Background The clinical presentation of COVID-19 in patients admitted to hospital is heterogeneous. We aimed to determine whether clinical phenotypes of patients with COVID-19 can be derived from clinical data, to assess the reproducibility of these phenotypes and correlation with prognosis, and to derive and validate a simplified probabilistic model for phenotype assignment. Phenotype identification was not primarily intended as a predictive tool for mortality. MethodsIn this study, we used data from two cohorts: the COVID-19@Spain cohort, a retrospective cohort including 4035 consecutive adult patients admitted to 127 hospitals in Spain with COVID-19 between Feb 2 and March 17, 2020, and the COVID-19@HULP cohort, including 2226 consecutive adult patients admitted to a teaching hospital in Madrid between Feb 25 and April 19, 2020. The COVID-19@Spain cohort was divided into a derivation cohort, comprising 2667 randomly selected patients, and an internal validation cohort, comprising the remaining 1368 patients. The COVID-19@HULP cohort was used as an external validation cohort. A probabilistic model for phenotype assignment was derived in the derivation cohort using multinomial logistic regression and validated in the internal validation cohort. The model was also applied to the external validation cohort. 30-day mortality and other prognostic variables were assessed in the derived phenotypes and in the phenotypes assigned by the probabilistic model. Findings Three distinct phenotypes were derived in the derivation cohort (n=2667)-phenotype A (516 [19%] patients), phenotype B (1955 [73%]) and phenotype C (196 [7%])-and reproduced in the internal validation cohort (n=1368)phenotype A (233 [17%] patients), phenotype B (1019 [74%]), and phenotype C (116 [8%]). Patients with phenotype A were younger, were less frequently male, had mild viral symptoms, and had normal inflammatory parameters. Patients with phenotype B included more patients with obesity, lymphocytopenia, and moderately elevated inflammatory parameters. Patients with phenotype C included older patients with more comorbidities and even higher inflammatory parameters than phenotype B. We developed a simplified probabilistic model (validated in the internal validation cohort) for phenotype assignment, including 16 variables. In the derivation cohort, 30-day mortality rates were 2•5% (95% CI 1•4-4•3) for patients with phenotype A, 30•5% (28•5-32•6) for patients with phenotype B, and 60•7% (53•7-67•2) for patients with phenotype C (log-rank test p<0•0001). The predicted phenotypes in the internal validation cohort and external validation cohort showed similar mortality rates to the assigned phenotypes (internal validation cohort: 5•3% [95% CI 3•4-8•1] for phenotype A, 31•3% [28•5-34•2] for phenotype B, and 59•5% [48•8-69•3] for phenotype C; external validation cohort: 3•7% [2•0-6•4] for phenotype A, 23•7% [21•8-25•7] for phenotype B, and 51•4% [41•9-60•7] for phenotype C).Interpretation Patients admitted to hospital with COVID-19 can be classified into three...

Aneurysmal diseases of the thoracic aorta are life-threatening conditions. In such cases, stent-graft treatment has been proposed as an alternative to surgery. The morbidity and mortality associated with endovascular repair are significantly lower than those associated with open surgery. In the largest surgical series, the mortality ranged from 5% to 20%. In studies of endovascular repair, the 30-day mortality was 0%-20% and the periprocedural stroke rate was 0%-7%. Often, open surgery is prohibited in patients with these high-risk lesions; thus, in many cases endovascular treatment is the only alternative. Thoracic aortic diseases that can be treated with endovascular stent-graft placement include aneurysms, dissection, traumatic rupture, traumatic pseudoaneurysms, intramural hematoma, penetrating atherosclerotic ulcers, and aortic rupture. Thorough preprocedure imaging is essential for selecting patients, choosing the stent-graft devices, and planning the intervention. Prerequisites for endovascular stent-graft placement are an adequate neck for graft attachment and adequate vascular access. When the ascending aorta or aortic arch is involved, surgical and endovascular procedures can be combined and performed simultaneously, allowing treatment of a wider range of cases. An experienced interdisciplinary team is needed to manage such cases.

