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Meta-Analysis
. 2021 Mar 16;3(3):CD013639.
doi: 10.1002/14651858.CD013639.pub4.

Thoracic imaging tests for the diagnosis of COVID-19

Nayaar Islam  1   2 , Sanam Ebrahimzadeh  1 , Jean-Paul Salameh  1 , Sakib Kazi  1 , Nicholas Fabiano  3 , Lee Treanor  3 , Marissa Absi  3 , Zachary Hallgrimson  3 , Mariska Mg Leeflang  4 , Lotty Hooft  5 , Christian B van der Pol  6 , Ross Prager  7 , Samanjit S Hare  8 , Carole Dennie  1   9 , René Spijker  5   10 , Jonathan J Deeks  11   12 , Jacqueline Dinnes  11   13 , Kevin Jenniskens  14 , Daniël A Korevaar  15 , Jérémie F Cohen  16 , Ann Van den Bruel  17 , Yemisi Takwoingi  11   12 , Janneke van de Wijgert  14   18 , Johanna Aag Damen  14 , Junfeng Wang  19 , Matthew Df McInnes  3   2 ; Cochrane COVID-19 Diagnostic Test Accuracy Group  11
Affiliations
Meta-Analysis

Thoracic imaging tests for the diagnosis of COVID-19

Nayaar Islam et al. Cochrane Database Syst Rev. .

Update in

  • Thoracic imaging tests for the diagnosis of COVID-19.
    Ebrahimzadeh S, Islam N, Dawit H, Salameh JP, Kazi S, Fabiano N, Treanor L, Absi M, Ahmad F, Rooprai P, Al Khalil A, Harper K, Kamra N, Leeflang MM, Hooft L, van der Pol CB, Prager R, Hare SS, Dennie C, Spijker R, Deeks JJ, Dinnes J, Jenniskens K, Korevaar DA, Cohen JF, Van den Bruel A, Takwoingi Y, van de Wijgert J, Wang J, Pena E, Sabongui S, McInnes MD; Cochrane COVID-19 Diagnostic Test Accuracy Group. Ebrahimzadeh S, et al. Cochrane Database Syst Rev. 2022 May 16;5(5):CD013639. doi: 10.1002/14651858.CD013639.pub5. Cochrane Database Syst Rev. 2022. PMID: 35575286 Free PMC article.

Abstract

Background: The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Our 2020 edition of this review showed thoracic (chest) imaging to be sensitive and moderately specific in the diagnosis of coronavirus disease 2019 (COVID-19). In this update, we include new relevant studies, and have removed studies with case-control designs, and those not intended to be diagnostic test accuracy studies.

Objectives: To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected COVID-19.

Search methods: 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 30 September 2020. We did not apply any language restrictions.

Selection criteria: We included studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19 and that reported estimates of test accuracy or provided data from which we could compute estimates.

Data collection and analysis: The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using the QUADAS-2 domain-list. We presented the results of estimated sensitivity and specificity using paired forest plots, and we summarised pooled estimates in tables. We used a bivariate meta-analysis model where appropriate. We presented the uncertainty of accuracy estimates using 95% confidence intervals (CIs).

Main results: We included 51 studies with 19,775 participants suspected of having COVID-19, of whom 10,155 (51%) had a final diagnosis of COVID-19. Forty-seven studies evaluated one imaging modality each, and four studies evaluated two imaging modalities each. All studies used RT-PCR as the reference standard for the diagnosis of COVID-19, with 47 studies using only RT-PCR and four studies using a combination of RT-PCR and other criteria (such as clinical signs, imaging tests, positive contacts, and follow-up phone calls) as the reference standard. Studies were conducted in Europe (33), Asia (13), North America (3) and South America (2); including only adults (26), all ages (21), children only (1), adults over 70 years (1), and unclear (2); in inpatients (2), outpatients (32), and setting unclear (17). Risk of bias was high or unclear in thirty-two (63%) studies with respect to participant selection, 40 (78%) studies with respect to reference standard, 30 (59%) studies with respect to index test, and 24 (47%) studies with respect to participant flow. For chest CT (41 studies, 16,133 participants, 8110 (50%) cases), the sensitivity ranged from 56.3% to 100%, and specificity ranged from 25.4% to 97.4%. The pooled sensitivity of chest CT was 87.9% (95% CI 84.6 to 90.6) and the pooled specificity was 80.0% (95% CI 74.9 to 84.3). There was no statistical evidence indicating that reference standard conduct and definition for index test positivity were sources of heterogeneity for CT studies. Nine chest CT studies (2807 participants, 1139 (41%) cases) used the COVID-19 Reporting and Data System (CO-RADS) scoring system, which has five thresholds to define index test positivity. At a CO-RADS threshold of 5 (7 studies), the sensitivity ranged from 41.5% to 77.9% and the pooled sensitivity was 67.0% (95% CI 56.4 to 76.2); the specificity ranged from 83.5% to 96.2%; and the pooled specificity was 91.3% (95% CI 87.6 to 94.0). At a CO-RADS threshold of 4 (7 studies), the sensitivity ranged from 56.3% to 92.9% and the pooled sensitivity was 83.5% (95% CI 74.4 to 89.7); the specificity ranged from 77.2% to 90.4% and the pooled specificity was 83.6% (95% CI 80.5 to 86.4). For chest X-ray (9 studies, 3694 participants, 2111 (57%) cases) the sensitivity ranged from 51.9% to 94.4% and specificity ranged from 40.4% to 88.9%. The pooled sensitivity of chest X-ray was 80.6% (95% CI 69.1 to 88.6) and the pooled specificity was 71.5% (95% CI 59.8 to 80.8). For ultrasound of the lungs (5 studies, 446 participants, 211 (47%) cases) the sensitivity ranged from 68.2% to 96.8% and specificity ranged from 21.3% to 78.9%. The pooled sensitivity of ultrasound was 86.4% (95% CI 72.7 to 93.9) and the pooled specificity was 54.6% (95% CI 35.3 to 72.6). Based on an indirect comparison using all included studies, chest CT had a higher specificity than ultrasound. For indirect comparisons of chest CT and chest X-ray, or chest X-ray and ultrasound, the data did not show differences in specificity or sensitivity.

