A systematic review and meta-analysis comparing the diagnostic accuracy of initial RT-PCR and CT scan in suspected COVID-19 patients

Diagnostic performance of deep learning models versus radiologists in COVID-19 pneumonia: A systematic review and meta-analysis

PURPOSE

Although several studies have compared the performance of deep learning (DL) models and radiologists for the diagnosis of COVID-19 pneumonia on CT of the chest, these results have not been collectively evaluated. We performed a meta-analysis of original articles comparing the performance of DL models versus radiologists in detecting COVID-19 pneumonia.

METHODS

A systematic search was conducted on the three main medical literature databases, Scopus, Web of Science, and PubMed, for articles published as of February 1st, 2023. We included original scientific articles that compared DL models trained to detect COVID-19 pneumonia on CT to radiologists. Meta-analysis was performed to determine DL versus radiologist performance in terms of model sensitivity and specificity, taking into account inter and intra-study heterogeneity.

RESULTS

Twenty-two articles met the inclusion criteria. Based on the meta-analytic calculations, DL models had significantly higher pooled sensitivity (0.933 vs. 0.829, p < 0.001) compared to radiologists with similar pooled specificity (0.905 vs. 0.897, p = 0.746). In the differentiation of COVID-19 versus community-acquired pneumonia, the DL models had significantly higher sensitivity compared to radiologists (0.915 vs. 0.836, p = 0.001).

CONCLUSIONS

DL models have high performance for screening of COVID-19 pneumonia on chest CT, offering the possibility of these models for augmenting radiologists in clinical practice.

Lung abnormalities on computed tomography of Vietnamese patients with COVID-19 and the association with medical variables

Objectives

Patients with COVID-19 may experience a lung injury without presenting clinical symptoms. Early detection of lung injury in patients with COVID-19 is required to enhance prediction and prevent severe progression.

Methods

Lung lesions in patients with COVID-19 were defined using the Fleischner Society terminology. Chest computed tomography lesions and their correlation with demographic characteristics and medical variables were identified.

Results

Patients with mild and moderate COVID-19 had up to 45% lung injuries, whereas critical patients had 55%. However, patients with mild and moderate COVID-19 typically had low-level lung injuries. Ground-glass (68.1%), consolidation (48.8%), opacity (36.3%), and nodular (6.9%) lung lesions were the most prevalent in patients with COVID-19. Patients with COVID-19 infected with the Delta variant had worse lung injury than those infected with the Alpha and Omicron. People vaccinated with ≥2 doses showed a lower risk of lung injury than those vaccinated with <1 dose. Patients <18 years old were less likely to have a lung injury than patients >18 years old. The treatment outcomes were unaffected by the severity of the lung injury.

Conclusion

Patients with mild COVID-19 had a similar risk of lung injury as patients with severe COVID-19. Thus, using chest computed tomography to detect lung injury can enhance the treatment outcomes and reduce the patient's risk of pulmonary complications.

Investigation of the diagnostic importance and accuracy of CT in the chest compared to the RT-PCR test for suspected COVID-19 patients in Jordan

This article aims to synthesize the existing literature on the implementation of public policies to incentivize the development of treatments for rare diseases, (diseases with very low prevalence and therefore with low commercial interest) otherwise known as orphan drugs. The implementation of these incentives in the United States (US), Japan, and in the European Union (EU) seems to be related to a substantial increase in treatments for these diseases, and has influenced the way the pharmaceutical research & development (R&D) system operates beyond this policy area. Despite the success of the Orphan Drug model, the academic literature also highlights the negative implications that these public policies have on affordability and access to orphan drugs, as well as on the prioritization of certain disease rare areas over others. The synthesis focuses mostly on the United States' Orphan Drug Act (ODA) as a model for subsequent policies in other regions and countries. It starts with a historical overview of the creation of the term "rare diseases", continues with a summary of the evidence available on the US ODA's positive and negative impacts, and provides a summary of the different proposals to reform these incentives in light of the negative outcomes described. Finally, it describes some key aspects of the Japanese and European policies, as well as some of the challenges captured in the literature related to their impact in Low- and Middle-Income Countries (LMICs).

Is it necessary to define new diagnostic reference levels during pandemics like the Covid19-?

Introduction

This study intended to assess the dose length product (DLP), effective cumulative radiation dose (E.D.), and additional cancer risk (ACR) due to a chest CT scan to detect or follow up the Covid-19 disease in four university-affiliated hospitals that used different imaging protocols. Indeed, this study aimed to examine the differences in decision-making between different imaging centers in choosing chest CT imaging protocols during the pandemic, and to assess whether a new diagnostic reference level (DRL) is needed in pandemic situations.

Methods

This retrospective study assessed the E.D. of all chest imagings for Covid-19 for six months in four different hospitals in our country. Imaging parameters and DLP (mGy.cm) were recorded. The E.D.s and ACRs from chest CT scans were calculated using an online calculator.

Results

Thousand-six hundred patients were included in the study. The mean cumulative dose due to chest CT was 3.97 mSv which might cause 2.59 × 10^-2 ACR. The mean cumulative E.D. in different hospitals was in the range of 1.96-9.51 mSv.

Conclusions

The variety of mean E.D.s shows that different hospitals used different imaging protocols. Since there is no defined DRL in the pandemic, some centers use routine protocols, and others try to reduce the dose but insufficiently.In pandemics such as Covid-19, when CT scan is used for screening or follow-up, DLPs can be significantly lower than in normal situations. Therefore, international regularized organizations such as the international atomic energy agency (IAEA) or the international commission on radiological protection (IRCP) should provide new DRL ranges.

Computed tomography severity score as a predictor of disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis

BACKGROUND

Prediction of outcomes in severe COVID-19 patients using chest computed tomography severity score (CTSS) may enable more effective clinical management and early, timely ICU admission. We conducted a systematic review and meta-analysis to determine the predictive accuracy of the CTSS for disease severity and mortality in severe COVID-19 subjects.

METHODS

The electronic databases PubMed, Google Scholar, Web of Science, and the Cochrane Library were searched to find eligible studies that investigated the impact of CTSS on disease severity and mortality in COVID-19 patients between 7 January 2020 and 15 June 2021. Two independent authors looked into the risk of bias using the Quality in Prognosis Studies (QUIPS) tool.

RESULTS

Seventeen studies involving 2788 patients reported the predictive value of CTSS for disease severity. The pooled sensitivity, specificity, and summary area under the curve (sAUC) of CTSS were 0.85 (95% CI 0.78-0.90, I² =83), 0.86 (95% CI 0.76-0.92, I² =96) and 0.91 (95% CI 0.89-0.94), respectively. Six studies involving 1403 patients reported the predictive values of CTSS for COVID-19 mortality. The pooled sensitivity, specificity, and sAUC of CTSS were 0.77 (95% CI 0.69-0.83, I² = 41), 0.79 (95% CI 0.72-0.85, I² = 88), and 0.84 (95% CI 0.81-0.87), respectively.

DISCUSSION

Early prediction of prognosis is needed to deliver the better care to patients and stratify them as soon as possible. Because different CTSS thresholds have been reported in various studies, clinicians are still determining whether CTSS thresholds should be used to define disease severity and predict prognosis.

CONCLUSION

Early prediction of prognosis is needed to deliver optimal care and timely stratification of patients.  CTSS has strong discriminating power for the prediction of disease severity and mortality in patients with COVID-19.

A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative

BACKGROUND

Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19).

OBJECTIVES

To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity.

METHODS

The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected.

RESULTS

A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user.

CONCLUSION

We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.

COVID-19 diagnostics: Molecular biology to nanomaterials

The SARS-CoV-2 pandemic has claimed around 6.4 million lives worldwide. The disease symptoms range from mild flu-like infection to life-threatening complications. The widespread infection demands rapid, simple, and accurate diagnosis. Currently used methods include molecular biology-based approaches that consist of conventional amplification by RT-PCR, isothermal amplification-based techniques such as RT-LAMP, and gene editing tools like CRISPR-Cas. Other methods include immunological detection including ELISA, lateral flow immunoassay, chemiluminescence, etc. Radiological-based approaches are also being used. Despite good analytical performance of these current methods, there is an unmet need for less costly and simpler tests that may be performed at point of care. Accordingly, nanomaterial-based testing has been extensively pursued. In this review, we discuss the currently used diagnostic techniques for SARS-CoV-2, their usefulness, and limitations. In addition, nanoparticle-based approaches have been highlighted as another potential means of detection. The review provides a deep insight into the current diagnostic methods and future trends to combat this deadly menace.

