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Background: The impact of diagnosis treatment and bronchial asthma on coronavirus disease 2019 (COVID-19) associated outcomes remains unclear. Objective: To identify the prevalence and outcomes associated with asthma among hospitalized patients with COVID-19. Methods: Electronic health records of 130 patients with asthma among hospitalized patients with COVID-19 were reviewed. Two subgroups of asthmatic patients were compared according to clinical outcomes during hospitalization. Patients with death results, intubation, and/or need of intensive care unit (ICU) stay were grouped as asthmatic patients with severe COVID-19 outcomes, and the rest were grouped as asthmatic patients with non-severe COVID-19 outcomes. Multivariable analyses were conducted with logistic regression to identify independent risk factors for severe outcomes. Results: The prevalence of asthma in COVID-19 hospitalized patients was 5%. The mean age was 59.4 years and 54% were women. 17% received treatment in GINA step 4-5 asthma at the time of admission. An allergic asthma phenotype was determined in 38%. There was no significant difference in hospital length of stay or need for intubation between asthmatic patients and global COVID-19 admitted patients. 17% of asthmatic patients developed a severe outcome, and 5% had a death result. Elevated Lactate Dehydrogenase (LDH) level, low transcutaneous pulse oximetry (SpO2), the coexistence of atrial fibrillation (AF), and need for moderate or high ICS at admission were independent risk factors for a worse outcome in asthmatics COVID-19 hospitalized patients. Conclusion: The prevalence of asthma in COVID-19 hospitalized patients was 5%, consistent with the asthma prevalence in the general population. The asthmatic patients with the previous prescription of moderate or high doses of ICS and/or coexistence of atrial fibrillation at admission had a higher risk of the severe outcome.
RESUMO
Background: Granulomatous-lymphocytic interstitial lung disease (GLILD) is a distinct clinic-radio-pathological interstitial lung disease (ILD) that develops in 9% to 30% of patients with common variable immunodeficiency (CVID). Often related to extrapulmonary dysimmune disorders, it is associated with long-term lung damage and poorer clinical outcomes. The aim of this study was to explore the potential use of the integration between clinical parameters, laboratory variables, and developed CT scan scoring systems to improve the diagnostic accuracy of non-invasive tools. Methods: A retrospective cross-sectional study of 50 CVID patients was conducted in a referral unit of primary immune deficiencies. Clinical variables including demographics and comorbidities; analytical parameters including immunoglobulin levels, lipid metabolism, and lymphocyte subpopulations; and radiological and lung function test parameters were collected. Baumann's GLILD score system was externally validated by two observers in high-resolution CT (HRCT) scans. We developed an exploratory predictive model by elastic net and Bayesian regression, assessed its discriminative capacity, and internally validated it using bootstrap resampling. Results: Lymphadenopathies (adjusted OR 9.42), splenomegaly (adjusted OR 6.25), Baumann's GLILD score (adjusted OR 1.56), and CD8+ cell count (adjusted OR 0.9) were included in the model. The larger range of values of the validated Baumann's GLILD HRCT scoring system gives it greater predictability. Cohen's κ statistic was 0.832 (95% CI 0.70-0.90), showing high concordance between both observers. The combined model showed a very good discrimination capacity with an internally validated area under the curve (AUC) of 0.969. Conclusion: Models integrating clinics, laboratory, and CT scan scoring methods may improve the accuracy of non-invasive diagnosis of GLILD and might even preclude aggressive diagnostic tools such as lung biopsy in selected patients.
