Systematic review and meta-analysis of chest radiograph (CXR) findings in COVID-19

Early association between respiratory mechanics and radiological changes in mechanically ventilated critically ill patients with COVID-19

The chest X-ray (CXR) Brixia scoring system was developed exclusively for COVID-19 severity assessment. However, no association between the score and respiratory mechanics during mechanical ventilation has been examined. Our aim was to evaluate the association between the CXR Brixia score and respiratory mechanics on the first day of mechanical ventilation in critically ill COVID-19 patients. A total of 77 COVID-19 patients who underwent mechanical ventilation and CXR in the ICU setting were retrospectively included. The CXR Brixia scoring system was applied, and respiratory mechanics data were recorded on the first day of invasive mechanical ventilation. Median Simplified Acute Physiologic Score II (SAPSII) and Sequential Organ Failure Assessment (SOFA) scores were 40 (31-54) and 6 (4-8), respectively. The median Brixia score was 14 (11-16). The correlation between the Brixia score and static compliance or driving pressure was significant, at r = -0.38, p < 0.001 and r = 0.33, p = 0.003, respectively. Using multivariable linear regression, the model with the B zone was significantly better associated with static compliance (F = 11.5, R2 = 0.14, p = 0.001) and driving pressure (F = 11.3, R2 = 0.13, p = 0.001). In logistic regression analysis, the Brixia score (OR 1.24; 95% CI 1.07, 1.45; p = 0.005), B zone (OR 2.60; 95% CI 1.30, 5.20; p = 0.007), C zone (OR 2.50; 95% CI 1.23, 5.11; p = 0.012), A zone (OR 2.01; 95% CI 1.16, 3.44; p = 0.012), and D zone (OR 1.84; 95% CI 1.07, 3.17; p = 0.027) significantly predicted a driving pressure > 14 cmH2O. There is a relationship between changes in Brixia-scored chest X-ray images and compliance and driving pressure on the first day of invasive mechanical ventilation. We identified some CXR areas using the Brixia score, and evaluation of the Brixia score may provide additional information for predicting respiratory mechanics.

COVID-19 in immunocompromised patients after hematopoietic stem cell transplantation: a pilot study

Data on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in patients at early stage of immune reconstitution after hematopoietic stem cell transplantation (HSCT) are limited. In the present study, we retrospectively investigated the incidence and clinical features of SARS-CoV-2 infection in patients who underwent HSCT in 2022. Patients (allo-HSCT, n = 80; auto-HSCT, n = 37) were consecutively included in the study. The SARS-CoV-2 infection rate was 59.8%, and the median interval of HSCT to coronavirus disease 2019 (COVID-19) was 4.8 (range: 0.5-12) months. Most patients were categorized as mild (41.4%) or moderate (38.6%), and 20% as severe/critical. No deaths were attributable to COVID-19. Further analysis showed that lower circulating CD8+ T-cell counts and calcineurin inhibitor administration increased the risk of SARS-CoV-2 infection. Exposure to rituximab significantly increased the probability of severe or critical COVID-19 compared with that of mild/moderate illness (P < .001). In the multivariate analysis, rituximab use was associated with severe COVID-19. Additionally, COVID-19 had no significant effect on immune reconstitution. Furthermore, it was found that Epstein-Barr virus infection and rituximab administration possibly increase the risk of developing severe illness. Our study provides preliminary insights into the effect of SARS-CoV-2 on immune reconstitution and the outcomes of allo-HSCT recipients.

CODENET: A deep learning model for COVID-19 detection

Conventional COVID-19 testing methods have some flaws: they are expensive and time-consuming. Chest X-ray (CXR) diagnostic approaches can alleviate these flaws to some extent. However, there is no accurate and practical automatic diagnostic framework with good interpretability. The application of artificial intelligence (AI) technology to medical radiography can help to accurately detect the disease, reduce the burden on healthcare organizations, and provide good interpretability. Therefore, this study proposes a new deep neural network (CNN) based on CXR for COVID-19 diagnosis - CodeNet. This method uses contrastive learning to make full use of latent image data to enhance the model's ability to extract features and generalize across different data domains. On the evaluation dataset, the proposed method achieves an accuracy as high as 94.20%, outperforming several other existing methods used for comparison. Ablation studies validate the efficacy of the proposed method, while interpretability analysis shows that the method can effectively guide clinical professionals. This work demonstrates the superior detection performance of a CNN using contrastive learning techniques on CXR images, paving the way for computer vision and artificial intelligence technologies to leverage massive medical data for disease diagnosis.

Texture analysis of chest X-ray images for the diagnosis of COVID-19 pneumonia

Purpose

Medical imaging is one of the main methods of diagnosing COVID-19, along with real-time reverse trans-cription-polymerase chain reaction (RT-PCR) tests. The purpose of the study was to analyse the texture parameters of chest X-rays (CXR) of patients suspected of having COVID-19.

Material and methods

Texture parameters of the CXRs of 70 patients with symptoms typical of COVID-19 infection were analysed using LIFEx software. The regions of interest (ROIs) included each lung separately, for which 57 para-meters were tested. The control group consisted of 30 healthy, age-matched patients with no pathological findings in CXRs.

Results

According to the ROC analysis, 13 of the tested parameters differentiate the radiological image of lungs with COVID-19 features from the image of healthy lungs: GLRLM_LRHGE (AUC 0.91); DISCRETIZED_Q3 (AUC 0.90); GLZLM_HGZE (AUC 0.90); GLRLM_HGRE (AUC 0.89); DISCRETIZED_mean (AUC 0.89); DISCRETIZED_Q2 (AUC 0.61); GLRLM_SRHGE (AUC 0.87); GLZLM_LZHGE (AUC 0.87); GLZLM_SZHGE (AUC 0.84); DISCRETIZED_Q1 (AUC 0.81); NGLDM_Coarseness (AUC 0.70); DISCRETIZED_std (AUC 0.64); CONVENTIONAL_Q2 (AUC 0.61).

Conclusions

Selected texture parameters of radiological CXRs make it possible to distinguish COVID-19 features from healthy ones.

Analysis of an Individualised Stepwise Approach to Non-Pharmacological Therapy in COVID-19

BACKGROUND

Early intubation versus use of conventional or high-flow nasal cannula oxygen therapy (COT/HFNC), continuous positive airway pressure (CPAP), and non-invasive ventilation (NIV) has been debated throughout the COVID-19 pandemic. Our centre followed a stepwise approach, in concordance with German national guidelines, escalating non-invasive modalities prior to invasive mechanical ventilation (IMV) or extracorporeal membrane oxygenation (ECMO), rather than early or late intubation.

OBJECTIVES

The aims of the study were to investigate the real-life usage of these modalities and analyse patient characteristics and survival.

METHOD

A retrospective monocentric observation was conducted of all consecutive COVID-19 hospital admissions between March 2020 and December 2021 at a university-affiliated pulmonary centre in Germany. Anthropometric data, therapy, and survival status were descriptively analysed.

RESULTS

From 1,052 COVID-19-related admissions, 835 patients were included (54% male, median 58 years). Maximum therapy was as follows: 34% (n = 284) no therapy, 40% (n = 337) COT, 3% (n = 22) HFNC, 9% (n = 73) CPAP, 7% (n = 56) NIV, 4% (n = 34) IMV, and 3% (n = 29) ECMO. Of 551 patients treated with at least COT, 12.3% required intubation. Overall, 183 patients required intensive unit care, and 106 (13%) died. Of the 68 patients who received IMV/ECMO, 48 died (74%). The strategy for non-pharmacological therapy was individual but remained consistent throughout the studied period.

CONCLUSIONS

This study provides valuable insight into COVID-19 care in Germany and shows how the majority of patients could be treated with the maximum treatment required according to disease severity following the national algorithm. Escalation of therapy modality is interlinked with disease severity and thus associated with mortality.

Gastrointestinal symptoms among COVID-19 patients presenting to a primary health care center of Nepal: A cross-sectional study

Background and Aim

Coronavirus disease 2019 (COVID-19) is a major public health problem causing significant morbidity and mortality worldwide. Apart from respiratory symptoms, gastrointestinal symptoms like nausea, vomiting, diarrhea, and abdominal discomfort are quite common among COVID-19 patients. The gastrointestinal tract can be a potential site for virus replication and feces a source of transmission. Thus, ignorance of enteric symptoms can hinder effective disease control. The objective of this study is to see the gastrointestinal manifestation of the disease and its effect on morbidity and mortality.

