Association between chest X-ray score and clinical outcome in COVID-19 patients: A study on modified radiographic assessment of lung edema score (mRALE) in Indonesia.

Radiological examinations such as chest X-rays (CXR) play a crucial role in the early diagnosis and determining disease severity in coronavirus disease 2019 (COVID-19). Various CXR scoring systems have been developed to quantitively assess lung abnormalities in COVID-19 patients, including CXR modified radiographic assessment of lung edema (mRALE). The aim of this study was to determine the relationship between mRALE scores and clinical outcome (mortality), as well as to identify the correlation between mRALE score and the severity of hypoxia (PaO2/FiO2 ratio). A retrospective cohort study was conducted among hospitalized COVID-19 patients at Dr. Soetomo General Academic Hospital Surabaya, Indonesia, from February to April 2022. All CXR data at initial admission were scored using the mRALE scoring system, and the clinical outcomes at the end of hospitalization were recorded. Of the total 178 COVID-19 patients, 62.9% survived after completing the treatment. Patients within non-survived had significantly higher quick sequential organ failure assessment (qSOFA) score (p<0.001), lower PaO2/FiO2 ratio (p=0.004), and higher blood urea nitrogen (p<0.001), serum creatinine (p<0.008) and serum glutamic oxaloacetic transaminase (p=0.001) levels. There was a significant relationship between mRALE score and clinical outcome (survived vs deceased) (p=0.024; contingency coefficient of 0.184); and mRALE score of ≥2.5 served as a risk factor for mortality among COVID-19 patients (relative risk of 1.624). There was a significant negative correlation between the mRALE score and PaO2/FiO2 ratio based on the Spearman correlation test (r=-0.346; p<0.001). The findings highlight that the initial mRALE score may serve as an independent predictor of mortality among hospitalized COVID-19 patients as well as proves its potential prognostic role in the management of COVID-19.

Artificial intelligence in the healthcare sector: comparison of deep learning networks using chest X-ray images.

Purpose: Artificial intelligence has led to significant developments in the healthcare sector, as in other sectors and fields. In light of its significance, the present study delves into exploring deep learning, a branch of artificial intelligence. Methods: In the study, deep learning networks ResNet101, AlexNet, GoogLeNet, and Xception were considered, and it was aimed to determine the success of these networks in disease diagnosis. For this purpose, a dataset of 1,680 chest X-ray images was utilized, consisting of cases of COVID-19, viral pneumonia, and individuals without these diseases. These images were obtained by employing a rotation method to generate replicated data, wherein a split of 70 and 30% was adopted for training and validation, respectively. Results: The analysis findings revealed that the deep learning networks were successful in classifying COVID-19, Viral Pneumonia, and Normal (disease-free) images. Moreover, an examination of the success levels revealed that the ResNet101 deep learning network was more successful than the others with a 96.32% success rate. Conclusion: In the study, it was seen that deep learning can be used in disease diagnosis and can help experts in the relevant field, ultimately contributing to healthcare organizations and the practices of country managers.

Triplet-constrained deep hashing for chest X-ray image retrieval in COVID-19 assessment.

Radiology images of the chest, such as computer tomography scans and X-rays, have been prominently used in computer-aided COVID-19 analysis. Learning-based radiology image retrieval has attracted increasing attention recently, which generally involves image feature extraction and finding matches in extensive image databases based on query images. Many deep hashing methods have been developed for chest radiology image search due to the high efficiency of retrieval using hash codes. However, they often overlook the complex triple associations between images; that is, images belonging to the same category tend to share similar characteristics and vice versa. To this end, we develop a triplet-constrained deep hashing (TCDH) framework for chest radiology image retrieval to facilitate automated analysis of COVID-19. The TCDH consists of two phases, including (a) feature extraction and (b) image retrieval. For feature extraction, we have introduced a triplet constraint and an image reconstruction task to enhance discriminative ability of learned features, and these features are then converted into binary hash codes to capture semantic information. Specifically, the triplet constraint is designed to pull closer samples within the same category and push apart samples from different categories. Additionally, an auxiliary image reconstruction task is employed during feature extraction to help effectively capture anatomical structures of images. For image retrieval, we utilize learned hash codes to conduct searches for medical images. Extensive experiments on 30,386 chest X-ray images demonstrate the superiority of the proposed method over several state-of-the-art approaches in automated image search. The code is now available online.

Integrated assessment of computed tomography density in pectoralis and erector spinae muscles as a prognostic biomarker for coronavirus disease 2019.

