Why is this question important?
People with suspected COVID-19 need to know quickly whether they are infected, so they can receive appropriate treatment, self-isolate, and inform close contacts.
Currently, formal diagnosis of COVID-19 requires a laboratory test (RT-PCR) of nose and throat samples. RT-PCR requires specialist equipment and takes at least 24 hours to produce a result. It is not completely accurate, and may require a second RT-PCR or a different test to confirm diagnosis.
COVID-19 is a respiratory disease. Clinicians may use chest imaging to diagnose people who have COVID-19 symptoms, while awaiting RT-PCR results or when RT-PCR results are negative, and the person has COVID-19 symptoms.
What did we want to find out?
We wanted to know whether chest imaging is accurate enough to diagnose COVID-19 in people with suspected infection. This is the first update of this review; in it we included studies in people with suspected COVID-19 only; we excluded studies in people with confirmed COVID-19.
The evidence is up to date to 22 June 2020.
What are chest imaging tests?
X-rays or scans produce an image of the organs and structures in the chest.
- X-rays (radiography) use radiation to produce a 2-D image. Usually done in hospitals, using fixed equipment by a radiographer, they can also be done on portable machines.
- Computed tomography (CT) scans use a computer to merge 2-D X-ray images and convert them to a 3-D image. They require highly specialised equipment and are done in hospital by a specialist radiographer.
- Ultrasound scans use high-frequency sound waves to produce an image. They can be done in hospital or other healthcare settings, such as a doctor’s office.
What did we do?
We searched for studies that assessed the accuracy of chest imaging to diagnose COVID-19 in people with suspected COVID-19. Studies could be of any design and take place anywhere.
What did we find?
We found 34 studies with 9339 people. All the studies confirmed SARS-CoV-2 infection using RT-PCR alone or RT-PCR with another test.
Most studies (31 studies; 8014 participants) evaluated chest CT; three evaluated chest X-rays (1243 participants) and one evaluated lung ultrasound (100 participants). Nineteen studies took place in Asia, 10 in Europe, four in North America and one in Australia. Participants were hospital inpatients (24 studies), and outpatients (4 studies); the setting was unclear in six studies.
Where four or more studies evaluated a particular type of chest imaging, we pooled their results and analysed them together.
Pooled results showed that chest CT correctly diagnosed COVID-19 in 89.9% of people who had COVID-19. However, it incorrectly identified COVID-19 in 38% of people who did not have COVID-19.
Correct diagnosis of COVID-19 with chest X-rays ranged from 57% to 89%. However, incorrect diagnosis of COVID-19 in people who did not have COVID-19 ranged from 11% to 89%.
Lung ultrasound correctly diagnosed COVID-19 in 96% of people with COVID-19. However, it incorrectly diagnosed COVID-19 in 38% of people who did not have COVID-19.
How reliable are the results?
The studies differed from each other and used different methods to report their results. About a quarter of the studies were published as preprints, which do not undergo the same rigorous checks as published studies. We cannot draw confident conclusions based on results from studies in this review.
What does this mean?
The evidence suggests that chest CT is better at ruling out COVID-19 infection than distinguishing it from other respiratory problems. So, its usefulness may be limited to excluding COVID-19 infection rather than distinguishing it from other causes of lung infection.
Chest CT accuracy has improved since our first review, perhaps because radiologists now use better definitions of a positive diagnosis. The stage of the pandemic may also have an effect – with later studies building on knowledge and experience gained earlier.
We plan to update this review as more evidence becomes available. Future studies should predefine what a positive test is, and compare different types of imaging tests on similar groups of people.
Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID‐19 in suspected patients, meaning that CT may have limited capability in differentiating SARS‐CoV‐2 infection from other causes of respiratory illness. However, we are limited in our confidence in these results due to the poor study quality and the heterogeneity of included studies. Because of limited data, accuracy estimates of chest X‐ray and ultrasound of the lungs for the diagnosis of suspected COVID‐19 cases should be carefully interpreted.