Methods

This observational cross-sectional retrospective study was carried out among 165 laboratory-confirmed COVID-19 patients in primary health care of Gorkha, Nepal from March 1, 2021 to March 1, 2022. A systematic random sampling method was adopted while data were entered and analyzed by Statistical Package for Social Sciences version 21.

Results

Of 165 patients, 97 patients (58.78%) had enteric involvement. Among gastrointestinal symptoms, diarrhea in 67 patients (40.6%) and nausea and/or vomiting in 66 patients (40%) were the most common symptoms, followed by abdominal pain in 27 patients (16.4%) and anorexia in 19 patients (11.5%). Of the majority of cases with gastrointestinal involvement, 63 (63%) were below 50 years of age. Many of the patients who received vaccination had gastrointestinal symptoms (79%). Complications like acute respiratory distress syndrome, shock, and arrhythmia developed in 9.7% of patients, with the death of eight patients. COVID-19 vaccination was associated with 4.32 times higher odds of having gastrointestinal involvement in subsequent COVID-19 infection.

Conclusions

Diarrhea followed by nausea/vomiting was among the most common gastrointestinal symptoms affecting younger age groups in our study. Enteric symptoms were more common among vaccinated people rather than among nonvaccinated ones.

Imaging Acute and Chronic Cardiac Complications of COVID-19 and after COVID-19 Vaccination

COVID-19 is associated with acute and longer-term cardiovascular manifestations including myocardial injury, myopericarditis, stress-induced cardiomyopathy, myocardial infarction, and thromboembolic disease. Although the morbidity and mortality related to acute COVID-19 have decreased substantially, there is growing concern about the longer-term cardiovascular effects of the disease and postacute sequelae. Myocarditis has also been reported after messenger ribonucleic acid (mRNA)-based COVID-19 vaccination, with the highest risk among adolescent boys and young adult men. Noninvasive imaging including cardiac MRI has a key role in identifying the presence of cardiovascular disease, evaluating for potential mechanisms of injury, stratifying risk of future adverse cardiovascular events, and potentially guiding treatment in patients with suspected cardiovascular injury after COVID-19 and vaccination. Patterns of injury identified at cardiac MRI after COVID-19 include myocarditis and pericarditis, myocardial ischemia, and infarction. Myocardial edema and late gadolinium enhancement have been described months after the initial infection in a minority of patients with persistent cardiac symptoms after COVID-19. In patients with myocarditis after receiving a COVID-19 vaccination, the most common pattern of late gadolinium enhancement is subepicardial at the basal inferolateral wall, and patients tend to have milder imaging abnormalities compared with those from other causes of myocarditis. This article describes the role of multimodality cardiac imaging and imaging findings in patients with acute and longer-term cardiovascular manifestations of COVID-19 and in patients with myocarditis after receiving an mRNA-based COVID-19 vaccination. ©RSNA, 2023 Online supplemental material is available for this article. Quiz questions for this article are available through the Online Learning Center.

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.

Magnetic Resonance Imaging of Cardiovascular Manifestations Following COVID-19

Globally, over 650 million people have had COVID-19 due to infection with the SARS-Cov-2 virus. Cardiac complications in the acute infectious and early recovery phase were recognized early in the pandemic, including myocardial injury and inflammation. With a decrease in the number of acute COVID-19 related deaths, there has been increased interest in postacute sequela of COVID-19 (PASC) and other longer-term cardiovascular complications. A proportion of patients recovered from COVID-19 have persistent cardiac symptoms and are at risk of cardiovascular disease. Cardiovascular imaging, including MRI, plays an important role in the detection of cardiovascular manifestations of COVID-19 in both the acute and longer-term phases after COVID-19. The purpose of this review is to highlight the role of cardiovascular imaging in the diagnosis and risk stratification of patients with acute and chronic cardiovascular manifestations of COVID-19 with a focus on cardiac MRI. EVIDENCE LEVEL: 4. TECHNICAL EFFICACY: Stage 3.

