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Introduction: Severe asthma is associated with a disproportionally high disease burden, including the risk of severe exacerbations. Accurate prediction of the risk of severe exacerbations may enable clinicians to tailor treatment plans to an individual patient. This study aims to develop and validate a novel risk prediction model for severe exacerbations in patients with severe asthma, and to examine the potential clinical utility of this tool.
Methods And Analysis: The target population is patients aged 18 years or older with severe asthma. Based on the data from the International Severe Asthma Registry (n=8925), a prediction model will be developed using a penalised, zero-inflated count model that predicts the rate or risk of exacerbation in the next 12 months. The risk prediction tool will be externally validated among patients with physician-assessed severe asthma in an international observational cohort, the NOVEL observational longiTudinal studY (n=1652). Validation will include examining model calibration (ie, the agreement between observed and predicted rates), model discrimination (ie, the extent to which the model can distinguish between high-risk and low-risk individuals) and the clinical utility at a range of risk thresholds.
Ethics And Dissemination: This study has obtained ethics approval from the Institutional Review Board of National University of Singapore (NUS-IRB-2021-877), the Anonymised Data Ethics and Protocol Transparency Committee (ADEPT1924) and the University of British Columbia (H22-01737). Results will be published in an international peer-reviewed journal.
Trial Registration Number: European Union electronic Register of Post-Authorisation Studies, EU PAS Register (EUPAS46088).
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http://dx.doi.org/10.1136/bmjopen-2022-070459 | DOI Listing |
Immunotherapy
September 2025
aGuangzhou Institute of Respiratory Health, State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, National Center for Respiratory Medicine, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Asthma is a chronic respiratory disorder characterized by airway inflammation, hyperresponsiveness, and reversible airflow obstruction. Despite therapeutic strategies, asthma remains inadequately controlled in many patients. Genetic predisposition plays a significant role in asthma pathogenesis, and the Proteinase-Activated Receptor 2 (PAR-2), encoded by the F2RL1 gene, has been associated with asthma.
View Article and Find Full Text PDFThorax
September 2025
Usher Institute, The University of Edinburgh, Edinburgh, UK
Background: The long-acting monoclonal antibody nirsevimab and respiratory syncytial virus (RSV) vaccines became available for prevention of severe RSV-associated disease in 2023. While clinical trials showed good efficacy and safety, their restrictive inclusion criteria, small sample sizes and short follow-up limit generalisability. We aimed to summarise real-world evidence on the effectiveness and safety of nirsevimab, RSV maternal vaccine and RSV vaccines for older adults.
View Article and Find Full Text PDFPharmacoepidemiol Drug Saf
September 2025
Sanofi, Cambridge, Massachusetts, USA.
Purpose: Given the increased likelihood for individuals with severe asthma to experience comorbidities, disease complications, emergency room visits, and hospitalizations, the ability to stratify asthma populations on severity is often important. Although pharmacoepidemiologic studies using administrative healthcare databases sometimes categorize asthma severity, there is no consensus on an approach.
Methods: Individuals with asthma (≥ 2 ICD-10-CM diagnosis codes J45) aged ≥ 6 years were identified in Optum's de-identified Clinformatics Data Mart Database between January 2017 and November 2023.
J Allergy Clin Immunol
September 2025
Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA; Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA. Electronic address:
Background: Genetic control of gene expression in asthma-related tissues is not well-characterized, particularly for African-ancestry populations, limiting advancement in our understanding of the increased prevalence and severity of asthma in those populations.
Objective: To create novel transcriptome prediction models for asthma tissues (nasal epithelium and CD4+ T cells) and apply them in transcriptome-wide association study to discover candidate asthma genes.
Methods: We developed and validated gene expression prediction databases for unstimulated CD4+ T cells and nasal epithelium using an elastic net framework.