ArticleChronic obstructive pulmonary diseases (Miami, Fla.)
Insight into Best Variables for COPD Case Identification: A Random Forests Analysis.
Article in Chronic obstructive pulmonary diseases (Miami, Fla.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed.
- Trial
- Development and Validation of an Electronic Health Record Algorithm to Predict the Presence of Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2026Article
- Article
- Unleashing the Power of Very Small Data to Predict Acute Exacerbations of Chronic Obstructive Pulmonary Disease.International journal of chronic obstructive pulmonary disease · 2023Article
- A literature review on the analysis of symptom-based clinical pathways: Time for a different approach?PLOS digital health · 2022Article
- Protocol Summary of the COPD Assessment in Primary Care To Identify Undiagnosed Respiratory Disease and Exacerbation Risk (CAPTURE) Validation in Primary Care Study.Chronic obstructive pulmonary diseases (Miami, Fla.) · 2021Article
- Article
- A New Approach for Identifying Patients with Undiagnosed Chronic Obstructive Pulmonary Disease.American journal of respiratory and critical care medicine · 2017Article
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
rationaleThis study is part of a larger, multi-method project to develop a questionnaire for identifying undiagnosed cases of chronic obstructive pulmonary disease (COPD) in primary care settings, with specific interest in the detection of patients with moderate to severe airway obstruction or risk of exacerbation.
objectivesTo examine 3 existing datasets for insight into key features of COPD that could be useful in the identification of undiagnosed COPD.
methodsRandom forests analyses were applied to the following databases: COPD Foundation Peak Flow Study Cohort (N=5761), Burden of Obstructive Lung Disease (BOLD) Kentucky site (N=508), and COPDGene® (N=10,214). Four scenarios were examined to find the best, smallest sets of variables that distinguished cases and controls:(1) moderate to severe COPD (forced expiratory volume in 1 second [FEV
resultsFrom 4 to 8 variables were able to differentiate cases from controls, with sensitivity ≥73 (range: 73-90) and specificity >68 (range: 68-93). Across scenarios, the best models included age, smoking status or history, symptoms (cough, wheeze, phlegm), general or breathing-related activity limitation, episodes of acute bronchitis, and/or missed work days and non-work activities due to breathing or health.
conclusionsResults provide insight into variables that should be considered during the development of candidate items for a new questionnaire to identify undiagnosed cases of clinically significant COPD.
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Registered trials
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