Early Diagnosis of COVID-19 Images Using Optimal CNN Hyperparameters

Coronavirus disease (COVID-19) is a worldwide epidemic that poses substantial health hazards. However, COVID-19 diagnostic test sensitivity is still restricted due to abnormalities in specimen processing. Meanwhile, optimizing the highly defined number of convolutional neural network (CNN) hyperparameters (hundreds to thousands) is a useful direction to improve its overall performance and overcome its cons. Hence, this paper proposes an optimization strategy for obtaining the optimal learning rate and momentum of a CNN's hyperparameters using the grid search method to improve the network performance. Therefore, three alternative CNN architectures (GoogleNet, VGG16, and ResNet) were used to optimize hyperparameters utilizing two different COVID-19 radiography data sets (Kaggle (X-ray) and China national center for bio-information (CT)). These architectures were tested with/without optimizing the hyperparameters. The results confirm effective disease classification using the CNN structures with optimized hyperparameters. Experimental findings indicate that the new technique outperformed the previous in terms of accuracy, sensitivity, specificity, recall, F-score, false positive and negative rates, and error rate. At epoch 25, the optimized Resnet obtained high classification accuracy, reaching 98.98% for X-ray images and 98.78% for CT images.

Do COVID-19 CT features vary between patients from within and outside mainland China? Findings from a meta-analysis

Background

Chest computerized tomography (CT) plays an important role in detecting patients with suspected coronavirus disease 2019 (COVID-19), however, there are no systematic summaries on whether the chest CT findings of patients within mainland China are applicable to those found in patients outside.

Methods

Relevant studies were retrieved comprehensively by searching PubMed, Embase, and Cochrane Library databases before 15 April 2022. Quality assessment of diagnostic accuracy studies (QUADAS) was used to evaluate the quality of the included studies, which were divided into two groups according to whether they were in mainland China or outside. Data on diagnostic performance, unilateral or bilateral lung involvement, and typical chest CT imaging appearances were extracted, and then, meta-analyses were performed with R software to compare the CT features of COVID-19 pneumonia between patients from within and outside mainland China.

Results

Of the 8,258 studies screened, 19 studies with 3,400 patients in mainland China and 14 studies with 554 outside mainland China were included. Overall, the risk of quality assessment and publication bias was low. The diagnostic value of chest CT is similar between patients from within and outside mainland China (93, 91%). The pooled incidence of unilateral lung involvement (15, 7%), the crazy-paving sign (31, 21%), mixed ground-glass opacities (GGO) and consolidations (51, 35%), air bronchogram (44, 25%), vascular engorgement (59, 33%), bronchial wall thickening (19, 12%), and septal thickening (39, 26%) in patients from mainland China were significantly higher than those from outside; however, the incidence rates of bilateral lung involvement (75, 84%), GGO (78, 87%), consolidations (45, 58%), nodules (12, 17%), and pleural effusion (9, 15%) were significantly lower.

Conclusion

Considering that the chest CT features of patients in mainland China may not reflect those of the patients abroad, radiologists and clinicians should be familiar with various CT presentations suggestive of COVID-19 in different regions.

Efficacy of drug regimen with and without oseltamivir in hospitalized patients with COVID-19: A retrospective study

Introduction

Coronavirus disease 2019 (COVID-19) is the most critical issue in nowadays medicine. We aimed to evaluate the use and therapeutic outcomes of oseltamivir, an antiviral drug for patients with COVID-19.

Materials and method

In an observational study conducted at Imam Khomeini Hospital in Amol, Iran, data for 544 patients with laboratory and CT scan result confirmed COVID-19 were retrospectively collected between February 24th and April 13th 2020. To compare the characteristics of patients based on gender, the chi-square test was used. Logistic regression was used to evaluate the effect of oseltamivir on the outcome of treatment. Logrank test were used to compare the length of hospital stay in people treated with oseltamivir and drugs other than oseltamivir.

Results

Kaplan–Meier and logrank test showed no significant reduction in hospitalization time and survival rate following treatment with oseltamivir. However, a significant increase in lymphocytes count and reduction of C-reactive protein (CRP) level detected.

Conclusion

Administration of oseltamivir for patients with COVID-19 didn't show any improvement in hospitalization duration and survival rate.

Performance of a serological IgM and IgG qualitative test for COVID-19 diagnosis: An experimental study in Brazil

Qualitative antibody tests are an easy, point-of-care diagnostic method that is useful in diagnosing coronavirus disease 2019, especially in situations where reverse transcription-polymerase chain reaction is negative. However, some factors are able to affect its sensitivity and accuracy, which may contribute to these tests not being used as a first-line diagnostic tool.

Excess deaths during the COVID-19 pandemic in Iran

BACKGROUND

The actual number of deaths during the COVID-19 pandemic is expected to be higher than the reported deaths. We aimed to estimate the number of deaths in Iran during the COVID-19 pandemic from December 22, 2019 to March 20, 2022.

METHODS

We compared the number of age- and sex-specific deaths reported by Iran's Bureau of Vital Statistics with the predicted deaths estimated using an improved Lee-Carter model. We estimated the number of all-cause excess deaths in three scenarios, including the baseline scenario (without any undercounting of deaths) and 4% and 8% undercounting of all-cause deaths.

RESULTS

We estimated 282,378 (95% confidence intervals [CI]: 225,439; 341,951) excess deaths in the baseline model. This number was 303,148 (95% CI: 246,417; 357,823) and 308,486 (95% CI: 250,607; 364,417) in the 4% and 8% scenarios, respectively. During the same period, Iran reported 139,610 deaths as being directly related to COVID-19. The ratio of reported COVID-19 deaths to total excess deaths ranged from 45.2% to 49.4% in the various scenarios. Most excess deaths occurred in the baseline scenario in males (157,552 [95% CI: 125,142; 191,265]) and those aged ≥75 years (102,369 [95% CI: 93,894; 111,188]).

CONCLUSIONS

The reported number of COVID-19 deaths was less than half of Iran's estimated number of excess deaths. The results of this study will be helpful for health policymakers' planning, and call for strengthening the timeliness and accuracy of Iran's death registration systems, planning for more accurate monitoring of epidemics, and planning to provide support services for survivors' families.

An Umbrella Review With Meta-Analysis of Chest Computed Tomography for Diagnosis of COVID-19: Considerations for Trauma Patient Management

Background

RT-PCR testing is the standard for diagnosis of COVID-19, although it has its suboptimal sensitivity. Chest computed tomography (CT) has been proposed as an additional tool with diagnostic value, and several reports from primary and secondary studies that assessed its diagnostic accuracy are already available. To inform recommendations and practice regarding the use of chest CT in the in the trauma setting, we sought to identify, appraise, and summarize the available evidence on the diagnostic accuracy of chest CT for diagnosis of COVID-19, and its application in emergency trauma surgery patients; overcoming limitations of previous reports regarding chest CT accuracy and discussing important considerations regarding its role in this setting.

Methods

We conducted an umbrella review using Living Overview of Evidence platform for COVID-19, which performs regular automated searches in MEDLINE, Embase, Cochrane Central Register of Controlled Trials, and more than 30 other sources. The review was conducted following the JBI methodology for systematic reviews. The Grading of Recommendations, Assessment, Development, and Evaluation approach for grading the certainty of the evidence is reported (registered in International Prospective Register of Systematic Reviews, CRD42020198267).

Results

Thirty studies that fulfilled selection criteria were included; 19 primary studies provided estimates of sensitivity (0.91, 95%CI = [0.88–0.93]) and specificity (0.73, 95%CI = [0.61; 0.82]) of chest CT for COVID-19. No correlation was found between sensitivities and specificities (ρ = 0.22, IC95% [–0.33; 0.66]). Diagnostic odds ratio was estimated at: DOR = 27.5, 95%CI (14.7; 48.5). Evidence for sensitivity estimates was graded as MODERATE, and for specificity estimates it was graded as LOW.

Conclusion

The value of chest CT appears to be that of an additional screening tool that can easily detect PCR false negatives, which are reportedly highly frequent. Upon the absence of PCR testing and impossibility to perform RT-PCR in trauma patients, chest CT can serve as a substitute with increased value and easy implementation.