Future diagnostic accuracy studies should pre‐define positive imaging findings, include direct comparisons of the various modalities of interest on the same participant population, and implement improved reporting practices. Planned updates of this review will aim to: increase precision around the accuracy estimates for chest CT (ideally with low risk of bias studies); obtain further data to inform accuracy of chest X-rays and ultrasound; and obtain data to further fulfil secondary objectives (e.g. ‘threshold’ effects, comparing accuracy estimates across different imaging modalities) to inform the utility of imaging along different diagnostic pathways.
The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Early research showed thoracic (chest) imaging to be sensitive but not specific in the diagnosis of coronavirus disease 2019 (COVID-19). However, this is a rapidly developing field and these findings need to be re-evaluated in the light of new research. This is the first update of this 'living systematic review'. This update focuses on people suspected of having COVID-19 and excludes studies with only confirmed COVID-19 participants.
To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X‐ray and ultrasound) in people with suspected COVID‐19.
We searched the COVID‐19 Living Evidence Database from the University of Bern, the Cochrane COVID‐19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID‐19 publications through to 22 June 2020. We did not apply any language restrictions.
We included studies of all designs that recruited participants of any age group suspected to have COVID-19, and which reported estimates of test accuracy, or provided data from which estimates could be computed. When studies used a variety of reference standards, we retained the classification of participants as COVID-19 positive or negative as used in the study.
We screened studies, extracted data, and assessed the risk of bias and applicability concerns using the QUADAS‐2 domain-list independently, in duplicate. We categorised included studies into three groups based on classification of index test results: studies that reported specific criteria for index test positivity (group 1); studies that did not report specific criteria, but had the test reader(s) explicitly classify the imaging test result as either COVID-19 positive or negative (group 2); and studies that reported an overview of index test findings, without explicitly classifying the imaging test as either COVID-19 positive or negative (group 3). We presented the results of estimated sensitivity and specificity using paired forest plots, and summarised in tables. We used a bivariate meta‐analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs).
We included 34 studies: 30 were cross-sectional studies with 8491 participants suspected of COVID-19, of which 4575 (54%) had a final diagnosis of COVID-19; four were case-control studies with 848 cases and controls in total, of which 464 (55%) had a final diagnosis of COVID-19. Chest CT was evaluated in 31 studies (8014 participants, 4224 (53%) cases), chest X-ray in three studies (1243 participants, 784 (63%) cases), and ultrasound of the lungs in one study (100 participants, 31 (31%) cases).
Twenty‐six per cent (9/34) of all studies were available only as preprints. Nineteen studies were conducted in Asia, 10 in Europe, four in North America and one in Australia. Sixteen studies included only adults, 15 studies included both adults and children and one included only children. Two studies did not report the ages of participants. Twenty-four studies included inpatients, four studies included outpatients, while the remaining six studies were conducted in unclear settings. The majority of included studies had a high or unclear risk of bias with respect to participant selection, index test, reference standard, and participant flow.
For chest CT in suspected COVID-19 participants (31 studies, 8014 participants, 4224 (53%) cases) the sensitivity ranged from 57.4% to 100%, and specificity ranged from 0% to 96.0%. The pooled sensitivity of chest CT in suspected COVID-19 participants was 89.9% (95% CI 85.7 to 92.9) and the pooled specificity was 61.1% (95% CI 42.3 to 77.1).
Sensitivity analyses showed that when the studies from China were excluded, the studies from other countries demonstrated higher specificity compared to the overall included studies. When studies that did not classify index tests as positive or negative for COVID-19 (group 3) were excluded, the remaining studies (groups 1 and 2) demonstrated higher specificity compared to the overall included studies. Sensitivity analyses limited to cross-sectional studies, or studies where at least two reverse transcriptase polymerase chain reaction (RT-PCR) tests were conducted if the first was negative, did not substantively alter the accuracy estimates. We did not identify publication status as a source of heterogeneity.
For chest X-ray in suspected COVID-19 participants (3 studies, 1243 participants, 784 (63%) cases) the sensitivity ranged from 56.9% to 89.0% and specificity from 11.1% to 88.9%. The sensitivity and specificity of ultrasound of the lungs in suspected COVID-19 participants (1 study, 100 participants, 31 (31%) cases) were 96.8% and 62.3%, respectively. We could not perform a meta-analysis for chest X-ray or ultrasound due to the limited number of included studies.