AI based prediction of severe exacerbation in Asian bronchiectasis patients using the KMBARC registry

Abstract

Classical scoring tools such as the Bronchiectasis Severity Index (BSI) and FACED have shown good performance for predicting adverse outcomes, yet they were developed using European cohorts and may not fully align with Asian clinical characteristics. This study aimed to develop an Asian population-specific artificial intelligence (AI) model for predicting severe acute exacerbations (AEs) in bronchiectasis. A total of 492 patients with 1-year follow-up data from the Korean Multicenter Bronchiectasis Audit and Research Collaboration registry were analyzed. Severe AE was defined as an event requiring an emergency department visit or hospitalization due to worsening respiratory symptoms. Three AI models-extreme gradient boosting, logistic regression, and multilayer perceptron (MLP)-were trained and compared with classical scoring models. Severe AE occurred in 56 patients (11.4%). Among the models, the MLP demonstrated the best performance, with higher sensitivity, specificity, F1-score, and area under the receiver operating characteristic curve compared with both classical scores. Shapley additive explanation (SHAP) analysis identified BSI, sputum characteristics, and histories of tuberculosis and pneumonia as key predictors. Although classical scores performed well, a population-specific AI model using local clinical data showed improved predictive performance and may support individualized risk assessment for Asian patients with bronchiectasis.