BACKGROUND ＆ AIMS: Muscle quantification using chest computed tomography (CT) is a useful prognostic biomarker for coronavirus disease 2019 (COVID-19). However, no studies have evaluated the clinical course through comprehensive assessment of the pectoralis and erector spinae muscles. Therefore, we compared the impact of the areas and densities of these muscles on COVID-19 infection outcome. METHODS: This multicenter retrospective cohort study was conducted by the COVID-19 Task Force. A total of 1410 patients with COVID-19 were included, and data on the area and density of the pectoralis and erector spinae muscles on chest CT were collected. The impact of each muscle parameter on the clinical outcome of COVID-19 was stratified according to sex. The primary outcome was the percentage of patients with severe disease, including those requiring oxygen supplementation and those who died. Additionally, 167 patients were followed up for changes in muscle parameters at three months and for the clinical characteristics in case of reduced CT density. RESULTS: For both muscles, low density rather than muscle area was associated with COVID-19 severity. Regardless of sex, lower erector spinae muscle density was associated with more severe disease than pectoralis muscle density. The muscles were divided into two groups using the receiver operating characteristic curve of CT density, and the population was classified into four (Group A: high CT density for both muscles, Group B: low CT density for pectoralis and high for erector spinae muscle. Group C: high CT density for pectoralis and low for erector spinae muscle, Group D: low CT density for both muscles). In univariate analysis, Group D patients exhibited worse outcomes than Group A (OR: 2.96, 95% CI: 2.03-4.34 in men; OR: 3.02, 95% CI: 2.66-10.4 in women). Multivariate analysis revealed that men in Group D had a significantly more severe prognosis than those in Group A (OR: 1.82, 95% CI: 1.16-2.87). Moreover, Group D patients tended to have the highest incidence of other complications due to secondary infections and acute kidney injury during the clinical course. Longitudinal analysis of both muscle densities over three months revealed that patients with decreased muscle density over time were more likely to have severe cases than those who did not. CONCLUSIONS: Muscle density, rather than muscle area, predicts the clinical outcomes of COVID-19. Integrated assessment of pectoralis and erector spinae muscle densities demonstrated higher accuracy in predicting the clinical course of COVID-19 than individual assessments.

Evaluating a novel accelerated free-breathing late gadolinium enhancement imaging sequence for assessment of myocardial injury.

INTRODUCTION: Cardiac magnetic resonance imaging (MRI), including late gadolinium enhancement (LGE), plays an important role in the diagnosis and prognostication of ischemic and non-ischemic myocardial injury. Conventional LGE sequences require patients to perform multiple breath-holds and require long acquisition times. In this study, we compare image quality and assessment of myocardial LGE using an accelerated free-breathing sequence to the conventional standard-of-care sequence. METHODS: In this prospective cohort study, a total of 41 patients post Coronavirus 2019 (COVID-19) infection were included. Studies were performed on a 1.5 Tesla scanner with LGE imaging acquired using a conventional inversion recovery rapid gradient echo (conventional LGE) sequence followed by the novel accelerated free-breathing (FB-LGE) sequence. Image quality was visually scored (ordinal scale from 1 to 5) and compared between conventional and free-breathing sequences using the Wilcoxon rank sum test. Presence of per-segment LGE was identified according to the American Heart Association 16-segment myocardial model and compared across both conventional LGE and FB-LGE sequences using a two-sided chi-square test. The perpatient LGE extent was also evaluated using both sequences and compared using the Wilcoxon rank sum test. Interobserver variability in detection of per-segment LGE and per-patient LGE extent was evaluated using Cohen's kappa statistic and interclass correlation (ICC), respectively. RESULTS: The mean acquisition time for the FB-LGE sequence was 17 s compared to 413 s for the conventional LGE sequence (P < 0.001). Assessment of image quality was similar between both sequences (P = 0.19). There were no statistically significant differences in LGE assessed using the FB-LGE versus conventional LGE on a per-segment (P = 0.42) and per-patient (P = 0.06) basis. Interobserver variability in LGE assessment for FB-LGE was good for per-segment (= 0.71) and per-patient extent (ICC = 0.92) analyses. CONCLUSIONS: The accelerated FB-LGE sequence performed comparably to the conventional standard-of-care LGE sequence in a cohort of patients post COVID-19 infection in a fraction of the time and without the need for breath-holding. Such a sequence could impact clinical practice by increasing cardiac MRI throughput and accessibility for frail or acutely ill patients unable to perform breath-holding.

Self-attention-driven retrieval of chest CT images for COVID-19 assessment.

Numerous methods have been developed for computer-aided diagnosis (CAD) of coronavirus disease-19 (COVID-19), based on chest computed tomography (CT) images. The majority of these methods are based on deep neural networks and often act as "black boxes" that cannot easily gain the trust of medical community, whereas their result is uniformly influenced by all image regions. This work introduces a novel, self-attention-driven method for content-based image retrieval (CBIR) of chest CT images. The proposed method analyzes a query CT image and returns a classification result, as well as a list of classified images, ranked according to similarity with the query. Each CT image is accompanied by a heatmap, which is derived by gradient-weighted class activation mapping (Grad-CAM) and represents the contribution of lung tissue and lesions to COVID-19 pathology. Beyond visualization, Grad-CAM weights are employed in a self-attention mechanism, in order to strengthen the influence of the most COVID-19-related image regions on the retrieval result. Experiments on two publicly available datasets demonstrate that the binary classification accuracy obtained by means of DenseNet-201 is 81.3% and 96.4%, for COVID-CT and SARS-CoV-2 datasets, respectively, with a false negative rate which is less than 3% in both datasets. In addition, the Grad-CAM-guided CBIR framework slightly outperforms the plain CBIR in most cases, with respect to nearest neighbour (NN) and first four (FF). The proposed method could serve as a computational tool for a more transparent decision-making process that could be trusted by the medical community. In addition, the employed self-attention mechanism increases the obtained retrieval performance.

Multiparametric Cardiovascular MRI Assessment of Post-COVID Syndrome in Children in Comparison to Matched Healthy Individuals.

BACKGROUND: Post-COVID syndrome (PCS) can adversely affect the quality of life of patients and their families. In particular, the degree of cardiac impairment in children with PCS is unknown. OBJECTIVE: The aim of this study was to identify potential cardiac inflammatory sequelae in children with PCS compared with healthy controls. METHODS: This single-center, prospective, intraindividual, observational study assesses cardiac function, global and segment-based strains, and tissue characterization in 29 age- and sex-matched children with PCS and healthy children using a 3 T magnetic resonance imaging (MRI). RESULTS: Cardiac MRI was carried out over 36.4 ± 24.9 weeks post-COVID infection. The study cohort has an average age of 14.0 ± 2.8 years, for which the majority of individuals experience from fatigue, concentration disorders, dyspnea, dizziness, and muscle ache. Children with PSC in contrast to the control group exhibited elevated heart rate (83.7 ± 18.1 beats per minute vs 75.2 ± 11.2 beats per minute, P = 0.019), increased indexed right ventricular end-diastolic volume (95.2 ± 19.2 mlm -2 vs 82.0 ± 21.5 mlm -2 , P = 0.018) and end-systolic volume (40.3 ± 7.9 mlm -2 vs 34.8 ± 6.2 mlm -2 , P = 0.005), and elevated basal and midventricular T1 and T2 relaxation times ( P < 0.001 to P = 0.013). Based on the updated Lake Louise Criteria, myocardial inflammation is present in 20 (69%) children with PCS. No statistically significant difference was observed for global strains. CONCLUSIONS: Cardiac MRI revealed altered right ventricular volumetrics and elevated T1 and T2 mapping values in children with PCS, suggestive for a diffuse myocardial inflammation, which may be useful for the diagnostic workup of PCS in children.

Assessment of alterations in the retina and vitreous in pre- and post-COVID-19 patients using swept-source optical coherence tomography and angiography: A comparative study.

Ocular manifestations have been well recognized in coronavirus disease 2019 (COVID-19) outbreak. Several studies have detected ocular manifestations in patients after COVID-19. However, little is known about the retinal and vitreal alterations in patients before and after COVID-19 infection. This study aimed to investigate the retinal and vitreal alterations in patients before and after contracting COVID-19 infection using swept-source optical coherence tomography (SS-OCT) and angiography (SS-OCTA). A total of 38 participants (76 eyes) were enrolled and followed-up 1 month after COVID-19 infection. Then, 26 patients (52 eyes) were evaluated 3 months after COVID-19 infection. Compared with the pre-COVID-19 status, patients with 1- and 3-month post-COVID-19 statuses had significant thinning of ganglion cell and inner plexiform layer, thickening of inner nuclear layer, a decrease in the vessel density (VD) of superficial vascular complex, and an increase in the VD of deep vascular complex. Meanwhile, alteration in parameters of foveal avascular zone (all p < 0.05) and hyper-reflective dots in the vitreous of 27 patients (54 eyes) (71.1% vs. pre-COVID-19, 34.2%, p = 0.006) were observed. These findings suggest significantly retinal and vitreal alterations occurred in patients after COVID-19 infection, possibly due to direct or indirect virus-induced injuries. Further longitudinal studies are required to investigate the long-term effects of COVID-19 infection on the human eyes.

Impact of the COVID-19 Pandemic on Iron Overload Assessment by MRI in Patients with Hemoglobinopathies: The E-MIOT Network Experience.

BACKGROUND: The E-MIOT (Extension-Myocardial Iron Overload in Thalassemia) project is an Italian Network assuring high-quality quantification of tissue iron overload by magnetic resonance imaging (MRI). We evaluated the impact of the COVID-19 pandemic on E-MIOT services. METHODS: The activity of the E-MIOT Network MRI centers in the year 2020 was compared with that of 2019. A survey evaluated whether the availability of MRI slots for patients with hemoglobinopathies was reduced and why. RESULTS: The total number of MRI scans was 656 in 2019 and 350 in 2020, with an overall decline of 46.4% (first MRI: 71.7%, follow-up MRI: 36.9%), a marked decline (86.9%) in the period March-June 2020, and a reduction in the gap between the two years in the period July-September. A new drop (41.4%) was recorded in the period October-December for two centers, due to the general reduction in the total amount of MRIs/day for sanitization procedures. In some centers, patients refused MRI scans for fear of getting COVID. Drops in the MRI services >80% were found for patients coming from a region without an active MRI site. CONCLUSIONS: The COVID-19 pandemic had a strong negative impact on MRI multi-organ iron quantification, with a worsening in the management of patients with hemoglobinopathies.

Assessment of the Suitability of the Fleischner Society Imaging Guidelines in Evaluating Chest Radiographs of COVID-19 Patients.

BACKGROUND: The Fleischner Society established consensus guidelines for imaging in patients with coronavirus disease 2019 (COVID-19). We investigated the prevalence of pneumonia and the adverse outcomes by dividing groups according to the symptoms and risk factors of patients and assessed the suitability of the Fleischner society imaging guidelines in evaluating chest radiographs of COVID-19 patients. METHODS: From February 2020 to May 2020, 685 patients (204 males, mean 58 ± 17.9 years) who were diagnosed with COVID-19 and hospitalized were included. We divided patients into four groups according to the severity of symptoms and presence of risk factors (age > 65 years and presence of comorbidities). The patient groups were defined as follows: group 1 (asymptomatic patients), group 2 (patients with mild symptoms without risk factors), group 3 (patients with mild symptoms and risk factors), and group 4 (patients with moderate to severe symptoms). According to the Fleischner society, chest imaging is not indicated for groups 1-2 but is indicated for groups 3-4. We compared the prevalence and score of pneumonia on chest radiographs and compare the adverse outcomes (progress to severe pneumonia, intensive care unit admission, and death) between groups. RESULTS: Among the 685 COVID-19 patients, 138 (20.1%), 396 (57.8%), 102 (14.9%), and 49 (7.1%) patients corresponded to groups 1 to 4, respectively. Patients in groups 3-4 were significantly older and showed significantly higher prevalence rates of pneumonia (group 1-4: 37.7%, 51.3%, 71.6%, and 98%, respectively, P < 0.001) than those in groups 1-2. Adverse outcomes were also higher in groups 3-4 than in groups 1-2 (group 1-4: 8.0%, 3.5%, 6.9%, and 51%, respectively, P < 0.001). Patients with adverse outcomes in group 1 were initially asymptomatic but symptoms developed during follow-up. They were older (mean age, 80 years) and most of them had comorbidities (81.8%). Consistently asymptomatic patients had no adverse events. CONCLUSION: The prevalence of pneumonia and adverse outcomes were different according to the symptoms and risk factors in COVID-19 patients. Therefore, as the Fleischner Society recommended, evaluation and monitoring of COVID-19 pneumonia using chest radiographs is necessary for old symptomatic patients with comorbidities.

Predicting the COVID-19 Patients Status Using Chest CT Scan Findings: A Risk Assessment Model Based on Decision Tree Analysis.

BACKGROUND: The role of chest computed tomography (CT) to diagnose coronavirus disease 2019 (COVID-19) is still an open field to be explored. The aim of this study was to apply the decision tree (DT) model to predict critical or non-critical status of patients infected with COVID-19 based on available information on non-contrast CT scans. METHODS: This retrospective study was performed on patients with COVID-19 who underwent chest CT scans. Medical records of 1078 patients with COVID-19 were evaluated. The classification and regression tree (CART) of decision tree model and k-fold cross-validation were used to predict the status of patients using sensitivity, specificity, and area under the curve (AUC) assessments. RESULTS: The subjects comprised of 169 critical cases and 909 non-critical cases. The bilateral distribution and multifocal lung involvement were 165 (97.6%) and 766 (84.3%) in critical patients, respectively. According to the DT model, total opacity score, age, lesion types, and gender were statistically significant predictors for critical outcomes. Moreover, the results showed that the accuracy, sensitivity and specificity of the DT model were 93.3%, 72.8%, and 97.1%, respectively. CONCLUSIONS: The presented algorithm demonstrates the factors affecting health conditions in COVID-19 disease patients. This model has the potential characteristics for clinical applications and can identify high-risk subpopulations that need specific prevention. Further developments including integration of blood biomarkers are underway to increase the performance of the model.

Comparison of artificial intelligence versus real-time physician assessment of pulmonary edema with lung ultrasound.

BACKGROUND: Lung ultrasound can evaluate for pulmonary edema, but data suggest moderate inter-rater reliability among users. Artificial intelligence (AI) has been proposed as a model to increase the accuracy of B line interpretation. Early data suggest a benefit among more novice users, but data are limited among average residency-trained physicians. The objective of this study was to compare the accuracy of AI versus real-time physician assessment for B lines. METHODS: This was a prospective, observational study of adult Emergency Department patients presenting with suspected pulmonary edema. We excluded patients with active COVID-19 or interstitial lung disease. A physician performed thoracic ultrasound using the 12-zone technique. The physician recorded a video clip in each zone and provided an interpretation of positive (≥3 B lines or a wide, dense B line) or negative (<3 B lines and the absence of a wide, dense B line) for pulmonary edema based upon the real-time assessment. A research assistant then utilized the AI program to analyze the same saved clip to determine if it was positive versus negative for pulmonary edema. The physician sonographer was blinded to this assessment. The video clips were then reviewed independently by two expert physician sonographers (ultrasound leaders with >10,000 prior ultrasound image reviews) who were blinded to the AI and initial determinations. The experts reviewed all discordant values and reached consensus on whether the field (i.e., the area of lung between two adjacent ribs) was positive or negative using the same criteria as defined above, which served as the gold standard. RESULTS: 71 patients were included in the study (56.3% female; mean BMI: 33.4 [95% CI 30.6-36.2]), with 88.3% (752/852) of lung fields being of adequate quality for assessment. Overall, 36.1% of lung fields were positive for pulmonary edema. The physician was 96.7% (95% CI 93.8%-98.5%) sensitive and 79.1% (95% CI 75.1%-82.6%) specific. The AI software was 95.6% (95% CI 92.4%-97.7%) sensitive and 64.1% (95% CI 59.8%-68.5%) specific. CONCLUSION: Both the physician and AI software were highly sensitive, though the physician was more specific. Future research should identify which factors are associated with increased diagnostic accuracy.

Artificial intelligence guided HRCT assessment predicts the severity of COVID-19 pneumonia based on clinical parameters.

BACKGROUND: The purpose of the study was to compare the results of AI (artificial intelligence) analysis of the extent of pulmonary lesions on HRCT (high resolution computed tomography) images in COVID-19 pneumonia, with clinical data including laboratory markers of inflammation, to verify whether AI HRCT assessment can predict the clinical severity of COVID-19 pneumonia. METHODS: The analyzed group consisted of 388 patients with COVID-19 pneumonia, with automatically analyzed HRCT parameters of volume: AIV (absolute inflammation), AGV (absolute ground glass), ACV (absolute consolidation), PIV (percentage inflammation), PGV (percentage ground glass), PCV (percentage consolidation). Clinical data included: age, sex, admission parameters: respiratory rate, oxygen saturation, CRP (C-reactive protein), IL6 (interleukin 6), IG - immature granulocytes, WBC (white blood count), neutrophil count, lymphocyte count, serum ferritin, LDH (lactate dehydrogenase), NIH (National Institute of Health) severity score; parameters of clinical course: in-hospital death, transfer to the ICU (intensive care unit), length of hospital stay. RESULTS: The highest correlation coefficients were found for PGV, PIV, with LDH (respectively 0.65, 0.64); PIV, PGV, with oxygen saturation (respectively - 0.53, -0.52); AIV, AGV, with CRP (respectively 0.48, 0.46); AGV, AIV, with ferritin (respectively 0.46, 0.45). Patients with critical pneumonia had significantly lower oxygen saturation, and higher levels of immune-inflammatory biomarkers on admission. The radiological parameters of lung involvement proved to be strong predictors of transfer to the ICU (in particular, PGV ≥ cut-off point 29% with Odds Ratio (OR): 7.53) and in-hospital death (in particular: AIV ≥ cut-off point 831 cm3 with OR: 4.31). CONCLUSIONS: Automatic analysis of HRCT images by AI may be a valuable method for predicting the severity of COVID-19 pneumonia. The radiological parameters of lung involvement correlate with laboratory markers of inflammation, and are strong predictors of transfer to the ICU and in-hospital death from COVID-19. TRIAL REGISTRATION: National Center for Research and Development CRACoV-HHS project, contract number SZPITALE-JEDNOIMIENNE/18/2020.

Right ventricular free wall longitudinal strain assessment using offline speckle tracking in COVID-19 patients requiring intensive medical care.

PURPOSE: The purpose of this study was to evaluate and confirm the prognostic utility of comprehensive transthoracic echocardiography (TTE) using offline myocardial strain analyses in a Japanese coronavirus disease (COVID-19) cohort hospitalized in intensive care units. METHODS: We performed a retrospective analysis of 90 consecutive adult patients with COVID-19 who underwent clinically indicated standard two-dimensional TTE in intensive care wards. Patients on extracorporeal membrane oxygenation (ECMO) at the time of TTE were excluded. Biventricular strain assessments using vendor-independent offline speckle tracking analysis were performed. Patients with inadequate TTE image quality were also excluded. RESULTS: Among the 90 COVID-19 patients, 15 (17%) patients required venovenous or venoarterial ECMO. There were 25 (28%) in-hospital deaths. A composite event, defined as the combination of in-hospital mortality and subsequent initiation of ECMO, occurred in 32 patients. Multivariate logistic regression for composite events indicated that right ventricular free wall longitudinal strain (RV-FWLS) and mechanical ventilation at the time of TTE were independent risk factors for composite events (p = 0.01, odds ratio [OR] 1.09, 95% confidence interval [CI] 1.01-1.18; p = 0.04, OR 3.24, 95% CI 1.03-10.20). Cumulative survival probability plots generated using the Kaplan-Meier method for composite events with log-rank tests revealed a significant difference between subgroups divided by the cutoff value of RV-FWLS (p < 0.001). CONCLUSION: Offline measurement of RV-FWLS may be a potent predictor of worse outcomes in COVID-19 requiring intensive care. Larger multicenter prospective studies are needed.

Ultrasound imaging assessment of the diaphragm and abdominal muscles in people with a recent history of moderate Covid-19 infection and healthy participants: A cross-sectional pilot study.

Coronavirus disease (Covid-19) is a highly infectious disease caused by the SARS-CoV-2 virus and is associated with a decrease of respiratory, physical, and psychological function, subsequently affecting quality of life. The aim of the present pilot study was to use ultrasound imaging (USI) to evaluate and compare the thickness of the diaphragm and abdominal muscles between individuals recently diagnosed with moderate Covid-19 infection and healthy individuals. METHODS: A cross-sectional observational pilot study was performed. A total sample of 24 participants were recruited from a private medical center (Madrid, Spain): Covid-19 (n = 12) and healthy controls (n = 12). The external oblique (EO), internal oblique (IO), transversus abdominis (TrA), rectus abdominis (RA), interrecti distance (IRD) and diaphragm thickness were assessed using USI during inspiration, expiration and during contraction. RESULTS: USI measurements of the thickness of EO, IO, TrA, RA, IRD and the diaphragm did not differ significantly between groups during inspiration, expiration or during contraction (all P > 0.05). CONCLUSIONS: These preliminary results suggest that the morphology of the abdominal muscles and diaphragm is not altered in people with a recent history of moderate Covid-19 infection.

Longitudinal Assessment of Chest CT Findings and Pulmonary Function after COVID-19 Infection.

Background Information on pulmonary sequelae and pulmonary function 2 years after recovery from SARS-CoV-2 infection is lacking. Purpose To longitudinally assess changes in chest CT abnormalities and pulmonary function in individuals after SARS-CoV-2 infection. Materials and Methods In this prospective study, participants discharged from the hospital after SARS-CoV-2 infection from January 20 to March 10, 2020, were considered for enrollment. Participants without chest CT scans at admission or with complete resolution of lung abnormalities at discharge were excluded. Serial chest CT scans and pulmonary function test results were obtained 6 months (June 20 to August 31, 2020), 12 months (December 20, 2020, to February 3, 2021), and 2 years (November 16, 2021, to January 10, 2022) after symptom onset. The term interstitial lung abnormality (ILA) and two subcategories, fibrotic ILAs and nonfibrotic ILAs, were used to describe residual CT abnormalities on follow-up CT scans. Differences between groups were compared with the χ2 test, Fisher exact test, or independent samples t test. Results Overall, 144 participants (median age, 60 years [range, 27-80 years]; 79 men) were included. On 2-year follow-up CT scans, 39% of participants (56 of 144) had ILAs, including 23% (33 of 144) with fibrotic ILAs and 16% (23 of 144) with nonfibrotic ILAs. The remaining 88 of 144 participants (61%) showed complete radiologic resolution. Over 2 years, the incidence of ILAs gradually decreased (54%, 42%, and 39% of participants at 6 months, 12 months, and 2 years, respectively; P < .001). Respiratory symptoms (34% vs 15%, P = .007) and abnormal diffusing capacity of lung for carbon monoxide (43% vs 20%, P = .004) occurred more frequently in participants with ILAs than in those with complete radiologic resolution. Conclusion More than one-third of participants had persistent interstitial lung abnormalities 2 years after COVID-19 infection, which were associated with respiratory symptoms and decreased diffusion pulmonary function. Chinese Clinical Trial Registry no. ChiCTR2000038609 © RSNA, 2023 Supplemental material is available for this article. See also the editorial by van Beek in this issue.

Inter-rater reliability and prognostic value of baseline Radiographic Assessment of Lung Edema (RALE) scores in observational cohort studies of inpatients with COVID-19.

OBJECTIVES: To reliably quantify the radiographic severity of COVID-19 pneumonia with the Radiographic Assessment of Lung Edema (RALE) score on clinical chest X-rays among inpatients and examine the prognostic value of baseline RALE scores on COVID-19 clinical outcomes. SETTING: Hospitalised patients with COVID-19 in dedicated wards and intensive care units from two different hospital systems. PARTICIPANTS: 425 patients with COVID-19 in a discovery data set and 415 patients in a validation data set. PRIMARY AND SECONDARY OUTCOMES: We measured inter-rater reliability for RALE score annotations by different reviewers and examined for associations of consensus RALE scores with the level of respiratory support, demographics, physiologic variables, applied therapies, plasma host-response biomarkers, SARS-CoV-2 RNA load and clinical outcomes. RESULTS: Inter-rater agreement for RALE scores improved from fair to excellent following reviewer training and feedback (intraclass correlation coefficient of 0.85 vs 0.93, respectively). In the discovery cohort, the required level of respiratory support at the time of CXR acquisition (supplemental oxygen or non-invasive ventilation (n=178); invasive-mechanical ventilation (n=234), extracorporeal membrane oxygenation (n=13)) was significantly associated with RALE scores (median (IQR): 20.0 (14.1-26.7), 26.0 (20.5-34.0) and 44.5 (34.5-48.0), respectively, p<0.0001). Among invasively ventilated patients, RALE scores were significantly associated with worse respiratory mechanics (plateau and driving pressure) and gas exchange metrics (PaO2/FiO2 and ventilatory ratio), as well as higher plasma levels of IL-6, soluble receptor of advanced glycation end-products and soluble tumour necrosis factor receptor 1 (p<0.05). RALE scores were independently associated with 90-day survival in a multivariate Cox proportional hazards model (adjusted HR 1.04 (1.02-1.07), p=0.002). We replicated the significant associations of RALE scores with baseline disease severity and mortality in the independent validation data set. CONCLUSIONS: With a reproducible method to measure radiographic severity in COVID-19, we found significant associations with clinical and physiologic severity, host inflammation and clinical outcomes. The incorporation of radiographic severity assessments in clinical decision-making may provide important guidance for prognostication and treatment allocation in COVID-19.

Magnetic resonance imaging spectrum of COVID-associated rhino-orbital-cerebral mucormycosis and assessment of anatomical severity.

OBJECTIVES: To describe the extent and imaging findings of COVID-associated rhino-orbital-cerebral mucormycosis on magnetic resonance imaging and to evaluate the value of MRI severity score in grading the extent of involvement. METHODS: Proven cases of ROCM with a history of concurrent or recently (<6 weeks) treated COVID-19 underwent MRI at the initial presentation. Findings were charted for each anatomical structure and the extent of involvement was scored for sinonasal, extra-sinus soft tissues, orbits, and brain. MR severity score was defined by summing up the individual scores of each compartment (sinonasal 20, orbital 20, soft tissue 10, and brain 10) and a total score out of 60 was assigned. RESULTS: A total of 47 patients were included in our study with variable involvement of sinonasal compartment (n = 43), extra-sinus soft tissue (n = 25), orbits (n = 23), and brain (n = 17). In the sinonasal compartment, T2, DWI, and post-contrast T1 were the most useful sequences. A significantly higher mean sinonasal score was associated with mortality (p = 0.007). In the orbits, a combination of STIR (orbital fat and extraconal muscles), DWI (optic nerves), and post-contrast images (superior ophthalmic vein) were the most accurate sequences. A higher mean orbital score was associated with vision loss (p = 0.001). Patients with uncontrolled diabetes had greater extent of cranial involvement. CONCLUSION: A combination of magnetic resonance sequences is required to correctly evaluate the involvement of individual structures and thus to assign the correct MR scoring. The proposed MR severity score can effectively and objectively evaluate the severity of COVID-associated ROCM.

MRI Assessment of Cerebral Blood Flow in Nonhospitalized Adults Who Self-Isolated Due to COVID-19.

BACKGROUND: Neurological symptoms associated with coronavirus disease 2019 (COVID-19), such as fatigue and smell/taste changes, persist beyond infection. However, little is known of brain physiology in the post-COVID-19 timeframe. PURPOSE: To determine whether adults who experienced flu-like symptoms due to COVID-19 would exhibit cerebral blood flow (CBF) alterations in the weeks/months beyond infection, relative to controls who experienced flu-like symptoms but tested negative for COVID-19. STUDY TYPE: Prospective observational. POPULATION: A total of 39 adults who previously self-isolated at home due to COVID-19 (41.9 ± 12.6 years of age, 59% female, 116.5 ± 62.2 days since positive diagnosis) and 11 controls who experienced flu-like symptoms but had a negative COVID-19 diagnosis (41.5 ± 13.4 years of age, 55% female, 112.1 ± 59.5 since negative diagnosis). FIELD STRENGTH AND SEQUENCES: A 3.0 T; T1-weighted magnetization-prepared rapid gradient and echo-planar turbo gradient-spin echo arterial spin labeling sequences. ASSESSMENT: Arterial spin labeling was used to estimate CBF. A self-reported questionnaire assessed symptoms, including ongoing fatigue. CBF was compared between COVID-19 and control groups and between those with (n = 11) and without self-reported ongoing fatigue (n = 28) within the COVID-19 group. STATISTICAL TESTS: Between-group and within-group comparisons of CBF were performed in a voxel-wise manner, controlling for age and sex, at a family-wise error rate of 0.05. RESULTS: Relative to controls, the COVID-19 group exhibited significantly decreased CBF in subcortical regions including the thalamus, orbitofrontal cortex, and basal ganglia (maximum cluster size = 6012 voxels and maximum t-statistic = 5.21). Within the COVID-19 group, significant CBF differences in occipital and parietal regions were observed between those with and without self-reported on-going fatigue. DATA CONCLUSION: These cross-sectional data revealed regional CBF decreases in the COVID-19 group, suggesting the relevance of brain physiology in the post-COVID-19 timeframe. This research may help elucidate the heterogeneous symptoms of the post-COVID-19 condition. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.

Admission chest CT findings and risk assessment for stroke-associated pneumonia.

INTRODUCTION: Stroke-associated pneumonia (SAP) is a significant cause of morbidity and mortality after stroke. Various factors, including dysphagia and stroke severity, are closely related to SAP risk; however, the contribution of the baseline pulmonary parenchymal status to this interplay is an understudied field. Herein, we evaluated the prognostic performance of admission chest computed tomography (CT) findings in predicting SAP. METHODS: We evaluated admission chest CT images, acquired as part of a COVID-19-related institutional policy, in a consecutive series of acute ischemic stroke patients. The pulmonary opacity load at baseline was quantified using automated volumetry and visual scoring algorithms. The relationship between pulmonary opacities with risk of pneumonia within 7 days of symptom onset (i.e., SAP) was evaluated by bivariate and multivariate analyses. RESULTS: Twenty-three percent of patients in our cohort (n = 100) were diagnosed with SAP. Patients with SAP were more likely to have atrial fibrillation, COPD, severe neurological deficits, and dysphagia. The visual opacity score on chest CT was significantly higher among patients who developed SAP (p = 0.014), while no such relationship was observed in terms of absolute or relative opacity volume. In multivariate analyses, admission stroke severity, presence of dysphagia and a visual opacity score of ≥ 3 (OR 6.37, 95% CI 1.61-25.16; p = 0.008) remained significantly associated with SAP risk. CONCLUSIONS: Pulmonary opacity burden, as evaluated on admission chest CT, is significantly associated with development of pneumonia within initial days of stroke. This association is independent of other well-known predisposing factors for SAP, including age, stroke severity, and presence of dysphagia.