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Severe eosinophilic asthma is a high-burden disease. Mepolizumab has been effective in several randomized clinical trials. However, such success might not be applicable to patients treated in usual clinical practice. The objectives of this article are to evaluate the efficacy of mepolizumab in severe uncontrolled eosinophilic asthma under usual clinical practice, and to determine characteristics associated with the response to this treatment.We have conducted a retrospective, multicentre study, including all adult patients with severe uncontrolled eosinophilic asthma in Galicia, Spain, on whom mepolizumab treatment was started before June 2020, at least 6 months before the time of inclusion, and had received at least one dose of the drug. Patient characteristics, clinical data, respiratory function and comorbidities were collected at baseline and at the 6-month-follow-up. Responders and super-responders were defined according to clinical response and requirement of systemic corticosteroids.122 patients (mean age 58 years old) were included. In the follow-up treatment 6 months later, 75.4% of the patients were well-controlled, displaying a significant reduction in blood eosinophil counts (p 0.001), hospital admissions and disease exacerbations (p 0.001), and had their systemic glucocorticosteroid dose significantly reduced (p 0.001). The inhaled corticosteroid dose was also lowered (p 0.01) after 6 months of treatment. Around two-thirds had a clinically significant increase in FEV1, 95% of the patients were considered responders and 43% super-responders.In routine clinical practice, mepolizumab is effective in patients with severe eosinophilic asthma and it has a good safety profile.

The coronavirus disease caused by SARS-Cov-2 is a pandemic with millions of confirmed cases around the world and a high death toll. Currently, the real-time polymerase chain reaction (RT-PCR) is the standard diagnostic method for determining COVID-19 infection. Various failures in the detection of the disease by means of laboratory samples have raised certain doubts about the characterisation of the infection and the spread of contacts.In clinical practice, chest radiography (RT) and chest computed tomography (CT) are extremely helpful and have been widely used in the detection and diagnosis of COVID-19. RT is the most common and widely available diagnostic imaging technique, however, its reading by less qualified personnel, in many cases with work overload, causes a high number of errors to be committed. Chest CT can be used for triage, diagnosis, assessment of severity, progression, and response to treatment. Currently, artificial intelligence (AI) algorithms have shown promise in image classification, showing that they can reduce diagnostic errors by at least matching the diagnostic performance of radiologists.This review shows how AI applied to thoracic radiology speeds up and improves diagnosis, allowing to optimise the workflow of radiologists. It can provide an objective evaluation and achieve a reduction in subjectivity and variability. AI can also help to optimise the resources and increase the efficiency in the management of COVID-19 infection. Resumen: La enfermedad causada por el coronavirus SARS-CoV-2 es una pandemia con millones de casos confirmados en todo el mundo, y un alto número de fallecimientos. Actualmente, la reacción en cadena de la polimerasa en tiempo real (RT-PCR) es el método de diagnóstico estándar para determinar la infección por COVID-19. Diversos fracasos en la detección de la enfermedad por medio de muestras de laboratorio han planteado ciertas dudas sobre la caracterización de la infección y la propagación a los contactos.En la práctica clínica, la radiografía de tórax (RT) y la tomografía computarizada (TC) de tórax son extremadamente útiles y se han utilizado extensamente en la detección y el diagnóstico de la COVID-19. La RT es la técnica de diagnóstico por imagen más común, y la que está más ampliamente disponible, sin embargo, su lectura por personal menos cualificado, en muchos casos con sobrecarga de trabajo, hace que se cometa un gran número de errores. La TC de tórax se puede utilizar para el triaje, el diagnóstico, la evaluación de la gravedad, la progresión y la respuesta al tratamiento. Actualmente, los algoritmos de inteligencia artificial (IA) han resultado prometedores en la clasificación de imágenes, mostrando que pueden reducir los errores de diagnóstico, como mínimo igualando el rendimiento diagnóstico de los radiólogos.Esta revisión muestra cómo la IA aplicada a la RT acelera y mejora el diagnóstico, lo que permite optimizar el flujo de trabajo de los radiólogos. Puede proporcionar una evaluación objetiva y lograr una reducción de la subjetividad y la variabilidad. La IA también puede ayudar a optimizar los recursos y aumentar la eficiencia en la gestión de la infección por COVID-19.