Chronic obstructive pulmonary disease (COPD) is significant cause of morbidity and mortality worldwide. There is mounting evidence suggesting that COPD patients are at increased risk of severe COVID-19 outcomes; however, it remains unclear whether they are more susceptible to acquiring SARS-CoV-2 infection. In this comprehensive review, we aim to provide an up-to-date perspective of the intricate relationship between COPD and COVID-19. We conducted a thorough review of the literature to examine the evidence regarding the susceptibility of COPD patients to COVID-19 infection and the severity of their disease outcomes. While most studies have found that pre-existing COPD is associated with worse COVID-19 outcomes, some have yielded conflicting results. We also discuss confounding factors such as cigarette smoking, inhaled corticosteroids, and socioeconomic and genetic factors that may influence this association. Furthermore, we review acute COVID-19 management, treatment, rehabilitation, and recovery in COPD patients and how public health measures impact their care. In conclusion, while the association between COPD and COVID-19 is complex and requires further investigation, this review highlights the need for careful management of COPD patients during the pandemic to minimize the risk of severe COVID-19 outcomes.

/pdf/the-complex-association-between-copd-and-covid-19-1ecy5tq7.pdf

The Complex Association between COPD and COVID-19

Rationale: There is controversy concerning the association of chronic obstructive pulmonary disease (COPD) as an independent risk factor for mortality in patients hospitalized with Coronavirus Disease 2019 (COVID-19). We hypothesize that patients with COPD hospitalized for COVID-19 have increased mortality risk. Objective: To assess whether COPD increased the risk of mortality among patients hospitalized for COVID-19. Methods: We conducted a retrospective cohort analysis of patients with COVID-19 between February 10, 2020, and November 10, 2020, and hospitalized within 14 days of diagnosis. Electronic health records from U.S. facilities (Optum COVID-19 data) were used. Results: In our cohort of 31,526 patients, 3030 (9.6%) died during hospitalization. Mortality in patients with COPD was higher than that of patients without COPD, 14.02% and 8.8%, respectively. Univariate (odds ratio [OR] 1.68; 95% confidence interval [CI] 1.54 to 1.84) and multivariate (OR 1.33; 95% CI 1.18 to 1.50) analysis showed that patients with COPD had greater odds of death due to COVID-19 than patients without COPD. We found significant interactions between COPD and sex and COPD and age. Specifically, the increased mortality risk associated with COPD was observed among female (OR 1.62; 95% CI 1.36 to 1.95) but not male patients (OR 1.14; 95% CI 0.97 to 1.34); and in patients aged 40 to 64 (OR 1.42; 95% CI 1.07 to 1.90) and 65 to 79 (OR 1.48; 95% CI 1.23 to 1.78) years. Conclusions: COPD is an independent risk factor for death in adults aged 40 to 79 years hospitalized with COVID-19 infection.

/pdf/outcomes-of-patients-with-copd-hospitalized-for-coronavirus-4kuedmvjd6.pdf

Outcomes of Patients with COPD Hospitalized for Coronavirus Disease 2019.

The purpose of this study was to analyze the imaging features of extraskeletal osteosarcomas (ESOS) on computed tomography (CT) and magnetic resonance imaging (MRI) and to investigate their associations with overall survival (OS) using uni- and multivariable survival analyses.This two-center retrospective study included all consecutive adult patients between 2008 and 2021 with histopathologically-proven ESOS who underwent pre-treatment CT and/or MRI. Clinical and histological characteristics, ESOS presentation on CT and MRI, treatment and outcomes were reported. Survival analyses were performed using Kaplan-Meier analysis and Cox regressions. Associations between imaging features and OS were searched using uni- and multivariable analyses.Fifty-four patients were included (30/54 [56%] men, median age: 67.5 years). Twenty-four died of ESOS (median OS: 18 months). ESOS were mostly deep-seated (46/54, 85%) in the lower limb (27/54, 50%) with a median size of 95 mm (interquartile range: 64, 142; range: 21-289 mm). Mineralization was seen on 26/42 (62%) patients, mainly gross-amorphous (18/26; 69%). ESOS were generally highly heterogeneous on T2-weighted images (38/48; 79%) and contrast-enhanced (CE) T1-weighted images (29/40; 72%), with necrosis (39/40; 97%), well-defined or focally infiltrative margins (39/47; 83%), with moderate peritumoral edema (39/47; 83%) and rim-like peripheral enhancement (17/40; 42%). Size, location, mineralization on CT, signal intensity heterogeneity on T1-, T2- and CE-T1-weighted images and hemorragic signal on MRI were associated with poorer OS (range of log-rank P = 0.0069-0.0485). At multivariable analysis, hemorragic signal and signal intensity heterogeneity on T2-weighted images remained predictive for poorer OS (hazard ratio [HR] = 2.68, P = 0.0299; HR = 9.85, P = 0.0262, respectively) CONCLUSION: ESOS typically presents as mineralized heterogeneous and necrotic soft tissue tumor with a possible rim-like enhancement and limited peritumoral abnormalities. MRI may help estimate outcome of patients with ESOS. 

Imaging presentation of extraskeletal osteosarcomas on CT and MRI and correlation with patients outcome: A two-center retrospective study of 54 patients.

http://www.clinicalimaging.org/article/S0899707122002881/pdf

Two in one: Overlapping CT findings of COVID-19 and underlying lung diseases

We propose a deep learning system to automatically detect four explainable emphysema signs on frontal and lateral chest radiographs. Frontal and lateral chest radiographs from 3000 studies were retrospectively collected. Two radiologists annotated these with 4 radiological signs of pulmonary emphysema identified from the literature. A patient with ≥2 of these signs present is considered emphysema positive. Using separate deep learning systems for frontal and lateral images we predict the presence of each of the four visual signs and use these to determine emphysema positivity. The ROC and AUC results on a set of 422 held-out cases, labeled by both radiologists, are reported. Comparison with a black-box model which predicts emphysema without the use of explainable visual features is made on the annotations from both radiologists, as well as the subset that they agreed on. DeLong’s test is used to compare with the black-box model ROC and McNemar’s test to compare with radiologist performance. In 422 test cases, emphysema positivity was predicted with AUCs of 0.924 and 0.946 using the reference standard from each radiologist separately. Setting model sensitivity equivalent to that of the second radiologist, our model has a comparable specificity (p = 0.880 and p = 0.143 for each radiologist respectively). Our method is comparable with the black-box model with AUCs of 0.915 (p = 0.407) and 0.935 (p = 0.291), respectively. On the 370 cases where both radiologists agreed (53 positives), our model achieves an AUC of 0.981, again comparable to the black-box model AUC of 0.972 (p = 0.289). Our proposed method can predict emphysema positivity on chest radiographs as well as a radiologist or a comparable black-box method. It additionally produces labels for four visual signs to ensure the explainability of the result. The dataset is publicly available at https://doi.org/10.5281/zenodo.6373392.

Explainable emphysema detection on chest radiographs with deep learning

Background: Comorbid disease is a risk factor for severe COVID-19 infection, however, initial rates of chronic obstructive pulmonary disease (COPD) in case series were low and severity of COVID-19 in COPD patients was variable. Methods: We performed a retrospective study of patients admitted with COVID-19 and evaluated outcomes in those with and without COPD and/or emphysema. Patients were identified as having COPD if they had a diagnosis in the medical record and a history of airflow-obstruction on spirometry, or a history of tobacco use and prescribed long-acting bronchodilator(s). Computed tomography scans were evaluated by radiologists. Propensity matching was performed for age, body-mass index (BMI), and serologic data correlated with severity of COVID-19 disease (D-dimer, C-reactive protein, ferritin, fibrinogen, absolute lymphocyte count, lymphocyte percentage, and lactate dehydrogenase). Results: Of 577 patients admitted with COVID-19, 103 had a diagnosis of COPD and/or emphysema. The COPD/emphysema cohort was older (67 vs 58, p<0.0001) than the other cohort and had a lower BMI. Among unmatched cohorts those with COPD/emphysema had higher rates of intensive care unit (ICU) admission (35% vs 24.9%, p=0.036) and maximal respiratory support requirements, with more frequent invasive mechanical ventilation (21.4% vs 11.8%), but no significant difference in mortality. After propensity-matching there was no difference in ICU admission, maximal respiratory support requirements, or mortality. Univariate and multivariate regression analyses yielded similar results. Discussion: Our propensity-matched retrospective cohort study suggests that patients hospitalized with COVID-19 that have COPD and/or emphysema may not have worse outcomes than those without these comorbid conditions.

/pdf/impact-of-chronic-obstructive-pulmonary-disease-and-4q9kj2tvwi.pdf

Impact of Chronic Obstructive Pulmonary Disease and Emphysema on Outcomes of Hospitalized Patients with COVID-19 Pneumonia.

Bone Tumors Occurring in the Soft Tissues: A Review of the Clinical, Imaging, and Histopathologic Findings.

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Impact of Chronic Obstructive Pulmonary Disease and Emphysema on Outcomes of Hospitalized Patients with COVID-19 Pneumonia.

Bone Tumors Occurring in the Soft Tissues: A Review of the Clinical, Imaging, and Histopathologic Findings.