Developing and validating a chronic obstructive pulmonary disease quick screening questionnaire using statistical learning models.

ABSTRACT:

Background

Active targeted case-finding is a cost-effective way to identify individuals with high-risk for early diagnosis and interventions of chronic obstructive pulmonary disease (COPD). A precise and practical COPD screening instrument is needed in health care settings.

Methods

We created four statistical learning models to predict the risk of COPD using a multi-center randomized cross-sectional survey database (n = 5281). The minimal set of predictors and the best statistical learning model in identifying individuals with airway obstruction were selected to construct a new case-finding questionnaire. We validated its performance in a prospective cohort (n = 958) and compared it with three previously reported case-finding instruments.

Results

A set o

SUBMITTER: Wang X 

PROVIDER: S-EPMC9373185 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature