Relationships between COVID-19 healthcare outcomes and county characteristics in the U.S. for Delta (B.1.617.2) and Omicron (B.1.1.529 and BA.1.1) variants.

Background: COVID-19 is constantly evolving, and highly populated communities consist of many different characteristics that may contribute to COVID-19 health outcomes. Therefore, we aimed to (1) quantify the relationships between county characteristics and severe and non-severe county-level health outcomes related to COVID-19. We also aimed to (2) compare these relationships across time periods where the Delta (B.1.617.2) and Omicron (B.1.1.529 and BA.1.1) variants were dominant in the U.S. Methods: We used multiple regression to measure the strength of relationships between healthcare outcomes and county characteristics in the 50 most populous U.S. counties. Results: We found many different significant predictors including the proportion of a population vaccinated, median household income, population density, and the proportion of residents aged 65+, but mainly found that socioeconomic factors and the proportion of a population vaccinated play a large role in the dynamics of the spread and severity of COVID-19 in communities with high populations. Discussion: The present study shines light on the associations between public health outcomes and county characteristics and how these relationships change throughout Delta and Omicron's dominance. It is important to understand factors underlying COVID-19 health outcomes to prepare for future health crises.

A comparison of neuroelectrophysiology databases.

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.

A Comparison of Neuroelectrophysiology Databases.

As data sharing has become more prevalent, three pillars - archives, standards, and analysis tools - have emerged as critical components in facilitating effective data sharing and collaboration. This paper compares four freely available intracranial neuroelectrophysiology data repositories: Data Archive for the BRAIN Initiative (DABI), Distributed Archives for Neurophysiology Data Integration (DANDI), OpenNeuro, and Brain-CODE. The aim of this review is to describe archives that provide researchers with tools to store, share, and reanalyze both human and non-human neurophysiology data based on criteria that are of interest to the neuroscientific community. The Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB) are utilized by these archives to make data more accessible to researchers by implementing a common standard. As the necessity for integrating large-scale analysis into data repository platforms continues to grow within the neuroscientific community, this article will highlight the various analytical and customizable tools developed within the chosen archives that may advance the field of neuroinformatics.

COVID-19 vaccination dynamics in the US: coverage velocity and carrying capacity based on socio-demographic vulnerability indices in California's pediatric population.

Introduction: COVID-19 vaccine inequities have been widespread across California, the United States, and globally. As COVID-19 vaccine inequities have not been fully understood in the youth population, it is vital to determine possible factors that drive inequities to enable actionable change that promotes vaccine equity among vulnerable minor populations. Methods: The present study used the social vulnerability index (SVI) and daily vaccination numbers within the age groups of 12-17, 5-11, and under 5 years old across all 58 California counties to model the growth velocity and the anticipated maximum proportion of population vaccinated. Results: Overall, highly vulnerable counties, when compared to low and moderately vulnerable counties, experienced a lower vaccination rate in the 12-17 and 5-11 year-old age groups. For age groups 5-11 and under 5 years old, highly vulnerable counties are expected to achieve a lower overall total proportion of residents vaccinated. In highly vulnerable counties in terms of socioeconomic status and household composition and disability, the 12-17 and 5-11 year-old age groups experienced lower vaccination rates. Additionally, in the 12-17 age group, high vulnerability counties are expected to achieve a higher proportion of residents vaccinated compared to less vulnerable counterparts. Discussion: These findings elucidate shortcomings in vaccine uptake in certain pediatric populations across California and may help guide health policies and future allocation of vaccines, with special emphasis placed on vulnerable populations, especially with respect to socioeconomic status and household composition and disability.

A Novel Threshold-Based Segmentation Method for Quantification of COVID-19 Lung Abnormalities.

Since December 2019, the novel coronavirus disease 2019 (COVID-19) has claimed the lives of more than 3.75 million people worldwide. Consequently, methods for accurate COVID-19 diagnosis and classification are necessary to facilitate rapid patient care and terminate viral spread. Lung infection segmentations are useful to identify unique infection patterns that may support rapid diagnosis, severity assessment, and patient prognosis prediction, but manual segmentations are time-consuming and depend on radiologic expertise. Deep learning-based methods have been explored to reduce the burdens of segmentation; however, their accuracies are limited due to the lack of large, publicly available annotated datasets that are required to establish ground truths. For these reasons, we propose a semi-automatic, threshold-based segmentation method to generate region of interest (ROI) segmentations of infection visible on lung computed tomography (CT) scans. Infection masks are then used to calculate the percentage of lung abnormality (PLA) to determine COVID-19 severity and to analyze the disease progression in follow-up CTs. Compared with other COVID-19 ROI segmentation methods, on average, the proposed method achieved improved precision ( 47.49 % ) and specificity ( 98.40 % ) scores. Furthermore, the proposed method generated PLAs with a difference of ± 3.89 % from the ground-truth PLAs. The improved ROI segmentation results suggest that the proposed method has potential to assist radiologists in assessing infection severity and analyzing disease progression in follow-up CTs.

Molecular and antigen tests, and sample types for diagnosis of COVID-19: a review.

Laboratory tests seeking to improve detection of COVID-19 have been widely developed by laboratories and commercial companies. This review provides an overview of molecular and antigen tests, presents the sensitivity and specificity for 329 assays that have received US FDA Emergency Use Authorization and evaluates six sample collection methods - nasal, nasopharyngeal, oropharyngeal swabs, saliva, blood and stool. Molecular testing is preferred for diagnosis of COVID-19, but negative results do not always rule out the presence of infection, especially when clinical suspicion is high. Sensitivity and specificity ranged from 88.1 to 100% and 88 to 100%, respectively. Antigen tests may be more easy to use and rapid. However, they have reported a wide range of detection sensitivities from 16.7 to 85%, which may potentially yield many false-negative results.

Key Radiological Features of COVID-19 Chest CT Scans with a Focus on Special Subgroups: A Literature Review.

BACKGROUND: In 2019, a series of novel pneumonia cases later known as Coronavirus Disease 2019 (COVID-19) were reported in Wuhan, China. Chest computed tomography (CT) has played a key role in the management and prognostication of COVID-19 patients. CT has demonstrated 98% sensitivity in detecting COVID-19, including identifying lung abnormalities that are suggestive of COVID-19, even among asymptomatic individuals. METHODS: We conducted a comprehensive literature review of 17 published studies, focusing on three subgroups, pediatric patients, pregnant women, and patients over 60 years old, to identify key characteristics of chest CT in COVID-19 patients. RESULTS: Our comprehensive review of the 17 studies concluded that the main CT imaging finding is ground glass opacities (GGOs) regardless of patient age. We also identified that crazy paving pattern, reverse halo sign, smooth or irregular septal thickening, and pleural thickening may serve as indicators of disease progression. Lesions on CT scans were dominantly distributed in the peripheral zone with multilobar involvement, specifically concentrated in the lower lobes. In the patients over 60 years old, the proportion of substantial lobe involvement was higher than the control group and crazy paving signs, bronchodilation, and pleural thickening were more commonly present. CONCLUSION: Based on all 17 studies, CT findings in COVID-19 have shown a predictable pattern of evolution over the disease. These studies have proven that CT may be an effective approach for early screening and detection of COVID-19.

Efficient and visualizable convolutional neural networks for COVID-19 classification using Chest CT.

With coronavirus disease 2019 (COVID-19) cases rising rapidly, deep learning has emerged as a promising diagnosis technique. However, identifying the most accurate models to characterize COVID-19 patients is challenging because comparing results obtained with different types of data and acquisition processes is non-trivial. In this paper we designed, evaluated, and compared the performance of 20 convolutional neutral networks in classifying patients as COVID-19 positive, healthy, or suffering from other pulmonary lung infections based on chest computed tomography (CT) scans, serving as the first to consider the EfficientNet family for COVID-19 diagnosis and employ intermediate activation maps for visualizing model performance. All models are trained and evaluated in Python using 4173 chest CT images from the dataset entitled "A COVID multiclass dataset of CT scans," with 2168, 758, and 1247 images of patients that are COVID-19 positive, healthy, or suffering from other pulmonary infections, respectively. EfficientNet-B5 was identified as the best model with an F1 score of 0.9769 ± 0.0046, accuracy of 0.9759 ± 0.0048, sensitivity of 0.9788 ± 0.0055, specificity of 0.9730 ± 0.0057, and precision of 0.9751 ± 0.0051. On an alternate 2-class dataset, EfficientNetB5 obtained an accuracy of 0.9845 ± 0.0109, F1 score of 0.9599 ± 0.0251, sensitivity of 0.9682 ± 0.0099, specificity of 0.9883 ± 0.0150, and precision of 0.9526 ± 0.0523. Intermediate activation maps and Gradient-weighted Class Activation Mappings offered human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools. With a prediction speed of under 0.1 s on GPUs and 0.5 s on CPUs, our proposed model offers a rapid, scalable, and accurate diagnostic for COVID-19.

Computed Tomography Angiographic Features of Anomalous Origination of the Coronary Arteries in Adult Patients: A Literature Review and Coronary Computed Tomography Angiographic Illustrations.

Computed tomography angiography not only detects atherosclerotic coronary artery disease but also helps delineate the anomalous coronary arterial anatomy that may be more than just an incidental finding and could contribute to patients' symptomatology. Additionally, identification of coronary artery anomalies is clinically significant for preoperative planning and optimizing the approach for coronary catheterizations or surgical treatments. In this work, we review rare origination anomalies of coronary arteries and illustrate their characteristics through computed tomography images.

Computed Tomography in Emergency Diagnosis and Management Considerations of Small Bowel Obstruction for Surgical vs. Non-surgical Approach.

BACKGROUND: Background: Small Bowel Obstruction (SBO) accounts for 15% of abdominal pain complaints referred to emergency departments and imposes significant financial burdens on the healthcare system. The most common symptom and sign of SBO is the absence of stool or flatus passsage and abdominal distension, respectively. Patients who do not demonstrate severe clinical or imaging findings are typically treated with conservative approaches. Patients with clinical signs of sepsis or physical findings of peritonitis are often instantly transferred to the operating room without supplementary imaging assessment. However, in cases where symptoms are non-specific or physical examination is challenging, such as in cases with loss of consciousness, the diagnosis can be complicated. This paper discusses the key findings identifiable on Computed Tomography (CT) which are vital for the emergent triage, proper treatment and appropriate decision making in patients with suspected SBO. METHODS: Narrative review of the literature. RESULTS: CT plays a key role in emergent triage, proper treatment and decision making and provides high sensitivity, specificity, and accuracy in the detection of early-stage obstruction and acute intestinal vascular compromise. CT can also differentiate between various etiologies of SBO entity which is considered an important criterion in the triage of patients into surgical vs. non-surgical treatment. CONCLUSION: There Key CT findings which may suggest a need for surgical treatment include mesenteric edema, lack of the small-bowel feces, bowel wall thickening, fat stranding in the mesentery, and intraperitoneal fluid which are predictive of urgent surgical exploration.

COVID-19 Vaccination Dynamics in the US: Coverage Velocity and Carrying Capacity Based on Socio-demographic Vulnerability Indices in California.

Coronavirus disease 2019 (COVID-19) disparities among vulnerable populations are of paramount concern that extend to vaccine administration. With recent uptick in infection rates, dominance of the delta variant, and authorization of a third booster shot, understanding the population-level vaccine coverage dynamics and underlying sociodemographic factors is critical for achieving equity in public health outcomes. This study aimed to characterize the scope of vaccine inequity in California counties through modeling the trends of vaccination using the Social Vulnerability Index (SVI). Overall SVI, its four themes, and 9228 data points of daily vaccination numbers from December 15, 2020, to May 23, 2021, across all 58 California counties were used to model the growth velocity and anticipated maximum proportion of population vaccinated, defined as having received at least one dose of vaccine. Based on the overall SVI, the vaccination coverage velocity was lower in counties in the high vulnerability category (v = 0.0346, 95% CI 0.0334, 0.0358) compared to moderate (v = 0.0396, 95% CI 0.0385, 0.0408) and low (v = 0.0414, 95% CI 0.0403, 0.0425) vulnerability categories. SVI Theme 3 (minority status and language) yielded the largest disparity in coverage velocity between low and high-vulnerable counties (v = 0.0423 versus v = 0.035, P < 0.001). Based on the current trajectory, while counties in low-vulnerability category of overall SVI are estimated to achieve a higher proportion of vaccinated individuals, our models yielded a higher asymptotic maximum for highly vulnerable counties of Theme 3 (K = 0.544, 95% CI 0.527, 0.561) compared to low-vulnerability counterparts (K = 0.441, 95% CI 0.432, 0.450). The largest disparity in asymptotic proportion vaccinated between the low and high-vulnerability categories was observed in Theme 2 describing the household composition and disability (K = 0.602, 95% CI 0.592, 0.612; versus K = 0.425, 95% CI 0.413, 0.436). Overall, the large initial disparities in vaccination rates by SVI status attenuated over time, particularly based on Theme 3 status which yielded a large decrease in cumulative vaccination rate ratio of low to high-vulnerability categories from 1.42 to 0.95 (P = 0.002). This study provides insight into the problem of COVID-19 vaccine disparity across California which can help promote equity during the current pandemic and guide the allocation of future vaccines such as COVID-19 booster shots.

Association between ABO blood types and coronavirus disease 2019 (COVID-19), genetic associations, and underlying molecular mechanisms: a literature review of 23 studies.

An association of various blood types and the 2019 novel coronavirus disease (COVID-19) has been found in a number of publications. The aim of this literature review is to summarize key findings related to ABO blood types and COVID-19 infection rate, symptom presentation, and outcome. Summarized findings include associations between ABO blood type and higher infection susceptibility, intubation duration, and severe outcomes, including death. The literature suggests that blood type O may serve as a protective factor, as individuals with blood type O are found COVID-19 positive at far lower rates. This could suggest that blood type O individuals are less susceptible to infection, or that they are asymptomatic at higher rates and therefore do not seek out testing. We also discuss genetic associations and potential molecular mechanisms that drive the relationship between blood type and COVID-19. Studies have found a strong association between a locus on a specific gene cluster on chromosome three (chr3p21.31) and outcome severity, such as respiratory failure. Cellular models have suggested an explanation for blood type modulation of infection, evidencing that spike protein/Angiotensin-converting enzyme 2 (ACE2)-dependent adhesion to ACE2-expressing cell lines was specifically inhibited by monoclonal or natural human anti-A antibodies, so individuals with non-A blood types, specifically O, or B blood types, which produce anti-A antibodies, may be less susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection due to the inhibitory effects of anti-A antibodies.

Reason for Exam Imaging Reporting and Data System: Consensus Reached on Quality Assessment of Radiology Requisitions.

OBJECTIVE: The aim of this study was to reach consensus on quality assessment of clinical information in imaging requisitions using Reason for exam Imaging Reporting and Data System (RI-RADS). METHODS: A Delphi study was conducted in September 2018 with a panel of 87 radiologists with diverse levels of experience from various settings (community hospitals, private hospitals, university hospitals, and clinics), of which 74.7% completed the survey. The agreement was assessed in the following subjects: (a) presumed effect of standardization, (b) the standardized system for information, (c) the scoring system for evaluation of requisitions, and (d) the implementation of RI-RADS. The consensus threshold was set at 51% responding (strongly) agree. The rate of lawsuits preventable with clinical information was also assessed. RESULTS: Consensus was reached on all objectives of the study with a high level of agreement. Radiologists agreed on the need for standardization of imaging requisitions and attributed it to increased speed and accuracy of interpretations. Three categories of information were determined as key indicators of quality: impression, clinical findings, and clinical question. The scoring system is intended to grade requisitions based on the presence of these categories. Radiologists also agreed that RI-RADS will encourage physicians to improve requisitions. Among radiologists who responded to the survey, 12.6% had experienced at least one lawsuit potentially preventable with sufficient information in requisitions. CONCLUSIONS: Reason for exam Imaging Reporting and Data System can be used as a standard for quality assessment of requisitions. Its use may improve the quality of patient care and reduce lawsuits against radiologists.

Current comments on contrast media administration in patients with renal insufficiency.

Contrast media administration has been associated with complications such as nephropathy, cardiovascular morbidity, and neurovascular events, particularly in patients with renal insufficiency. This association has been questioned in recent studies. This review was performed to summarize the most current evidence on contrast induced nephropathy (CIN), contributing factors, and considerations in patients with renal insufficiency. The risk of CIN was over-estimated by the previous studies, due to a lack of control groups or presence of non-randomized control groups, which led to a selection bias. However, the thresholds associated with an increased risk of CIN are controversial and require risk-benefit analysis on an individual basis. Regarding the administration of contrast media (CM) in the emergency setting, the majority of studies suggested that CM exposure does not meaningfully increase the risk of acute kidney injury in critically ill patients (including trauma patients). Several strategies have been suggested to reduce the risk of CIN, including volume expansion to increase renal blood flow, sodium bicarbonate or N-acetylcysteine administration, and use of low-osmolal contrast media in end-stage renal disease.

Impacts of the Coronavirus Disease 2019 (COVID-19) pandemic on healthcare workers: A nationwide survey of United States radiologists.

BACKGROUND: Efforts to reduce nosocomial spread of COVID-19 have resulted in unprecedented disruptions in clinical workflows and numerous unexpected stressors for imaging departments across the country. Our purpose was to more precisely evaluate these impacts on radiologists through a nationwide survey. METHODS: A 43-item anonymous questionnaire was adapted from the AO Spine Foundation's survey and distributed to 1521 unique email addresses using REDCap™ (Research Electronic Data Capture). Additional invitations were sent out to American Society of Emergency Radiology (ASER) and Association of University Radiologists (AUR) members. Responses were collected over a period of 8 days. Descriptive analyses and multivariate modeling were performed using SAS v9.4 software. RESULTS: A total of 689 responses from radiologists across 44 different states met the criteria for inclusion in the analysis. As many as 61% of respondents rated their level of anxiety with regard to COVID-19 to be a 7 out of 10 or greater, and higher scores were positively correlated the standardized number of COVID-19 cases in a respondent's state (RR = 1.11, 95% CI: 1.02-1.21, p = 0.01). Citing the stressor of "personal health" was a strong predictor of higher anxiety scores (RR 1.23; 95% CI: 1.13-1.34, p < 0.01). By contrast, participants who reported needing no coping methods were more likely to self-report lower anxiety scores (RR 0.4; 95% CI: 0.3-0.53, p < 0.01). CONCLUSION: COVID-19 has had a significant impact on radiologists across the nation. As these unique stressors continue to evolve, further attention must be paid to the ways in which we may continue to support radiologists working in drastically altered practice environments and in remote settings.

PET/Computed Tomography Scans and PET/MR Imaging in the Diagnosis and Management of Musculoskeletal Diseases.

PET/computed tomography scans and PET/MR imaging have been applied in imaging tumors of the musculoskeletal system for their ability to provide information about metabolic activity. However, applications of these imaging modalities are now being extended to nononcologic musculoskeletal pathologies, such as osteoarthritis, rheumatoid arthritis, and osteoporosis. This article aims to explore the alternative uses of these imaging modalities in oncologic and nononcologic musculoskeletal pathologies. It also discusses the various strengths and some weaknesses that are seen in particular situations.

Chest Computed Tomography Manifestation of Coronavirus Disease 2019 (COVID-19) in Patients With Cardiothoracic Conditions.

The coronavirus disease 2019 (COVID-19) pandemic is a serious public health concern, with an exponentially growing number of patients worldwide. Computed tomography (CT) has been suggested as a highly sensitive modality for the diagnosis of pulmonary involvement in the early stages of COVID-19. The typical features of COVID-19 in chest CT include bilateral, peripheral, and multifocal ground-glass opacities with or without superimposed consolidations. Patients with underlying medical conditions are at higher risks of complications and mortality. The diagnosis of COVID-19 on the basis of the imaging features may be more challenging in patients with preexisting cardiothoracic conditions, such as chronic obstructive pulmonary disease, interstitial lung disease, cardiovascular disease, and malignancies with cardiothoracic involvement. The extensive pulmonary involvement in some of these pathologies may obscure the typical manifestation of COVID-19, whereas other preexisting pathologies may resemble the atypical or rare CT manifestations of this viral pneumonia. Thus, understanding the specific CT manifestations in these special subgroups is essential for a prompt diagnosis.