chest computed tomography for the diagnosis of patients with coronavirus disease 2019 CORD-Papers-2021-10-25 (Version 1)

Title: Chest Computed Tomography for the Diagnosis of Patients with Coronavirus Disease 2019 (COVID-19): A Rapid Review and Meta-Analysis
Abstract: Background: The outbreak of the coronavirus disease 2019 (COVID-19) has had a massive impact on the whole world. Computed tomography (CT) has been widely used in the diagnosis of this novel pneumonia. This study aims to understand the role of CT for the diagnosis and the main imaging manifestations of patients with COVID-19. Methods: We conducted a rapid review and meta-analysis on studies about the use of chest CT for the diagnosis of COVID-19. We comprehensively searched databases and preprint servers on chest CT for patients with COVID-19 between 1 January 2020 and 31 March 2020. The primary outcome was the sensitivity of chest CT imaging. We also conducted subgroup analyses and evaluated the quality of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results: A total of 104 studies with 5694 patients were included. Using RT-PCR results as reference, a meta-analysis based on 64 studies estimated the sensitivity of chest CT imaging in COVID-19 was 99% (95% CI, 0.97-1.00). If case reports were excluded, the sensitivity in case series was 96% (95% CI, 0.93-0.99). The sensitivity of CT scan in confirmed patients under 18 years old was only 66% (95% CI, 0.11-1.00). The most common imaging manifestation was ground-glass opacities (GGO) which was found in 75% (95% CI, 0.68-0.82) of the patients. The pooled probability of bilateral involvement was 84% (95% CI, 0.81-0.88). The most commonly involved lobes were the right lower lobe (84%, 95% CI, 0.78-0.90) and left lower lobe (81%, 95% CI, 0.74-0.87). The quality of evidence was low across all outcomes. Conclusions: In conclusion, this meta-analysis indicated that chest CT scan had a high sensitivity in diagnosis of patients with COVID-19. Therefore, CT can potentially be used to assist in the diagnosis of COVID-19.
Published: 4/17/2020
DOI: 10.1101/2020.04.14.20064733
DOI_URL: http://doi.org/10.1101/2020.04.14.20064733
Author Name: Lv, M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lv_m
Author Name: Wang, M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_m
Author Name: Yang, N
Author link: https://covid19-data.nist.gov/pid/rest/local/author/yang_n
Author Name: Luo, X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/luo_x
Author Name: Li, W
Author link: https://covid19-data.nist.gov/pid/rest/local/author/li_w
Author Name: Chen, X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chen_x
Author Name: Liu, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_y
Author Name: Ren, M
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ren_m
Author Name: Zhang, X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/zhang_x
Author Name: Wang, L
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_l
Author Name: Ma, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ma_y
Author Name: Lei, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lei_j
Author Name: Fukuoka, T
Author link: https://covid19-data.nist.gov/pid/rest/local/author/fukuoka_t
Author Name: Ahn, H S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/ahn_h_s
Author Name: Lee, M S
Author link: https://covid19-data.nist.gov/pid/rest/local/author/lee_m_s
Author Name: Luo, Z
Author link: https://covid19-data.nist.gov/pid/rest/local/author/luo_z
Author Name: Chen, Y
Author link: https://covid19-data.nist.gov/pid/rest/local/author/chen_y
Author Name: Liu, E
Author link: https://covid19-data.nist.gov/pid/rest/local/author/liu_e
Author Name: Tian, J
Author link: https://covid19-data.nist.gov/pid/rest/local/author/tian_j
Author Name: Wang, X
Author link: https://covid19-data.nist.gov/pid/rest/local/author/wang_x
sha: e3f209f007921fcbe574781b183e4501183ff907
license: medrxiv
source_x: MedRxiv; WHO
source_x_url: https://www.who.int/
url: https://doi.org/10.1101/2020.04.14.20064733 http://medrxiv.org/cgi/content/short/2020.04.14.20064733v1?rss=1
has_full_text: TRUE
Keywords Extracted from Text Content: CIs children coronavirus disease 2019 COVID-19 lobes chest CT CT Freeman-Tukey left lower lobe patients ≤18 medRxiv preprint pleural extract lung lesions https://www.biorxiv.org/ human Wuhan seafood https://www.medrxiv.org/ medRxiv preprint 85 case(s Wuhan-Cov bioRxiv lobe patient children medRxiv preprint 1 1 COVID-19 patients people 96.74 SARS-CoV-2 participants Patients CBM lesions Wuhan Coronavirus bronchogram chest CT medRxiv preprint medRxiv interlobular septum lymphadenopathy M Lv GGO coronavirus I 2 94.32 peripheral https://doi.org/10.1101/2020.04.14.20064733 doi medRxiv preprint Figure 2 lobes Coronavirus disease 2019 left lower lobe Geneva Estill
