Systematic review and meta-analysis of chest radiograph (CXR) findings in COVID-19.
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ABSTRACT:
SUBMITTER: Sadiq Z
PROVIDER: S-EPMC8313779 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:AimTo determine the overall rate of chest imaging findings in asymptomatic cases, describe the most common patterns found, and determine the rate of later symptom development in these initially asymptomatic cases.Materials and methodsThe PubMed and EMBASE databases were searched until 1 May 2020, for studies examining the proportion of positive chest imaging findings in asymptomatic cases diagnosed with COVID-19 and a random-effects meta-analysis of proportions was performed. Heterogeneity was assessed using the I2 statistic.ResultsAmong 858 non-duplicate studies, seven studies with a total of 231 asymptomatic cases met the inclusion criteria. In the primary analysis, the pooled estimate of the overall rate of positive chest computed tomography (CT) findings among asymptomatic cases was 63% (95% confidence interval [CI]: 44-78%). Among 155/231 cases that were followed up for later symptom development, 90/155 remained asymptomatic and 65/155 developed symptoms during the study period (that ranged between seven and 30 days of follow-up). The pooled estimate of the rate of positive chest CT findings was 62% (95% CI: 38-81%) in cases that remained asymptomatic, while it was 90% (95% CI: 49-99%) in cases that developed symptoms. Among CT findings, the pooled estimate of the overall rate of ground-glass opacities (GGO) at CT alone was 71% (95% CI: 50-86%). Among other CT findings reported, 22/231 patients had GGO with consolidation, 7/231 patients had stripe shadows with or without GGO, and 8/231 patients had GGO with interlobular septal thickening. Among initially asymptomatic cases with positive CT findings, the pooled estimate of the overall rate of later symptom development was 26% (95% CI: 14-43%).ConclusionIn COVID-19, asymptomatic cases can have positive chest CT findings, and COVID-19 should be considered among cases with CT abnormalities even when there are no other symptoms. There is a need for close clinical monitoring of asymptomatic cases with radiographic findings as a significant percentage will develop symptoms.
| S-EPMC7420957 | biostudies-literature
Project description:Chest computed tomography (CT) is frequently used in diagnosing coronavirus disease 2019 (COVID-19) for detecting abnormal changes in the lungs and monitoring disease progression during the treatment process. Furthermore, CT imaging appearances are correlated with patients presenting with different clinical scenarios, such as early versus advanced stages, asymptomatic versus symptomatic patients, and severe versus nonsevere situations. However, its role as a screening and diagnostic tool in COVID-19 remains to be clarified. This article provides a systematic review and meta-analysis of the current literature on chest CT imaging findings with the aim of highlighting the contribution and judicious use of CT in the diagnosis of COVID-19. A search of PubMed/Medline, Web of Science, ScienceDirect, Google Scholar and Scopus was performed to identify studies reporting chest imaging findings in COVID-19. Chest imaging abnormalities associated with COVID-19 were extracted from the eligible studies and diagnostic value of CT in detecting these abnormal changes was compared between studies consisting of both COVID-19 and non-COVID-19 patients. A random-effects model was used to perform meta-analysis for calculation of pooled mean values and 95% confidence intervals (95% CI) of abnormal imaging findings. Fifty-five studies met the selection criteria and were included in the analysis. Pulmonary lesions more often involved bilateral lungs (78%, 95% CI: 45-100%) and were more likely to have a peripheral (65.35%, 95% CI: 25.93-100%) and peripheral plus central distribution (31.12%, 95% CI: 1.96-74.07%), but less likely to have a central distribution (3.57%, 95% CI: 0.99-9.80%). Ground glass opacities (GGO) (58.05%, 95% CI: 16.67-100%), consolidation (44.18%, 95% CI: 1.61-71.46%) and GGO plus consolidation (52.99%, 95% CI: 19.05-76.79%) were the most common findings reported in 94.5% (52/55) of the studies, followed by air bronchogram (42.50%, 95% CI: 7.78-80.39%), linear opacities (41.29%, 95% CI: 7.44-65.06%), crazy-paving pattern (23.57%, 95% CI: 3.13-91.67%) and interlobular septal thickening (22.91%, 95% CI: 0.90-80.49%). CT has low specificity in differentiating pneumonia-related lung changes due to significant overlap between COVID-19 and non-COVID-19 patients with no significant differences in most of the imaging findings between these two groups (P>0.05). Furthermore, normal CT (13.31%, 95% CI: 0.74-38.36%) was reported in 26 (47.3%) studies. Despite widespread use of CT in the diagnosis of COVID-19 patients based on the current literature, CT findings are not pathognomonic as it lacks specificity in differentiating imaging appearances caused by different types of pneumonia. Further, there is a relatively high percentage of normal CT scans. Use of CT as a first-line diagnostic or screening tool in COVID-19 is not recommended.