Systematic Review Registration

[www.crd.york.ac.uk/prospero], identifier [CRD42020198267].

Diagnostic Performance of Antigen Rapid Diagnostic Tests, Chest Computed Tomography, and Lung Point-of-Care-Ultrasonography for SARS-CoV-2 Compared with RT-PCR Testing: A Systematic Review and Network Meta-Analysis

(1) Background: The comparative performance of various diagnostic methods for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection remains unclear. This study aimed to investigate the comparison of the 3 index test performances of rapid antigen diagnostic tests (RDTs), chest computed tomography (CT), and lung point-of-care-ultrasonography (US) with reverse transcription-polymerase chain reaction (RT-PCR), the reference standard, to provide more evidence-based data on the appropriate use of these index tests. (2) Methods: We retrieved data from electronic literature searches of PubMed, Cochrane Library, and EMBASE from 1 January 2020, to 1 April 2021. Diagnostic performance was examined using bivariate random-effects diagnostic test accuracy (DTA) and Bayesian network meta-analysis (NMA) models. (3) Results: Of the 3992 studies identified in our search, 118 including 69,445 participants met our selection criteria. Among these, 69 RDT, 38 CT, and 15 US studies in the pairwise meta-analysis were included for DTA with NMA. CT and US had high sensitivity of 0.852 (95% credible interval (CrI), 0.791–0.914) and 0.879 (95% CrI, 0.784–0.973), respectively. RDT had high specificity, 0.978 (95% CrI, 0.960–0.996). In accuracy assessment, RDT and CT had a relatively higher than US. However, there was no significant difference in accuracy between the 3 index tests. (4) Conclusions: This meta-analysis suggests that, compared with the reference standard RT-PCR, the 3 index tests (RDTs, chest CT, and lung US) had similar and complementary performances for diagnosis of SARS-CoV-2 infection. To manage and control COVID-19 effectively, future large-scale prospective studies could be used to obtain an optimal timely diagnostic process that identifies the condition of the patient accurately.

Prognostic value of semi-quantitative CT-based score integrated with cardiovascular risk factors during the first peak of the COVID-19 pandemic: A new score to predict poor outcome

Purpose

Predicting the clinical course of COVID-19 pneumonia is of high clinical importance and may change treatment strategies. This study aimed to compare the semi-quantitative CT score (radiological score), mCHA₂DS₂-VASc score (clinical score), and R-mCHA₂DS₂-VASc score (clinical and radiological score) to predict the risk of ICU admission and mortality in COVID 19 pneumonia.

Methods

This study retrospectively evaluated 901 COVID-19 pneumonia cases with positive PCR results. The mCHA₂DS₂-VASc score was calculated based on clinical risk factors. CT images were evaluated, and the semi-quantitative CT scores were obtained. A new scoring method (R-mCHA₂DS₂-VASc score) was developed by combining these scores. The performance of the mCHA₂DS₂-VASc score, semi-quantitative CT score, and a combination of these scores (R-mCHA₂DS₂-VASc score) was evaluated using ROC analysis.

Results

The ROC curves of the semi-quantitative CT, mCHA₂DS₂-VASc, and R-mCHA₂DS₂-VASc scores were examined. The semi-quantitative CT, mCHA₂DS₂-VASc, and R-mCHA₂DS₂-VASc scores were significant in predicting intensive care unit (ICU) admission and mortality (p < 0.001). The R-mCHA₂DS₂-VASc score performed best in predicting a severe clinical course, and the cut-off value of 8 for the R-mCHA₂DS₂-VASc score had 83.9% sensitivity and 91.6% specificity for mortality.

Conclusions

The R-mCHA₂DS₂-VASc score includes both clinical and radiological parameters. It is a feasible scoring method for predicting a severe clinical course at an early stage with high sensitivity and specificity values. However, prospective studies with larger sample sizes are warranted.

Distinct Outcomes in COVID-19 Patients with Positive or Negative RT-PCR Test

Identification of the SARS-CoV-2 virus by RT-PCR from a nasopharyngeal swab sample is a common test for diagnosing COVID-19. However, some patients present clinical, laboratorial, and radiological evidence of COVID-19 infection with negative RT-PCR result(s). Thus, we assessed whether positive results were associated with intubation and mortality. This study was conducted in a Brazilian tertiary hospital from March to August of 2020. All patients had clinical, laboratory, and radiological diagnosis of COVID-19. They were divided into two groups: positive (+) RT-PCR group, with 2292 participants, and negative (−) RT-PCR group, with 706 participants. Patients with negative RT-PCR testing and an alternative most probable diagnosis were excluded from the study. The RT-PCR(+) group presented increased risk of intensive care unit (ICU) admission, mechanical ventilation, length of hospital stay, and 28-day mortality, when compared to the RT-PCR(−) group. A positive SARS-CoV-2 RT-PCR result was independently associated with intubation and 28 day in-hospital mortality. Accordingly, we concluded that patients with a COVID-19 diagnosis based on clinical data, despite a negative RT-PCR test from nasopharyngeal samples, presented more favorable outcomes than patients with positive RT-PCR test(s).

Diagnostic sensitivity of RT-PCR assays on nasopharyngeal specimens for detection of SARS-CoV-2 infection: A Systematic Review and Meta-Analysis

Background

Reverse transcription polymerase chain reaction (RT-PCR) is the current standard of reference in the diagnosis of SARS-CoV-2 infection. In outpatient clinical practice, nasopharyngeal swab RT-PCR testing is still the most common procedure. The purpose of this systematic review and meta-analysis was to evaluate the sensitivity of RT-PCR nasopharyngeal assays.

Methods

We searched three databases, including PubMed/MEDLINE, EMBASE, and Cochrane Library, using a comprehensive strategy. Studies investigating the sensitivity of SARS-CoV-2 RT-PCR nasopharyngeal assays in adults were included. Two reviewers extracted data and assessed trial quality independently. Pooled sensitivity and its confidence interval were computed using the meta package in R.

Results

Thirteen studies were found eligible for the inclusion in the systematic review. Out of these, 25 different sub-studies were identified and included in the meta-analysis, which reported the sensitivities of 25 different nasopharyngeal RT-PCR assays. Finally, the overall pooled sensitivity resulted 89% (95% CI, 85.4 to 91.8%).

Conclusion

Our study suggests that RT-PCR assays on nasopharyngeal specimens have a substantial sensitivity for diagnosing SARS-CoV-2 infection.

Diagnostic performance of the RSNA-proposed classification for COVID-19 pneumonia versus pre-pandemic controls

Objective

To evaluate the diagnostic accuracy of the Radiological Society of North America (RSNA) classification system for coronavirus disease 2019 (COVID-19) pneumonia compared to pre-pandemic chest computed tomography (CT) scan images to mitigate the risk of bias regarding the reference standard.

Materials and methods

This was a retrospective, cross-sectional, diagnostic test accuracy study. Chest CT scans, carried out from May 1 to June 30, 2020, and from May 1 to July 17, 2017, were consecutively selected for the COVID-19 (positive reverse transcription-polymerase chain reaction [RT-PCR] for severe acute respiratory syndrome coronavirus 2 result) and control (pre-pandemic) groups, respectively. Four expert thoracic radiologists blindly interpreted each CT scan image. Sensitivity and specificity were calculated.

Results

A total of 160 chest CT scan images were included: 79 in the COVID-19 group (56 [43.5–67] years old, 41 men) and 81 in the control group (62 [52–72] years old, 44 men). Typically, an estimated specificity of 98.5% (95% confidence interval [CI] 98.1%–98.4%) was obtained. For the indeterminate classification as a diagnostic threshold, an estimated sensitivity of 88.3% (95% CI 84.7%–91.7%) and a specificity of 79.0% (95% CI 74.5%–83.4%), with an area under the curve of 0.865 (95% CI 0.838–0.895), were obtained.

Conclusion

The RSNA classification system shows strong diagnostic accuracy for COVID-19 pneumonia, even against pre-pandemic controls. It can be an important aid in clinical decision-making, especially when a typical or indeterminate pattern is found, possibly advising retesting following an initial negative RT-PCR result and streamlining early management and isolation.