Extracted Text Content in Record: First 5000 Characters:The outbreak of the coronavirus disease 2019 has had a massive impact on the whole world. Computed tomography (CT) has been widely used in the diagnosis of this novel pneumonia. This study aims to understand the role of CT for the diagnosis and the main imaging manifestations of patients with COVID-19. We conducted a rapid review and meta-analysis on studies about the use of chest CT for the diagnosis of COVID-19. We comprehensively searched databases and preprint servers on chest CT for patients with COVID-19 between 1 January 2020 and 31 March 2020. The primary outcome was the sensitivity of chest CT imaging. We also conducted subgroup analyses and evaluated the quality of evidence using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results: A total of 104 studies with 5694 patients were included. Using RT-PCR results as reference, a meta-analysis based on 64 studies estimated the sensitivity of chest CT imaging in COVID-19 was 99% (95% CI, 0.97-1.00). If case reports were excluded, the sensitivity in case series was 96% (95% CI, 0.93-0.99). The sensitivity of CT scan in confirmed patients under 18 years old was only 66% (95% CI, 0.11-1.00). The most common imaging manifestation was ground-glass opacities (GGO) which was found in 75% (95% CI, 0.68-0.82) of the patients. The pooled probability of bilateral involvement was 84% (95% CI, 0.81-0.88). The most commonly involved lobes were the right lower lobe (84%, 95% CI, 0.78-0.90) and left lower lobe (81%, 95% CI, 0.74-0.87). The quality of evidence was low across all outcomes. In conclusion, this meta-analysis indicated that chest CT scan had a high sensitivity in diagnosis of patients with COVID-19. Therefore, CT can potentially be used to assist in the diagnosis of COVID-19. : medRxiv preprint We performed a meta-analysis using STATA 15.1. We present data from eligible studies in an evidence table and using descriptive statistics. The percentages of the sensitivity of CT examination and the probability of imaging manifestations in patients with COVID-19 were computed using the metaprop command (Stata) for the meta-analysis of proportions. metaprop allows the inclusion of studies with proportions equal to 0 or 100% and avoids CIs surpassing the 0 to 1 range, where normal approximation procedures often break down. It achieves this by using the binomial distribution to model within-study variability or by allowing Freeman-Tukey double arcsine transformation to stabilize the variances. We generated a forest plot to show the individual and pooled probabilities of positive initial CT examination, their 95% CI and study weights. We conducted subgroup analyses based on case series, and children (≤18). Quality of the evidence assessment In early January 2020, a disease caused by a novel coronavirus rapidly spread and across the whole world. The disease was later named as Coronavirus disease 2019 (COVID-19). On 11 March 2020, COVID-19 was declared by the WHO a pandemic (1) . As of 12 April 2020, the World Health Organization (WHO) has reported 1,614,951 confirmed cases across more than 200 countries (2) . COVID-19 is a respiratory illness that can spread from human to human. Patients with the disease have mild to severe respiratory illness with symptoms such as fever, cough, dyspnea, as well as other non-specific symptoms including, fatigue, myalgia, and headache (3) (4) (5) . Based on current knowledge, the median basic reproductive number (R 0 ) value of COVID-19 is 5.7 (95%CI 3.8-8.9) (6) , which means that COVID-19 is highly contagious. COVID-19 is mainly diagnosed by viral nucleic acid test, immunological detection, and radiological examination. However, the sensitivity of the nucleic acid test may be as low as 50% (7) , and some diagnoses may be missed. As a respiratory disease, imaging detection plays an important role in the diagnosis of COVID-19. On one hand, when COVID-19 cannot be diagnosed by nucleic acid, CT can be used as an auxiliary diagnostic method; on the other hand, CT can show lesions and also plays an important role in patient follow-up. Since February 2020, several case-control studies (8, 9) , case series (10, 11) , and case-report (12, 13) of CT diagnosis of COVID-19 have been published. However, there is no systematic review and meta-analysis to find out the performance of chest CT in the diagnosis of COVID-19. We therefore conducted this study to estimate the sensitivity of chest CT and the probability of imaging findings in cases with COVID-19 to guide the diagnosis of COVID-19. We searched Medline (via PubMed), Embase, Cochrane library, Web of Science, China Biology Medicine disc (CBM), China National Knowledge Infrastructure (CNKI) between 1 January 2020 and 31 March 2020, using terms with ("2019-novel coronavirus" OR "Novel CoV" OR "2019-nCoV" OR "2019-CoV" OR "Wuhan-Cov" OR "Wuhan Coronavirus" OR "Wuhan seafood market pneumonia virus" OR COVID-19 OR SARS-CoV-2 OR "novel coronavirus pneumonia"
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