Cardiovascular Testing in the United States during the COVID-19 Pandemic: Volume Recovery and Worldwide Comparison.

Purpose: To characterize the recovery of diagnostic cardiovascular procedure volumes in U.S. and non-U.S. facilities in the year following the initial COVID-19 outbreak. Materials and Methods: The International Atomic Energy Agency (IAEA) coordinated a worldwide study called the IAEA Noninvasive Cardiology Protocols Study of COVID-19 2 (INCAPS COVID 2), collecting data from 669 facilities in 107 countries, including 93 facilities in 34 U.S. states, to determine the impact of the pandemic on diagnostic cardiovascular procedure volumes. Participants reported volumes for each diagnostic imaging modality used at their facility for March 2019 (baseline), April 2020, and April 2021. This secondary analysis of INCAPS COVID 2 evaluated differences in changes in procedure volume between U.S. and non-U.S. facilities and among U.S. regions. Factors associated with return to prepandemic volumes in the United States were also analyzed in a multivariable regression analysis. Results: Reduction in procedure volumes in April 2020 compared with baseline was similar for U.S. and non-U.S. facilities (-66% vs -71%, P = .27). U.S. facilities reported greater return to baseline in April 2021 than did all non-U.S. facilities (4% vs -6%, P = .008), but there was no evidence of a difference when comparing U.S. facilities with non-U.S. high-income country (NUHIC) facilities (4% vs 0%, P = .18). U.S. regional differences in return to baseline were observed between the Midwest (11%), Northeast (9%), South (1%), and West (-7%, P = .03), but no studied factors were significant predictors of 2021 change from prepandemic baseline. Conclusion: The reductions in cardiac testing during the early pandemic have recovered within a year to prepandemic baselines in the United States and NUHICs, while procedure volumes remain depressed in lower-income countries.Keywords: SPECT, Cardiac, Epidemiology, Angiography, CT Angiography, CT, Echocardiography, SPECT/CT, MR Imaging, Radionuclide Studies, COVID-19, Cardiovascular Imaging, Diagnostic Cardiovascular Procedure, Cardiovascular Disease, Cardiac Testing Supplemental material is available for this article. © RSNA, 2023.

Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data.

For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient's 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis.

Influence of thoracic radiology training on classification of interstitial lung diseases.

INTRODUCTION: Interpretation of high-resolution CT images plays an important role in the diagnosis and management of interstitial lung diseases. However, interreader variation may exist due to varying levels of training and expertise. This study aims to evaluate interreader variation and the role of thoracic radiology training in classifying interstitial lung disease (ILD). METHODS: This is a retrospective study where seven physicians (radiologists, thoracic radiologists, and a pulmonologist) classified the subtypes of ILD of 128 patients from a tertiary referral center, all selected from the Interstitial Lung Disease Registry which consists of patients from November 2014 to January 2021. Each patient was diagnosed with a subtype of interstitial lung disease by a consensus diagnosis from pathology, radiology, and pulmonology. Each reader was provided with only clinical history, only CT images, or both. Reader sensitivity and specificity and interreader agreements using Cohen's κ were calculated. RESULTS: Interreader agreement based only on clinical history, only on radiologic information, or combination of both was most consistent amongst readers with thoracic radiology training, ranging from fair (Cohen's κ: 0.2-0.46), moderate to almost perfect (Cohen's κ: 0.55-0.92), and moderate to almost perfect (Cohen's κ: 0.53-0.91) respectively. Radiologists with any thoracic training showed both increased sensitivity and specificity for NSIP as compared to other radiologists and the pulmonologist when using only clinical history, only CT information, or combination of both (p < 0.05). CONCLUSIONS: Readers with thoracic radiology training showed the least interreader variation and were more sensitive and specific at classifying certain subtypes of ILD. SUMMARY SENTENCE: Thoracic radiology training may improve sensitivity and specificity in classifying ILD based on HRCT images and clinical history.

Evaluation of Lower Extremity Calcium Score as a Measure of Peripheral Arterial Disease Burden and Amputation Risk.

BACKGROUND: The ankle-brachial pressure index (ABI) and toe-brachial pressure index (TBI) are commonly used diagnostic tools for peripheral artery disease (PAD) that are unreliable in the presence of calcified vessels. In this study, we aimed to demonstrate the utility of the lower extremity calcium score (LECS) in addition to ABI and TBI in measuring disease burden and predicting the risk of amputation in patients with PAD. METHODS: Patients who were evaluated in the vascular surgery clinic at Emory University for PAD and who underwent noncontrast computed tomography of the aorta and lower extremities were included in the study. Aortoiliac, femoral-popliteal, and tibial calcium scores were measured using the Agatston method. ABI and TBI that were obtained within 6 months of the computed tomography scan were noted and divided into categories of PAD severity. Associations between ABI, TBI, and LECS of each anatomic segment were evaluated. Univariate and multivariate ordinal regression analyses were performed to predict the outcome of amputation. Receiver operating characteristic analysis was performed to compare LECS with other variables in its ability to predict amputation. RESULTS: Fifty patients included in the study cohort were divided into LECS quartiles, with 12-13 patients in each quartile. The highest quartile tended to be older (P = 0.016), had a higher percentage of diabetics (P = 0.034), and had a higher frequency of major amputations (P = 0.004) compared to the other quartiles. Patients in the highest quartile of tibial calcium score were more likely to have stage 3 chronic kidney disease (CKD) or greater (P = 0.011) and also had a higher frequency of amputation (P < 0.005) and mortality (P = 0.041). We found no significant association between each anatomic LECS and ABI/TBI categories. On univariate analysis, CKD (Odds Ratio [OR] 12.92 (95% CI 2.01 to 82.83), P = 0.007), diabetes mellitus (OR 5.47 (95% CI 1.27 to 23.64), P = 0.023), tibial calcium score (OR 6.62 (95% CI 1.79 to 24.54), P = 0.005), and total bilateral calcium score (OR 6.32 (95% CI 1.18 to 33.78), P = 0.031) were associated with increased risk of amputation. On multivariate stepwise ordinal regression, TBI and tibial calcium score were identified as important predictors of amputation, with hyperlipidemia and CKD increasing the overall prediction of the model. On Receiver operating characteristic analysis, the addition of the tibial calcium score (area under the curve 0.94, standard error 0.048) significantly improved the prediction of amputation compared to hyperlipidemia, CKD, and TBI alone (area under the curve 0.82, standard error 0.071, P = 0.022). CONCLUSIONS: The addition of tibial calcium score to other known PAD risk factors may improve the prediction of amputation in patients with PAD.

Performance of spiral UTE-MRI of the lung in post-COVID patients.

Patients recovered from COVID-19 may develop long-COVID symptoms in the lung. For this patient population (post-COVID patients), they may benefit from longitudinal, radiation-free lung MRI exams for monitoring lung lesion development and progression. The purpose of this study was to investigate the performance of a spiral ultrashort echo time MRI sequence (Spiral-VIBE-UTE) in a cohort of post-COVID patients in comparison with CT and to compare image quality obtained using different spiral MRI acquisition protocols. Lung MRI was performed in 36 post-COVID patients with different acquisition protocols, including different spiral sampling reordering schemes (line in partition or partition in line) and different breath-hold positions (inspiration or expiration). Three experienced chest radiologists independently scored all the MR images for different pulmonary structures. Lung MR images from spiral acquisition protocol that received the highest image quality scores were also compared against corresponding CT images in 27 patients for evaluating diagnostic image quality and lesion identification. Spiral-VIBE-UTE MRI acquired with the line in partition reordering scheme in an inspiratory breath-holding position achieved the highest image quality scores (score range = 2.17-3.69) compared to others (score range = 1.7-3.29). Compared to corresponding chest CT images, three readers found that 81.5% (22 out of 27), 81.5% (22 out of 27) and 37% (10 out of 27) of the MR images were useful, respectively. Meanwhile, they all agreed that MRI could identify significant lesions in the lungs. The Spiral-VIBE-UTE sequence allows for fast imaging of the lung in a single breath hold. It could be a valuable tool for lung imaging without radiation and could provide great value for managing different lung diseases including assessment of post-COVID lesions.

Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients.

Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest computed tomography (CT) in combination with plasma cytokines using a machine learning and k-fold cross-validation approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n = 152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within five days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α), were collected from the electronic medical record. We found that CT quantitative alone was better at predicting severity (AUC 0.81) than death (AUC 0.70), while cytokine measurements alone better-predicted death (AUC 0.70) compared to severity (AUC 0.66). When combined, chest CT and plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82). Finally, we provide a simple scoring system (nomogram) using plasma IL-6, IL-8, TNF-α, ground-glass opacities (GGO) to aerated lung ratio and age as new metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.

Barotrauma in COVID 19: Incidence, pathophysiology, and effect on prognosis.

OBJECTIVES: To investigate the incidence, risk factors, and outcomes of barotrauma (pneumomediastinum and subcutaneous emphysema) in mechanically ventilated COVID-19 patients. To describe the chest radiography patterns of barotrauma and understand the development in relation to mechanical ventilation and patient mortality. METHODS: We performed a retrospective study of 363 patients with COVID-19 from March 1 to April 8, 2020. Primary outcomes were pneumomediastinum or subcutaneous emphysema with or without pneumothorax, pneumoperitoneum, or pneumoretroperitoneum. The secondary outcomes were length of intubation and death. In patients with pneumomediastinum and/or subcutaneous emphysema, we conducted an imaging review to determine the timeline of barotrauma development. RESULTS: Forty three out of 363 (12%) patients developed barotrauma radiographically. The median time to development of either pneumomediastinum or subcutaneous emphysema was 2 days (IQR 1.0-4.5) after intubation and the median time to pneumothorax was 7 days (IQR 2.0-10.0). The overall incidence of pneumothorax was 28/363 (8%) with an incidence of 17/43 (40%) in the barotrauma cohort and 11/320 (3%) in those without barotrauma (p ≤ 0.001). In total, 257/363 (71%) patients died with an increase in mortality in those with barotrauma 33/43 (77%) vs. 224/320 (70%). When adjusting for covariates, barotrauma was associated with increased odds of death (OR 2.99, 95% CI 1.25-7.17). CONCLUSION: Barotrauma is a frequent complication of mechanically ventilated COVID-19 patients. In comparison to intubated COVID-19 patients without barotrauma, there is a higher rate of pneumothorax and an increased risk of death.

Evaluation of interstitial lung disease: An algorithmic review using ILD-RADS.

Interstitial lung diseases (ILDs) may present a diagnostic dilemma due to their many classifications and overlapping imaging findings. In this review, we present an algorithmic approach for assessing ILDs based on identifying and understanding key imaging features to aid in narrowing a differential diagnosis or reaching a specific diagnosis. We use the recently introduced Interstitial Lung Disease Reporting And Data System (ILD-RADS) as a framework for our discussion.

Review of Thoracic Imaging Manifestations of COVID-19 and Other Pathologic Coronaviruses.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an easily transmissible coronavirus that emerged in late 2019 and has caused a global pandemic characterized by acute respiratory disease named coronavirus disease 2019 (COVID-19). Diagnostic imaging can be helpful as a complementary tool in supporting the diagnosis of COVID-19 and identifying alternative pathology. This article presents an overview of acute and postacute imaging findings in COVID-19.

Imaging of COVID-19.

The novel coronavirus disease 2019 (COVID-19) emerged as the source of a global pandemic in late 2019 and early 2020 and quickly spread throughout the world becoming one of the worst pandemics in recent history. This chapter reviews the most up to date radiological literature and outlines the utility of thoracic imaging in COVID-19, defining both the common and the less typical imaging appearances during the acute and subacute phases of COVID-19. The short term complications and the long term sequela will also be discussed in the context of radiology, including pulmonary emboli, acute respiratory distress syndrome, superimposed infections, barotrauma, cardiac manifestations, pulmonary parenchymal scarring and fibrosis.

Radiology Implementation Considerations for Artificial Intelligence (AI) Applied to COVID-19, From the AJR Special Series on AI Applications.

Hundreds of imaging-based artificial intelligence (AI) models have been developed in response to the COVID-19 pandemic. AI systems that incorporate imaging have shown promise in primary detection, severity grading, and prognostication of outcomes in COVID-19, and have enabled integration of imaging with a broad range of additional clinical and epidemiologic data. However, systematic reviews of AI models applied to COVID-19 medical imaging have highlighted problems in the field, including methodologic issues and problems in real-world deployment. Clinical use of such models should be informed by both the promise and potential pitfalls of implementation. How does a practicing radiologist make sense of this complex topic, and what factors should be considered in the implementation of AI tools for imaging of COVID-19? This critical review aims to help the radiologist understand the nuances that impact the clinical deployment of AI for imaging of COVID-19. We review imaging use cases for AI models in COVID-19 (e.g., diagnosis, severity assessment, and prognostication) and explore considerations for AI model development and testing, deployment infrastructure, clinical user interfaces, quality control, and institutional review board and regulatory approvals, with a practical focus on what a radiologist should consider when implementing an AI tool for COVID-19.

Quantitative chest CT combined with plasma cytokines predict outcomes in COVID-19 patients.

Despite extraordinary international efforts to dampen the spread and understand the mechanisms behind SARS-CoV-2 infections, accessible predictive biomarkers directly applicable in the clinic are yet to be discovered. Recent studies have revealed that diverse types of assays bear limited predictive power for COVID-19 outcomes. Here, we harness the predictive power of chest CT in combination with plasma cytokines using a machine learning approach for predicting death during hospitalization and maximum severity degree in COVID-19 patients. Patients (n=152) from the Mount Sinai Health System in New York with plasma cytokine assessment and a chest CT within 5 days from admission were included. Demographics, clinical, and laboratory variables, including plasma cytokines (IL-6, IL-8, and TNF-α) were collected from the electronic medical record. We found that chest CT combined with plasma cytokines were good predictors of death (AUC 0.78) and maximum severity (AUC 0.82), whereas CT quantitative was better at predicting severity (AUC 0.81 vs 0.70) while cytokine measurements better predicted death (AUC 0.70 vs 0.66). Finally, we provide a simple scoring system using plasma IL-6, IL-8, TNF-α, GGO to aerated lung ratio and age as novel metrics that may be used to monitor patients upon hospitalization and help physicians make critical decisions and considerations for patients at high risk of death for COVID-19.

CT Morphologic Characteristics and Variant Patterns of Interstitial Pulmonary Fibrosis in Systemic Lupus Erythematosus.

PURPOSE: To assess CT features of pulmonary fibrosis in patients with systemic lupus erythematosus (SLE) and to assess the presence of several distinctive patterns of fibrosis associated with connective tissue disease. MATERIALS AND METHODS: A cross-sectional retrospective analysis was performed. An institutional clinical database was queried for the years of 2005-2015 to identify CT examination reports of patients with SLE and fibrotic lung disease, which yielded 50 patients (median age, 49 years; age range, 22-71 years; 46 women). CT examination reports were scored by two subspecialty thoracic radiologists using a standard multilevel semiquantitative system. Readers noted the presence or absence of several recently described CT signs of variant patterns of fibrosis in connective tissue disease (the "anterior upper lobe," "straight-edge," and "exuberant honeycombing" signs), as well as two other morphologic characteristics (an "island-like" appearance of areas of well-defined fibrosis with angular margins surrounded by normal lung and confluent regions of lucent lung destruction). RESULTS: The most common CT patterns were characterized as either fibrotic nonspecific interstitial pneumonia (38%, 19 of 50) or variant fibrosis (44%, 22 of 50). CT signs of variant fibrosis were identified by both readers in up to 62% of patients, with good κ agreement (0.44-0.64); the island-like sign (62%) and anterior upper lobe sign (52%) were most commonly observed. Pulmonary function test results showed correlations with several imaging findings but did not show correlations with CT signs of variant fibrosis. CONCLUSION: When present, pulmonary fibrosis in SLE often has a distinctive appearance and may also manifest as several variant fibrotic patterns.Keywords: CT, Lung© RSNA, 2021See also the commentary by White in this issue.

Combining Initial Radiographs and Clinical Variables Improves Deep Learning Prognostication in Patients with COVID-19 from the Emergency Department.

PURPOSE: To train a deep learning classification algorithm to predict chest radiograph severity scores and clinical outcomes in patients with coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: In this retrospective cohort study, patients aged 21-50 years who presented to the emergency department (ED) of a multicenter urban health system from March 10 to 26, 2020, with COVID-19 confirmation at real-time reverse-transcription polymerase chain reaction screening were identified. The initial chest radiographs, clinical variables, and outcomes, including admission, intubation, and survival, were collected within 30 days (n = 338; median age, 39 years; 210 men). Two fellowship-trained cardiothoracic radiologists examined chest radiographs for opacities and assigned a clinically validated severity score. A deep learning algorithm was trained to predict outcomes on a holdout test set composed of patients with confirmed COVID-19 who presented between March 27 and 29, 2020 (n = 161; median age, 60 years; 98 men) for both younger (age range, 21-50 years; n = 51) and older (age >50 years, n = 110) populations. Bootstrapping was used to compute CIs. RESULTS: The model trained on the chest radiograph severity score produced the following areas under the receiver operating characteristic curves (AUCs): 0.80 (95% CI: 0.73, 0.88) for the chest radiograph severity score, 0.76 (95% CI: 0.68, 0.84) for admission, 0.66 (95% CI: 0.56, 0.75) for intubation, and 0.59 (95% CI: 0.49, 0.69) for death. The model trained on clinical variables produced an AUC of 0.64 (95% CI: 0.55, 0.73) for intubation and an AUC of 0.59 (95% CI: 0.50, 0.68) for death. Combining chest radiography and clinical variables increased the AUC of intubation and death to 0.88 (95% CI: 0.79, 0.96) and 0.82 (95% CI: 0.72, 0.91), respectively. CONCLUSION: The combination of imaging and clinical information improves outcome predictions.Supplemental material is available for this article.© RSNA, 2020.

A Review of Clinical and Imaging Features of Diffuse Pulmonary Hemorrhage.

OBJECTIVE. The purpose of this article is to review the clinical and imaging features of diffuse pulmonary hemorrhage. CONCLUSION. Diffuse pulmonary hemorrhage is a life-threatening syndrome associated with a wide variety of underlying pathologic categories. Nonspecific clinical and imaging features pose challenges to promptly diagnosing this condition. Chest radiography commonly shows alveolar opacification, and CT reveals the extent of disease. Integration of clinical, radiologic, laboratory, and pathologic findings facilitates timely diagnosis and etiologic identification.

Coronary artery calcification in COVID-19 patients: an imaging biomarker for adverse clinical outcomes.

BACKGROUND: Recent studies have demonstrated a complex interplay between comorbid cardiovascular disease, COVID-19 pathophysiology, and poor clinical outcomes. Coronary artery calcification (CAC) may therefore aid in risk stratification of COVID-19 patients. METHODS: Non-contrast chest CT studies on 180 COVID-19 patients ≥ age 21 admitted from March 1, 2020 to April 27, 2020 were retrospectively reviewed by two radiologists to determine CAC scores. Following feature selection, multivariable logistic regression was utilized to evaluate the relationship between CAC scores and patient outcomes. RESULTS: The presence of any identified CAC was associated with intubation (AOR: 3.6, CI: 1.4-9.6) and mortality (AOR: 3.2, CI: 1.4-7.9). Severe CAC was independently associated with intubation (AOR: 4.0, CI: 1.3-13) and mortality (AOR: 5.1, CI: 1.9-15). A greater CAC score (UOR: 1.2, CI: 1.02-1.3) and number of vessels with calcium (UOR: 1.3, CI: 1.02-1.6) was associated with mortality. Visualized coronary stent or coronary artery bypass graft surgery (CABG) had no statistically significant association with intubation (AOR: 1.9, CI: 0.4-7.7) or death (AOR: 3.4, CI: 1.0-12). CONCLUSION: COVID-19 patients with any CAC were more likely to require intubation and die than those without CAC. Increasing CAC and number of affected arteries was associated with mortality. Severe CAC was associated with higher intubation risk. Prior CABG or stenting had no association with elevated intubation or death.

COVID-19 Imaging: What We Know Now and What Remains Unknown.

Infection with SARS-CoV-2 ranges from an asymptomatic condition to a severe and sometimes fatal disease, with mortality most frequently being the result of acute lung injury. The role of imaging has evolved during the pandemic, with CT initially being an alternative and possibly superior testing method compared with reverse transcriptase-polymerase chain reaction (RT-PCR) testing and evolving to having a more limited role based on specific indications. Several classification and reporting schemes were developed for chest imaging early during the pandemic for patients suspected of having COVID-19 to aid in triage when the availability of RT-PCR testing was limited and its level of performance was unclear. Interobserver agreement for categories with findings typical of COVID-19 and those suggesting an alternative diagnosis is high across multiple studies. Furthermore, some studies looking at the extent of lung involvement on chest radiographs and CT images showed correlations with critical illness and a need for mechanical ventilation. In addition to pulmonary manifestations, cardiovascular complications such as thromboembolism and myocarditis have been ascribed to COVID-19, sometimes contributing to neurologic and abdominal manifestations. Finally, artificial intelligence has shown promise for use in determining both the diagnosis and prognosis of COVID-19 pneumonia with respect to both radiography and CT.

Portable Chest Radiography as an Exclusionary Test for Adverse Clinical Outcomes During the COVID-19 Pandemic.

BACKGROUND: Chest radiography (CXR) often is performed in the acute setting to help understand the extent of respiratory disease in patients with COVID-19, but a clearly defined role for negative chest radiograph results in assessing patients has not been described. RESEARCH QUESTION: Is portable CXR an effective exclusionary test for future adverse clinical outcomes in patients suspected of having COVID-19? STUDY DESIGN AND METHODS: Charts of consecutive patients suspected of having COVID-19 at five EDs in New York City between March 19, 2020, and April 23, 2020, were reviewed. Patients were categorized based on absence of findings on initial CXR. The primary outcomes were hospital admission, mechanical ventilation, ARDS, and mortality. RESULTS: Three thousand two hundred forty-five adult patients, 474 (14.6%) with negative initial CXR results, were reviewed. Among all patients, negative initial CXR results were associated with a low probability of future adverse clinical outcomes, with negative likelihood ratios of 0.27 (95% CI, 0.23-0.31) for hospital admission, 0.24 (95% CI, 0.16-0.37) for mechanical ventilation, 0.19 (95% CI, 0.09-0.40) for ARDS, and 0.38 (95% CI, 0.29-0.51) for mortality. Among the subset of 955 patients younger than 65 years and with a duration of symptoms of at least 5 days, no patients with negative CXR results died, and the negative likelihood ratios were 0.17 (95% CI, 0.12-0.25) for hospital admission, 0.09 (95% CI, 0.02-0.36) for mechanical ventilation, and 0.09 (95% CI, 0.01-0.64) for ARDS. INTERPRETATION: Initial CXR in adult patients suspected of having COVID-19 is a strong exclusionary test for hospital admission, mechanical ventilation, ARDS, and mortality. The value of CXR as an exclusionary test for adverse clinical outcomes is highest among young adults, patients with few comorbidities, and those with a prolonged duration of symptoms.