Predictive modeling of COPD exacerbation rates using baseline risk factors.

ABSTRACT:

Background

Demographic and disease characteristics have been associated with the risk of chronic obstructive pulmonary disease (COPD) exacerbations. Using previously collected multinational clinical trial data, we developed models that use baseline risk factors to predict an individual's rate of moderate/severe exacerbations in the next year on various pharmacological treatments for COPD.

Methods

Exacerbation data from 20,054 patients in the ETHOS, KRONOS, TELOS, SOPHOS, and PINNACLE-1, PINNACLE-2, and PINNACLE-4 studies were pooled. Machine learning was used to identify predictors of moderate/severe exacerbation rates. Important factors were selected for generalized linear modeling, further informed by backward variable selection. An independent test set was held back for v

SUBMITTER: Singh D 

PROVIDER: S-EPMC9340368 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature