ArticleChronic respiratory disease
Developing and validating a chronic obstructive pulmonary disease quick screening questionnaire using statistical learning models.
Article in Chronic respiratory disease. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Developing an interpretable machine learning predictive model of chronic obstructive pulmonary disease by serum PFAS concentration.Frontiers in public health · 2025Article
- Using machine learning for early detection of chronic obstructive pulmonary disease: a narrative review.Respiratory research · 2024Review
- The Impact of Different Smoking Behavior on Pulmonary Function and Pulmonary Hypertension Among Chinese Male Patients with Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2024Article
Corrections and comments
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Authors and funding
14 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundActive 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.
methodsWe created four statistical learning models to predict the risk of COPD using a multi-center randomized cross-sectional survey database (
resultsA 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.
conclusionsWe developed a COPD case-finding questionnaire, which is an efficient and cost-effective tool for identifying high-risk population of COPD.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.