CT Manifestations and Clinical Characteristics of 1115 Patients with Coronavirus Disease 2019 (COVID-19): A Systematic Review and Meta-analysis

Early predictors of intensive care unit admission among COVID-19 patients in Qatar

Background

COVID-19 is associated with significant morbidity and mortality. This study aimed to explore the early predictors of intensive care unit (ICU) admission among patients with COVID-19.

Methods

This was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29-May 29, 2020. For each case enrolled, one control was matched by age and gender.

Results

A total of 1,560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range = 18). Predictors independently associated with ICU admission were cardiovascular disease (adjusted odds ratio (aOR) = 1.64, 95% confidence interval (CI): 1.16-2.32, p = 0.005), diabetes (aOR = 1.52, 95% CI: 1.08-2.13, p = 0.016), obesity (aOR = 1.46, 95% CI: 1.03-2.08, p = 0.034), lymphopenia (aOR = 2.69, 95% CI: 1.80-4.02, p < 0.001), high AST (aOR = 2.59, 95% CI: 1.53-4.36, p < 0.001), high ferritin (aOR = 1.96, 95% CI: 1.40-2.74, p < 0.001), high CRP (aOR = 4.09, 95% CI: 2.81-5.96, p < 0.001), and dyspnea (aOR = 2.50, 95% CI: 1.77-3.54, p < 0.001).

Conclusion

Having cardiovascular disease, diabetes, obesity, lymphopenia, dyspnea, and increased AST, ferritin, and CRP were independent predictors for ICU admission in patients with COVID-19.

A Multiclass Radiomics Method-Based WHO Severity Scale for Improving COVID-19 Patient Assessment and Disease Characterization From CT Scans

OBJECTIVES

The aim of this study was to evaluate the severity of COVID-19 patients' disease by comparing a multiclass lung lesion model to a single-class lung lesion model and radiologists' assessments in chest computed tomography scans.

MATERIALS AND METHODS

The proposed method, AssessNet-19, was developed in 2 stages in this retrospective study. Four COVID-19-induced tissue lesions were manually segmented to train a 2D-U-Net network for a multiclass segmentation task followed by extensive extraction of radiomic features from the lung lesions. LASSO regression was used to reduce the feature set, and the XGBoost algorithm was trained to classify disease severity based on the World Health Organization Clinical Progression Scale. The model was evaluated using 2 multicenter cohorts: a development cohort of 145 COVID-19-positive patients from 3 centers to train and test the severity prediction model using manually segmented lung lesions. In addition, an evaluation set of 90 COVID-19-positive patients was collected from 2 centers to evaluate AssessNet-19 in a fully automated fashion.

RESULTS

AssessNet-19 achieved an F1-score of 0.76 ± 0.02 for severity classification in the evaluation set, which was superior to the 3 expert thoracic radiologists (F1 = 0.63 ± 0.02) and the single-class lesion segmentation model (F1 = 0.64 ± 0.02). In addition, AssessNet-19 automated multiclass lesion segmentation obtained a mean Dice score of 0.70 for ground-glass opacity, 0.68 for consolidation, 0.65 for pleural effusion, and 0.30 for band-like structures compared with ground truth. Moreover, it achieved a high agreement with radiologists for quantifying disease extent with Cohen κ of 0.94, 0.92, and 0.95.

CONCLUSIONS

A novel artificial intelligence multiclass radiomics model including 4 lung lesions to assess disease severity based on the World Health Organization Clinical Progression Scale more accurately determines the severity of COVID-19 patients than a single-class model and radiologists' assessment.

Digital tomosynthesis improves chest radiograph accuracy and reduces microbiological false negatives in COVID-19 diagnosis

PURPOSE

Diagnosing pneumonia by radiograph is improvable. We aimed (a) to compare radiograph and digital thoracic tomosynthesis (DTT) performances and agreement for COVID-19 pneumonia diagnosis, and (b) to assess the DTT ability for COVID-19 diagnosis when polymerase chain reaction (PCR) and radiograph are negative.

METHODS

Two emergency radiologists with 11 (ER1) and 14 experience-years (ER2) retrospectively evaluated radiograph and DTT images acquired simultaneously in consecutively clinically suspected COVID-19 pneumonia patients in March 2020-January 2021. Considering PCR and/or serology as reference standard, DTT and radiograph diagnostic performance and interobserver agreement, and DTT contributions in unequivocal, equivocal, and absent radiograph opacities were analysed by the area under the curve (AUC), Cohen's Kappa, Mc-Nemar's and Wilcoxon tests.

RESULTS

We recruited 480 patients (49 ± 15 years, 277 female). DTT increased ER1 (from 0.76, CI95% 0.7-0.8 to 0.79, CI95% 0.7-0.8; P=.04) and ER2 (from 0.77 CI95% 0.7-0.8 to 0.80 CI95% 0.8-0.8, P=.02) radiograph-AUCs, sensitivity, specificity, predictive values, and positive likelihood ratio. In false negative microbiological cases, DTT suggested COVID-19 pneumonia in 13% (4/30; P=.052, ER1) and 20% (6/30; P=.020, ER2) more than radiograph. DTT showed new or larger opacities in 33-47% of cases with unequivocal opacities in radiograph, new opacities in 2-6% of normal radiographs and reduced equivocal opacities by 13-16%. Kappa increased from 0.64 (CI95% 0.6-0.8) to 0.7 (CI95% 0.7-0.8) for COVID-19 pneumonia probability, and from 0.69 (CI95% 0.6-0.7) to 0.76 (CI95% 0.7-0.8) for pneumonic extension.

CONCLUSION

DTT improves radiograph performance and agreement for COVID-19 pneumonia diagnosis and reduces PCR false negatives.

The Threat of Adversarial Attack on a COVID-19 CT Image-Based Deep Learning System

The coronavirus disease 2019 (COVID-19) rapidly spread around the world, and resulted in a global pandemic. Applying artificial intelligence to COVID-19 research can produce very exciting results. However, most research has focused on applying AI techniques in the study of COVID-19, but has ignored the security and reliability of AI systems. In this paper, we explore adversarial attacks on a deep learning system based on COVID-19 CT images with the aim of helping to address this problem. Firstly, we built a deep learning system that could identify COVID-19 CT images and non-COVID-19 CT images with an average accuracy of 76.27%. Secondly, we attacked the pretrained model with an adversarial attack algorithm, i.e., FGSM, to cause the COVID-19 deep learning system to misclassify the CT images, and the classification accuracy of non-COVID-19 CT images dropped from 80% to 0%. Finally, in response to this attack, we proposed how a more secure and reliable deep learning model based on COVID-19 medical images could be built. This research is based on a COVID-19 CT image recognition system, which studies the security of a COVID-19 CT image-based deep learning system. We hope to draw more researchers' attention to the security and reliability of medical deep learning systems.

COVID19 biomarkers: What did we learn from systematic reviews?

