ArticleNPJ primary care respiratory medicine2019
An accurate prediction model to identify undiagnosed at-risk patients with COPD: a cross-sectional case-finding study.
Article in NPJ primary care respiratory medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- How to Enhance the Diagnosis of Early Stages of Chronic Obstructive Pulmonary Disease (COPD)? The Role of Mobile Spirometry in COPD Screening and Diagnosis-A Systematic Review.Advances in respiratory medicine · 2024Pooled it
- A Two-Staged, Risk-Stratified Strategy Combining FEVRespirology (Carlton, Vic.) · 2025Observational
- Article
- Current Progress of COPD Early Detection: Key Points and Novel Strategies.International journal of chronic obstructive pulmonary disease · 2023Review
- Development and validation of the EHS-COPD model to predict sex-specific risk of chronic obstructive pulmonary disease (COPD) in older Chinese adults: Hong Kong's Elderly Health Service Cohort.Annals of translational medicine · 2022Article
- Novel App-Based Portable Spirometer for the Early Detection of COPD.Diagnostics (Basel, Switzerland) · 2021Article
- The Construction of Primary Screening Model and Discriminant Model for Chronic Obstructive Pulmonary Disease in Northeast China.International journal of chronic obstructive pulmonary disease · 2020Article
- Associations of Clinical Characteristics and Intestinal Flora Imbalance in Stable Chronic Obstructive Pulmonary Disease (COPD) Patients and the Construction of an Early Warning Model.International journal of chronic obstructive pulmonary diseaseArticle
- Performance and Clinical Utility of Various Chronic Obstructive Pulmonary Disease Case-Finding Tools.International journal of chronic obstructive pulmonary diseaseArticle
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Underuse or unavailability of spirometry is one of the most important factors causing underdiagnosis of COPD. We reported the development of a COPD prediction model to identify at-risk, undiagnosed COPD patients when spirometry was unavailable. This cross-sectional study enrolled subjects aged ≥40 years with respiratory symptoms and a smoking history (≥20 pack-years) in a medical center in two separate periods (development and validation cohorts). All subjects completed COPD assessment test (CAT), peak expiratory flow rate (PEFR) measurement, and confirmatory spirometry. A binary logistic model with calibration (Hosmer-Lemeshow test) and discrimination (area under receiver operating characteristic curve [AUROC]) was implemented. Three hundred and one subjects (development cohort) completed the study, including non-COPD (154, 51.2%) and COPD cases (147; stage I, 27.2%; II, 55.8%; III-IV, 17%). Compared with non-COPD and GOLD I cases, GOLD II-IV patients exhibited significantly higher CAT scores and lower lung function, and were considered clinically significant for COPD. Four independent variables (age, smoking pack-years, CAT score, and percent predicted PEFR) were incorporated developing the prediction model, which estimated the COPD probability (P
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