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DOI: 10.4103/ijri.IJRI_377_20
Diagnostic accuracy and false-positive rate of chest CT as compared to RT-PCR in coronavirus disease 2019 (COVID-19) pneumonia: A prospective cohort of 612 cases from India and review of literature
Financial support and sponsorship Nil.Abstract
Background: At present, the diagnosis of COVID-19 depends on real-time reverse transcriptase polymerase chain reaction (RT-PCT). On imaging, computed tomography (CT) manifestations resemble those seen in viral pneumonias, with multifocal ground-glass opacities and consolidation in a peripheral distribution being the most common findings. Although these findings lack specificity for COVID-19 diagnosis on imaging grounds, CT could be used to provide objective assessment about the extension of the lung opacities, which could be used as an imaging surrogate for disease burden. Chest CT scan may be helpful in early diagnosing of COVID-19. Objective: The current study investigated the diagnostic accuracy and false-positive rate of chest CT in detecting COVID-19 pneumoniain a population with clinical suspicion using RT-PCR testing as reference standard. Materials and Methods: In this prospective single centerstudy performed on 612 cases with clinical suspicion of COVID-19, all adult symptomatic ED patients had both a CT scan and a PCR upon arrival at the hospital. CT results were compared with PCR test (s) and diagnostic accuracy was calculated. Results: Between February 15, 2020 to July 15, 2020, 612 symptomatic ED patients were included. In total, 78.5% of patients had a positive PCR and 82.8% a positive CT, resulting in a sensitivity of 94.2%, specificity 76.4%, likelihood ratio (LR) + 2.94 and (LR) - 0.18.The PPV was 76.7% and NPV 94.1%. The sensitivity of the CT tended to be higher (100.0%) in those with severe risk pneumonia than in patients with low/medium risk pneumonia (90.3%, P = 0.42). In patients with sepsis, sensitivity was significantly higher than in those without sepsis (99.5 vs. 63.5%, P < 0.001). The diagnostic ability of chest CT was found to be rather high with 92.1%, concordance rate between findings of CT and PCR. In 48 (7.8%) patients discordant findings between CT and PCR were observed. The positive predictive values (PPV) and accuracy of chest CT in diagnosing COVID-19 were higher in patients ≥60 years than that in patients <60 years (P = 0.001 and 0.004, respectively). The specificity and NPV of chest CT in diagnosing COVID-19 were greater for women than that for men (P = 0.007 and 0.03, respectively); and no difference existed for sensitivity, PPV and accuracy (P = 0.43, 0.69 and 0.31, respectively). In most cases, the CT scan was considered suspicious for COVID-19, while the PCR was negative (37/48, 70.8%). In the majority of these, the diagnosis at discharge was pulmonary infection (n = 26; 74.3%). The current study included repeated PCRs and explored discordant test results, which showed that in about 45.9% of patients with false-positive CT scans, other viral pathogens were detected. The false-positive rate of CT findings in the diagnosis of COVID-19 pneumonia was 7.2%. Conclusion: High diagnostic accuracy of chest CT findings with typical and relatively atypical CT manifestations of COVID-19 leads to a low rate of missed diagnosis. Normal chest CT can be found in RT-PCR positive COVID-19 cases, and typical CT manifestations can be found in RT-PCR negative cases. Therefore, a combination of both CT and RT-PCR for future follow-up, management and medical surveillance is recommended considering the false-positive results of chest CT in the diagnosis of COVID-19 pneumonia.
Publication History
Received: 16 May 2020
Accepted: 02 January 2020
Article published online:
13 July 2021
© 2021. Indian Radiological Association. This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial-License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/).
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