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Thoracic imaging tests for the diagnosis of COVID-19.
Cochrane Database Syst Rev. 2020 09 30; 9:CD013639.CD

Abstract

BACKGROUND

The diagnosis of infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents major challenges. Reverse transcriptase polymerase chain reaction (RT-PCR) testing is used to diagnose a current infection, but its utility as a reference standard is constrained by sampling errors, limited sensitivity (71% to 98%), and dependence on the timing of specimen collection. Chest imaging tests are being used in the diagnosis of COVID-19 disease, or when RT-PCR testing is unavailable.

OBJECTIVES

To determine the diagnostic accuracy of chest imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected or confirmed COVID-19.

SEARCH METHODS

We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, and The Stephen B. Thacker CDC Library. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. We conducted searches for this review iteration up to 5 May 2020.

SELECTION CRITERIA

We included studies of all designs that produce estimates of test accuracy or provide data from which estimates can be computed. We included two types of cross-sectional designs: a) where all patients suspected of the target condition enter the study through the same route and b) where it is not clear up front who has and who does not have the target condition, or where the patients with the target condition are recruited in a different way or from a different population from the patients without the target condition. When studies used a variety of reference standards, we included all of them.

DATA COLLECTION AND ANALYSIS

We screened studies and extracted data independently, in duplicate. We also assessed the risk of bias and applicability concerns independently, in duplicate, using the QUADAS-2 checklist and presented the results of estimated sensitivity and specificity, using paired forest plots, and summarised in tables. We used a hierarchical meta-analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs).

MAIN RESULTS

We included 84 studies, falling into two categories: studies with participants with confirmed diagnoses of COVID-19 at the time of recruitment (71 studies with 6331 participants) and studies with participants suspected of COVID-19 (13 studies with 1948 participants, including three case-control studies with 549 cases and controls). Chest CT was evaluated in 78 studies (8105 participants), chest X-ray in nine studies (682 COVID-19 cases), and chest ultrasound in two studies (32 COVID-19 cases). All evaluations of chest X-ray and ultrasound were conducted in studies with confirmed diagnoses only. Twenty-five per cent (21/84) of all studies were available only as preprints, 15/71 studies in the confirmed cases group and 6/13 of the studies in the suspected group. Among 71 studies that included confirmed cases, 41 studies had included symptomatic cases only, 25 studies had included cases regardless of their symptoms, five studies had included asymptomatic cases only, three of which included a combination of confirmed and suspected cases. Seventy studies were conducted in Asia, 2 in Europe, 2 in North America and one in South America. Fifty-one studies included inpatients while the remaining 24 studies were conducted in mixed or unclear settings. Risk of bias was high in most studies, mainly due to concerns about selection of participants and applicability. Among the 13 studies that included suspected cases, nine studies were conducted in Asia, and one in Europe. Seven studies included inpatients while the remaining three studies were conducted in mixed or unclear settings. In studies that included confirmed cases the pooled sensitivity of chest CT was 93.1% (95%CI: 90.2 - 95.0 (65 studies, 5759 cases); and for X-ray 82.1% (95%CI: 62.5 to 92.7 (9 studies, 682 cases). Heterogeneity judged by visual assessment of the ROC plots was considerable. Two studies evaluated the diagnostic accuracy of point-of-care ultrasound and both reported zero false negatives (with 10 and 22 participants having undergone ultrasound, respectively). These studies only reported True Positive and False Negative data, therefore it was not possible to pool and derive estimates of specificity. In studies that included suspected cases, the pooled sensitivity of CT was 86.2% (95%CI: 71.9 to 93.8 (13 studies, 2346 participants) and specificity was 18.1% (95%CI: 3.71 to 55.8). Heterogeneity judged by visual assessment of the forest plots was high. Chest CT may give approximately the same proportion of positive results for patients with and without a SARS-CoV-2 infection: the chances of getting a positive CT result are 86% (95% CI: 72 to 94) in patient with a SARS-CoV-2 infection and 82% (95% CI: 44 to 96) in patients without.

AUTHORS' CONCLUSIONS

The uncertainty resulting from the poor study quality and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results. Our findings indicate that chest CT is sensitive but not specific for the diagnosis of COVID-19 in suspected patients, meaning that CT may not be capable of differentiating SARS-CoV-2 infection from other causes of respiratory illness. This low specificity could also be the result of the poor sensitivity of the reference standard (RT-PCR), as CT could potentially be more sensitive than RT-PCR in some cases. Because of limited data, accuracy estimates of chest X-ray and ultrasound of the lungs for the diagnosis of COVID-19 should be carefully interpreted. Future diagnostic accuracy studies should avoid cases-only studies and pre-define positive imaging findings. Planned updates of this review will aim to: increase precision around the accuracy estimates for CT (ideally with low risk of bias studies); obtain further data to inform accuracy of chest X rays and ultrasound; and continue to search for studies that fulfil secondary objectives to inform the utility of imaging along different diagnostic pathways.

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Publisher Full Text (DOI)

Authors+Show Affiliations

Salameh JP
Department of Radiology, University of Ottawa, Ottawa, Canada. Faculty of Health Sciences, Queen's University, Kingston, Canada.
Leeflang MM
Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands.
Hooft L
Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Islam N
Department of Radiology, University of Ottawa, Ottawa, Canada.
McGrath TA
Department of Radiology, University of Ottawa, Ottawa, Canada.
van der Pol CB
Department of Radiology, McMaster University, Hamilton, Canada.
Frank RA
Department of Radiology, University of Ottawa, Ottawa, Canada.
Prager R
Department of Medicine, University of Ottawa, Ottawa, Canada.
Hare SS
Department of Radiology, Royal Free London NHS Trust, London, UK.
Dennie C
Department of Radiology, University of Ottawa, Ottawa, Canada. Department of Medical Imaging, The Ottawa Hospital, Ottawa, Canada.
Spijker R
Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands. Medical Library, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Amsterdam, Netherlands.
Deeks JJ
Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK. NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.
Dinnes J
Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK. NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.
Jenniskens K
Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Korevaar DA
Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands.
Cohen JF
Obstetrical, Perinatal and Pediatric Epidemiology Research Team (EPOPé), Centre de Recherche Épidémiologie et Statistique Sorbonne Paris Cité (CRESS), Inserm UMR1153, Paris Descartes University, Paris, France.
Van den Bruel A
NIHR Diagnostic Evidence Cooperative, University of Oxford, Oxford, UK.
Takwoingi Y
Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK. NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.
van de Wijgert J
Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands. Institute of Infection, Veterinary, and Ecological Sciences, University of Liverpool, Liverpool, UK.
Damen JA
Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Wang J
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrehct, Netherlands.
Cochrane COVID-19 Diagnostic Test Accuracy Group
NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK.
McInnes MD
Department of Radiology, University of Ottawa, Ottawa, Canada.

MeSH

AdultBetacoronavirusCOVID-19COVID-19 TestingChildClinical Laboratory TechniquesCoronavirus InfectionsHumansLungPandemicsPneumonia, ViralRadiography, ThoracicSARS-CoV-2Sensitivity and SpecificityTomography, X-Ray ComputedUltrasonography

Pub Type(s)

Journal Article
Meta-Analysis
Research Support, Non-U.S. Gov't
Systematic Review

Language

eng

PubMed ID

32997361

Citation

Salameh, Jean-Paul, et al. "Thoracic Imaging Tests for the Diagnosis of COVID-19." The Cochrane Database of Systematic Reviews, vol. 9, 2020, p. CD013639.
Salameh JP, Leeflang MM, Hooft L, et al. Thoracic imaging tests for the diagnosis of COVID-19. Cochrane Database Syst Rev. 2020;9:CD013639.
Salameh, J. P., Leeflang, M. M., Hooft, L., Islam, N., McGrath, T. A., van der Pol, C. B., Frank, R. A., Prager, R., Hare, S. S., Dennie, C., Spijker, R., Deeks, J. J., Dinnes, J., Jenniskens, K., Korevaar, D. A., Cohen, J. F., Van den Bruel, A., Takwoingi, Y., van de Wijgert, J., ... McInnes, M. D. (2020). Thoracic imaging tests for the diagnosis of COVID-19. The Cochrane Database of Systematic Reviews, 9, CD013639. https://doi.org/10.1002/14651858.CD013639.pub2
Salameh JP, et al. Thoracic Imaging Tests for the Diagnosis of COVID-19. Cochrane Database Syst Rev. 2020 09 30;9:CD013639. PubMed PMID: 32997361.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Thoracic imaging tests for the diagnosis of COVID-19. AU - Salameh,Jean-Paul, AU - Leeflang,Mariska Mg, AU - Hooft,Lotty, AU - Islam,Nayaar, AU - McGrath,Trevor A, AU - van der Pol,Christian B, AU - Frank,Robert A, AU - Prager,Ross, AU - Hare,Samanjit S, AU - Dennie,Carole, AU - Spijker,René, AU - Deeks,Jonathan J, AU - Dinnes,Jacqueline, AU - Jenniskens,Kevin, AU - Korevaar,Daniël A, AU - Cohen,Jérémie F, AU - Van den Bruel,Ann, AU - Takwoingi,Yemisi, AU - van de Wijgert,Janneke, AU - Damen,Johanna Aag, AU - Wang,Junfeng, AU - ,, AU - McInnes,Matthew Df, Y1 - 2020/09/30/ PY - 2020/9/30/entrez PY - 2020/10/1/pubmed PY - 2020/10/21/medline SP - CD013639 EP - CD013639 JF - The Cochrane database of systematic reviews JO - Cochrane Database Syst Rev VL - 9 N2 - BACKGROUND: The diagnosis of infection by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents major challenges. Reverse transcriptase polymerase chain reaction (RT-PCR) testing is used to diagnose a current infection, but its utility as a reference standard is constrained by sampling errors, limited sensitivity (71% to 98%), and dependence on the timing of specimen collection. Chest imaging tests are being used in the diagnosis of COVID-19 disease, or when RT-PCR testing is unavailable. OBJECTIVES: To determine the diagnostic accuracy of chest imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected or confirmed COVID-19. SEARCH METHODS: We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, and The Stephen B. Thacker CDC Library. In addition, we checked repositories of COVID-19 publications. We did not apply any language restrictions. We conducted searches for this review iteration up to 5 May 2020. SELECTION CRITERIA: We included studies of all designs that produce estimates of test accuracy or provide data from which estimates can be computed. We included two types of cross-sectional designs: a) where all patients suspected of the target condition enter the study through the same route and b) where it is not clear up front who has and who does not have the target condition, or where the patients with the target condition are recruited in a different way or from a different population from the patients without the target condition. When studies used a variety of reference standards, we included all of them. DATA COLLECTION AND ANALYSIS: We screened studies and extracted data independently, in duplicate. We also assessed the risk of bias and applicability concerns independently, in duplicate, using the QUADAS-2 checklist and presented the results of estimated sensitivity and specificity, using paired forest plots, and summarised in tables. We used a hierarchical meta-analysis model where appropriate. We presented uncertainty of the accuracy estimates using 95% confidence intervals (CIs). MAIN RESULTS: We included 84 studies, falling into two categories: studies with participants with confirmed diagnoses of COVID-19 at the time of recruitment (71 studies with 6331 participants) and studies with participants suspected of COVID-19 (13 studies with 1948 participants, including three case-control studies with 549 cases and controls). Chest CT was evaluated in 78 studies (8105 participants), chest X-ray in nine studies (682 COVID-19 cases), and chest ultrasound in two studies (32 COVID-19 cases). All evaluations of chest X-ray and ultrasound were conducted in studies with confirmed diagnoses only. Twenty-five per cent (21/84) of all studies were available only as preprints, 15/71 studies in the confirmed cases group and 6/13 of the studies in the suspected group. Among 71 studies that included confirmed cases, 41 studies had included symptomatic cases only, 25 studies had included cases regardless of their symptoms, five studies had included asymptomatic cases only, three of which included a combination of confirmed and suspected cases. Seventy studies were conducted in Asia, 2 in Europe, 2 in North America and one in South America. Fifty-one studies included inpatients while the remaining 24 studies were conducted in mixed or unclear settings. Risk of bias was high in most studies, mainly due to concerns about selection of participants and applicability. Among the 13 studies that included suspected cases, nine studies were conducted in Asia, and one in Europe. Seven studies included inpatients while the remaining three studies were conducted in mixed or unclear settings. In studies that included confirmed cases the pooled sensitivity of chest CT was 93.1% (95%CI: 90.2 - 95.0 (65 studies, 5759 cases); and for X-ray 82.1% (95%CI: 62.5 to 92.7 (9 studies, 682 cases). Heterogeneity judged by visual assessment of the ROC plots was considerable. Two studies evaluated the diagnostic accuracy of point-of-care ultrasound and both reported zero false negatives (with 10 and 22 participants having undergone ultrasound, respectively). These studies only reported True Positive and False Negative data, therefore it was not possible to pool and derive estimates of specificity. In studies that included suspected cases, the pooled sensitivity of CT was 86.2% (95%CI: 71.9 to 93.8 (13 studies, 2346 participants) and specificity was 18.1% (95%CI: 3.71 to 55.8). Heterogeneity judged by visual assessment of the forest plots was high. Chest CT may give approximately the same proportion of positive results for patients with and without a SARS-CoV-2 infection: the chances of getting a positive CT result are 86% (95% CI: 72 to 94) in patient with a SARS-CoV-2 infection and 82% (95% CI: 44 to 96) in patients without. AUTHORS' CONCLUSIONS: The uncertainty resulting from the poor study quality and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results. Our findings indicate that chest CT is sensitive but not specific for the diagnosis of COVID-19 in suspected patients, meaning that CT may not be capable of differentiating SARS-CoV-2 infection from other causes of respiratory illness. This low specificity could also be the result of the poor sensitivity of the reference standard (RT-PCR), as CT could potentially be more sensitive than RT-PCR in some cases. Because of limited data, accuracy estimates of chest X-ray and ultrasound of the lungs for the diagnosis of COVID-19 should be carefully interpreted. Future diagnostic accuracy studies should avoid cases-only studies and pre-define positive imaging findings. Planned updates of this review will aim to: increase precision around the accuracy estimates for CT (ideally with low risk of bias studies); obtain further data to inform accuracy of chest X rays and ultrasound; and continue to search for studies that fulfil secondary objectives to inform the utility of imaging along different diagnostic pathways. SN - 1469-493X UR - https://www.unboundmedicine.com/medline/citation/32997361/Thoracic_imaging_tests_for_the_diagnosis_of_COVID_19_ DB - PRIME DP - Unbound Medicine ER -