Authors' conclusions: Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19. Chest X-ray is moderately sensitive and moderately specific for the diagnosis of COVID-19. Ultrasound is sensitive but not specific for the diagnosis of COVID-19. Thus, chest CT and ultrasound may have more utility for excluding COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest in the same participant population, and implement improved reporting practices.

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Conflict of interest statement

Nayaar Islam has no known conflicts of interest.

Sanam Ebrahimzadeh has no known conflicts of interest.

Jean‐Paul Salameh has no known conflicts of interest.

Sakib Kazi has no known conflicts of interest.

Nicholas Fabiano has no known conflicts of interest.

Lee Treanor has no known conflicts of interest.

Marissa Absi has no known conflicts of interest.

Zachary Hallgrimson has no known conflicts of interest.

Mariska MG Leeflang has no known conflicts of interest.

Lotty Hooft has no known conflicts of interest.

Christian B van der Pol has no known conflicts of interest.

Ross Prager has no known conflicts of interest.

Samanjit Singh Hare has no known conflicts of interest.

Carole Dennie has no known conflicts of interest.

René Spijker: the Dutch Cochrane Centre (DCC) has received grants for performing commissioned systematic reviews. In no situation did the commissioner have any influence on the results of the work.

Jonathan J Deeks has no known conflicts of interest.

Jacqueline Dinnes has no known conflicts of interest.

Kevin Jenniskens has no known conflicts of interest.

Daniel Korevaar has no known conflicts of interest.

Jérémie F Cohen has no known conflicts of interest.

Ann Van den Bruel has no known conflicts of interest.

Yemisi Takwoingi has no known conflicts of interest.

Janneke van de Wijgert has no known conflicts of interest.

Johanna AAG Damen has no known conflicts of interest.

Junfeng Wang received a consultancy fee from Biomind, an Artificial Intelligence (AI) company providing machine intelligence solutions in medical imaging. The consultancy service was about design of clinical studies, not related to this review. The company had no influence on the results of the work.

Matthew McInnes has no known conflicts of interest.

Figures

1
1
Study flow diagram.
2
2
Risk of bias and applicability concerns graph: review authors' judgements about each domain presented as percentages across included studies.
3
3
Risk of bias and applicability concerns summary: review authors' judgements about each domain for each included study.
4
4
Forest plot of chest CT in suspected cases.
5
5
Summary ROC plot of chest CT in suspected cases. The summary point is indicated by the solid black circle, individual studies are indicated by outlined circles (scale=study sample size). The dotted border and the dashed border represent 95% confidence regions and 95% prediction regions, respectively.
6
6
Forest plot of A) chest X‐ray and B) ultrasound in suspected cases.
7
7
Summary ROC plot of chest X‐ray in suspected cases. The summary point is indicated by the solid black circle, individual studies are indicated by outlined circles (scale=study sample size). The dotted border and the dashed border represent 95% confidence regions and 95% prediction regions, respectively.
8
8
Summary ROC plot of ultrasound of the lungs in suspected cases. The summary point is indicated by the solid black circle, individual studies are indicated by outlined circles (scale=study sample size). The dotted border and the dashed border represent 95% confidence regions and 95% prediction regions, respectively.
9
9
Forest plot of chest CT studies in suspected cases that used the CO‐RADS scoring system. Threshold (i.e. 2, 3 4, or 5) indicated in parentheses ahead of Study ID. Grouped by threshold.
10
10
Pooled sensitivity and specificity estimates and 95% confidence intervals at varying CO‐RADS thresholds: CO‐RADS 2 (n = 7), CO‐RADS 3 (n = 9), CO‐RADS 4 (n = 7), and CO‐RADS 5 (n = 7).
11
11
Summary ROC plot of comparative studies: chest CT versus chest X‐ray. The dotted line connects index tests pairs evaluated in the same study (i.e. a comparative study).
12
12
Summary ROC plot of comparative studies: chest CT versus ultrasound of the lungs. The dotted lines connect index tests pairs evaluated in the same study (i.e. a comparative study).
13
13
Summary ROC plot of comparative studies: chest X‐ray versus ultrasound of the lungs. The dotted line connects index tests pairs evaluated in the same study (i.e. a comparative study).
14
14
Cumulative meta‐analysis of chest CT A) logit‐sensitivity and B) logit‐specificity. Includes 41 studies in the current review. Studies are ranked according to date of publication (1 = oldest, 41 = most recent).
15
15
Pooled sensitivity and specificity estimates and 95% confidence intervals across all versions of this review.
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1. Test
Chest CT in suspected cases
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2. Test
Chest X‐ray in suspected cases
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3. Test
Ultrasound of the lungs in suspected cases
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4. Test
CT CO‐RADS 2
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5. Test
CT CO‐RADS 3
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6. Test
CT CO‐RADS 4
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7. Test
CT CO‐RADS 5
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References

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References to other published versions of this review

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