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

ABSTRACT: 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.

SUBMITTER: Wang JM 

PROVIDER: S-EPMC10161244 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature