Machine learning for screening of at-risk, mild and moderate COPD patients at risk of FEV₁ decline: results from...

Purpose: The purpose of this study was to train and validate machine learning models for predicting rapid decline of forced expiratory volume in 1 s (FEV₁) in individuals with a smoking history at-risk-for chronic obstructive pulmonary disease (COPD), Global Initiative for Chronic Obstructive Lung Disease (GOLD 0), or with mild-to-moderate (GOLD 1-2) COPD. We trained multiple models to predict rapid FEV₁ decline using demographic, clinical and radiologic biomarker data. Training and internal validation data were obtained from the COPDGene study and prediction models were validated against the SPIROMICS cohort. Methods: We used GOLD 0-2 participants (n = 3,821) from COPDGene (60.0 ± 8.8 years, 49.9% male) for variable selection and model training. Accelerated lung function decline was defined as a mean drop in FEV₁% predicted of > 1.5%/year at 5-year follow-up. We built logistic regression models predicting accelerated decline based on 22 chest CT imaging biomarker, pulmonary function, symptom, and demographic features. Models were validated using n = 885 SPIROMICS subjects (63.6 ± 8.6 years, 47.8% male). Results: The most important variables for predicting FEV₁ decline in GOLD 0 participants were bronchodilator responsiveness (BDR), post bronchodilator FEV₁% predicted (FEV₁.pp.post), and CT-derived expiratory lung volume; among GOLD 1 and 2 subjects, they were BDR, age, and PRM_{lower lobes fSAD}. In the validation cohort, GOLD 0 and GOLD 1-2 full variable models had significant predictive performance with AUCs of 0.620 ± 0.081 (p = 0.041) and 0.640 ± 0.059 (p < 0.001). Subjects with higher model-derived risk scores had significantly greater odds of FEV₁ decline than those with lower scores. Conclusion: Predicting FEV₁ decline in at-risk patients remains challenging but a combination of clinical, physiologic and imaging variables provided the best performance across two COPD cohorts.