Development and validation of a multimodal risk prediction model for early COPD progression: a retrospective study...

Early identification of rapid progression in patients with preserved ratio but chronic cough remains challenging.

To develop and validate an integrated multimodal prediction model that combines clinical phenotypes, small airway function indices, and chest CT quantitative features for stratifying progression risk in individuals with preserved ratio, chronic cough, and early COPD features.

Retrospective cohort study with external validation.

In this retrospective cohort study of 408 participants with chronic cough (⩾3 months) and preserved spirometry (FEV₁/FVC ⩾ 0.7), we integrated clinical profiles, pulmonary function (including small airway indices), and AI-quantified chest CT quantitative features. A three-stage modeling approach was used: variable screening with generalized estimating equations, predictor selection via LASSO regression, and construction of a Random Survival Forest-Cox hybrid model to predict a composite outcome of accelerated lung function decline (annual FEV₁ decline >40 mL) or acute exacerbation. External validation was performed using an independent cohort (COPDGene, n = 312, 82 outcome events), with 3-year follow-up for prediction. The model underwent internal validation (10-fold cross-validation), with interpretability assessed via SHapley Additive exPlanations (SHAP).

Multivariable analysis confirmed small airway dysfunction and mucus plug burden as independent predictors of accelerated FEV₁ decline (all p < 0.001), with a significant synergistic interaction (β = 2.34, p = 0.003). The final nine-feature model showed excellent discrimination, with a time-dependent AUC of 0.872 (95% CI: 0.843-0.901) on internal validation and 0.843 (95% CI: 0.812-0.874) on external validation. SHAP analysis identified small airway parameters and mucus plug burden as the top contributors, jointly accounting for 68.4% of predictive output. A nomogram and online calculator were developed for clinical use.

We developed and validated a robust multimodal prediction model that accurately stratifies progression risk in individuals with preserved ratio, chronic cough, and early COPD features. This tool facilitates personalized risk assessment and holds promise for guiding precision management strategies to improve outcomes in this high-risk population.