Development and Validation of the Risk of Exacerbation in Severe Asthma (RESA) Model.
prospective_cohort · Level II
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- Record sourced from PubMed, PMID 41903878.
- Also identified by DOI 10.1016/j.jaip.2026.03.017.
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Abstract
Severe asthma (SA) is associated with frequent exacerbations and high treatment costs. To develop and validate an individualized risk calculator for severe exacerbations in SA, and evaluate its clinical utility for guiding personalized clinical decisions. Patients with SA were identified from combined data from the International Severe Asthma Registry (2015-2022) and NOVEL observational longiTudinal studY (2016-2023) across 30 countries and regions. The prediction end point was the 12-month risk of 1 or more or 2 or more severe exacerbations. Using expert input and Bayesian network analysis, 11 routinely measured predictors were identified, measured within the past 12 months. A mixed-effects, zero-inflated negative binomial model was developed, adjusting for between-country variability and biologic drop-in effects. Internal-external cross-validation was performed using the natural clustering by country settings. Data from 9911 patients with SA were used. Essential predictors included age, sex, past 12-month severe exacerbations, asthma control, chronic rhinosinusitis, FEV<sub>1</sub> to forced vital capacity ratio, percent predicted FEV<sub>1</sub>, blood eosinophils, fractional exhaled nitric oxide, and long-term oral corticosteroid and macrolide use. The model also adapted setting-specific baseline risks. In the internal-external cross-validation, across broad geographical and health care variability, the model showed excellent calibration and informative, generalizable discrimination (pooled area under the time-dependent receiver-operating characteristics curve of 0.63 [95% CI, 0.60-0.66] for ≥1 and 0.68 [95% CI, 0.64-0.72] for ≥2 exacerbations). Decision curve analysis showed clear net benefit across risk thresholds. The Risk of Exacerbation in Severe Asthma model quantifies SA exacerbation risk using routinely available predictors and demonstrates potential clinical utility.
Medical subject headings
- Asthma