Development and validation of the Risk of Exacerbation in Severe Asthma (RESA) model.

Abstract

BACKGROUND: Severe asthma (SA) is associated with frequent exacerbations and high treatment costs. OBJECTIVE(S): Develop and validate an individualized risk calculator for severe exacerbations in SA, and evaluate its clinical utility for guiding personalized clinical decisions. METHOD(S): 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. The prediction endpoint was the 12-month risk of >=1 or >=2 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 (IECV) was performed using the natural clustering by country-settings. RESULT(S): Data from 9,911 SA patients were used. Essential predictors included age, sex, past 12-month severe exacerbations, asthma control, chronic rhinosinusitis, forced expiratory volume in one second (FEV1) to forced vital capacity ratio, percent predicted FEV1, blood eosinophils, fractional exhaled nitric oxide, long-term oral corticosteroids and macrolide use. The model also adapted setting-specific baseline risks. In the IECV, across broad geographical and healthcare variability, the model showed excellent calibration and informative, generalizable discrimination (pooled AUC 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. CONCLUSION(S): The Risk of Exacerbation in Severe Asthma (RESA) model quantifies SA exacerbation risk using routinely available predictors and demonstrates potential clinical utility. Copyright © 2026. Published by Elsevier Inc.