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Table of Contents
Year : 2021  |  Volume : 4  |  Issue : 1  |  Page : 78-87

Assessment of COVID-19 severity using computed tomography imaging: A systematic review and meta-analysis

1 Sharma Diagnostic Centre, Wardha, Maharashtra, India
2 Tata Memorial Centre, Tata Memorial Centre and Homi Bhabha National Institute, Mumbai, Maharashtra, India
3 Tata Memorial Centre-ACTREC, Tata Memorial Centre and Homi Bhabha National Institute, Mumbai, Maharashtra, India
4 Centre for Cancer Epidemiology, Tata Memorial Centre and Homi Bhabha National Institute, Mumbai, Maharashtra, India

Date of Submission19-Sep-2020
Date of Decision21-Nov-2020
Date of Acceptance06-Mar-2021
Date of Web Publication26-Mar-2021

Correspondence Address:
Abhishek Mahajan
Department of Radiodiagnosis, Tata Memorial Hospital, HNBI University, Mumbai
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/crst.crst_292_20

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Computed tomography (CT) imaging has been reported to be a reliable tool for the evaluation of suspected cases and follow-up of confirmed cases of coronavirus disease 2019 (COVID-19). Despite the generation of a considerable amount of imaging data related to COVID-19, there is a need for an updated systematic review and meta-analysis pertaining to the questions of clinical significance. We aimed to analyze the correlation between abnormal chest CT findings and disease severity in patients with COVID-19. We searched for case series/studies published in the English language until March 24, 2020 that reported the clinical and chest CT imaging features of confirmed cases of COVID-19 in the PubMed database. A total of 208 studies were screened, and 71 were finally included in the meta-analysis. Study characteristics and relative risk (RR) estimates were extracted from each article and pooled using the random-effects meta-analysis model. There were a total of 6406 patients studied in a total of 71 studies; the male to female ratio was 1.08:1, and the mean age was 45.76 years; of these, 2057 patients from 14 studies were categorized into severe (24.3%) and mild (75.7%) disease groups. Imaging features that were more frequently noted in patients with severe disease than in those with mild disease included bilateral lung involvement (88.7% vs. 49.8%), scattered distribution (80.4% vs. 46.5%), multiple lobe involvement (95.7% vs. 59.6%), consolidation (88.3% vs. 60.3%), crazy-paving pattern (45.4% vs. 27.6%), air-bronchogram sign (29.7% vs. 15.1%), interlobular septal thickening (84.2% vs. 55.8%), and subpleural line (36.8% vs. 26.4%) differences between the two disease groups were statistically significant (P < 0.001). For 3778 patients in 29 studies, a significant pooled RR estimate was associated with abnormal chest CT findings in patients with COVID-19 (RR, 5.46%; 95% confidence interval [CI], 3.72%–8.04%; I2 = 86%). Individual assessment of the CT features revealed that a significant pooled RR estimate was associated with pure ground-glass opacity (GGO) (RR, 1.63%; 95% CI, 1.12%–2.38%; I2 = 79%), while lower pooled RR estimates were associated with CT features like crazy-paving pattern (RR, 1.37%; 95% CI, 1.10%–1.71%; I2 = 60%), consolidation (RR, 0.47%; 95% CI, 0.32%–0.7%; I2 = 83.5%), GGO with consolidation (RR, 0.73%; 95% CI, 0.52%–1.02%; I2 = 75%), and air-bronchogram sign (RR, 0.58%; 95% CI, 0.36%–0.96%; I2 = 94%). In conclusion, the number, location, extent, and type of radiological lesions are associated with COVID-19 progression and severity, suggesting the feasibility of using CT imaging in the assessment of disease severity in all age groups and efficient allocation of resources for patient management at the institutional level.

