Objective: To evaluate the efficacy and safety of mepolizumab in patients with severe eosinophilic asthma (SEA) in a real-world clinical setting. Methods: This retrospective, observational cohort study included data from 45 SEA patients who initiated mepolizumab treatment, with 41 completing a 12-month follow-up. Demographic, clinical, and laboratory data-including exacerbation rates, forced expiratory volume in one second (FEV1), Asthma Control Test (ACT) and CARAT scores, and blood eosinophil counts-were extracted from patient medical records. Paired statistical tests were used to compare pre- and post-treatment outcomes, with significance set at p < 0.05. Results: Mepolizumab therapy was associated with an 81% reduction in annual exacerbation rates (from 2.68 to 0.51 events; p < 0.001) and a 21.6% improvement in FEV1 (from 1.71 L to 2.08 L; p < 0.001). ACT scores increased significantly from 11.91 to 21.29 (p < 0.001), with 73.2% of patients achieving well-controlled asthma (ACT = 20). CARAT scores also showed significant improvement, reflecting better control of both asthma and rhinitis symptoms. Blood eosinophil counts decreased by 64% (from 525.9 to 189.4 cells/µL; p < 0.001). Overall, the treatment was well tolerated, with only one discontinuation due to a mild headache. Conclusion: In this real-world cohort, mepolizumab significantly reduced exacerbation frequency, improved lung function and symptom control, and lowered eosinophil levels over 12 months. These findings support its use as an effective and safe therapeutic option for managing severe eosinophilic asthma.

Keywords: Mepolizumab, Eosinophilic asthma, Severe asthma.

Objective: To describe the sociodemographic and clinical characteristics of individuals exposed to smoking or biomass smoke and followed at primary health care (PHC) centers across three states in Brazil. Methods: This was a cross-sectional multicenter study including patients followed at any of four PHC centers in Brazil. Patients = 35 years of age who were smokers or former smokers, or were exposed to biomass smoke were included, the exception being those with physical/mental disabilities and those who were pregnant. Face-to-face assessments included a questionnaire assessing clinical and sociodemographic characteristics, as well as the COPD Assessment Test (CAT) and the modified Medical Research Council (mMRC) dyspnea scale. Results: Of a total of 737 patients, 56.3% were female and 64.2% were White, with a mean age of 57.7 ± 11.8 years. Most (54.4%) had < 9 years of schooling, 50.2% had low socioeconomic status, and 71.5% were overweight/obese. Smokers accounted for 43.4% of the study sample, whereas 15.0% had no direct exposure to cigarette smoke. Common symptoms included cough, in 37.3%; wheezing, in 33.8%; and phlegm, in 27.4%. Most (75.1%) of the study participants had mMRC dyspnea scale scores of 0 or 1. CAT scores were 0-10, in 40.2%; 11-20, in 44.6%; 21-30, in 14.1%; and 31-40, in 1.1%. Binary logistic regression showed that sex and age significantly impacted mMRC dyspnea scale predictions, whereas BMI and socioeconomic status influenced CAT predictions. Common comorbidities included hypertension, in 51.3%; depression, in 27.4%; and diabetes, in 24.3%. No association was found between hypertension and obesity or smoking, or between diabetes and obesity or smoking. Conclusions: PHC patients with risk factors such as smoking and exposure to biomass smoke have a high comorbidity burden, with over half experiencing mild to moderate quality-of-life impacts. This study emphasizes the need for targeted preventive measures in PHC settings.

Keywords: Smoking; Risk factors; Biomass; Primary health care.

Objective: This study sought to estimate body composition values by disease severity in patients recovered from COVID-19. Methods: This was an observational, analytical, prospective cross-sectional study involving patients recovered from COVID-19 and employing the following assessment methods: bioelectrical impedance analysis, dual-energy X-ray absorptiometry, and air displacement plethysmography. Results: A total of 210 volunteers were included. Demographic characteristics, comorbidities, and body composition values highlighted significant differences between men and women across disease severity levels. Conclusions: Sex differences influence the severity of COVID-19. Our results provide a cross-sectional analysis of the impact of body composition on COVID-19 patients, showing risk factors and parameters that can contribute to the treatment, health recovery, and quality of life of this population.

Keywords: COVID-19; Body composition; Risk factors; Quality of life.

Objective: Thymic tumors are a rare group of anterior mediastinal tumors. Surgery is the primary treatment. Adjuvant treatment is used in select cases. The purpose of this study was to evaluate the outcomes of patients with thymic tumors, submitted or not to radiotherapy, and identify risk factors that could impact the outcomes to better support patient selection for RT. Methods: This is a single institution retrospective cohort study. Patients with histologically proven thymoma or thymic carcinoma treated from July of 2009 to November of 2020 were included. Analysis was based on the use of radiation therapy (RT). Overall survival and disease-free survival were assessed from the date of diagnosis. To minimize selection bias, propensity score matching (PSM) regression using Kernel matching was used, estimated on the population for average treatment effect. Results: Overall, 101 patients were analyzed, with mean age at diagnosis of 54.6 years (range 25-84 years). Unfavorable histology and more advanced stages predominated in the cohort. Nevertheless, most (69.3%) were treated with radical intent. RT was delivered in 52.9% of these patients. Five-year OS, local progression and distant progression free survivals were 81.0%, 95.0% and 88.1%, respectively for the radical intent cohort. PSM showed that RT reduced the chances of death by 6.3% (matched sample size was 60, p = 0.02). Conclusions: In this retrospective cohort, RT had a positive impact in OS after PSM analysis. Prospective data regarding the role of RT in this disease is needed to validate these findings.