| S-EPMC7242306 | biostudies-literature
Project description:While ground-glass opacity, consolidation, and fibrosis in the lungs are some of the hallmarks of acute SAR-CoV-2 infection, it remains unclear whether these pulmonary radiological findings would resolve after acute symptoms have subsided. We conducted a systematic review and meta-analysis to evaluate chest computed tomography (CT) abnormalities stratified by COVID-19 disease severity and multiple timepoints post-infection. PubMed/MEDLINE was searched for relevant articles until 23 May 2023. Studies with COVID-19-recovered patients and follow-up chest CT at least 12 months post-infection were included. CT findings were evaluated at short-term (1-6 months) and long-term (12-24 months) follow-ups and by disease severity (severe and non-severe). A generalized linear mixed-effects model with random effects was used to estimate event rates for CT findings. A total of 2517 studies were identified, of which 43 met the inclusion (N = 8858 patients). Fibrotic-like changes had the highest event rate at short-term (0.44 [0.3-0.59]) and long-term (0.38 [0.23-0.56]) follow-ups. A meta-regression showed that over time the event rates decreased for any abnormality (β = -0.137, p = 0.002), ground-glass opacities (β = -0.169, p < 0.001), increased for honeycombing (β = 0.075, p = 0.03), and did not change for fibrotic-like changes, bronchiectasis, reticulation, and interlobular septal thickening (p > 0.05 for all). The severe subgroup had significantly higher rates of any abnormalities (p < 0.001), bronchiectasis (p = 0.02), fibrotic-like changes (p = 0.03), and reticulation (p < 0.001) at long-term follow-ups when compared to the non-severe subgroup. In conclusion, significant CT abnormalities remained up to 2 years post-COVID-19, especially in patients with severe disease. Long-lasting pulmonary abnormalities post-SARS-CoV-2 infection signal a future public health concern, necessitating extended monitoring, rehabilitation, survivor support, vaccination, and ongoing research for targeted therapies.
| S-EPMC10969005 | biostudies-literature
Project description:The Coronavirus Disease (COVID-19) pandemic first broke out in December 2019 in Wuhan, China, and has now spread worldwide. Laboratory findings have been only partially described in some observational studies. To date, more comprehensive systematic reviews of laboratory findings on COVID-19 are missing. We performed a systematic review with a meta-analysis to assess laboratory findings in patients with COVID-19. Observational studies from three databases were selected. We calculated pooled proportions and 95% confidence interval (95% CI) using the random-effects model meta-analysis. A total of 1106 articles were identified from PubMed, Web of Science, CNKI (China), and other sources. After screening, 28 and 7 studies were selected for a systematic review and a meta-analysis, respectively. Of the 4,663 patients included, the most prevalent laboratory finding was increased C-reactive protein (CRP; 73.6%, 95% CI 65.0-81.3%), followed by decreased albumin (62.9%, 95% CI 28.3-91.2%), increased erythrocyte sedimentation rate (61.2%, 95% CI 41.3-81.0%), decreased eosinophils (58.4%, 95% CI 46.5-69.8%), increased interleukin-6 (53.1%, 95% CI 36.0-70.0%), lymphopenia (47.9%, 95% CI 41.6-54.9%), and increased lactate dehydrogenase (LDH; 46.2%, 95% CI 37.9-54.7%). A meta-analysis of seven studies with 1905 patients showed that increased CRP (OR 3.0, 95% CI: 2.1-4.4), lymphopenia (OR 4.5, 95% CI: 3.3-6.0), and increased LDH (OR 6.7, 95% CI: 2.4-18.9) were significantly associated with severity. These results demonstrated that more attention is warranted when interpreting laboratory findings in patients with COVID-19. Patients with elevated CRP levels, lymphopenia, or elevated LDH require proper management and, if necessary, transfer to the intensive care unit.
| S-EPMC7256350 | biostudies-literature
Project description:BACKGROUND:The majority of the children with SARS-CoV-2 infection present with respiratory symptoms, hence various chest imaging modalities have been used in the management. Knowledge about the radiological findings of coronavirus disease (COVID-19) in children is limited. Hence, we systematically synthesized the available data that will help in better management of COVID-19 in children. METHODS:Four different electronic databases (MEDLINE, EMBASE, Web of Science and CENTRAL) were searched for articles reporting radiological findings in children with COVID-19. Studies reporting thoracic radiological findings of COVID-19 in patients aged <19?years were included. A random-effect meta-analysis (wherever feasible) was performed to provide pooled estimates of various findings. RESULTS:A total of 1984 records were screened of which forty-six studies (923 patients) fulfilled the eligibility criteria and were included in this systematic review. A chest computed tomography (CT) scan was the most frequently used imaging modality. While one-third of the patients had normal scans, a significant proportion (19%) of clinically asymptomatic children had radiological abnormalities too. Unilateral lung involvement (55%) was frequent when compared with bilateral and ground-glass opacities were the most frequent (40%) definitive radiological findings. Other common radiological findings were non-specific patchy shadows (44%), consolidation (23%), halo sign (26%), pulmonary nodules and prominent bronchovascular marking. Interstitial infiltration being the most frequent lung ultrasound finding. CONCLUSION:CT scan is the most frequently used imaging modality for COVID-19 in children and can detect pneumonia before the appearance of clinical symptoms. Undefined patchy shadows, grand-glass opacities and consolidation are commonly observed imaging findings in COVID-19 pneumonia.
| S-EPMC7454935 | biostudies-literature
Project description:ObjectiveCentral nervous system involvement in coronavirus disease 2019 (COVID-19) has been increasingly reported. We performed a systematic review and meta-analysis to evaluate the incidence of radiologically demonstrated neurologic complications and detailed neuroimaging findings associated with COVID-19.Materials and methodsA systematic literature search of MEDLINE/PubMed and EMBASE databases was performed up to September 17, 2020, and studies evaluating neuroimaging findings of COVID-19 using brain CT or MRI were included. Several cohort-based outcomes, including the proportion of patients with abnormal neuroimaging findings related to COVID-19 were evaluated. The proportion of patients showing specific neuroimaging findings was also assessed. Subgroup analyses were also conducted focusing on critically ill COVID-19 patients and results from studies that used MRI as the only imaging modality.ResultsA total of 1394 COVID-19 patients who underwent neuroimaging from 17 studies were included; among them, 3.4% of the patients demonstrated COVID-19-related neuroimaging findings. Olfactory bulb abnormalities were the most commonly observed (23.1%). The predominant cerebral neuroimaging finding was white matter abnormality (17.6%), followed by acute/subacute ischemic infarction (16.0%), and encephalopathy (13.0%). Significantly more critically ill patients had COVID-19-related neuroimaging findings than other patients (9.1% vs. 1.6%; p = 0.029). The type of imaging modality used did not significantly affect the proportion of COVID-19-related neuroimaging findings.ConclusionAbnormal neuroimaging findings were occasionally observed in COVID-19 patients. Olfactory bulb abnormalities were the most commonly observed finding. Critically ill patients showed abnormal neuroimaging findings more frequently than the other patient groups. White matter abnormalities, ischemic infarctions, and encephalopathies were the common cerebral neuroimaging findings.
| S-EPMC8546134 | biostudies-literature
Project description:BackgroundAbnormal laboratory findings are common in patients infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The aim of this systematic review was to investigate the effect of the level of some laboratory factors (C-reactive protein (CRP), creatinine, leukocyte count, hemoglobin, and platelet count) on the severity and outcome of coronavirus disease 2019 (COVID-19).MethodsWe searched PubMed, Web of Science, Scopus, and Google Scholar. We collected the articles published before May 26, 2020. We gathered the laboratory factors in groups of patients with COVID-19, and studied the relation between level of these factors with severity and outcome of the disease.ResultsMean CRP level, creatinine, hemoglobin, and the leukocytes count in the critically ill patients were significantly higher than those of the other groups (non-critical patients); mean CRP = 54.81 mg/l, mean creatinine = 86.82 μmol/l, mean hemoglobin = 144.05 g/l, and mean leukocyte count = 7.41 × 109. The lymphocyte count was higher in patients with mild/moderate disease (mean: 1.32 × 109) and in the invasive ventilation group (mean value of 0.72 × 109), but it was considerably lower than those of the other two groups. The results showed that the platelet count was higher in critically ill patients (mean value of 205.96 × 109). However, the amount was lower in the invasive ventilation group compared with the other groups (mean level = 185.67 × 109).ConclusionWith increasing disease severity, the leukocyte count and the level of CRP increase significantly and the lymphocyte count decreases. There seems to be a significant relation between platelet level, hemoglobin, and creatinine level with severity of the disease. However, more studies are required to confirm this.
| S-EPMC7962428 | biostudies-literature
Project description:BackgroundAccurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR).MethodsIn this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation.ResultsUsing the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19).ConclusionsA two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.
| S-EPMC9929417 | biostudies-literature
Project description:Purpose:Real-time polymerase chain reaction (RT-PCR) and chest computed tomography (CT) scan are main diagnostic modalities of coronavirus disease 2019 (COVID-19). However, there is still no consensus on which of these methods is superior to the other. Therefore, the present meta-analysis was designed to answer to the question whether CT scan can be used in diagnosis of COVID-19 or not. Methods:Searches were performed in Medline, Embase, Scopus, and Web of Science databases until the end of April 2020. Two researchers gathered the data of diagnostic accuracy studies that had attempted to evaluate sensitivity and specificity of CT scan in diagnosis of COVID-19. Results:Data of 9 studies were included. Area under the curve of ground glass opacity (GGO), consolidation, pleural effusion, other CT features, and simultaneous observation of GGO with other CT features was 0.64 (95% CI 0.60-0.69), 0.30 (95% CI 0.26-0.34), 0.60 (95% CI 0.56-0.64), 0.61 (95% CI 0.56-0.65), and 0.90 (95% CI 0.87-0.92), respectively. Sensitivity and specificity of simultaneous observation of GGO with other CT scan features was higher than all of the other signs. Sensitivity, specificity, and diagnostic odds ratio of this sign was calculated to be 0.90, 0.89, and 20, respectively. Conclusion:Simultaneous observation of GGO and other features of viral pneumonia in CT scan had optimum performance in detection of COVID-19. However, it is suggested to make the final diagnosis based on both CT scan and RT-PCR, as none of the two diagnostic modalities are reliable alone.
| S-EPMC7549426 | biostudies-literature
Project description:ObjectiveThe 2019 Coronavirus (COVID-19) results in a wide range of clinical severity and there remains a need for prognostic tools which identify patients at risk of rapid deterioration and who require critical care. Chest radiography (CXR) is routinely obtained at admission of COVID-19 patients. However, little is known regarding correlates between CXR severity and time to intubation. We hypothesize that the degree of opacification on CXR at time of admission independently predicts need and time to intubation.MethodsIn this retrospective cohort study, we reviewed COVID-19 patients who were admitted to an urban medical center during March 2020 that had a CXR performed on the day of admission. CXRs were divided into 12 lung zones and were assessed by two blinded thoracic radiologists. A COVID-19 opacification rating score (CORS) was generated by assigning one point for each lung zone in which an opacity was observed. Underlying comorbidities were abstracted and assessed for association.ResultsOne hundred forty patients were included in this study and 47 (34%) patients required intubation during the admission. Patients with CORS ≥ 6 demonstrated significantly higher rates of early intubation within 48 h of admission and during the hospital stay (ORs 24 h, 19.8, p < 0.001; 48 h, 28.1, p < 0.001; intubation during hospital stay, 6.1, p < 0.0001). There was no significant correlation between CORS ≥ 6 and age, sex, BMI, or any underlying cardiac or pulmonary comorbidities.ConclusionsCORS ≥ 6 at the time of admission predicts need for intubation, with significant increases in intubation at 24 and 48 h, independent of comorbidities.Key points• Chest radiography at the time of admission independently predicts time to intubation within 48 h and during the hospital stay in COVID-19 patients. • More opacities on chest radiography are associated with several fold increases in early mechanical ventilation among COVID-19 patients. • Chest radiography is useful in identifying COVID-19 patients whom may rapidly deteriorate and help inform clinical management as well as hospital bed and ventilation allocation.
| S-EPMC7553374 | biostudies-literature