Keywords: Computed tomography, COVID-19, crazy-paving pattern, ground-glass opacities, meta-analysis, systematic review

How to cite this article:
Sharma PJ, Mahajan A, Rane S, Bhattacharjee A. Assessment of COVID-19 severity using computed tomography imaging: A systematic review and meta-analysis. Cancer Res Stat Treat 2021;4:78-87

How to cite this URL:
Sharma PJ, Mahajan A, Rane S, Bhattacharjee A. Assessment of COVID-19 severity using computed tomography imaging: A systematic review and meta-analysis. Cancer Res Stat Treat [serial online] 2021 [cited 2021 Sep 21];4:78-87. Available from: https://www.crstonline.com/text.asp?2021/4/1/78/312078

  Introduction Top

A report describing a cluster of early cases of lower respiratory tract infection, initially labeled as “pneumonia of unknown etiology,” from the Wuhan City in the Hubei province of China, was submitted to the World Health Organization (WHO) China Country Office on December 31, 2019. The causative agent was later isolated from the lower respiratory tract of the affected individuals and identified as the 2019-novel coronavirus (2019-nCoV) by the WHO. The origin of the 2019-nCoV was suspected to be the Phinolophus bat, and it was subsequently named the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) by the International Committee on Taxonomy of Viruses. The WHO declared it a Public Health Emergency of International Concern (PHEIC) on January 30, 2020. The United States of America reported its first case of coronavirus disease 2019 (COVID-19) on February 26, 2020. On March 11, 2020, COVID-19 was declared a pandemic.[1]

Radiological examination has played a critical role in the diagnostic workup and treatment assessment of suspected cases of pneumonia, particularly because of its feasibility and rapidity. Chest radiography (CXR) detects ground-glass opacities (GGOs) with a lower sensitivity; compared to CXR, computed tomography (CT) imaging has been reported to be a more efficient and reliable diagnostic tool for the evaluation of clinically suspected cases and follow-up of confirmed cases of COVID-19 by various studies.[2],[3],[4]

The first systematic review on CT imaging features of patients with COVID-19 was reported by Salehi et al.; the authors reviewed 30 initial studies providing preliminary insights into the early and follow-up CT imaging findings of 919 patients.[5] This was followed by four more systematic reviews and meta-analyses, of which the latest was by Xu et al. in which they analyzed the pooled sensitivity of chest CT imaging in the diagnosis of COVID-19.[6] Even though a considerable amount of imaging data relating to COVID-19 is available in the published literature, an updated systematic review and meta-analysis pertaining to the questions of clinical significance is warranted.

This meta-analysis was aimed at generalizing, consolidating, and analyzing the data related to the specific clinical symptoms and their correlation with ominous chest CT findings in patients with COVID-19, to help the public health-care officials, radiologists, and physicians to optimize resources, accurately triage patients, and effectively manage patients with COVID-19 at an institutional level.

  Methods Top


We followed the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement.[7]

Eligibility criteria

Two reviewers (PS and AM) systematically searched the PubMed database for case series and studies published in the English language that reported the demographic, clinical, laboratory, and CT imaging features of reverse transcription-polymerase chain reaction (RT-PCR)-confirmed cases of COVID-19 up to March 24, 2020. Studies related to other coronavirus-related illnesses, such as the Middle East respiratory syndrome, case reports, review articles, opinion pieces, and letters to the editor were excluded.

Information sources and search strategy

We searched the PubMed database directly as well as using pubmed.mineR on March 24, 2020, using the keywords “coronavirus,” “nCoV,” “2019-nCoV,” “COVID-19,” “coronavirus + CT imaging,” “nCoV + CT imaging,” “2019-nCoV + CT imaging,” and “COVID-19 + CT imaging.” The WHO database of publications on novel coronavirus was screened for additional literature.[1]

Study selection

The search results were initially screened by title and abstract, followed by examination of the full texts of relevant articles by two independent reviewers (PS and AM). Articles fulfilling the eligibility criteria were included. Additional articles were retrieved by screening the reference lists of the included studies and archives of the reviewers. Potential duplicates were excluded.

Data collection process and data items

We recorded the country where the studies were performed, number of reported cases, age, sex, clinical features (fever and cough) along with disease severity (according to the Chinese Centre of Disease Control [CDC] guidelines in most studies), imaging findings (chest CT), intensive care unit (ICU) admissions, outcomes (discharge and death), and data for quality assessment. We evaluated and combined granular data such as laterality of the lesion, number of involved lobes, and general pattern of lesions. In our analysis, we performed a clinical categorization of the patients based on the fifth edition of the China Guidelines for the Diagnosis and Treatment Plan of Novel Coronavirus (2019-nCoV) Infection by the National Health Commission (Trial Version 5), which was the most relevant guideline at the time of our analysis.[8] This guideline categorized the patients as having mild, moderate, severe, or critical disease. For the purpose of this analysis, we merged the categories and classified the patients as having non-severe/mild (mild + moderate) or severe (severe + critical) disease.

Any discrepancy related to data inclusion/extraction was resolved in a timely manner by review of the full text of the articles by the reviewers with no cases of disagreement.

Assessment of methodological quality

For quality assessment, we used the Quality Appraisal of Case Series Studies Checklist of the Institute of Health Economics,[9] and the general quality was rated as poor, fair, or good using this tool.

Statistical approach and assessment of the risk of bias

The meta-analysis was performed with “metafor” available in the R software (version 3.5.0), with the incidence of CT features and clinical symptoms of patients with COVID-19 as the outcomes. Forest plots were prepared with relative risk (RR) and confidence intervals. Pooled statistics with fixed and random effect models were used for each severity parameter, given the variable degrees of data heterogeneity, in order to provide a more generalized result. Heterogeneity among the included studies was assessed using the Cochran's Q-test (P) and inconsistency index (I2). Publication bias was assessed using Egger's test at 5% significance level.[10] We created 14 by 14 matrices through all types of RRs to calculate the feature-wise separate and combined risks with all combinations.

  Results Top

Our literature search returned 208 studies, 138 of which were excluded as they were not eligible [Supplementary Figure S1]. A total of 71 studies describing 6406 patients were finally selected for this systematic review and meta-analysis. The patient details, presenting features, radiological findings, and relevant diagnostic details were recorded. There were 4 studies exclusively reporting 31 cases of COVID-19 in children, and 3 studies exclusively reported 65 cases of COVID-19 in pregnant women.[11],[12],[13],[14],[15],[16] Out of a total of 71 studies, 48 (67.6%) describing a total of 6364 patients reported the population averages and frequencies of different radiological findings without individual patient-level information. Individual patient data were available in 22 studies and 1 database[17] for a total of 142 patients.

A total of 37 variables were analyzed, of which 12 were found to be suitable for our study. Most of the studies were from China, two were from Japan, one from Korea, two from Italy, and one from the USA. The retrospective study by Chen et al. on a total of 99 patients was the earliest to be published in late January 2020.[18] Thereafter, more than 1000 patients each were reported in two large studies by Guan et al. and Ai et al.[19],[20] Chest CT was the imaging modality used in all the studies, although there were a few studies that reported some nonspecific chest radiography findings. The methodologic quality of the studies was generally fair, with the exception of two studies that were rated poor because of insufficient data [Supplementary Table S1].

Demographics of the pooled patient population

There was no gender predisposition in the pooled study population that had a male-to-female ratio of 1.08:1 (ratios for individual studies ranged from 0.1:1 to 4:1) for 60 84.5% out of the 71 studies describing 5776 (90.2%) of the 6402 patients. The mean age of the pooled population was 45.8 years, ranging from 1 day to 95 years (reported mean range: 2.1–66.5 years). Clinical and imaging features for individual patients were available in a total of 142 cases. Of these 142 patients, 21 (14.78%) were children with COVID-19. Of the remaining 121 (85.2%) adult patients, 9 (7.4%) were pregnant women.

Clinical features

Fever was the most common presenting feature observed in 74.7% (2588/3463) of the patients, followed by cough observed in 53.2% (1808/3401). In our meta-analysis, the RR for developing fever was 3.3% (95% confidence interval [CI] 2.39%–4.45%) and for cough was 1.37% (95% CI 1.1–1.71%) [Figure 1]. Among the less common features were fatigue, observed in 19.7% (490/2478) of the patients and cough with expectoration observed in 17.8% (496/2795) of the patients in 58 (81.7%) of the total of 71 studies that reported the symptoms; the above-mentioned statistical findings associated with fever, cough, fatigue, and cough with expectoration were in accordance with both the larger studies (total 2113 patients), one by Guan et al.(43%, 473/1099) and another by Ai et al. (53.9%, 547/1014).[19],[20] A summary of other less common clinical presenting symptoms is provided in [Supplementary Table S2].
Figure 1: Forest plots of the incidence of fever (a and c) and cough (b and d) in coronavirus disease 2019 cases