Keywords: Thymoma; Thymus neoplasms; Radiotherapy.

Objectives: The 9th edition of the Tumor, Node, Metastasis (TNM-9) lung cancer classification is set to replace the 8th edition (TNM-8) starting in 2025. Key updates include the splitting of the mediastinal nodal category N2 into single- and multiple-station involvement, as well as the classification of multiple extrathoracic metastatic lesions as involving a single organ system (M1c1) or multiple organ systems (M1c2). This study aimed to assess how the TNM-9 revisions affect the final staging of lung cancer patients and how these changes correlate with overall survival (OS). Methods: This retrospective cohort study included patients diagnosed with lung cancer between 2018 and 2021, who were staged according to both TNM-8 and TNM-9 criteria. The staging classifications were analyzed and compared in relation to OS. Results: Among a total of 914 patients, 42 were re-staged using TNM-9. Of the 382 patients classified as stage IVB, 55.9% were reclassified as M1c2. Despite an absolute increase in mean OS for patients re-staged from IIB to IIA and from IIIA to IIB, the observed differences were not statistically significant. Median OS differed significantly both within stage IVB and between patients with M1c2 disease and other stage IV subgroups. Multi-organ metastatic disease was an independent predictor of poorer OS, regardless of age, sex, performance status, and oncologic treatment. Conclusions: TNM-9 improves prognostic accuracy in lung cancer. Although patients with multiple extrathoracic metastases involving different organ systems are not yet independently staged from IVB, they demonstrated significantly poorer OS compared to other advanced-stage patients.

Keywords: Lung cancer, Metastasis, Lymph nodes, Prognosis, Survival.

Objectives: This study explores the relationship between inhaler visual identification, naming, and adherence outcomes, and evaluates the potential of combining these factors into a screening tool for identifying poor adherence. Methods: This observational, prospective study included adult patients with COPD, asthma, or asthma+COPD who had been on chronic inhalation therapy for at least the past year. Data were collected through patient interviews and medical records. Adherence was assessed using the Test of Adherence to Inhalers (TAI) questionnaire and prescription records, calculated as the Proportion of Days Covered (PDC). The patients completed a questionnaire to evaluate their ability to visually identify and name their inhalers. Results: Among the 196 participants, significant differences in adherence levels were observed across the COPD, asthma, and asthma+COPD groups, with COPD patients demonstrating higher adherence rates (p=0.001). Concordance between TAI and PDC was highest in the COPD group (75.0%), compared to the asthma (51.3%) and asthma+COPD (55.5%) groups. Correct naming of inhalers was not significantly correlated with adherence. However, correct inhaler visual identification was associated with better adherence. Incorrect visual identification showed low sensitivity (15.9%) but high specificity (92.6%) for detecting poorly adherent patients. Conclusions: The ability to visually identify inhalers was associated with better adherence, while the ability to name inhalers was not. Although incorrect visual identification has limited utility as a screening tool, it may still serve as a rapid and practical method for identifying poorly adherent patients in clinical practice.

Keywords: COPD, asthma, inhalers, therapeutic adherence, visual perception.

Objective: To evaluate the quality of ChatGPT answers to asthma-related questions, as assessed from the perspectives of asthma specialists and laypersons. Methods: Seven asthma-related questions were asked to ChatGPT (version 4) between May 3, 2024 and May 4, 2024. The questions were standardized with no memory of previous conversations to avoid bias. Six pulmonologists with extensive expertise in asthma acted as judges, independently assessing the quality and reproducibility of the answers from the perspectives of asthma specialists and laypersons. A Likert scale ranging from 1 to 4 was used, and the content validity coefficient was calculated to assess the level of agreement among the judges. Results: The evaluations showed variability in the quality of the answers provided by ChatGPT. From the perspective of asthma specialists, the scores ranged from 2 to 3, with greater divergence in questions 2, 3, and 5. From the perspective of laypersons, the content validity coefficient exceeded 0.80 for four of the seven questions, with most answers being correct despite a lack of significant depth. Conclusions: Although ChatGPT performed well in providing answers to laypersons, the answers that it provided to specialists were less accurate and superficial. Although AI has the potential to provide useful information to the public, it should not replace medical guidance. Critical analysis of AI-generated information remains essential for health care professionals and laypersons alike, especially for complex conditions such as asthma.

Keywords: Asthma; Artificial intelligence; Pulmonologists