sensitivity, specificity and accuracy of 0.91 (range 0.82–0.98), 0.775 (0.25–1.00) and 0.87 (0.68–0.99), respectively, with RT-PCR as the reference. Importantly, early small, China-based studies tended to favour chest CT versus later larger, non-China studies.

Conclusions

A relatively high false positive rate can be expected with chest CT. It is possible it may still be useful to provide circumstantial evidence, however, in some patients with a suspicious clinical presentation of COVID-19 and negative initial Severe Acute Respiratory Syndrome Coronavirus 2 RT-PCR tests, but more evidence is required in this context. In acute cardiorespiratory presentations, negative CT scan and RT-PCR tests is likely to be reassuring.

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  1. Version published to 10.1177/20542704211011837
  2. ScreenIT

    SciScore for 10.1101/2020.06.22.20136846: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Secondary Outcomes: The secondary outcomes were positive predictive value (in the event of a positive test the probability the patient is truly positive), negative predictive value (in the event of a negative test the probability the patient is truly negative), positive (PLR) and negative likelihood (NLR) ratios (respectively, the probability a person who has COVID-19 testing positive divided by the probability of a person who does not have COVID-19 testing positive [PLR] and the probability of a person who has COVID-19 testing negative divided by the probability of a person who does not have COVID-19 testing negative [NLR]), and discrepancy of findings between both investigations. 2.4. Search Strategy: Three authors independently searched online databases, including MEDLINE, EMBASE, EMCARE, CINAHL and the Cochrane Central Register of Controlled
    MEDLINE
    suggested: (MEDLINE, RRID:SCR_002185)
    EMBASE
    suggested: (EMBASE, RRID:SCR_001650)
    Data Extraction: An electronic data extraction spreadsheet was created in line with Cochrane’s data collection form for intervention reviews.
    Cochrane’s
    suggested: None
    Three authors cooperatively collected and recorded the results with any disagreements resolved via discussion. 2.7. Data Synthesis: Data synthesis was conducted using the Review Manager 5.3 software and Microsoft Excel.
    Microsoft Excel
    suggested: (Microsoft Excel, RRID:SCR_016137)

    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:
    Diagnostic tests always have inherent strengths and weaknesses. In the case of the COVID-19 pandemic it would be preferable to have a simple, rapid test with high sensitivity leading to a low number of false negatives thereby allowing accurate decision-making regarding, for example, who may be infectious and requires isolation in the hospital setting or home isolation in the community. Based on those who had a suggestive CT but tested negative with RT-PCR, our findings suggest that CT scan may be able to detect a high proportion of hospitalised COVID-19 cases overall and may be useful when PCR testing is initially negative but clinical suspicion high [4]. There were relatively few patients who had a negative CT scan, but who were initially RT-PCR positive. Of the studies that reported patients who were initially RT-PCR negative, but subsequently became positive, 79.2% of patients (42/53) had a suggestive CT. It is important to note that across the studies, however, only a small proportion who were initially RT-PCR negative were systematically retested. Overall, our results suggest that chest CT scan may be a useful adjunct to RT-PCR in the initial detection of COVID-19 in certain circumstances, but not routinely, which supports published guidance and thus we do not advocate a change in practice. CT scans are subject to their inherent limitations. Firstly, they are best avoided during pregnancy due to the excessive radiation and risk of harming the foetus [26]. In addition, ov...

    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.

  3. Version published to 10.1101/2020.06.22.20136846v4 on medRxiv
  4. Version published to 10.1101/2020.06.22.20136846v3 on medRxiv
  5. Version published to 10.1101/2020.06.22.20136846v2 on medRxiv
  6. Version published to 10.1101/2020.06.22.20136846v1 on medRxiv