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Radiological findings

About 59 (83.1%) out of 71 studies reported the detailed radiological findings of patients with COVID-19; these studies discussed the CT imaging findings primarily in terms of the extent, distribution and type of the lesions. In this meta-analysis, the RR for the presentation of abnormal CT findings in patients with COVID-19 was 5.5% (95% CI, 3.72%–8.04%, I2 = 86%) across 29 studies describing a total of 3778 patients [Figure 2].
Figure 2: (a and b) Forest plots of the incidence of abnormal and normal computed tomography in coronavirus disease 2019 cases

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Extent and distribution of lung lesions

This meta-analysis included 42 studies that described the laterality of the lesions for a total of 3853 patients. Of these, in 36 studies, bilateral lung involvement was more commonly reported (1783/3853, 46.3%), whereas only 3 studies reported higher unilateral lung involvement (512/3853; 13.3%); 2 small case series reported an equal number of patients with unilateral and bilateral lung involvement.[17],[20],[21],[22],[23],[24] Across 36 studies in a total of 2811 patients that reported the axial spatial distribution of the lesions, peripheral/subpleural distribution was most commonly reported (1375/2811, 48.91%), whereas co-occurrence of peripheral and central lesions (183/2811, 6.5%) and isolated central lesions (51/2811, 1.8%) were less commonly seen.

Lobes distribution and the number of lobes involved

In 19 out of 71 studies, describing a total of 1527 patients, that reported the lobe distribution, multilobar involvement was reported in 1023 (66.9%) patients, whereas unilobar involvement was reported in 288 (18.9%) patients. Among the 70 patients for whom individual information was reported, lower lobe involvement was reported in 51 (72.9%) patients, middle lobe involvement was reported in 45 (64.3%) patients, and upper lobe involvement was reported in 37 (52.9%) patients [Supplementary Table S3].

Types of lung lesions

Various radiological features were broadly classified as parenchymal (GGOs, consolidation, GGO with consolidation, emphysematous changes, lung cavitation, bronchial [bronchiectasis, bronchus distortion, and bronchial wall thickening], and special signs [air-bronchogram, tree-in-bud, air trapping, vacuolar, and reverse halo signs]); interstitial (interstitial septal thickening [inter/intralobular septal thickening and subpleural line/band], linear, reticular [fine-mesh shadow, reticular opacities, and reticular pattern], nodular [pulmonary nodules with/without halo sign], and vascular thickening [dilation sign and wall thickening]); mixed parenchymal and interstitial (crazy paving, interlobular septal thickening with crazy paving, and spider web signs); lymph nodal; pleural (effusion, thickening, and retraction); and fibrotic changes. Our meta-analysis of 40 studies with 3239 patients presenting with pure GGO on chest CT images indicated a 1.6% RR for developing pure GGO with an incidence of 55.4% (95% CI, 1.12%–2.38%, I2 = 79%). A total of 37 studies with 3095 patients were included in the analysis of consolidative lesions, and we observed a RR of 0.5% for developing consolidation with an incidence of 35.5% (95% CI, 0.32%–0.7%, I2 = 83.5%) [Supplementary Figure S2].

A total of 17 studies with 918 patients with findings of GGO and consolidation together showed an associated RR of 0.7% with an incidence of 41.1% (95% CI, 0.52%–1.02%, I2 = 75%). Among the less common findings were “crazy-paving pattern” observed in 1780 (60.8%) out of the 2928 reported cases described across 48 studies (RR – 1.4%, 95% CI, 1.10%–1.71%, I2 = 60%) [Supplementary Figure S3], and “air-bronchogram sign” observed in 359 (34.7%) out of the 1032 confirmed cases described across 16 studies (RR – 0.6%, 95% CI 0.36–0.96%, I2 = 94%) [Supplementary Figure S4]. The individual patient-level analysis revealed interlobular septal thickening (31/135, 23% of patients) and reticular opacities (22/135, 16.3% of patients) as other less common findings.

Association of the specific clinical symptoms and radiological findings with disease severity

Out of 71 studies describing a total of 6406 patients, 14 (19.7%) studies describing 2057 (32.1%) patients that mentioned the disease severity according to the Chinese CDC guidelines were used for the analysis of disease severity.[8] This guideline categorized the patients as having mild, moderate, severe, and critical disease. For the purpose of this analysis, we merged the categories and classified the patients as having non-severe/mild (mild + moderate) or severe (severe + critical) disease. Of a total of 2057 patients, 1559 (75.7%) were categorized as having mild disease (patients with mild clinical symptoms such as fever and no/mild pneumonia [unilateral, unilobar (mostly lower lobe), peripheral GGOs/pulmonary nodules with or without halo/consolidatory patches with evidence of air-bronchogram with or without vascular dilation/tree-in-bud sign] on imaging), and the remaining 498 (24.2%) patients were categorized as having severe disease (patients with dyspnea, respiratory frequency ≥30/min, blood oxygen saturation [SpO2] ≤93%, PaO2/FiO2 <300, and/or lung infiltrates >50% within 24–48 h or critically ill patients with acute respiratory distress syndrome [ARDS] or respiratory failure, septic shock, and/or multiple organ dysfunction [MOD] or failure [MOF] requiring urgent care). Patients with delayed presentation, progressive symptoms, and those needing ICU admission were included in the severe disease group; the rest of the patients were included in the mild disease group. All symptoms were more prevalent in patients with severe disease, with a significantly higher prevalence of fever, cough, expectoration, and dyspnea [Table 1].
Table 1: Comparison of patient demographics and clinical features between mild and severe disease groups in patients infected with COVID-19

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Bilateral lung involvement was significantly higher in patients with severe disease (283/319; 88.7%) as compared to patients with non-severe disease (636/1277; 49.8%) (P < 0.001). Involvement of peripheral zones was not different between the two patient groups, whereas multiple lobe involvement (95.7% vs. 59.6%), simultaneous peripheral and central respiratory zone involvement (80.4% vs. 46.5%), and presence of consolidation (88.3% vs. 60.3%) (P < 0.001) were more frequent in the severe disease group as compared to the non-severe disease group [Table 2].
Table 2: Comparison of extent and distribution of lesions on computed tomography imaging between mild and severe disease groups in patients infected with COVID-19

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Bronchial changes such as bronchial distortion (31.8% vs. 10%; P = 0.03), bronchial wall thickening (29.7% vs. 15.1%; P < 0.001), and “air-bronchogram sign” (29.7% vs. 15.1%; P < 0.001) were 2–3 times more frequent in patients with severe disease compared to those with mild disease. Interstitial changes such as interlobular septal thickening (84.2% vs. 55.8%; P < 0.001), subpleural line (36.8% vs. 26.4%; P < 0.001), pleural effusion (16.3% vs. 6.6%; P < 0.001), “crazy-paving pattern” (45.4% vs. 27.6%; P = 0.004), reticular opacities (41.7% vs. 29.8%; P = 0.01), pulmonary nodules (27.7% vs. 22.4%; P = 0.03), and fibrosis (38% vs. 23%; P = 0.03) were also more frequent in the severe disease group as compared to the mild disease group [Table 3]. The derived RR of a combination of all radiological features of disease severity versus none present using the fixed effect model was 25.7% (95% CI, 22.84%–28.95%); on using the random effect model, the derived RR was 28.6% (95% CI, 20.57%–39.99%) [Supplementary Table S4] and [Supplementary Table S5]. The characteristics and CT findings of the patients with COVID-19 from the various studies included in this analysis are depicted in [Supplementary Table S6]. A comparison of the type of lesions on CT imaging between the mild and severe disease groups is shown in [Supplementary Table S7]. The combination of relative risk point estimates about the disease severity with feature-wise combination using the random and fixed effect models are shown in [Supplementary Table S8] and [Supplementary Table S9], respectively. A comparison of the different radiological findings on CT imaging between the different age groups is depicted in [Supplementary Table S10]. A comparison of the results obtained from the different systematic analyses and meta-analyses on CT imaging in patients with COVID-19 published until May 15, 2020 is shown in [Supplementary Table S11].
Table 3: Comparison of type of lesions on computed tomography imaging between mild and severe disease groups in patients infected with COVID-19