An XAI approach for COVID-19 detection using transfer learning with X-ray images

The coronavirus disease (COVID-19) has continued to cause severe challenges during this unprecedented time, affecting every part of daily life in terms of health, economics, and social development. There is an increasing demand for chest X-ray (CXR) scans, as pneumonia is the primary and vital complication of COVID-19. CXR is widely used as a screening tool for lung-related diseases due to its simple and relatively inexpensive application. However, these scans require expert radiologists to interpret the results for clinical decisions, i.e., diagnosis, treatment, and prognosis. The digitalization of various sectors, including healthcare, has accelerated during the pandemic, with the use and importance of Artificial Intelligence (AI) dramatically increasing. This paper proposes a model using an Explainable Artificial Intelligence (XAI) technique to detect and interpret COVID-19 positive CXR images. We further analyze the impact of COVID-19 positive CXR images using heatmaps. The proposed model leverages transfer learning and data augmentation techniques for faster and more adequate model training. Lung segmentation is applied to enhance the model performance further. We conducted a pre-trained network comparison with the highest classification performance (F1-Score: 98%) using the ResNet model.

The association of clinically relevant variables with chest radiograph lung disease burden quantified in real-time by radiologists upon initial presentation in individuals hospitalized with COVID-19

Objectives

We aimed to correlate lung disease burden on presentation chest radiographs (CXR), quantified at the time of study interpretation, with clinical presentation in patients hospitalized with coronavirus disease 2019 (COVID-19).

Material and methods

This retrospective cross-sectional study included 5833 consecutive adult patients, aged 18 and older, hospitalized with a diagnosis of COVID-19 with a CXR quantified in real-time while hospitalized in 1 of 12 acute care hospitals across a multihospital integrated healthcare network between March 24, 2020, and May 22, 2020. Lung disease burden was quantified in real-time by 118 radiologists on 5833 CXR at the time of exam interpretation with each lung annotated by the degree of lung opacity as clear (0%), mild (1–33%), moderate (34–66%), or severe (67–100%). CXR findings were classified as (1) clear versus disease, (2) unilateral versus bilateral, (3) symmetric versus asymmetric, or (4) not severe versus severe. Lung disease burden was characterized on initial presentation by patient demographics, co-morbidities, vital signs, and lab results with chi-square used for univariate analysis and logistic regression for multivariable analysis.

Results

Patients with severe lung disease were more likely to have oxygen impairment, an elevated respiratory rate, low albumin, high lactate dehydrogenase, and high ferritin compared to non-severe lung disease. A lack of opacities in COVID-19 was associated with a low estimated glomerular filtration rate, hypernatremia, and hypoglycemia.

Conclusions

COVID-19 lung disease burden quantified in real-time on presentation CXR was characterized by demographics, comorbidities, emergency severity index, Charlson Comorbidity Index, vital signs, and lab results on 5833 patients. This novel approach to real-time quantified chest radiograph lung disease burden by radiologists needs further research to understand how this information can be incorporated to improve clinical care for pulmonary-related diseases.. An absence of opacities in COVID-19 may be associated with poor oral intake and a prerenal state as evidenced by the association of clear CXRs with a low eGFR, hypernatremia, and hypoglycemia.

Automated prediction of COVID-19 severity upon admission by chest X-ray images and clinical metadata aiming at accuracy and explainability

In the past few years COVID-19 posed a huge threat to healthcare systems around the world. One of the first waves of the pandemic hit Northern Italy severely resulting in high casualties and in the near breakdown of primary care. Due to these facts, the Covid CXR Hackathon-Artificial Intelligence for Covid-19 prognosis: aiming at accuracy and explainability challenge had been launched at the beginning of February 2022, releasing a new imaging dataset with additional clinical metadata for each accompanying chest X-ray (CXR). In this article we summarize our techniques at correctly diagnosing chest X-ray images collected upon admission for severity of COVID-19 outcome. In addition to X-ray imagery, clinical metadata was provided and the challenge also aimed at creating an explainable model. We created a best-performing, as well as, an explainable model that makes an effort to map clinical metadata to image features whilst predicting the prognosis. We also did many ablation studies in order to identify crucial parts of the models and the predictive power of each feature in the datasets. We conclude that CXRs at admission do not help the predicting power of the metadata significantly by itself and contain mostly information that is also mutually present in the blood samples and other clinical factors collected at admission.

Does the variant positivity and negativity affect the clinical course in COVID-19?: A cohort study

The primary aim of the current study is to analyze the clinical, laboratory, and demographic data comparing the patients with Coronavirus Disease 2019 (COVID-19) admitted to our intensive care unit before and after the UK variant was first diagnosed in December 2020. The secondary objective was to describe a treatment approach for COVID-19. Between Mar 12, 2020, and Jun 22, 2021, 159 patients with COVID-19 were allocated into 2 groups: the variant negative group (77 patients before December 2020) and the variant positive group (82 patients after December 2020). The statistical analyses included early and late complications, demographic data, symptoms, comorbidities, intubation and mortality rates, and treatment options. Regarding early complications, unilateral pneumonia was more common in the variant (-) group (P = .019), whereas bilateral pneumonia was more common in the variant (+) group (P < .001). Regarding late complications, only cytomegalovirus pneumonia was observed more frequently in the variant (-) group (P = .023), whereas secondary gram (+) infection, pulmonary fibrosis (P = .048), acute respiratory distress syndrome (ARDS) (P = .017), and septic shock (P = .051) were more common in the variant (+) group. The therapeutic approach showed significant differences in the second group such as plasma exchange and extracorporeal membrane oxygenation which is more commonly used in the variant (+) group. Although mortality and intubation rates did not differ between the groups, severe challenging early and late complications were observed mainly in the variant (+) group, necessitating invasive treatment options. We hope that our data from the pandemic will shed light on this field. Regarding the COVID-19 pandemic, it is clear that there is much to be done to deal with future pandemics.

Risk stratification of hospital admissions for COVID-19 pneumonia by chest radiographic scoring in a Johannesburg tertiary hospital

BACKGROUND

Chest radiographic scoring systems for COVID-19 pneumonia have been developed. However, little is published on the utilityof these scoring systems in low- and middle-income countries.

OBJECTIVES

To perform risk stratification of COVID-19 pneumonia in Johannesburg, South Africa (SA), by comparing the Brixia score withclinical parameters, disease course and clinical outcomes. To assess inter-rater reliability and developing predictive models of the clinicaloutcome using the Brixia score and clinical parameters.

METHODS

Retrospective investigation was conducted of adult participants with established COVID-19 pneumonia admitted at a tertiaryinstitution from 1 May to 30 June 2020. Two radiologists, blinded to clinical data, assigned Brixia scores. Brixia scores were compared withclinical parameters, length of stay and clinical outcomes (discharge/death). Inter-rater agreement was determined. Multivariable logisticregression extracted variables predictive of in-hospital demise.

RESULTS

The cohort consisted of 263 patients, 51% male, with a median age of 47 years (interquartile range (IQR) = 20; 95% confidenceinterval (CI) 46.5 - 49.9). Hypertension (38.4%), diabetes (25.1%), obesity (19.4%) and HIV (15.6%) were the most common comorbidities.The median length of stay for 258 patients was 7.5 days (IQR = 7; 95% CI 8.2 - 9.7) and 6.5 days (IQR = 8; 95% CI 6.5 - 12.5) for intensivecare unit stay. Fifty (19%) patients died, with a median age of 55 years (IQR = 23; 95% CI 50.5 - 58.7) compared with survivors, of medianage 46 years (IQR = 20; 95% CI 45 - 48.6) (p=0.01). The presence of one or more comorbidities resulted in a higher death rate (23% v. 9.2%;p=0.01) than without comorbidities. The median Brixia score for the deceased was higher (14.5) than for the discharged patients (9.0)(p<0.001). Inter-rater agreement for Brixia scores was good (intraclass correlation coefficient 0.77; 95% CI 0.6 - 0.85; p<0.001). A modelcombining Brixia score, age, male gender and obesity (sensitivity 84%; specificity 63%) as well as a model with Brixia score and C-reactiveprotein (CRP) count (sensitivity 81%; specificity 63%) conferred the highest risk for in-hospital mortality.

CONCLUSION

We have demonstrated the utility of the Brixia scoring system in a middle-income country setting and developed the first SArisk stratification models incorporating comorbidities and a serological marker. When used in conjunction with age, male gender, obesityand CRP, the Brixia scoring system is a promising and reliable risk stratification tool. This may help inform the clinical decision pathway inresource-limited settings like ours during future waves of COVID-19.

Current and Emerging Knowledge in COVID-19

COVID-19 has emerged as a pandemic leading to a global public health crisis of unprecedented morbidity. A comprehensive insight into the imaging of COVID-19 has enabled early diagnosis, stratification of disease severity, and identification of potential sequelae. The evolution of COVID-19 can be divided into early infectious, pulmonary, and hyperinflammatory phases. Clinical features, imaging features, and management are different among the three phases. In the early stage, peripheral ground-glass opacities are predominant CT findings, and therapy directly targeting SARS-CoV-2 is effective. In the later stage, organizing pneumonia or diffuse alveolar damage pattern are predominant CT findings and anti-inflammatory therapies are more beneficial. The risk of severe disease or hospitalization is lower in breakthrough or Omicron variant infection compared with nonimmunized or Delta variant infections. The protection rates of the fourth dose of mRNA vaccination were 34% and 67% against overall infection and hospitalizations for severe illness, respectively. After acute COVID-19 pneumonia, most residual CT abnormalities gradually decreased in extent, but they may remain as linear or multifocal reticular or cystic lesions. Advanced insights into the pathophysiologic and imaging features of COVID-19 along with vaccine benefits have improved patient care, but emerging knowledge of post-COVID-19 condition, or long COVID, also presents radiology with new challenges.

Comparative analysis of chest radiography and lung ultrasound to predict intra-hospital prognosis of patients admitted for acute SARS-CoV-2 pneumonia (COVID-19)

Background

Point of care lung ultrasound (POCUS) has been recently used to assess prognosis in COVID-19 patients. However, there are no data comparing POCUS and chest-X ray, a technique widely used.

Patients and methods

Retrospective analysis in stable COVID-19 patients. Schalekamp radiological lung scale and LUZ-Score ultrasound scale were compared. Primary end-point was in-hospital death and/or need for Intensive Care Unit admission.

Results

A total of 138 patients were included. Median Schalekamp scale was 2 (2) and median LUZ-Score scale was 21 (10). No significant correlation was observed between both techniques. Patients with a LUZ-Score ≥ 21 points at admission had worse lung function and higher concentrations of LDH, CRP and Interleuquine-6. Schalekamp scale failed to identify patients at a higher risk at admission for the primary end-point. Addition of POCUS to a previous clinical model, improved risk prediction (AUC 0.805 [95% CI: 0.662-0.948]; P = <0.001).

Conclusions

Chest X-ray and POCUS showed no correlation at admission in this analysis. Only POCUS identified a group of patients with greater clinical and analytical involvement. POCUS improved, previous clinical model, while chest X-ray did not add relevant predictive information for the primary endpoint.

COVID-19 pneumonia: lessons learned, challenges, and preparing for the future

Coronavirus disease 2019 (COVID-19) is a viral disease that causes life-threatening health problems during acute illness, causing a pandemic and millions of deaths. Although computed tomography (CT) was used as a diagnostic tool for COVID-19 in the early period of the pan demic due to the inaccessibility or long duration of the polymerase chain reaction tests, cur rent studies have revealed that CT scan should not be used to diagnose COVID-19. However, radiologic findings are vital in assessing pneumonia severity and investigating complications in patients with COVID-19. Long-term symptoms, also known as long COVID, in people recovering from COVID-19 affect patients' quality of life and cause global health problems. Herein, we aimed to summarize the lessons learned in COVID-19 pneumonia, the challenges in diagnosing the disease and complications, and the prospects for future studies.

Molecular imaging as a tool for evaluation of COVID-19 sequelae – A review of literature

Coronavirus disease 2019 (COVID-19) is caused by the novel viral pathogen, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). COVID-19 primarily involves the lungs. Nucleic acid testing based on reverse-transcription polymerase chain reaction of respiratory samples is the current gold standard for the diagnosis of SARS-CoV-2 infection. Imaging modalities have an established role in triaging, diagnosis, evaluation of disease severity, monitoring disease progression, extra-pulmonary involvement, and complications. As our understanding of the disease improves, there has been substantial evidence to highlight its potential for multi-systemic involvement and development of long-term sequelae. Molecular imaging techniques are highly sensitive, allowing non-invasive visualization of physiological or pathological processes at a cellular or molecular level with potential for detection of functional changes earlier than conventional radiological imaging. The purpose of this review article is to highlight the evolving role of molecular imaging in evaluation of COVID-19 sequelae. Though not ideal for diagnosis, the various modalities of molecular imaging play an important role in assessing pulmonary and extra-pulmonary sequelae of COVID-19. Perfusion imaging using single photon emission computed tomography fused with computed tomography (CT) can be utilized as a first-line imaging modality for COVID-19 related pulmonary embolism. ¹⁸F-fluorodeoxyglucose positron emission tomography (PET)/CT is a sensitive tool to detect multi-systemic inflammation, including myocardial and vascular inflammation. PET in conjunction with magnetic resonance imaging helps in better characterization of neurological sequelae of COVID-19. Despite the fact that the majority of published literature is retrospective in nature with limited sample sizes, it is clear that molecular imaging provides additional valuable information (complimentary to anatomical imaging) with semi-quantitative or quantitative parameters to define inflammatory burden and can be used to guide therapeutic strategies and assess response. However, widespread clinical applicability remains a challenge owing to longer image acquisition times and the need for adoption of infection control protocols.

Clinical update on COVID-19 for the emergency clinician: Presentation and evaluation

INTRODUCTION

Coronavirus disease of 2019 (COVID-19) has resulted in millions of cases worldwide. As the pandemic has progressed, the understanding of this disease has evolved.

OBJECTIVE

This first in a two-part series on COVID-19 updates provides a focused overview of the presentation and evaluation of COVID-19 for emergency clinicians.

DISCUSSION

COVID-19, caused by Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), has resulted in significant morbidity and mortality worldwide. Several variants exist, including a variant of concern known as Delta (B.1.617.2 lineage) and the Omicron variant (B.1.1.529 lineage). The Delta variant is associated with higher infectivity and poor patient outcomes, and the Omicron variant has resulted in a significant increase in infections. While over 80% of patients experience mild symptoms, a significant proportion can be critically ill, including those who are older and those with comorbidities. Upper respiratory symptoms, fever, and changes in taste/smell remain the most common presenting symptoms. Extrapulmonary complications are numerous and may be severe, including the cardiovascular, neurologic, gastrointestinal, and dermatologic systems. Emergency department evaluation includes focused testing for COVID-19 and assessment of end-organ injury. Imaging may include chest radiography, computed tomography, or ultrasound. Several risk scores may assist in prognostication, including the 4C (Coronavirus Clinical Characterisation Consortium) score, quick COVID Severity Index (qCSI), NEWS2, and the PRIEST score, but these should only supplement and not replace clinical judgment.

CONCLUSION

This review provides a focused update of the presentation and evaluation of COVID-19 for emergency clinicians.

Análisis comparativo de la radiografía de tórax y la ecografía pulmonar para predecir el pronóstico intra-hospitalario de pacientes ingresados por neumonía secundaria a SARS-COV2 (COVID-19)

Antecedentes

La ecografía torácica es una técnica novedosa para estratificar el riesgo de los pacientes COVID-19. Sin embargo, no existen datos que comparen dicha técnica con la radiografía de tórax, una técnica ampliamente utilizada en esta enfermedad.

Pacientes y métodos

Análisis retrospectivo en pacientes estables COVID-19. Se compararon la escala de daño pulmonar radiológica de Schalekamp y ecográfica de LUZ-Score. El objetivo primario fue la muerte intrahospitalaria o la necesidad de ingreso en la UCI para tratamiento con ventilación mecánica.

Resultados

Se reclutaron 138 pacientes. La mediana de la escala de Schalekamp fue de 2 (2) y la del LUZ-Score de 21 (10). No se objetivó una correlación significativa entre ambas escalas. Los pacientes con un LUZ-Score ≥ 21 puntos al ingreso presentaron peor función pulmonar y mayores concentraciones de LDH, PCR e interleucina-6. La escala radiológica de Schalekamp no logró identificar a una población de mayor riesgo. Únicamente la adición de la ecografía pulmonar a un modelo de valoración clínica mejoró de manera significativa el área bajo la curva para el objetivo primario (ABC 0,805 [IC 95%: 0,662-0,948]; p ≤ 0,001).

Conclusiones

No se objetivó una correlación entre la afectación radiológica y la ecográfica. Únicamente la ecografía pulmonar identificó un subgrupo de pacientes con una mayor afectación clínico-analítica. La ecografía pulmonar mejoró el modelo de predicción clínico, mientras que la radiografía de tórax no añadió información relevante.

Post-infectious ST elevation myocardial infarction following a COVID-19 infection: A case report

Severe Acute Respiratory Syndrome-Coronavirus-2 (SARS-CoV-2) has been shown to increase the risk of thrombotic events due to a hypercoagulable state caused by several factors. The case of a 59-year-old woman affected by hypertension and metabolic disorders, treated for a COVID-19 infection who developed cardiac symptoms during the first days of hospitalization is reported. Electrocardiogram analysis and cardiac-ultrasound confirmed ST-segment elevation myocardial infarction (STEMI) diagnosis, thus the patient underwent percutaneous coronary intervention, which was successful. This case highlights a possible association between respiratory infection, particularly SARS-CoV-2 infection, and cardiovascular events, in particular Acute Coronary Syndrome. The association between these phenomena seems related to a range of factors, including a proinflammatory state and the hypoxemia. Moreover, the association amongst SARS-CoV-2 and cardiovascular diseases may be also linked to long-term sequelae. Thus, further studies are required to better understand the multifaceted and severe complications of this disease.

Still a role for the chest radiograph - humble but helpful!

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Multireader evaluation of radiologist performance for COVID-19 detection on emergency department chest radiographs

BACKGROUND

Chest radiographs (CXR) are frequently used as a screening tool for patients with suspected COVID-19 infection pending reverse transcriptase polymerase chain reaction (RT-PCR) results, despite recommendations against this. We evaluated radiologist performance for COVID-19 diagnosis on CXR at the time of patient presentation in the Emergency Department (ED).

MATERIALS AND METHODS

We extracted RT-PCR results, clinical history, and CXRs of all patients from a single institution between March and June 2020. 984 RT-PCR positive and 1043 RT-PCR negative radiographs were reviewed by 10 emergency radiologists from 4 academic centers. 100 cases were read by all radiologists and 1927 cases by 2 radiologists. Each radiologist chose the single best label per case: Normal, COVID-19, Other - Infectious, Other - Noninfectious, Non-diagnostic, and Endotracheal Tube. Cases labeled with endotracheal tube (246) or non-diagnostic (54) were excluded. Remaining cases were analyzed for label distribution, clinical history, and inter-reader agreement.

RESULTS

1727 radiographs (732 RT-PCR positive, 995 RT-PCR negative) were included from 1594 patients (51.2% male, 48.8% female, age 59 ± 19 years). For 89 cases read by all readers, there was poor agreement for RT-PCR positive (Fleiss Score 0.36) and negative (Fleiss Score 0.46) exams. Agreement between two readers on 1638 cases was 54.2% (373/688) for RT-PCR positive cases and 71.4% (679/950) for negative cases. Agreement was highest for RT-PCR negative cases labeled as Normal (50.4%, n = 479). Reader performance did not improve with clinical history or time between CXR and RT-PCR result.

CONCLUSION

At the time of presentation to the emergency department, emergency radiologist performance is non-specific for diagnosing COVID-19.

The role of chest imaging in the diagnosis, management, and monitoring of coronavirus disease 2019 (COVID-19)

Coronavirus disease 2019 (COVID-19) pandemic has posed a major public health crisis all over the world. The role of chest imaging, especially computed tomography (CT), has evolved during the pandemic paralleling the accumulation of scientific evidence. In the early stage of the pandemic, the performance of chest imaging for COVID-19 has widely been debated especially in the context of comparison to real-time reverse transcription polymerase chain reaction. Current evidence is against the use of chest imaging for routine screening of COVID-19 contrary to the initial expectations. It still has an integral role to play, however, in its work up and staging, especially when assessing complications or disease progression. Chest CT is gold standard imaging modality for COVID-19 pneumonia; in some situations, chest X-ray or ultrasound may be an effective alternative. The most important role of radiologists in this context is to be able to identify those patients at greatest risk of imminent clinical decompensation by learning to stratify cases of COVID-19 on the basis of radiologic imaging in the most efficient and timely fashion possible. The present availability of multiple and more refined CT grading systems and classification is now making this task easier and thereby contributing to the recent improvements achieved in COVID-19 treatment and outcomes. In this article, evidence of chest imaging regarding diagnosis, management and monitoring of COVID-19 will be chronologically reviewed.