Development of a prediction model to identify undiagnosed chronic obstructive pulmonary disease patients in primary care settings in China.

ABSTRACT:

Background

At present, a large number of chronic obstructive pulmonary disease (COPD) patients are undiagnosed in China. Thus, this study aimed to develop a simple prediction model as a screening tool to identify patients at risk for COPD.

Methods

The study was based on the data of 22,943 subjects aged 30 to 79 years and enrolled in the second resurvey of China Kadoorie Biobank during 2012 and 2013 in China. We stepwisely selected the predictors using logistic regression model. Then we tested the model validity through P-P graph, area under the receiver operating characteristic curve (AUROC), ten-fold cross validation and an external validation in a sample of 3492 individuals from the Enjoying Breathing Program in China.

Results

The final prediction model involved 14

SUBMITTER: Zhang B 

PROVIDER: S-EPMC10129090 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature