Annals of medicine · 2026
Identification of clinical phenotypes and development of a predictive model for rapid lung function decline in COPD patients.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Yang Zehua, Li Wei, Cui Yanan, Huang Tingting, Tang Xingyao, Peng Yaodie, Su Rui, Chu Xu, Li Yong, Zhang Chunyu, Jia Cunbo, Huang Ke, Yang Ting
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.