IntroductionAir pollution has a significant impact on the morbidity and mortality of various respiratory diseases. However, this has not been widely studied in diffuse interstitial lung diseases, specifically in idiopathic pulmonary fibrosis.ObjectiveIn this study we aimed to assess the relationship between four major air pollutants individually [carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), and nitrogen oxides (NOx)] and the development of chronic respiratory failure, hospitalization due to respiratory causes and mortality in patients with idiopathic pulmonary fibrosis.MethodsWe conducted an exploratory retrospective panel study from 2011 to 2020 in 69 patients with idiopathic pulmonary fibrosis from the pulmonary medicine department of a tertiary hospital. Based on their geocoded residential address, levels of each pollutant were estimated 1, 3, 6, 12, and 36 months prior to each event (chronic respiratory failure, hospital admission and mortality). Data was collected from the air quality monitoring stations of the Community of Madrid located &lt;3.5 km (2.2 miles) from each patient's home.ResultsThe increase in average values of CO [OR 1.62 (1.11–2.36) and OR 1.84 (1.1–3.06)], NO2 [OR 1.64 (1.01–2.66)], and NOx [OR 1.11 (1–1.23) and OR 1.19 (1.03–1.38)] were significantly associated with the probability of developing chronic respiratory failure in different periods. In addition, the averages of NO2, O3, and NOx were significantly associated with the probability of hospital admissions due to respiratory causes and mortality in these patients.ConclusionAir pollution is associated with an increase in the probability of developing chronic respiratory failure, hospitalization due to respiratory causes and mortality in patients with idiopathic pulmonary fibrosis.

No abstract

La mayor parte de los pacientes que superan la infección por SARS-CoV-2 no presentan complicaciones ni requieren un seguimiento específico, pero una proporción significativa (especialmente aquellos con formas clínicas moderadas/graves de la enfermedad) necesitan un seguimiento clínico-radiológico. Aunque apenas existen referencias o guías clínicas sobre el seguimiento a largo plazo de estos pacientes post-COVID-19, se están realizando pruebas radiológicas y constituyendo consultas monográficas de vigilancia en la mayor parte de los centros hospitalarios para atender sus necesidades. El propósito de este trabajo es compartir nuestra experiencia en el manejo del paciente post-COVID-19 en dos instituciones que han tenido una elevada incidencia de la COVID-19 y proponer unas recomendaciones generales de seguimiento desde una perspectiva clínica y radiológica.

We report the MRI findings in a 3-week-old boy with D-transposition of the great arteries and an abnormal origin of the right subclavian artery from the pulmonary artery. This anomaly of the subclavian arteries is called isolation. It is infrequent in patients with a right aortic arch, but exceedingly rare in those with a left aortic arch. This is a unique report of the MRI findings in this congenital abnormality of the aortic arch.

The main objective of this work is to develop and evaluate an artificial intelligence system based on deep learning capable of automatically identifying, quantifying, and characterizing COVID-19 pneumonia patterns in order to assess disease severity and predict clinical outcomes, and to compare the prediction performance with respect to human reader severity assessment and whole lung radiomics. We propose a deep learning based scheme to automatically segment the different lesion subtypes in nonenhanced CT scans. The automatic lesion quantification was used to predict clinical outcomes. The proposed technique has been independently tested in a multicentric cohort of 103 patients, retrospectively collected between March and July of 2020. Segmentation of lesion subtypes was evaluated using both overlapping (Dice) and distance-based (Hausdorff and average surface) metrics, while the proposed system to predict clinically relevant outcomes was assessed using the area under the curve (AUC). Additionally, other metrics including sensitivity, specificity, positive predictive value and negative predictive value were estimated. 95% confidence intervals were properly calculated. The agreement between the automatic estimate of parenchymal damage (%) and the radiologists’ severity scoring was strong, with a Spearman correlation coefficient (R) of 0.83. The automatic quantification of lesion subtypes was able to predict patient mortality, admission to the Intensive Care Units (ICU) and need for mechanical ventilation with an AUC of 0.87, 0.73 and 0.68 respectively. The proposed artificial intelligence system enabled a better prediction of those clinically relevant outcomes when compared to the radiologists’ interpretation and to whole lung radiomics. In conclusion, deep learning lesion subtyping in COVID-19 pneumonia from noncontrast chest CT enables quantitative assessment of disease severity and better prediction of clinical outcomes with respect to whole lung radiomics or radiologists’ severity score.

No abstract