Severe asthma is a subset of difficult-to-treat asthma that requires the verification of inhaler technique, the correction of modifiable risk factors, as well as diagnosis and comorbidity review. When severe asthma is suspected, patients should undergo proper phenotyping (T2-high or T2-low) and be referred to a specialized severe asthma clinic. The current biologics for severe asthma treatment include omalizumab (anti-IgE), mepolizumab and reslizumab (anti-IL-5), benralizumab (anti-IL-5 receptor), dupilumab (anti-IL-4/IL-13), and tezepelumab (anti-TSLP). The outcomes to evaluate are the reduction in systemic corticosteroid use, the reduction in exacerbations and healthcare use, and improvement in symptoms and lung function. Comorbidities should be carefully considered, and if possible, addressed with the same biologic. Dupilumab, mepolizumab, and omalizumab are also approved for chronic rhinosinusitis with nasal polyps (CRSwNP), the most common asthma comorbidity. There are currently several clinical trials on biologics for severe asthma. Depemokimab is an ultra-long-acting anti-IL-5 antibody with promising results in phase III trials as a twice-yearly biologic for T2-high asthma. Verekitug follows a similar dosing concept, targeting TSLP, but is still undergoing phase II trials. Itepekimab and astegolimab are two anti-IL-33 antibodies that could have a role in the future treatment of severe asthma. Tezepelumab is in a phase III clinical trial for CRSwNP. Besides new drugs, there is still a need for major research into biologics in severe asthma cases, namely with comparative studies, better biomarkers for predicting response, and the determination of optimal treatment duration.

Endobronchial ultrasound‐guided transbronchial needle aspiration (EBUS‐TBNA) is an established and essential tool in lung cancer diagnosis and staging. While most studies show a specificity of 100%, sensitivity is often sub-optimal. The aim of this study was to develop a prediction model for malignancy, based on patient and lymph node characteristics. A retrospective cohort analysis of lymph nodes submitted to EBUS-TBNA between January 2016 and December 2017 was conducted. Lymph node characteristics (station, morphology, size, and vascularization) and histology were evaluated. A multivariate logistic regression analysis was performed and a prediction score for malignancy was developed from this model using a regression coefficient-based scoring method. A cut-off point was chosen for optimal sensitivity. 308 lymph node results were analysed, corresponding to 222 patients, with a mean age of 61 (range 27 - 85). 76 (33.8%) were female. Punctured lymph nodes had a mean short axis of 13.3 mm. 66 (21.4%) were malignant. On univariable analysis, age, lymph node size, borders, vascularization and station (pretracheal vs others) were associated with malignancy. Only lymph node size (β=0.169), vascularization (β=-1.370) and station (β=1.035) were retained in the final multivariate model. This model [ sizex0.169 + (1.035 if pre-tracheal) – (1.370 if vascularized)] had an area under curve of 0.777 (95% CI 0.715 - 0.839), p<0.001. For a cut-off of 1.5 in this score, sensitivity was 95%. A score based on lymph node size, vascularization and station can improve accuracy of EBUS-TBNA and help to optimize false-negative results.

The development of malignant pleural effusion (MPE) is often viewed by clinicians and patients alike as a significant hallmark of overall poor prognosis in the follow-up of different primary cancer...