Some patients with severe asthma may benefit from treatment with biologics, but evidence has been mostly collected from randomized controlled trials (RCTs), in which patients' characteristics are different from those encountered in asthma patients in the real-world setting. The aim of this study was to describe the clinical features of complete responders versus non-complete responders to long-term treatment with biologics in patients with severe asthma attended in routine daily practice. Data of a cohort of 90 patients with severe asthma who were treated with biologics (omalizumab, benralizumab, and mepolizumab) for at least 12 months and were followed up to March 2022. Data recorded included clinical characteristics and effectiveness of treatment (exacerbation, Asthma Control Test [ACT] score, lung function, use of maintenance oral corticosteroids [mOCS]), FeNO, and blood eosinophils at baseline, at 12 months, and at the end of follow-up. Complete response is considered if, in addition to not presenting exacerbations or the use of mOCS, the ACT score was >20 and, the FEV₁ >80% predicted. An improvement in all asthma control parameters was observed after 12 months of treatment and a mean follow-up of 55 months. After 12 months of treatment 27.2% of patients met the criteria of complete response and this percentage even increased to 35.3% at the end of follow-up. Long-term complete response was associated to better lung function with mepolizumab and omalizumab treatment and to less previous exacerbations in the benralizumab group. The main cause of not achieving a complete response was the persistence of an airflow obstructive pattern. This study shows that omalizumab, benralizumab, and mepolizumab improved the clinical outcomes of patients with severe asthma in a clinic environment with similar effect sizes to RCTs in the long term follow-up. Airflow obstruction, however, was a predictor of a non-complete response to biologics.

A study was conducted in 98 adult patients diagnosed with severe eosinophilic asthma (73.5% women, mean age 47.2 years) and followed prospectively for 1 year. The aim of the study was to characterize this population and to identify factors associated with poor prognosis at 1 year of follow-up. At the initial visit, uncontrolled severe asthma was diagnosed in 87.7% of patients. Allergic sensitization was observed in 81.7% (polysensitization in 17.3%), with clinically significant allergic asthma in 45%. The mean percentage of sputum eosinophils was 4.7% (standard deviation(SD) 6.3%) and the mean (SD) blood eosinophil count 467 (225) cells/µL. Almost half of the patients (48.3%) had sputum eosinophilia (>3% eosinophils). Sputum eosinophils correlated significantly with peripheral eosinophilia (p = 0.004) and, to a lesser extent, with fractional exhaled nitric oxide (FeNO) (p = 0.04). After 1 year, 48 patients (49%) had uncontrolled asthma in all visits, and 50 (51%) had controlled asthma in some visits. Airway obstruction (FEV₁ < 80% predicted) was the main reason for uncontrolled asthma. In the multivariate analysis, an obstructive pattern (odds ratio (OR) 7.45, 95% confidence interval (CI) 2.41–23.03, p < 0.0001) and the patient’s age (OR 1.045, 95% CI 1.005–1.086, p = 0.026) were independent predictors of poor asthma control. In adult-onset and long-standing asthma, serum interleukin (IL) IL-17 was higher in the uncontrolled asthma group. This study contributes to characterizing patients with severe eosinophilic asthma in real-world clinical practice.

Objectives: During the COVID-19 pandemic, the risk of collapse for the health system created great difficulties. We will demonstrate that intermediate respiratory care units (IRCU) provide adequate management of patients with non-invasive respiratory support, which is particularly important for patients with SARS-CoV-2 pneumonia. Methods: A prospective observational study of patients with COVID-19 admitted to the ICU of a tertiary hospital. Sociodemographic data, comorbidities, pharmacological, respiratory support, laboratory and blood gas variables were collected. The overall cost of the unit was subsequently analyzed. Results: 991 patients were admitted, 56 to the IRCU (from a of 81 admitted to the critical care unit). Mean age was 65 years (SD 12.8), Barthel index 75 (SD 8.3), Charlson comorbidity index 3.1 (SD 2.2), HTN 27%, COPD 89% and obesity 24%. A significant relationship (p < 0.05) with higher mortality was noted for the following parameters: fever greater than or equal to 39 °C [OR 5.6; 95% CI (1.2–2.7); p = 0.020], protocolized pharmacological treatment [OR 0.3; 95% CI (0.1–0.9); p = 0.023] and IOI [OR 3.7; 95% CI (1.1–12.3); p = 0.025]. NIMV had less of a negative impact [OR 1.8; 95% CI (0.4–8.4); p = 0.423] than IOI. The total cost of the IRCU amounted to €66,233. The cost per day of stay in the IRCU was €164 per patient. The total cost avoided was €214,865. Conclusions: The pandemic has highlighted the importance of IRCUs in facilitating the management of a high patient volume. The treatment carried out in IRCUs is effective and efficient, reducing both admissions to and stays in the ICU.

The efficiency of lung cancer screening for reducing mortality is hindered by the high rate of false positives. Artificial intelligence applied to radiomics could help to early discard benign cases from the analysis of CT scans. The available amount of data and the fact that benign cases are a minority, constitutes a main challenge for the successful use of state of the art methods (like deep learning), which can be biased, over-fitted and lack of clinical reproducibility. We present an hybrid approach combining the potential of radiomic features to characterize nodules in CT scans and the generalization of the feed forward networks. In order to obtain maximal reproducibility with minimal training data, we propose an embedding of nodules based on the statistical significance of radiomic features for malignancy detection. This representation space of lesions is the input to a feed forward network, which architecture and hyperparameters are optimized using own-defined metrics of the diagnostic power of the whole system. Results of the best model on an independent set of patients achieve 100% of sensitivity and 83% of specificity (AUC = 0.94) for malignancy detection.

The measurement of specific T-cell responses can be a useful tool for COVID-19 diagnostics and clinical management. In this study, we evaluated the IFN-γ T-cell response against the main SARS-CoV-2 antigens (spike, nucleocapsid and membrane) in acute and convalescent individuals classified according to severity, and in vaccinated and unvaccinated controls. IgG against spike and nucleocapsid were also measured. Spike antigen triggered the highest number of T-cell responses. Acute patients showed a low percentage of positive responses when compared to convalescent (71.6% vs. 91.7%, respectively), but increased during hospitalization and with severity. Some convalescent patients showed an IFN-γ T-cell response more than 200 days after diagnosis. Only half of the vaccinated individuals displayed an IFN-γ T-cell response after the second dose. IgG response was found in a higher percentage of individuals compared to IFN-γ T-cell responses, and moderate correlations between both responses were seen. However, in some acute COVID-19 patients specific T-cell response was detected, but not IgG production. We found that the chances of an IFN-γ T-cell response against SARS-CoV-2 is low during acute phase, but may increase over time, and that only half of the vaccinated individuals had an IFN-γ T-cell response after the second dose.