Computed tomography findings in 3,557 COVID-19 infected children: a systematic review

Background

Although it was assumed in the early stages of the coronavirus disease 2019 (COVID-19) outbreak that the novel coronavirus infection was uncommon among children, the number of infected children has since been increasing significantly. Real-time polymerase chain reaction (RT-PCR) is the gold standard modality for the diagnosis of COVID-19 infection. In adults, chest CT is performed as an adjunct for identifying suspected COVID-19 cases along with patients' management and follow-up. However, CT findings in COVID-19 children studies have shown a diverse incidence of abnormal CT and finding patterns that made CT scan necessity to have remained controversial. The aim of the present review was to comprehensively determine the imaging findings of chest CT scans of confirmed COVID-19-infected pediatric patients through a systematic review of the available published studies.

Methods

A systematic literature search was performed in the PubMed, Embase, Scopus, and Web of Science core collection databases (four databases including SSCI, SCIE, AHCI, and ESCI) to find original articles containing chest CT findings in children with COVID-19 through May 7, 2021. This review included 81 articles published in English that in total included 3,557 pediatric patients.

Results

This review included 81 articles published in English that in total included 3,557 pediatric patients. Among the total confirmed coronavirus-infected cases (via RT-PCR test), two-thirds had abnormal chest CT findings; among these patients, 549 (37.8%) had bilateral lung involvement, and 475 (32.7%) had unilateral disease. Regarding the types of lung lesions, ground glass opacities were observed in 794 (54.7%) of patients, and consolidation was observed in 10.2%; moreover, halo sign, discrete pulmonary nodules, interstitial abnormalities or reticulations, and vascular thickening shadows were reported in 7.4%, 2.6%, 9.7% and 1.7% of the patients, respectively.

Discussion

This review revealed that chest CT scan manifestations in majority of COVID-19 positive children are mild, so regarding the risk of radiation exposure, it is reasonable to confine CT scan to individual cases that its benefits outweigh the risks.

Role of chest imaging in the diagnosis and treatment of COVID-19

Background: Thousands of new patients are diagnosed with coronavirus disease 2019 (COVID-19) daily worldwide. We reviewed the role of chest imaging in the diagnosis and treatment of patients with COVID-19.Current Concepts: Chest imaging is not recommended as a primary diagnostic tool for COVID-19. However, when real-time polymerase chain reaction is difficult to perform or when COVID-19 is strongly suspected, chest imaging can assist in the diagnosis. Thus, chest imaging is recommended for high-risk patients and patients with worsening respiratory symptoms, but not for asymptomatic patients. Bilateral peripheral pneumonia is a typical imaging finding in patients with COVID-19. However, there are cases where chest imaging shows atypical findings or appears normal. The extent of COVID-19 pneumonia on chest imaging is related to the severity of the disease. The presence and extent of pneumonia on chest imaging can help monitor patients, select appropriate treatment agents, determine whether the patient should be hospitalized, and predict the prognosis.Discussion and Conclusion: Appropriate use of chest imaging is needed for clinicians to help triage patients with COVID-19 and decide on the treatment plan.

Successful management of a pregnant woman with COVID-19 and multiple severe complications

We report a case of a 36-year-old gravida 2 para 1 woman at 38 weeks of gestation. A caesarean section was performed for severe pre-eclampsia, intrauterine growth restriction and oligohydramnios. The patient suffered postoperative bleeding, and exploratory laparotomy was performed. Uterine atonia, Couvelaire uterus and left adnexal haematoma were found, requiring a supracervical hysterectomy. As COVID-19 pneumonia and superimposed bacterial infection developed, the patient was mechanically ventilated in the intensive care unit. Remdesivir and meropenem were initially administered, but were changed to levofloxacin and ciprofloxacin following antibiotic sensitivity tests. Blood culture grew Enterococcus galinarum. Meanwhile, bleeding of the incisional wound occurred, which was controlled by the cessation of heparin therapy and regular wound care. With intensive monitoring and multidisciplinary management, the patient’s condition improved, and she was discharged from the hospital on day 25 from admission.

