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

• Published in: Frontiers in physiology 2023-04, Vol.14, p.1144192
• Main Authors: Wang, Jennifer M., Labaki, Wassim W., Murray, Susan, Martinez, Fernando J., Curtis, Jeffrey L., Hoffman, Eric A., Ram, Sundaresh, Bell, Alexander J., Galban, Craig J., Han, MeiLan K., Hatt, Charles
• Format: Article
• Language: English
• Subjects: chronic obstructive pulmonary disease; computed tomography; lung function decline; machine learning; Physiology; quantitative imaging
• ISSN: 1664-042X; 1664-042X
• DOI: 10.3389/fphys.2023.1144192

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 (FEV 1 ) 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 FEV 1 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 FEV 1 % 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 FEV 1 decline in GOLD 0 participants were bronchodilator responsiveness (BDR), post bronchodilator FEV 1 % predicted (FEV 1 .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 FEV 1 decline than those with lower scores. Conclusion: Predicting FEV 1 decline in at-risk patients remains challenging but a combination of clinical, physiologic and imaging variables provided the best performance across two COPD cohorts.