Identification of clinical phenotypes and development of a predictive model for rapid lung function decline in COPD patients.
Chronic obstructive pulmonary disease (COPD) exhibits significant clinical heterogeneity. This study integrates phenotype identification with longitudinal lung function analysis to develop a prediction tool for rapid decline in a large Chinese cohort.
This study included 38,863 COPD patients from a national multicenter screening program. Latent class analysis identified clinical phenotypes. Among 1,215 patients with baseline and 24-month follow-up spirometry, rapid decline was defined as annual FEV1 decline ≥40 mL or FEV1% predicted decline >1.5%. LASSO regression and the Boruta algorithm selected predictive variables. Six machine learning models were constructed and evaluated for discrimination, calibration, and utility. SHAP analysis was employed for interpretation.
Three distinct phenotypes were identified: Phenotype 1 (GOLD Stage 2, non-smoking females, 25.8%), Phenotype 2 (GOLD Stage 1, smoking males, 44.8%), and Phenotype 3 (GOLD Stage 3, severe smokers, 29.4%). Phenotype 2 exhibited the highest rapid decline rate (55.8%). The CatBoost model achieved optimal performance (AUC 0.712) using six variables: region, fuel type, income level, wheezing, hip circumference, and baseline FEV1. SHAP identified baseline FEV1 as the top predictor. At a threshold of 0.365, the model attained 95.5% sensitivity and 86.4% negative predictive value, with significant net benefit.
Three clinical phenotypes were identified among COPD patients. Phenotype 2 exhibits the highest risk of rapid progression, representing a key target group for intervention. The predictive model, based on six simple indicators, serves as a valuable tool for screening high-risk rapid decliners.
This study included 38,863 COPD patients from a national multicenter screening program. Latent class analysis identified clinical phenotypes. Among 1,215 patients with baseline and 24-month follow-up spirometry, rapid decline was defined as annual FEV1 decline ≥40 mL or FEV1% predicted decline >1.5%. LASSO regression and the Boruta algorithm selected predictive variables. Six machine learning models were constructed and evaluated for discrimination, calibration, and utility. SHAP analysis was employed for interpretation.
Three distinct phenotypes were identified: Phenotype 1 (GOLD Stage 2, non-smoking females, 25.8%), Phenotype 2 (GOLD Stage 1, smoking males, 44.8%), and Phenotype 3 (GOLD Stage 3, severe smokers, 29.4%). Phenotype 2 exhibited the highest rapid decline rate (55.8%). The CatBoost model achieved optimal performance (AUC 0.712) using six variables: region, fuel type, income level, wheezing, hip circumference, and baseline FEV1. SHAP identified baseline FEV1 as the top predictor. At a threshold of 0.365, the model attained 95.5% sensitivity and 86.4% negative predictive value, with significant net benefit.
Three clinical phenotypes were identified among COPD patients. Phenotype 2 exhibits the highest risk of rapid progression, representing a key target group for intervention. The predictive model, based on six simple indicators, serves as a valuable tool for screening high-risk rapid decliners.
Authors
Yang Yang, Li Li, Cui Cui, Huang Huang, Tang Tang, Peng Peng, Su Su, Chu Chu, Li Li, Zhang Zhang, Jia Jia, Huang Huang, Yang Yang
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