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Development of a model for predicting the severity of chronic obstructive pulmonary disease

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...

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Autores principales: Gu, Yu-Feng, Chen, Long, Qiu, Rong, Wang, Shu-Hong, Chen, Ping
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800610/
https://www.ncbi.nlm.nih.gov/pubmed/36590951
http://dx.doi.org/10.3389/fmed.2022.1073536
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author Gu, Yu-Feng
Chen, Long
Qiu, Rong
Wang, Shu-Hong
Chen, Ping
author_facet Gu, Yu-Feng
Chen, Long
Qiu, Rong
Wang, Shu-Hong
Chen, Ping
author_sort Gu, Yu-Feng
collection PubMed
description 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; P < 0.001), high-density lipoprotein (HDL) (OR: 1.83; P < 0.001), plasma fibrinogen (OR: 1.08; P = 0.002), fructosamine (OR: 1.12; P = 0.002), standard bicarbonate concentration (OR: 1.09; P < 0.001), partial pressure of carbon dioxide (OR: 1.09; P < 0.001), age (OR: 0.97; P < 0.001), eosinophil count (OR: 0.66; P = 0.042), lymphocyte ratio (OR: 0.97; P < 0.001), and apolipoprotein A1 (OR: 0.56; P = 0.003). The factors that could distinguish between mild/moderate and very severe cases were vascular disorders (OR: 1.59; P < 0.001), HDL (OR: 2.54; P < 0.001), plasma fibrinogen (OR: 1.10; P = 0.012), fructosamine (OR: 1.18; P = 0.001), partial pressure of oxygen (OR: 1.00; P = 0.007), plasma carbon dioxide concentration (OR: 1.01; P < 0.001), standard bicarbonate concentration (OR: 1.13; P < 0.001), partial pressure of carbon dioxide (OR: 1.16; P < 0.001), age (OR: 0.91; P < 0.001), sex (OR: 0.71; P = 0.010), allergic diseases (OR: 0.51; P = 0.009), eosinophil count (OR: 0.42; P = 0.014), lymphocyte ratio (OR: 0.93; P < 0.001), and apolipoprotein A1 (OR: 0.45; P = 0.005). The prediction model correctly predicted disease severity in 60.17% of patients, and kappa coefficient was 0.35 (95% CI: 0.33–0.37). 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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spelling pubmed-98006102022-12-31 Development of a model for predicting the severity of chronic obstructive pulmonary disease Gu, Yu-Feng Chen, Long Qiu, Rong Wang, Shu-Hong Chen, Ping Front Med (Lausanne) Medicine 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; P < 0.001), high-density lipoprotein (HDL) (OR: 1.83; P < 0.001), plasma fibrinogen (OR: 1.08; P = 0.002), fructosamine (OR: 1.12; P = 0.002), standard bicarbonate concentration (OR: 1.09; P < 0.001), partial pressure of carbon dioxide (OR: 1.09; P < 0.001), age (OR: 0.97; P < 0.001), eosinophil count (OR: 0.66; P = 0.042), lymphocyte ratio (OR: 0.97; P < 0.001), and apolipoprotein A1 (OR: 0.56; P = 0.003). The factors that could distinguish between mild/moderate and very severe cases were vascular disorders (OR: 1.59; P < 0.001), HDL (OR: 2.54; P < 0.001), plasma fibrinogen (OR: 1.10; P = 0.012), fructosamine (OR: 1.18; P = 0.001), partial pressure of oxygen (OR: 1.00; P = 0.007), plasma carbon dioxide concentration (OR: 1.01; P < 0.001), standard bicarbonate concentration (OR: 1.13; P < 0.001), partial pressure of carbon dioxide (OR: 1.16; P < 0.001), age (OR: 0.91; P < 0.001), sex (OR: 0.71; P = 0.010), allergic diseases (OR: 0.51; P = 0.009), eosinophil count (OR: 0.42; P = 0.014), lymphocyte ratio (OR: 0.93; P < 0.001), and apolipoprotein A1 (OR: 0.45; P = 0.005). The prediction model correctly predicted disease severity in 60.17% of patients, and kappa coefficient was 0.35 (95% CI: 0.33–0.37). CONCLUSION: This study developed a prediction model for COPD severity based on biochemical and immunological parameters, which should be validated in additional cohorts. Frontiers Media S.A. 2022-12-16 /pmc/articles/PMC9800610/ /pubmed/36590951 http://dx.doi.org/10.3389/fmed.2022.1073536 Text en Copyright © 2022 Gu, Chen, Qiu, Wang and Chen. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Medicine
Gu, Yu-Feng
Chen, Long
Qiu, Rong
Wang, Shu-Hong
Chen, Ping
Development of a model for predicting the severity of chronic obstructive pulmonary disease
title Development of a model for predicting the severity of chronic obstructive pulmonary disease
title_full Development of a model for predicting the severity of chronic obstructive pulmonary disease
title_fullStr Development of a model for predicting the severity of chronic obstructive pulmonary disease
title_full_unstemmed Development of a model for predicting the severity of chronic obstructive pulmonary disease
title_short Development of a model for predicting the severity of chronic obstructive pulmonary disease
title_sort development of a model for predicting the severity of chronic obstructive pulmonary disease
topic Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9800610/
https://www.ncbi.nlm.nih.gov/pubmed/36590951
http://dx.doi.org/10.3389/fmed.2022.1073536
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