The coronavirus disease 2019 (COVID19) pandemic continues to represent a substantial public health concern. It can rapidly progress to severe disease, with poor prognosis and a high mortality risk. An early diagnosis and specific prognostic tools can help healthcare providers to start interventions promptly, understand the likely prognosis and to identify and treat timely individuals likely to develop severe disease with enhanced mortality risk. Here we focused on an impressive set of systematic reviews and meta-analyses that were performed since the start of the COVID19 pandemic and summarized their results related to the levels of hematologic, inflammatory, immunologic biomarkers as well as markers of cardiac, respiratory, hepatic, gastrointestinal and renal systems and their association with the disease progression, severity and mortality. The evidence outlines the significance of specific biomarkers, including inflammatory and immunological parameters (C-reactive protein, procalcitonin, interleukin-6), hematological (lymphocytes count, neutrophil-to-lymphocyte ratio, D-dimer, ferritin, red blood cell distribution width), cardiac (troponin, CK-MB, myoglobin), liver (AST, ALT, total bilirubin, albumin) and lung injury (Krebs von den Lungen-6) that can be used as prognostic biomarkers to aid the identification of high-risk patients and the prediction of serious outcomes, including mortality, in COVID19. Thus, these parameters should be used as essential tools for an early risk stratification and adequate intervention in improving disease outcomes in COVID19 patients.

Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge — 2020.

Association of chest CT severity score with mortality of COVID-19 patients: a systematic review and meta-analysis

Purpose

Chest computed tomography (CT) is a high-sensitivity diagnostic tool for depicting interstitial pneumonia and may lay a critical role in the evaluation of the severity and extent of pulmonary involvement. In this study, we aimed to evaluate the association of chest CT severity score (CT-SS) with the mortality of COVID-19 patients using systematic review and meta-analysis.

Methods

Web of Science, PubMed, Embase, Scopus, and Google Scholar were used to search for primary articles. The meta-analysis was performed using the random-effects model, and odds ratios (ORs) with 95% confidence intervals (95%CIs) were calculated as the effect sizes.

Results

This meta-analysis retrieved a total number of 7106 COVID-19 patients. The pooled estimate for the association of CT-SS with mortality of COVID-19 patients was calculated as 1.244 (95% CI 1.157–1.337). The pooled estimate for the association of CT-SS with an optimal cutoff and mortality of COVID-19 patients was calculated as 7.124 (95% CI 5.307–9.563). There was no publication bias in the results of included studies. Radiologist experiences and study locations were not potential sources of between-study heterogeneity (both P > 0.2). The shapes of Begg’s funnel plots seemed symmetrical for studies evaluating the association of CT-SS with/without the optimal cutoffs and mortality of COVID-19 patients (Begg’s test P = 0.945 and 0.356, respectively).

Conclusions

The results of this study point to an association between CT-SS and mortality of COVID-19 patients. The odds of mortality for COVID-19 patients could be accurately predicted using an optimal CT-SS cutoff in visual scoring of lung involvement.

Clinical indicators, nursing diagnoses, and mortality risk in critically ill patients with COVID-19: a retrospective cohort

OBJECTIVE

To identify clinical indicators and nursing diagnoses with the highest risk of mortality in critically ill patients with COVID-19.

METHOD

Retrospective cohort with the population of adults and elderly people with COVID-19 from an Intensive Care Unit. Categorical variables were described using absolute and relative frequencies and risk factors for mortality using Cox regression, with a confidence interval of 95%.

RESULTS

The main clinical indicators of COVID-19 patients were dyspnea, fever, fatigue, cough, among others, and the Nursing Diagnoses at higher risk of mortality were Ineffective protection, Ineffective tissue perfusion, Contamination, Ineffective Breathing Pattern, Impaired spontaneous ventilation, Acute confusion, Frailty syndrome, Obesity, and Decreased cardiac output. It is worth mentioning that there was little information about the diagnoses of Domains 9, 10, and 12.

CONCLUSION

This research infers the need to monitor the clinical indicators dyspnea, fever, fatigue, cough, among others, and the Nursing Diagnoses with the highest risk of mortality Ineffective protection, Ineffective tissue perfusion, Contamination, Ineffective Breathing Pattern, Impaired spontaneous ventilation in critically ill patients.

Rapid 8-Zone Lung Ultrasound Protocol is Comparable to a Full 12-Zone Protocol for Outcome Prediction in Hospitalized COVID-19 Patients

OBJECTIVES

Safety precautions limit the clinical assessment of hospitalized Coronavirus disease 2019 (COVID-19) patients. The minimal exposure required to perform lung ultrasound (LUS) paired with its high accuracy, reproducibility, and availability make it an attractive solution for initial assessment of COVID-19 patients. We aim to evaluate whether the association between sonographic findings and clinical outcomes among COVID 19 patients is comparable between the validated 12-zone protocol and a shorter, 8-zone protocol, in which the posterior lung regions are omitted.

METHODS

One hundred and one COVID-19 patients hospitalized in a dedicated COVID-19 ward in a tertiary referral hospital were examined upon admission and scored by 2 LUS protocols. The association between the scores and a composite outcome consisting of death, transfer to the intensive care unit (ICU) or initiation of invasive or noninvasive mechanical ventilation was estimated and compared.

RESULTS

LUS scores in both the 8- and the 12-zone protocols were associated with the composite outcome during hospitalization (hazard ratio [HR] 1.21 [1.03-1.42, P = .022] and HR 1.13 [1.01-1.27, P = .037], respectively). The observed difference in the discriminatory ROC-AUC values for the 8- and 12-zone scores was not significant (0.767 and 0.754 [P = .647], respectively).

CONCLUSION

A short 8-zone LUS protocol is as accurate as the previously validated, 12-zone protocol for prognostication of clinical deterioration in nonventilated COVID-19 patients.

Prevalence, Features and Degree of Association of Oral Lesions in COVID-19: A Systematic Review of Systematic Reviews

Regardless of rapidly emerging findings on oral lesions described in adult SARS-CoV-2-positive subjects, the evidence level remains quite low and rather contrasting; therefore, the present systematic review of systematic reviews primarily aims to point out the overall prevalence of diagnosed cases. Secondary aims are to estimate the degree of association between oral lesions and SARS-CoV-2 infection and to grade, based on the reported frequency, the primary oral lesions, with related clinical presentations and microscopic features, in relation to COVID-19 forms. A study protocol compliant with the PRISMA statement was developed. Twelve studies were included, reporting highly heterogeneous and incomplete findings, thus precluding a meta-analysis. Further studies should be conducted to assess the overall prevalence of cases diagnosed with oral lesions among adult SARS-CoV-2-positive subjects, especially considering novel viral variants, and to determine their degree of association with SARS-CoV-2 infection and COVID-19 forms. Moreover, the reported findings noticed the need to evaluate the putative role both of SARS-CoV-2 in oral lesions genesis and of periodontitis and periodontal microbiome in COVID-19 worsening and re-activations. Deeper insights into oral lesions in adult SARS-CoV-2-positive subjects could enhance the comprehension of illness pathogenesis, natural history and clinical presentation, thus improving the preparedness of health professionals in the inter-disciplinary management of COVID-19.

In-Patient Trajectories and Effects of Training in Survivors of COVID-19-Associated Acute Respiratory Failure

BACKGROUND

Pulmonary rehabilitation (PR) is useful in survivors of COVID-19-associated acute respiratory failure (ARF). The aim of this retrospective study on in-patient PR was to report rehabilitative trajectories and effects of cycle training.

METHODS

According to the Short Physical Performance Battery (SPPB) score at admission (T0), participants were allocated to stage 1 (SPPB < 6), stage 2 (SPPB ≥ 6 and < 10), or stage 3 (SPPB ≥ 10) and performed increasing level of activities from passive exercises to free walking, balance exercises, strength exercises, and tailored cycle-ergometer endurance training. The primary outcome was SPPB. 6-min walk distance (6MWD), Medical Research Council score, Barthel dyspnea index, and rate of subjects able to cycling were also assessed.

RESULTS

Data of 123 participants were analyzed. At T0, 44 (35.8%), 50 (40.6%), and 29 (23.6%) participants were allocated to stages 1-3, respectively. At discharge, participants showed significant improvements in SPPB, independent of the initial stage, 81 (65.8%) improving more than its minimal clinically important difference. At T1, the proportion of participants in stages 1 and 2 decreased, whereas significantly increased in stage 3 (P = .003), (being 9.8%, 33.3%, and 56.9% for stages 1-3, respectively; P <.001). Sixty-nine of 123 participants (56.1%) underwent cycle exercise training. In participants able to perform it, 6MWD improved by 115 (65-240) m and 60 (40-118) m in participants with and without exercise-induced desaturation, respectively, with significant difference between groups (P = .044).

CONCLUSIONS

In-patient PR could be tailored and progressively increased to survivors of COVID-19-associated ARF; cycle training was feasible in half of the participants. Benefits were independent of initial stage of physical performance and allowed participants to move from lower to higher levels of activities.

Molecular and serology methods in the diagnosis of COVID-19: An overview

Coronavirus disease-19 (COVID-19) has become a pandemic, being a global health concern since December 2019 when the first cases were reported. Severe acute respiratory syndrome coronavirus 2, the COVID-19 causal agent, is a β-coronavirus that has on its surface the spike protein, which helps in its virulence and pathogenicity towards the host. Thus, effective and applicable diagnostic methods to this disease come as an important tool for the management of the patients. The use of the molecular technique PCR, which allows the detection of the viral RNA through nasopharyngeal swabs, is considered the gold standard test for the diagnosis of COVID-19. Moreover, serological methods, such as enzyme-linked immunosorbent assays and rapid tests, are able to detect severe acute respiratory syndrome coronavirus 2-specific immunoglobulin A, immunoglobulin M, and immunoglobulin G in positive patients, being important alternative techniques for the diagnostic establishment and epidemiological surveillance. On the other hand, reverse transcription loop-mediated isothermal amplification also proved to be a useful diagnostic method for the infection, mainly because it does not require a sophisticated laboratory apparatus and has similar specificity and sensitivity to PCR. Complementarily, imaging exams provide findings of typical pneumonia, such as the ground-glass opacity radiological pattern on chest computed tomography scanning, which along with laboratory tests assist in the diagnosis of COVID-19.

Air trapping in COVID-19 patients following hospital discharge: retrospective evaluation with paired inspiratory/expiratory thin-section CT

Objectives

The study reports our experience with paired inspiration/expiration thin-section computed tomographic (CT) scans in the follow-up of COVID-19 patients with persistent respiratory symptoms.

Methods

From August 13, 2020, to May 31, 2021, 48 long-COVID patients with respiratory symptoms (27 men and 21 women; median age, 62.0 years; interquartile range: 54.0–69.0 years) underwent follow-up paired inspiration-expiration thin-section CT scans. Patient demographics, length of hospital stay, intensive care unit admission rate, and clinical and laboratory features of acute infection were also included. The scans were obtained on a median of 72.5 days after onset of symptoms (interquartile range: 58.5–86.5) and at least 30 days after hospital discharge. Thin-section CT findings included ground-glass opacity, mosaic attenuation pattern, consolidation, traction bronchiectasis, reticulation, parenchymal bands, bronchial wall thickening, and air trapping. We used a quantitative score to determine the degree of air trapping in the expiratory scans.

Results

Parenchymal abnormality was found in 50% (24/48) of patients and included air trapping (37/48, 77%), ground-glass opacities (19/48, 40%), reticulation (18/48, 38%), parenchymal bands (15/48, 31%), traction bronchiectasis (9/48, 19%), mosaic attenuation pattern (9/48, 19%), bronchial wall thickening (6/48, 13%), and consolidation (2/48, 4%). The absence of air trapping was observed in 11/48 (23%), mild air trapping in 20/48 (42%), moderate in 13/48 (27%), and severe in 4/48 (8%). Independent predictors of air trapping were, in decreasing order of importance, gender (p = 0.0085), and age (p = 0.0182).

Conclusions

Our results, in a limited number of patients, suggest that follow-up with paired inspiratory/expiratory CT in long-COVID patients with persistent respiratory symptoms commonly displays air trapping.

Key Points

• Our experience indicates that paired inspiratory/expiratory CT in long-COVID patients with persistent respiratory symptoms commonly displays air trapping.

• Iterative reconstruction and dose-reduction options are recommended for demonstrating air trapping in long-COVID patients.

Molecular and Physiological Aspects of SARS-CoV-2 Infection in Women and Pregnancy

Whilst scientific knowledge about SARS-CoV-2 and COVID-19 is rapidly increasing, much of the effects on pregnant women is still unknown. To accommodate pregnancy, the human endometrium must undergo a physiological transformation called decidualization. These changes encompass the remodeling of endometrial immune cells leading to immunotolerance of the semi-allogenic conceptus as well as defense against pathogens. The angiotensin converting enzyme 2 (ACE2) plays an important regulatory role in the renin-angiotensin-system (RAS) and has been shown to be protective against comorbidities known to worsen COVID-19 outcomes. Furthermore, ACE2 is also crucial for decidualization and thus for early gestation. An astounding gender difference has been found in COVID-19 with male patients presenting with more severe cases and higher mortality rates. This could be attributed to differences in sex chromosomes, hormone levels and behavior patterns. Despite profound changes in the female body during pregnancy, expectant mothers do not face worse outcomes compared with non-pregnant women. Whereas mother-to-child transmission through respiratory droplets during labor or in the postnatal period is known, another question of in utero transmission remains unanswered. Evidence of placental SARS-CoV-2 infection and expression of viral entry receptors at the maternal-fetal interface suggests the possibility of in utero transmission. SARS-CoV-2 can cause further harm through placental damage, maternal systemic inflammation, and hindered access to health care during the pandemic. More research on the effects of COVID-19 during early pregnancy as well as vaccination and treatment options for gravid patients is urgently needed.

Indicadores clínicos, diagnósticos de enfermagem e risco de mortalidade em pacientes críticos com COVID-19: coorte retrospectiva

RESUMO Objetivo: Identificar os indicadores clínicos e os diagnósticos de enfermagem com maior risco de mortalidade em pacientes críticos com COVID-19. Método: Coorte retrospectiva com a população de adultos e idosos com COVID-19 de uma Unidade de Terapia Intensiva. As variáveis categóricas foram descritas por frequências absoluta e relativa e os fatores de risco para mortalidade, pela regressão de Cox, com intervalo de confiança de 95%. Resultados: Os principais indicadores clínicos de pacientes com COVID-19 foram dispneia, febre, fadiga, tosse, entre outros, e os Diagnósticos de Enfermagem de maior risco de mortalidade: Proteção ineficaz; Perfusão tissular ineficaz; Contaminação; Padrão Respiratório Ineficaz; Ventilação espontânea prejudicada; Confusão aguda; Síndrome do idoso frágil; Obesidade e Débito cardíaco diminuído. Vale ressaltar que havia poucas informações sobre os diagnósticos dos Domínios 9, 10 e 12. Conclusão: Esta pesquisa infere a necessidade de vigiar os indicadores clínicos dispneia, febre, fadiga, tosse, entre outros e os Diagnósticos de Enfermagem de maior risco de mortalidade Proteção ineficaz; Perfusão tissular ineficaz; Contaminação; Padrão Respiratório Ineficaz; Ventilação espontânea prejudicada em pacientes críticos.

Clinical Features of COVID-19 Patients in the First Year of Pandemic: A Systematic Review and Meta-Analysis

Background

The new coronavirus disease (COVID-19) carries a high risk of infection and has spread rapidly around the world. However, there are limited data about the clinical symptoms globally. The purpose of this systematic review and meta-analysis is to identify the prevalence of the clinical symptoms of patient with COVID-19.

Methods

A systematic review and meta-analysis were carried out. The following databases were searched: PubMed, CINAHL, MEDLINE, EMBASE, PsycINFO, medRxiv, and Google Scholar, from December 1st, 2019 to January 1st, 2021. Prevalence rates were pooled with meta-analysis using a random-effects model. Heterogeneity was tested using I-squared (I²) statistics.

Results

A total of 215 studies, involving 132,647 COVID-19 patients, met the inclusion criteria. The pooled prevalence of the four most common symptoms were fever 76.2% (n = 214; 95% CI 73.9–78.5); coughing 60.4% (n = 215; 95% CI 58.6–62.1); fatigue 33.6% (n = 175; 95% CI 31.2–36.1); and dyspnea 26.2% (n = 195; 95% CI 24.1–28.5). Other symptoms from highest to lowest in terms of prevalence include expectorant (22.2%), anorexia (21.6%), myalgias (17.5%), chills (15%), sore throat (14.1%), headache (11.7%), nausea or vomiting (8.7%), rhinorrhea (8.2%), and hemoptysis (3.3%). In subgroup analyses by continent, it was found that four symptoms have a slight prevalence variation—fever, coughing, fatigue, and diarrhea.

Conclusion

This meta-analysis found the most prevalent symptoms of COVID-19 patients were fever, coughing, fatigue, and dyspnea. This knowledge might be beneficial for the effective treatment and control of the COVID-19 outbreak. Additional studies are required to distinguish between symptoms during and after, in patients with COVID-19.

Lung ultrasound in predicting COVID-19 clinical outcomes: A prospective observational study

STUDY OBJECTIVE

We sought to determine the ability of lung point-of-care ultrasound (POCUS) to predict mechanical ventilation and in-hospital mortality in patients with coronavirus disease 2019 (COVID-19).

METHODS

This was a prospective observational study of a convenience sample of patients with confirmed COVID-19 presenting to 2 tertiary hospital emergency departments (EDs) in Iran between March and April 2020. An emergency physician attending sonographer performed a 12-zone bilateral lung ultrasound in all patients. Research associates followed the patients on their clinical course. We determined the frequency of positive POCUS findings, the geographic distribution of lung involvement, and lung severity scores. We used multivariable logistic regression to associate lung POCUS findings with clinical outcomes.

RESULTS

A total of 125 patients with COVID-like symptoms were included, including 109 with confirmed COVID-19. Among the included patients, 33 (30.3%) patients were intubated, and in-hospital mortality was reported in 19 (17.4%). Lung POCUS findings included pleural thickening 95.4%, B-lines 90.8%, subpleural consolidation 86.2%, consolidation 46.8%, effusions 19.3%, and atelectasis 18.3%. Multivariable logistic regression incorporating binary and scored POCUS findings were able to identify those at highest risk for need of mechanical ventilation (area under the curve 0.80) and in-hospital mortality (area under the curve 0.87). In the binary model ultrasound (US) findings in the anterior lung fields were significantly associated with a need for intubation and mechanical ventilation (odds ratio [OR] 3.67; 0.62-21.6). There was an inverse relationship between mortality and posterior lung field involvement (OR 0.05; 0.01-0.23; and scored OR of 0.57; 0.40-0.82). Anterior lung field involvement was not associated with mortality.

CONCLUSIONS

In patients with COVID-19, the anatomic distribution of findings on lung ultrasound is associated with outcomes. Lung POCUS-based models may help clinicians to identify those patients with COVID-19 at risk for clinical deterioration.Key Words: COVID-19; Lung Ultrasound; Mechanical ventilation; Prediction; ICU admission; Mortality; Clinical outcome; Risk stratification; Diagnostic accuracy.

Correlation Between Low-Dose Chest Computed Tomography and RT-PCR Results for the Diagnosis of COVID-19: A Report of 27,824 Cases in Tehran, Iran

RATIONALE AND OBJECTIVES

Real-time polymerase chain reaction (RT-PCR) remains the gold standard for confirmation of Coronavirus Disease 2019 (COVID-19) despite having many disadvantages. Here, we investigated the diagnostic performance of chest computed tomography (CT) as an alternative to RT-PCR in patients with clinical suspicion of COVID-19 infection.

METHODS

In this descriptive cross-sectional study, 27,824 patients with clinical suspicion of COVID-19 infection who underwent unenhanced low-dose chest CT from 20 February, 2020 to 21 May, 2020 were evaluated. Patients were recruited from seven specifically designated hospitals for patients with COVID-19 infection affiliated to Shahid Beheshti University of Medical Sciences. In each hospital, images were interpreted by two independent radiologists. CT findings were considered as positive/negative for COVID-19 infection based on RSNA diagnostic criteria. Then, the correlation between the number of daily positive chest CT scans and number of daily PCR-confirmed cases and COVID-19-related deaths in Tehran province during this three-month period was assessed. The trends of admission rate and patients with positive CT scans were also evaluated.

RESULTS

A strong positive correlation between the numbers of daily positive CT scans and daily PCR-confirmed COVID-19 cases (r = 0.913, p < 0.001) was observed. Furthermore, in hospitals located in regions with a lower socioeconomic status, the admission rate and number of positive cases within this three-month period was higher as compared to other hospitals.

CONCLUSION

Low-dose chest CT is a safe, rapid and reliable alternative to RT-PCR for the diagnosis of COVID-19 in high-prevalence regions. In addition, our study provides further evidence for considering patients' socioeconomic status as an important risk factor for COVID-19.

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.

Computerized tomography of the Thorax for surgical patients during the COVID-19 pandemic: Was it useful?

OBJECTIVES

Diagnostic challenges during the corona virus disease (COVID-19) pandemic forced the radiology regulating body to adopt the use of CT Chest as a triage and diagnostic tool, which was subsequently abandoned. The Royal Wolverhampton hospital followed both protocols. Here, we investigate the evidence behind this decision within the context of surgical admissions during the COVID-19 peak in our hospital.

METHODS

Retrospective data collection and analysis of all surgical admissions between the 1st of March to the 31st of May. Data were collected from the radiology and electronic portal looking into patients undergoing CT chest to diagnose the presence of COVID-19 as well as swab results.

RESULTS

Seventy-eight patients fulfilled our inclusion criteria. The scan either confirmed the presence or absence (4, 63 patients) of COVID-19 but was sometimes inconclusive (11 patients). Comparing these to the results of the swabs; CT showed sensitivity 42.86%, Specificity 97.92%, and accuracy 90.91%. In the inconclusive CT report group, chances of having a positive swab result were 45%: None of the scan results changed any of the surgical planning. Lymphocyte count in the context of surgical presentation did not have any statistical significance to predict the presence of COVID-19 (P = .7). Cost implications on our cohort of patients for adding the chest CT is estimated to be around £31 000.

CONCLUSION

CT Thorax during the pandemic was a good negative predictor but had limited diagnostic value and did not change patient management. Newer, faster techniques of PCR swabs and antibody testing would be a better and cheaper alternative.

Differentiation of Chest CT Findings Between Influenza Pneumonia and COVID-19: Interobserver Agreement Between Radiologists

OBJECTIVES

To investigate the chest CT and clinical characteristics of COVID-19 pneumonia and H1N1 influenza, and explore the radiologist diagnosis differences between COVID-19 and influenza.

MATERIALS AND METHODS

This cross-sectional study included a total of 43 COVID-19-confirmed patients (24 men and 19 women, 49.90 ± 18.70 years) and 41 influenza-confirmed patients (17 men and 24 women, 61.53 ± 19.50 years). Afterwards, the chest CT findings were recorded and 3 radiologists recorded their diagnoses of COVID-19 or of H1N1 influenza based on the CT findings.

RESULTS

The most frequent clinical symptom in patients with COVID-19 and H1N1 pneumonia were dyspnea (96.6%) and cough (62.5%), respectively. The CT findings showed that the COVID-19 group was characterized by GGO (88.1%), while the influenza group had features such as GGO (68.4%) and consolidation (66.7%). Compared to the influenza group, the COVID-19 group was more likely to have GGO (88.1% vs. 68.4%, p = 0.032), subpleural sparing (69.0% vs. 7.7%, p <0.001) and subpleural band (50.0% vs. 20.5%, p = 0.006), but less likely to have pleural effusion (4.8% vs. 33.3%, p = 0.001). The agreement rate between the 3 radiologists was 65.8%.

CONCLUSION

Considering similarities of respiratory infections especially H1N1 and COVID-19, it is essential to introduce some clinical and para clinical modalities to help differentiating them. In our study we extracted some lung CT scan findings from patients suspected to COVID-19 as a newly diagnosed infection comparing with influenza pneumonia patients.

Human genetic basis of coronavirus disease 2019

Coronavirus disease 2019 (COVID-19) caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in considerable morbidity and mortality worldwide. COVID-19 incidence, severity, and mortality rates differ greatly between populations, genders, ABO blood groups, human leukocyte antigen (HLA) genotypes, ethnic groups, and geographic backgrounds. This highly heterogeneous SARS-CoV-2 infection is multifactorial. Host genetic factors such as variants in the angiotensin-converting enzyme gene (ACE), the angiotensin-converting enzyme 2 gene (ACE2), the transmembrane protease serine 2 gene (TMPRSS2), along with HLA genotype, and ABO blood group help to explain individual susceptibility, severity, and outcomes of COVID-19. This review is focused on COVID-19 clinical and viral characteristics, pathogenesis, and genetic findings, with particular attention on genetic diversity and variants. The human genetic basis could provide scientific bases for disease prediction and targeted therapy to address the COVID-19 scourge.

Chest CT abnormalities in COVID-19: a systematic review

Computed tomography (CT) of the chest is one of the main diagnositic tools for coronavirus disease 2019 (COVID-19) infection. To document the chest CT findings in patients with confirmed COVID-19 and their association with the clinical severity, we searched related literatures through PubMed, MEDLINE, Embase, Web of Science (inception to May 4, 2020) and reviewed reference lists of previous systematic reviews. A total of 31 case reports (3768 patients) on CT findings of COVID-19 were included. The most common comorbid conditions were hypertension (18.4%) and diabetes mellitus (8.3%). The most common symptom was fever (78.7%), followed by cough (60.2%). It took an average of 5.6 days from symptom onset to admission. The most common chest CT finding was vascular enlargement (84.8%), followed by ground-glass opacity (GGO) (60.1%), air-bronchogram (47.8%), and consolidation (41.4%). Most lung lesions were located in the lung periphery (72.2%) and involved bilateral lung (76%). Most patients showed normal range of laboratory findings such as white blood cell count (96.4%) and lymphocyte (87.2%). Compared to previous published meta-analyses, our study is the first to summarize the different radiologic characteristics of chest CT in a total of 3768 COVID-19 patients by compiling case series studies. A comprehensive diagnostic approach should be adopted for patients with known COVID-19, suspected cases, and for exposed individuals.

Alkaline phosphatase and score of HRCT as indicators for predicting the severity of COVID-19

BACKGROUND

The clinical symptoms, blood laboratory data, O2 saturation and high-resolution computed tomography (HRCT) findings are critical factors in diagnosis of COVID-19 infection.

METHODS

In this study, 105 hospitalized patients suspected of having COVID-19 were evaluated. Finally, the laboratory and HRCT and related factors data of 83 confirmed cases by HRCT and RT-PCR were analyzed. To compare the median of quantitative variables in the two groups, the Mann-Whitney U test was used. Also, to determine the factors associated with the positiveness of the HRCT result, a univariate logistic model was fitted. Moreover, receiver operating characteristic (ROC) curves were constructed to test the ability of the final model to predict the positiveness of HRCT result.

RESULTS

61.40% of the patients had a comorbidity disease. 89.20% had fever, 92.00% cough, 91.40% dyspnea. Abnormal CRP was seen in 77.80% of the patients, followed by 66.70% lymphopenia, and 60.30% neutrophilia. Also, ALP (abnormal vs. normal) and score of HRCT assessment variables had a significant effect on the positiveness of HRCT findings. 87.95% had abnormal HRCT with 41% bilateral multi lobar patchy ground glass opacity (GGO). Moreover, there was a statistically significant association between the level of O2 saturation and HRCT results.

CONCLUSION

Our findings showed that male patients with middle age and comorbidity disease were more susceptible to the COVID-19 infection. Additionally, clinical features, blood laboratory findings, O2 saturation and HRCT findings are critical factors in the prognosis of COVID-19 infection.

Covid-19 imaging: A narrative review

Background

The 2019 novel coronavirus disease (COVID-19) imaging data is dispersed in numerous publications. A cohesive literature review is to be assembled.

Objective

To summarize the existing literature on Covid-19 pneumonia imaging including precautionary measures for radiology departments, Chest CT's role in diagnosis and management, imaging findings of Covid-19 patients including children and pregnant women, artificial intelligence applications and practical recommendations.

Methods

A systematic literature search of PubMed/med line electronic databases.

Results

The radiology department's staff is on the front line of the novel coronavirus outbreak. Strict adherence to precautionary measures is the main defense against infection's spread. Although nucleic acid testing is Covid-19's pneumonia diagnosis gold standard; kits shortage and low sensitivity led to the implementation of the highly sensitive chest computed tomography amidst initial diagnostic tools. Initial Covid-19 CT features comprise bilateral, peripheral or posterior, multilobar ground-glass opacities, predominantly in the lower lobes. Consolidations superimposed on ground-glass opacifications are found in few cases, preponderantly in the elderly. In later disease stages, GGO transformation into multifocal consolidations, thickened interlobular and intralobular lines, crazy paving, traction bronchiectasis, pleural thickening, and subpleural bands are reported. Standardized CT reporting is recommended to guide radiologists. While lung ultrasound, pulmonary MRI, and PET CT are not Covid-19 pneumonia's first-line investigative diagnostic modalities, their characteristic findings and clinical value are outlined. Artificial intelligence's role in strengthening available imaging tools is discussed.

Conclusion

This review offers an exhaustive analysis of the current literature on imaging role and findings in COVID-19 pneumonia.

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Chest computed tomography is a highly sensitive Covid −19 pneumonia's diagnostic tool.

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Initial Covid-19 CT features are bilateral, multifocal, peripheral or posterior ground-glass opacities, mainly in the lower lobes.

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Multifocal consolidations, bronchiectasis, pleural thickening, and subpleural bands are late disease stages features.

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Standardized CT reporting is recommended to guide radiologists.

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Artificial intelligence could strengthen available imaging tools.

Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

Chest Computed Tomography (CT) Severity Scales in COVID-19 Disease: A Validation Study

Background

Imaging-based quantitative assessment of lung lesions plays a key role in patient triage and therapeutic decision-making processes. The aim of our study was to validate the Total Severity Score (TSS), Chest Computed Tomography Score (CT-S), and Chest CT Severity Score (CT-SS) scales, which were used to assess the extent of lung inflammation in patients with SARS-CoV-2 infection in terms of interobserver agreement and the correlation of scores with patient clinical condition on the day of the study.

Material/Methods

A total of 77 chest CT scans collected from 77 consecutive patients hospitalized because of SARS-CoV-2 were included. The scans were assessed independently by 2 radiologists aware of the patients’ positive results of RT-PCR tests. Each chest CT was assessed according to the 3 scales. To assess the interobserver agreement of CT scan assessments, Cohen’s κ and intraclass correlation coefficient (ICC) were calculated.

Results

For the overall assessment, the κ was 0.944 and the ICC was 0.948 for the TSS; the κ was 0.909 and the ICC was 0.919 for the CT-S; and the κ was 0.888 and the ICC was 0.899 for the CT-SS. The CT-SS (r=0.627 for Radiologist 1 and r=0.653 for Radiologist 2) revealed the strongest positive correlation with the patient clinical condition as expressed using the Modified Early Warning Score.

Conclusions

The interobserver agreement for the 3 evaluated scales was very good. The CT-SS was found to have the strongest positive relationship with the Modified Early Warning Score.

Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds

CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After deep learning segmentation of the lungs, histogram-based and threshold-based analyses of lung attenuation were performed and compared. The derived imaging biomarkers were correlated with parameters of clinical and biochemical severity (modified WHO severity scale; c-reactive protein). For non-enhanced CTs (n = 526), all imaging biomarkers significantly differed between healthy lungs and lungs with viral pneumonia (all p < 0.001), a finding that was not reproduced for CTPAs (n = 504). Standard deviation (histogram-derived) and relative high attenuation area [600-0 HU] (HU-thresholding) differed most. The strongest correlation with disease severity was found for absolute high attenuation area [600-0 HU] (r = 0.56, 95% CI = 0.46-0.64). Deep-learning segmentation-based histogram and HU threshold analysis could be deployed in chest CT evaluation for the differentiating of healthy lungs from AP lungs.

Correlation of liver-to-spleen ratio, lung CT scores, clinical, and laboratory findings of COVID-19 patients with two consecutive CT scans

PURPOSE

Given the lack of information about abdominal imaging findings and correlation with clinical features of COVID-19, we aimed to evaluate the changes in hepatic attenuation during the course of disease. Our aim was to correlate the liver-to-spleen ratio (L/S), clinical, laboratory findings, and lung CT scores of patients with COVID-19 who had two consecutive chest CTs.

METHODS

A retrospective search was performed between March 1, 2020 and April 26, 2020 to identify patients who had positive RT-PCR tests and two unenhanced chest CTs. Scans that were obtained at hospital admission and follow-up were reviewed to assess L/S and lung CT scores. Patients were divided into two groups based on lung CT scores (non-progressive vs progressive). Patient demographics, laboratory findings, length of hospital stay, and survival were noted from electronic medical records.

RESULTS

Twenty patients in the progressive group and 7 patients in the non-progressive group were identified. The mean L/S of the progressive group (1.13 ± 0.3) was lower than that of the non-progressive group (1.21 ± 0.29) at hospital admission but there was no significant difference between the two groups (p = 0.547). L/S at follow-up was significantly different between the groups as the mean L/S values of the progressive and non-progressive groups were 1.02 ± 0.23 and 1.25 ± 0.29, respectively (p = 0.009). L/S was negatively correlated with AST and ALT (r =  - 0.46, p = 0.016 and r =  - 0.534, p = 0.004, respectively). There were significant differences between the two groups in terms of WBC, neutrophil, lymphocyte, monocyte, and platelet counts that were obtained at hospital admission. Length of hospital stay was significantly longer in patients in the progressive group (p = 0.035).

CONCLUSIONS

Decrease in L/S may be observed in patients with elevated lung CT scores at follow-up. WBC, neutrophil, lymphocyte, monocyte, and platelet counts at hospital admission may predict the progression of COVID-19.

Combining initial chest CT with clinical variables in differentiating coronavirus disease 2019 (COVID-19) pneumonia from influenza pneumonia

Coronavirus disease 2019 (COVID-19) has spread in more than 100 countries and regions around the world, raising grave global concerns. COVID-19 has a similar pattern of infection, clinical symptoms, and chest imaging findings to influenza pneumonia. In this retrospective study, we analysed clinical and chest CT data of 24 patients with COVID-19 and 79 patients with influenza pneumonia. Univariate analysis demonstrated that the temperature, systolic pressure, cough and sputum production could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the clinical features are 0.783 and 0.747, and the AUC value is 0.819. Univariate analysis demonstrates that nine CT features, central-peripheral distribution, superior-inferior distribution, anterior-posterior distribution, patches of GGO, GGO nodule, vascular enlargement in GGO, air bronchogram, bronchiectasis within focus, interlobular septal thickening, could distinguish COVID-19 from influenza pneumonia. The diagnostic sensitivity and specificity for the CT features are 0.750 and 0.962, and the AUC value is 0.927. Finally, a multivariate logistic regression model combined the variables from the clinical variables and CT features models was made. The combined model contained six features: systolic blood pressure, sputum production, vascular enlargement in the GGO, GGO nodule, central-peripheral distribution and bronchiectasis within focus. The diagnostic sensitivity and specificity for the combined features are 0.87 and 0.96, and the AUC value is 0.961. In conclusion, some CT features or clinical variables can differentiate COVID-19 from influenza pneumonia. Moreover, CT features combined with clinical variables had higher diagnostic performance.

Impact of the Coronavirus Disease 2019 (COVID-19) Pandemic on Invasive Pneumococcal Disease and Risk of Pneumococcal Coinfection With Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2): Prospective National Cohort Study, England

BACKGROUND

Streptococcus pneumoniae coinfection with influenza results in synergistic lethality, but there are limited data on pneumococcal coinfection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

METHODS

Public Health England conducts invasive pneumococcal disease (IPD) and SARS-CoV-2 surveillance in England. IPD trends during 2000/2001-2019/2020 epidemiological years were analyzed and cases during February-June 2020 linked with laboratory-confirmed SARS-CoV-2 infections. Multivariable logistic regression was used to assess risk factors for death.

RESULTS

IPD incidence in 2019/2020 (7.6/100 000; n = 3964) was 30% (IRR, .70; 95% CI, .18-2.67) lower compared with 2018/2019 (10.9/100 000; n = 5666), with large reductions observed across all age groups during March-June 2020. There were 160 886 SARS-CoV-2 and 1137 IPD cases during February-June 2020, including 40 IPD/coronavirus disease 2019 (COVID-19) co-infections (.025% [95% CI, .018-.034] of SARS-CoV-2 infections; 3.5% [2.5-4.8] of IPD cases), 21 with COVID-19 diagnosed 3-27 days after IPD, and 27 who developed COVID-19 ≥28 days after IPD. Case-fatality rates (CFRs) were 62.5 (25/40), 47.6% (10/21), and 33.3% (9/27), respectively (P < .001). In addition to an independent association with increasing age and serotype group, CFR was 7.8-fold (95% CI, 3.8-15.8) higher in those with IPD/COVID-19 coinfection and 3.9-fold (95% CI, 1.4-10.7) higher in patients who developed COVID-19 3-27 days after IPD compared with patients with IPD only.

CONCLUSIONS

Large declines in IPD were observed following COVID-19 lockdown. IPD/COVID-19 coinfections were rare but associated with high CFR, mainly in older adults. The rarity, age and serotype distribution of IPD/COVID-19 coinfections do not support wider extension of pneumococcal vaccination.

Prediction of Patient Management in COVID-19 Using Deep Learning-Based Fully Automated Extraction of Cardiothoracic CT Metrics and Laboratory Findings

Objective

To extract pulmonary and cardiovascular metrics from chest CTs of patients with coronavirus disease 2019 (COVID-19) using a fully automated deep learning-based approach and assess their potential to predict patient management.

Materials and Methods

All initial chest CTs of patients who tested positive for severe acute respiratory syndrome coronavirus 2 at our emergency department between March 25 and April 25, 2020, were identified (n = 120). Three patient management groups were defined: group 1 (outpatient), group 2 (general ward), and group 3 (intensive care unit [ICU]). Multiple pulmonary and cardiovascular metrics were extracted from the chest CT images using deep learning. Additionally, six laboratory findings indicating inflammation and cellular damage were considered. Differences in CT metrics, laboratory findings, and demographics between the patient management groups were assessed. The potential of these parameters to predict patients' needs for intensive care (yes/no) was analyzed using logistic regression and receiver operating characteristic curves. Internal and external validity were assessed using 109 independent chest CT scans.

Results

While demographic parameters alone (sex and age) were not sufficient to predict ICU management status, both CT metrics alone (including both pulmonary and cardiovascular metrics; area under the curve [AUC] = 0.88; 95% confidence interval [CI] = 0.79–0.97) and laboratory findings alone (C-reactive protein, lactate dehydrogenase, white blood cell count, and albumin; AUC = 0.86; 95% CI = 0.77–0.94) were good classifiers. Excellent performance was achieved by a combination of demographic parameters, CT metrics, and laboratory findings (AUC = 0.91; 95% CI = 0.85–0.98). Application of a model that combined both pulmonary CT metrics and demographic parameters on a dataset from another hospital indicated its external validity (AUC = 0.77; 95% CI = 0.66–0.88).

Conclusion

Chest CT of patients with COVID-19 contains valuable information that can be accessed using automated image analysis. These metrics are useful for the prediction of patient management.

Pediatric lung imaging features of COVID-19: A systematic review and meta-analysis

RATIONALE

Pediatric COVID-19 studies have been mostly restricted to case reports and small case series, which have prevented the identification of specific pediatric lung disease patterns in COVID-19. The overarching goal of this systematic review and meta-analysis is to provide the first comprehensive summary of the findings of published studies thus far describing COVID-19 lung imaging data in the pediatric population.

METHODS

A systematic literature search of PubMed was performed to identify studies assessing lung-imaging features of COVID-19 pediatric patients (0-18 years). A single-arm meta-analysis was conducted to obtain the pooled prevalence and 95% confidence interval (95% CI).

RESULTS

A total of 29 articles (n = 1026 children) based on chest computerized tomography (CT) images were included. The main results of this comprehensive analysis are as follows: (1) Over a third of pediatric patients with COVID-19 (35.7%, 95% CI: 27.5%-44%) had normal chest CT scans and only 27.7% (95% CI: 19.9%-35.6%) had bilateral lesions. (2) The most typical pediatric chest CT findings of COVID-19 were ground-glass opacities (GGO) (37.2%, 95% CI: 29.3%-45%) and the presence of consolidations or pneumonic infiltrates (22.3%, 95% CI: 17.8%-26.9%). (3) The lung imaging findings in children with COVID-19 were overall less frequent and less severe than in adult patients. (4) Typical lung imaging features of viral respiratory infections in the pediatric population such as increased perihilar markings and hyperinflation were not reported in children with COVID-19.

CONCLUSION

Chest CT manifestations in children with COVID-19 could potentially be used for early identification and prompt intervention in the pediatric population.

Independent role of CT chest scan in COVID-19 prognosis: Evidence from the machine learning classification

Background: The current coronavirus disease-19 (COVID-19) pandemic call attention to the key role informatics play in healthcare. The present study discovers an independent role of computerised tomography chest (CT) scans in prognosis of COVID-19 using classification learning algorithms. Methods: In this retrospective study, 57 RT PCR positive COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India) after approval from the Institutional Ethics Committee. A set of 21 features including clinical findings and laboratory parameters and chest CT severity score were recorded. The CT score with mild, moderate and severe categories was chosen as response variable. The dimensionality reduction of feature space was performed and classifiers including, decision trees, K-nearest neighbours, support vector machine and ensemble learning were trained with principal components. The model with highest accuracy and area under the ROC curve (AUC) was selected. Results: The median age of patients was 55 years (range: 20-99 years) with 37 males. The feature space was reduced from 21 to 7 predictors, that included fever, cough, fibrin degradation products, haemoglobin, neutrophil-lymphocyte ratio, ferritin and procalcitonin. The linear support vector machine was chosen as the best classifier with 73.7 % and 0.69 accuracy and AUC for severe CT chest score, respectively. The variance contributed by first three principal components were 97.5 %, 2.4 % and 0.0 %, respectively. Conclusion: In view of low degree of relationships between predictors and chest CT scan severity score category as interpreted from accuracy and AUC it can be concluded that chest CT scan has an independent role in the prognosis of COVID-19 patients.

Measures of physical performance in COVID-19 patients: a mapping review

BACKGROUND AND OBJECTIVE

There is evidence of short- and long-term impairment of physical performance in patients with COVID-19 infection, but a verification of measures of physical impairment in this condition is lacking. We reviewed the measures used to assess physical performance in these patients. Secondary targets were measures of exercise or daily life activities induced symptoms.

METHODS

Medline, CINAHL, and Pedro databases were searched from January 2020 to February 2021 for articles in the English language. Two investigators independently conducted the search, screened all titles and/or abstracts based on the inclusion criteria and independently scored the studies. The quality of the studies was evaluated by two reviewers according to the NIH quality assessment tool for observational cohort and cross-sectional studies. Discrepancies were resolved through consensus.

RESULTS

Out of 156 potentially relevant articles, 31 observational studies (8 cross-sectional), 1 randomized controlled trial, and 1 protocol were included. The quality of most of the 31 evaluable studies was judged as low (11 studies) or fair (14 studies). Sample sizes of the studies ranged from 14 to 20,889 patients. among the 28 reported measures, Barthel Index (42.4% of studies), Six-Minute Walking Distance Test (36.4%), Short Physical Performance Battery (21.2%) and 1-Minute Sit-to-Stand (12.1%) were the most used. Fifteen% and 36% of studies reported exercise induced desaturation and dyspnoea when performing the assessments, respectively. Other exercise induced symptoms were fatigue and pain. Studies reported wide ranges of impairment in physical performance as compared to "reference" values (range of mean or median reported values vs "reference values": 11-77 vs 100 points for Barthel Index; 11-22 vs 22-37 repetitions/min for 1m-STS; 0.5-7.9 vs 11.4 ± 1.3 points for SPPB; and 45-223 vs 380-782 m for 6MWT respectively).

