ArticleFrontiers in medicine2022
Development of a model for predicting the severity of chronic obstructive pulmonary disease.
Article in Frontiers in medicine, 2022. 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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Who cites it
2 citing papers in PubMed.
- Development and validation of a multimodal clinical-radiomics-deep learning nomogram based on automated chest CT segmentation for classifying COPD severity: a multicenter study.Frontiers in medicine · 2026Article
- High density lipoprotein-cholesterol is inversely associated with blood eosinophil counts among asthmatic adults in the USA: NHANES 2011-2018.Frontiers in immunology · 2023Article
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Authors and funding
5 authors.
Funding
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Abstract
Background: Several models have been developed to predict the severity and prognosis of chronic obstructive pulmonary disease (COPD). This study aimed to identify potential predictors and construct a prediction model for COPD severity using biochemical and immunological parameters. Methods: A total of 6,274 patients with COPD were recruited between July 2010 and July 2018. COPD severity was classified into mild, moderate, severe, and very severe based on the Global Initiative for Chronic Obstructive Lung Disease guidelines. A multivariate logistic regression model was constructed to identify predictors of COPD severity. The predictive ability of the model was assessed by measuring sensitivity, specificity, accuracy, and concordance. Results: Of 6,274 COPD patients, 2,644, 2,600, and 1,030 had mild/moderate, severe, and very severe disease, respectively. The factors that could distinguish between mild/moderate and severe cases were vascular disorders (OR: 1.44; Conclusion: This study developed a prediction model for COPD severity based on biochemical and immunological parameters, which should be validated in additional cohorts.
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