Ruling Out Coronavirus Disease 2019 in Patients with Pneumonia: The Role of Blood Cell Count and Lung Ultrasound

Coronavirus disease 2019 (COVID-19) is characterized by a distinctive blood leucocyte pattern and B-lines on lung ultrasound (LUS) as marker of alveolar-interstitial syndrome. We aimed to evaluate the accuracy of blood leucocyte count alone or in combination with LUS for COVID-19 diagnosis. We retrospectively enrolled consecutive patients diagnosed with community acquired pneumonia (CAP) at hospital admission to derive and validate cutoff values for blood cell count that could be predictive of COVID-19 before confirmation by the nucleic acid amplification test (NAAT). Cutoff values, generated and confirmed in inception (41/115, positive/negative patients) and validation (100/180, positive/negative patients) cohorts, were ≤17 and ≤10 cells/mm³ for basophils and eosinophils, respectively. Basophils and/or eosinophils below cutoff were associated with sensitivity of 98% (95%CI, 94–100) and negative likelihood ratio of 0.04 (95%CI, 0.01–0.11). In a subgroup of 265 subjects, the sensitivity of B-line on LUS was 15% lower (p < 0.001) than that of basophils and/or eosinophils below cutoff. The combination of B-lines with basophils and eosinophils below cutoff was associated with a moderate increase of the positive likelihood ratio: 5.0 (95%CI, 3.2–7.7). In conclusion, basophil and eosinophil counts above the generated cutoff virtually rule out COVID-19 in patients with CAP. Our findings can help optimize patient triage pending the NAAT results.

The risk factors and related hospitalizations for cases with positive and negative COVID-19 tests: A case-control study

BACKGROUND

The aim of this study was to evaluate the risk factors for hospitalizations of cases with positive and negative COVID-19 tests.

METHODS

In this case-control study, the case and control groups consisted of 292 COVID-19 patients and 296 non-COVID-19 patients. Patients who referred to a reference laboratory in Tehran (Iran) in March 2020 were selected and interviewed. The patients were contacted by telephone and data were recorded through a questionnaire.

RESULTS

The sample of this study consisted of 588 patients (349 [59%] females, 239 [41%] males) with a mean age of 42 ± 15. The results of this study showed that comorbidities like diabetes (OR = 7.42), hypertension (OR = 4.85), asthma and respiratory diseases (OR = 5.64) in addition to symptoms including fever (OR = 6.67), chills (OR = 11.2), anorexia (OR = 11.3), dyspnea (OR = 4.8), weakness and lethargy (OR = 5.7) were the most predictive variables for hospitalization of non-COVID-19 cases. Furthermore, demographical variables like male gender (OR = 3.71), high age (>50; OR = 3.12), BMI (>25; OR = 2.37), travel (OR = 2.79), comorbidities including diabetes (OR = 5.26), hypertension (OR = 3.7) and underlying immunosuppressant patients receiving corticosteroid therapy (OR = 3.62) in addition to symptoms like anorexia [OR = 2.55] and dyspnea (OR = 6.99) tend to increase the risk of hospital admission in COVID-19 patients, suggesting their predictive values for hospitalization of COVID-19 patients.

CONCLUSION

Our results indicated that different factors tend to increase the odds of hospital admission in patients with positive and negative COVID-19 tests, suggesting their predictive values for hospitalization.

Defining Post-COVID Symptoms (Post-Acute COVID, Long COVID, Persistent Post-COVID): An Integrative Classification

The pandemic of the coronavirus disease 2019 (COVID-19) has provoked a second pandemic, the "long-haulers", i.e., individuals presenting with post-COVID symptoms. We propose that to determine the presence of post-COVID symptoms, symptoms should appear after the diagnosis of SARS-CoV-2 infection; however, this situation has some problems due to the fact that not all people infected by SARS-CoV-2 receive such diagnosis. Based on relapsing/remitting nature of post-COVID symptoms, the following integrative classification is proposed: potentially infection related-symptoms (up to 4-5 weeks), acute post-COVID symptoms (from week 5 to week 12), long post-COVID symptoms (from week 12 to week 24), and persistent post-COVID symptoms (lasting more than 24 weeks). The most important topic is to establish the time reference points. The classification also integrates predisposing intrinsic and extrinsic factors and hospitalization data which could promote post-COVID symptoms. The plethora of symptoms affecting multiple systems exhibited by "long-haulers" suggests the presence of different underlying mechanisms.