CONCLUSION

This review found that a wide variety of functional status tests have been used, making comparisons difficult between studies. These measures show impairment in physical performance in COVID-19 patients. However, the quality of most of the studies was judged as low or fair.

RSNA International Trends: A Global Perspective on the COVID-19 Pandemic and Radiology in Late 2020

The coronavirus disease 2019 pandemic has challenged and changed health care systems around the world. There has been a heterogeneity of disease burden, health care resources, and nonimaging testing availability, both geographically and over time. In parallel, there has been a continued increase in understanding how the disease affects patients, effectiveness of therapeutic options, and factors that modulate transmission risk. In this report, radiology experts in representative countries from around the world share insights gained from local experience. These insights provide a guidepost to help address management challenges as cases continue to rise in many parts of the world and suggest modifications in workflow that are likely to continue after this pandemic subsides.

CT findings of 795 COVID-19 positive cases: a multicenter study in Egypt

Background

Corona Virus Disease 2019 (COVID-19) outbreak was officially announced as a global pandemic by the WHO on March 11th 2020. Thorough understanding of CT imaging features of COVID-19 is essential for effective patient management; rationalizing the need for relevant research. The aim of this study was to analyze the chest CT findings of patients with real-time polymerase chain reaction (RT-PCR) proved COVID-19 admitted to four Egyptian hospitals. The recently published RSNA expert consensus statement on reporting COVID-19 chest CT findings was taken into consideration.

Results

Normal CT “negative for COVID-19” was reported in 26.1% of our RT-PCR proved COVID-19 cases. In descending order of prevalence, imaging findings of the positive CT studies (73.9%) included GGO (69%), consolidation (49.7%), crazy paving (15.4%), and peri-lobular fibrosis (40.6%). These showed a dominantly bilateral (68.2%), peripheral (72.4%), and patchy (64.7%) distribution. Remarkably, thymic hyperplasia was identified in 14.3% of studies. According to the RSNA consensus, CT findings were classified as typical in 68.9%, indeterminate in 3.6%, and atypical in 1.4% of the evaluated CT studies.

Conclusion

Although COVID-19 cannot be entirely excluded by chest CT, it can be distinguished in more than two-thirds of cases; making CT a widely available, non-invasive, and rapid diagnostic tool.

Maternal Coronavirus Infections and Neonates Born to Mothers with SARS-CoV-2: A Systematic Review

The coronavirus disease 2019 (COVID-19) pandemic is continuously affecting the lives of all people. Understanding the impact of COVID-19 on pregnancy in terms of morbidity, mortality, and perinatal maternal and fetal outcomes is essential to propose strategies for prevention and infection control. Here, we conducted a systematic review to investigate pregnant women infected with COVID-19 in terms of signs and symptoms, type of delivery, comorbidities, maternal and neonatal outcomes, and the possibility of vertical transmission. A search on Embase and PubMed databases was performed on 31 October 2020. Observational studies and case reports on pregnant women infected with COVID-19 were included without language restrictions. The 70 selected studies included a total of 1457 pregnant women diagnosed with COVID-19 in the first, second, and third trimesters of pregnancy. The most common signs and symptoms were fever, cough, and nausea. The most frequent comorbidities were obesity, hypertensive disorders, and gestational diabetes. Among maternal and fetal outcomes, premature birth (n = 64), maternal death (n = 15), intrauterine fetal death or neonatal death (n = 16), cases of intrauterine fetal distress (n = 28), miscarriage (n = 7), decreased fetal movements (n = 19), and severe neonatal asphyxia (n = 5) were the most frequent. Thirty-nine newborns tested positive for SARS-CoV-2. Additionally, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA was detected in the placenta (n = 13) and breast milk (n = 6). This review indicates that COVID-19 during pregnancy can result in maternal, fetal, and neonatal complications. In addition, SARS-CoV-2 viral exposure of neonates during pregnancy and delivery cannot be ruled out. Thus, we highlight the need for long-term follow-up of newborns from mothers diagnosed with COVID-19 to establish the full implications of SARS-CoV-2 infection in these children.

Thorax Magnetic Resonance Imaging Findings in Patients with Coronavirus Disease (COVID-19)

Rationale and Objectives

The aim of this study was to compare the findings found in thorax computed tomography (CT), which is increasingly used in the diagnosis of the important public health problem of coronavirus disease (COVID-19), and the findings of magnetic resonance imaging (MRI) as an important diagnostic alternative.

Materials and Methods

Thirty-two patients diagnosed with COVID-19 who underwent thorax CT for COVID pneumonia and MRI for any reason within 24 hours after CT were included in the study. The number of lobes affected, number of lobes containing ground-glass opacities and consolidation, number of nodules, distribution of lesions (central, peripheral, or diffuse), lobes with centrilobular nodular pattern, and the presence of pleural effusion were recorded separately for both imaging methods.

Results

Seventeen of the patients were female (53%) and 15 were male (47%). The mean age of the patients was 60.5 (range, 20–85) years. A total of 31 patients (96%) had signs of pneumonia on CT. The most common finding in CT was ground-glass opacities in 29 patients (90.6%), followed by consolidation in 14 patients (43.75%). Both consolidation and ground-glass opacities were also observed in MRI in all of these patients. Nodules were detected in 12 patients (37.5%) on CT and 11 patients (34.4%) on MRI. The sensitivity and specificity of MRI in nodule detection were calculated as 91.67% and 100%, respectively.

Conclusion

Although thorax CT is widely used in the imaging of COVID-19 infection, due to its advantages, MRI can also be used as an alternative diagnostic tool.

Angiotensin-converting Enzyme 2 roles in the Pathogenesis of COVID-19

The new pandemic Coronavirus Disease 2019 (COVID-19) causes a wide range of clinical consequences, from asymptomatic infection to acute respiratory failure and it is very heterogeneous. The renin-angiotensin system (RAS) is well recognized as a key regulating system in circulatory homeostasis that play prominent roles in pathophysiological processes in abnormal activation for instance renal and cardiovascular diseases, obesity, and stroke. Angiotensin converting enzyme 2(ACE2) as a component of the RAS system. However, unlike the ACE, its activity is not inhibited by the ACE inhibitors. The major product of ACE2 is Ang1-7, known as a vasodilator peptide and part of the depressant arm of the RAS. There are two form of ACE2. Coronavirus cover with some proteins in order to help viral attachment to the cell membrane ACE2 as a receptor and then fuse and enter the cells. ACE2 was expressed in oral Cavity, salivary glands of the mouth, esophagus, myocardial cells, kidney, and enterocytes, along all the respiratory tract, intestine, and blood vessels. In this article, we explain the renin-angiotensin system and its components. Also, we shortly explain the organs involved in COVID-19 disease and we will talk about the possible causes of damage to these organs. We also reviewed the probable mechanism of using ACE2 in viral attachment and the probable treatment processes will also be discussed based on the surface proteins of the virus and ACE2. In addition, we briefly discuss the anti-angiotensin drugs and why patients with chronic disease are more susceptible to COVID-19 infection and show worse progression.