As a guest user you are not logged in or recognized by your IP address. You have
access to the Front Matter, Abstracts, Author Index, Subject Index and the full
text of Open Access publications.
Chronic obstructive pulmonary disease (COPD) is highly underdiagnosed. Early diagnosis is, therefore, essential to reduce costs and exacerbations and prevent disease progression. This calls for the development of a COPD case-finding tool. The present study aimed to develop a model for early identification of COPD with an eye to optimizing COPD case finding. The study was based on data from the US National Health and Nutrition Examination Survey (NHANES) dataset (2007–2012). For the analysis, 772 participants with spirometry defined COPD were included. Potential predictors for COPD (n=42) were extracted. The model was based on logistic regression, and the predictors were included using a stepwise forward selection. A five-fold cross-validation was used to train and validate the model. The predictors included age, gender, and pack-years of smoking. The model obtained an AUC of 0.71. In conclusion, such a model can be useful for identifying individuals that should perform post-bronchodilator spirometry to aid early identification of COPD.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.
This website uses cookies
We use cookies to provide you with the best possible experience. They also allow us to analyze user behavior in order to constantly improve the website for you. Info about the privacy policy of IOS Press.