Dataset Information

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 of seven predictors was selected from 643 variables, including age, morning productive cough, wheeze, years of smoking cessation, gender, job, and pack-year of smoking. In four statistical learning models, generalized additive model model had the highest area under curve (AUC) value both on the developing cross-sectional data set (AUC = 0.813) and the prospective validation data set (AUC = 0.880). Our questionnaire outperforms the other three tools on the cross-sectional validation data set.

Conclusions

We developed a COPD case-finding questionnaire, which is an efficient and cost-effective tool for identifying high-risk population of COPD.

SUBMITTER: Wang X

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

REPOSITORIES: biostudies-literature

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Publications

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

Wang Xiaoyue X He Hong H Xu Liang L Chen Cuicui C Zhang Jieqing J Li Na N Chen Xianxian X Jiang Weipeng W Li Li L Wang Linlin L Song Yuanlin Y Xiao Jing J Zhang Jun J Hou Dongni D

Chronic respiratory disease 20220101

<h4>Background</h4>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.<h4>Methods</h4>We created four statistical learning models to predict the risk of COPD using a multi-center randomized cross-sectional survey database (<i>n</i> = 5281). The minimal set of predictors and the best stat ...[more]

PMID: 35943965

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

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