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  Discussion Top

For the present systematic review and meta-analysis, we segregated the studies based on the disease severity. We then categorized the patients as per the Chinese CDC guidelines into mild (patients with mild clinical symptoms such as fever and no/mild pneumonia on imaging-1559/2057, 75.7%) and severe (patients with dyspnea, respiratory frequency ≥30/min, SpO2 ≤93%, PaO2/FiO2 <300 and/or lung infiltrates >50% within 24–48 h or critically ill patients with ARDS or respiratory failure, septic shock, and/or MOD or MOF requiring urgent care-498/2057, 24.2%) disease groups. The consolidated data were then analyzed both qualitatively and quantitatively to determine the correlation between disease severity and radiological findings.[19],[21],[25],[26],[27],[28],[29],[30],[31],[32]

With an almost equal distribution of male and female patients, we observed that fever and cough (largely without expectoration) were the most common presenting features in early disease, while progressive symptoms such as productiveness of cough and dyspnea represented a more severe disease course. On CT imaging, the unifocal GGOs that appeared in the earlier stages of symptom onset further evolved into multifocal GGOs with consolidation or diffuse consolidative patches along with the subsequent appearance of “crazy-paving pattern” (symptom duration of more than 1 week) and pleural effusion (more than 2 weeks).[33] It was observed that diffuse multilobar consolidation in both the lungs with bronchial changes (bronchial distortion, bronchial wall thickening, and “air-bronchogram sign”) and less common findings such as interlobular septal thickening, subpleural line, or pleural effusion were indicative of poor prognosis. It is prudent that we assess these features on CT imaging at the earliest to prevent complications such as ARDS presenting as “white lungs” on CT imaging, secondary pulmonary fibrosis, and permanent deterioration of pulmonary function. These secondary complications may lead to higher ICU admission rates and increased mortality.

This study established a significant correlation between the COVID-19 symptomatology and imaging findings, which makes the role of CT imaging even more clinically relevant, even though this correlation was more obvious in the severe disease group. For instance, dyspnea had a significant correlation with scattered peripheral + central zone involvement (P < 0.001), “crazy-paving pattern” (P < 0.001), reticular opacities (P < 0.001), interlobular septal thickening (P < 0.001), pleural effusion (P < 0.001), upper and middle lobe involvement (P = 0.01), vacuolar sign (P = 0.020), bilateral lobe involvement (P = 0.026), and “air-bronchogram sign” (P = 0.034); fatigue was associated with peripheral and central lung lesions (P = 0.013), while chest discomfort was associated with the co-occurrence of scattered peripheral and central lung lesions (P = 0.018), bronchial wall thickening (P = 0.030), and interlobular septal defect (P = 0.038).

Apart from assessing the data related to the severe cases of COVID-19, we also statistically analyzed the peculiar diagnostic imaging findings in specific groups of patients such as pediatric, geriatric, and pregnant women. Pooled data from studies reporting on pediatric cases indicate that the presenting symptoms and the CT findings in the pediatric patients are usually milder as compared to the adult and older age groups. This makes it mandatory to closely monitor the suspected pediatric cases for the development of any early symptoms, especially the ones with a definite exposure history. On CT imaging, the distribution of lesions was predominantly unilateral (P = 0.020), with middle lobe involvement (P < 0.001) and central zone distribution (P < 0.001); abnormal CT findings such as crazy paving (P < 0.001), reticular opacities (P < 0.001), interlobular septal thickening (P < 0.001), pleural effusion (P = 0.003), and air-bronchogram sign (P = 0.004) were significantly more frequent in the older adult patients as compared to children.[11],[12],[13],[14]

Conglomerated data on all the cases of COVID-19 in pregnant women from various studies showed that most cases were mild with a few showing consolidation earlier on chest CT imaging as compared to the non-pregnant adult patients. This suggests a possibility of delayed detection of infection in this particular group, with rapid deterioration of the patients' condition leading to poor prognosis in a small group of pregnant women.[14],[15],[16] Even though there was not a single case of vertical intrauterine transmission of the SARS-CoV-2 infection in the studies included in our analysis, one study suggested that neonates born to infected mothers might be at a higher risk of infection either through maternal aerosols or close proximity to another infected caregiver. This calls for efficient screening of all caregivers before handling of the neonate and strict observation for any signs of respiratory distress in all neonates.[16]

In light of the unavailability of enough vaccines to inoculate the entire global population at once, a definitive curative treatment option, and a strong herd immunity, this pandemic is going to potentially cause a huge global health crisis and socioeconomic setback, especially in the resource-constrained countries. Taking the recently published Fleischner guidelines into consideration, in case of high clinical suspicion, CT imaging has the potential to serve as a compelling diagnostic tool, especially in scenarios of a community transmission with an overwhelming number of patients.[34] Furthermore, a recent meta-analysis published by Xu et al. concluded that during a severe epidemic, chest CT was more sensitive (92%) at detecting COVID-19, but had a low specificity (25% and 33%) which could possibly be due to the recent emergence of COVID-19, limited experience of radiologists in the imaging of 2019-nCoV pneumonia, and very little published data on COVID-19.[6],[35]

A noteworthy feature of our systematic review and meta-analysis is that it includes various studies highlighting the clinical, epidemiological, microbiological, biochemical, and radiological characteristics of the patients with COVID-19 across different age-groups and with different levels of disease severity.[36],[37],[38],[39],[40],[41],[42],[43],[44],[45],[46],[47],[48],[49],[50],[51],[52],[53],[54],[55],[56],[57],[58],[59],[60],[61],[62],[63],[64],[65],[66],[67],[68],[69],[70],[71],[72],[73] Considering imaging has its own learning curve, we are convinced that in due course of time, the first-hand clinical experience that we have obtained and the abundance of published literature on radiological imaging features of COVID-19 will enable the radiologists to diagnose COVID-19 with a soaring level of confidence, thereby resulting in an increased specificity.

A major limitation of this systematic review and meta-analysis is that all the included studies were retrospective. Furthermore, taking into account the recent dreadful emergence of COVID-19, there have been inconsistencies in the ways the clinical and imaging features were dealt with by various health-care professionals. We are convinced that in due course of time, universally accepted guidelines will be established and will help the clinicians by providing definite reference standards. The high heterogeneity among studies for a few features helped us analyze a wide variety of data being reported. We realized that the clinical severity of COVID-19 is reflected in radiological findings, with the location, extent, and the types of observed lesions being different between the mild and severe disease groups, making it possible to predict the clinical disease severity with radiological imaging. In this meta-analysis, we have discussed all the imaging features that were mentioned in the various studies included in our review (studies published upto March 24, 2020). Imaging features such as pneumothorax and thromboembolism were not described in these initial studies. As per the recently published studies, the incidence of pulmonary embolism in patients with COVID-19 was between 23% and 30%; another study highlighted that it was significantly higher in critically ill patients requiring ICU admission (upto 26.6% in these patients).[74],[75],[76],[77] Zantah et al. reported a very small incidence of spontaneous pneumothorax (0.66% of total 3368 suspected patients with COVID-19) in their study.[78]

In summary, there exists a clear relationship between the number, location, extent, and type of radiological lesions and COVID-19 progression and severity, suggesting the feasibility of using CT imaging in the assessment of disease severity in all age groups and efficient allocation of resources for patient management at the institutional level.

Main points

  • On pulmonary CT imaging, bilateral lung involvement, scattered distribution, multiple lobe involvement, consolidation, crazy-paving pattern, air-bronchogram sign, interlobular septal thickening, and subpleural line were reported to be significantly higher in the severe disease group as compared to the mild disease group
  • Pooled RR estimates for typical pulmonary CT features associated with COVID-19 were significant for pure GGO while marginally significant for crazy-paving pattern; RR estimates were less significant for GGO with consolidation, pure consolidation, and air-bronchogram sign.

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Conflicts of interest

There are no conflicts of interest.

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