ArticleChronic obstructive pulmonary diseases (Miami, Fla.)2023
A Model to Predict Residual Volume from Forced Spirometry Measurements in Chronic Obstructive Pulmonary Disease.
Article in Chronic obstructive pulmonary diseases (Miami, Fla.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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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
2 citing papers in PubMed, 3 citations in OpenAlex.
- Estimation of Static Lung Volumes and Capacities From Spirometry Using Machine Learning: Algorithm Development and Validation.JMIR AI · 2025Article
- Development and validation of a mortality risk prediction model for chronic obstructive pulmonary disease: a cross-sectional study using probabilistic graphical modelling.EClinicalMedicine · 2024Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Background: Lung hyperinflation with elevated residual volume (RV) is associated with poor prognosis in adults with chronic obstructive pulmonary disease (COPD) and is a critical criterion for lung volume reduction selection. Here, we proposed that patterns within spirometric measures could represent the degree of hyperinflation. Methods: Fractional polynomial multivariate regression was used to develop a prediction model based on age, biological sex, forced expiratory volume in 1 second (FEV Results: The best fit model: RV %est=[FVC %predicted] x 3.46-[FEV Conclusion: In patients with COPD, a model using only spirometry, age, and biological sex can estimate elevated RV. This tool could facilitate the identification of candidates for lung volume reduction procedures and can be integrated into existing epidemiologic databases to investigate the clinical impact of hyperinflation.
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Registered trials
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