Predictors of success/failure in the control of asthmatic smoking patients under conditions of clinical practice.

BACKGROUND: Tobacco smoking directly affects the airway, where it triggers a very strong local inflammatory response. OBJECTIVE: To determine the predictors of improvement or worsening of asthma control in asthmatic smokers. METHODS: Observational, prospective, multicenter, single cohort study, carried out in the outpatient pulmonology departments with a follow-up period of 6 months. The treatment was adjusted according to the indications of standard clinical practice. RESULTS: 196 patients were included, with a mean age of 54.64 years.39% of the patients were active smokers. Interpreting an Asthma Control Questionnaire (ACQ) score of ≤ 0.75 as asthma control, this was achieved in 30.2% of the cases. Patients with greater adherence were more likely to improve their asthma symptoms (p < 0.05), defined as a decrease in ACQ of 0.5 points or more at the final visit, while taking concomitant medication was a negative risk factor for improvement (p < 0.001). An eosinophil value >300 was a predictor for achieving control (p < 0.01). Patients treated with fluticasone propionate/formoterol versus those receiving budesonide/formoterol or beclomethasone/formoterol had a lower ACQ score (p < 0.01 and p < 0.01, respectively). CONCLUSION: Asthmatic patients with active tobacco exposure and a higher number of anti-asthma medications are more likely to have poorer control. Correct adherence to treatment is the main intervention to be performed to achieve the control. An eosinophil count greater than 300 was the main predictor for achieving control. Fluticasone propionate/formoterol FP/FORM was associated with a greater likelihood of improving ACQ score.

Serum exosome inflamma-miRs are surrogate biomarkers for asthma phenotype and severity.

BACKGROUND: Asthma is a heterogeneous disease with several phenotypes, endotypes and severity degrees, in which different T-cell subpopulations are involved. These cells express specific miRNAs (i.e. inflamma-miRs) that can be released to serum in exosomes after activation and be used as biomarkers of underlying inflammation. Thus, we aim to evaluate specific T-cell miRNA signatures in serum exosomes from different subgroups of asthmatic patients. METHODS: Samples from healthy donors (N = 30) and patients (N = 119) with different asthma endotypes (T2high -Atopic/T2high -Non-atopic/T2low ) and severity degrees (mild/MA and moderate-severe/MSA) were used. Demographic, clinical, haematological and biochemical characteristics were collected. Twelve miRNAs previously associated with different Th subsets were preselected and their levels in serum exosome samples were measured using RTqPCR. RESULTS: We detected five miRNAs with high confidence in serum exosomes: miR-16-5p, miR-21-5p, miR-126-3p, miR146a-5p and miR-215-5p. All of them, except miR-16-5p were upregulated in MSA patients compared to MA. A logistic regression model including each of these miRNAs was created to discriminate both conditions, rendering a ROC curve AUC of 0.896 (0.830-0.961). miR-21-5p and miR-126-3p, both involved in Th1/Th2 differentiation, were specifically augmented in T2high -Atopic patients. Of note, all these changes were found in samples collected in autumn. On the contrary, IL-6high patients with MSA, which were more obese, older, with higher neutrophil and basophil counts and TNF levels, displayed a decrease of miR-21-5p, miR-126-3p and miR-146a-5p. CONCLUSION: Immune-related miRNAs, including miR-21-5p, miR-126-3p, miR-146a-5p and miR-215-5p, can be used as clinically relevant non-invasive biomarkers of the phenotype/endotype and severity of asthma.

Efficacy of mepolizumab in usual clinical practice and characteristics of responders.

BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSION: In routine clinical practice, mepolizumab is effective in patients with severe eosinophilic asthma and it has a good safety profile.

Inteligencia artificial en radiología torácica. ¿Un reto en tiempos de la COVID-19? / Artificial intelligence in thoracic radiology. A challenge in COVID-19 times?

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Use of Conventional Chest Imaging and Artificial Intelligence in COVID-19 Infection. A Review of the Literature.

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.

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.