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Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study

PURPOSE: There is an unmet clinical need for an accurate and objective diagnostic tool for early detection of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). DETECT (NCT03556475) was a multicenter, observational, cross-sectional study aiming to develop and validate multivariabl...

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Autores principales: Yin, Yan, Xu, Jinfu, Cai, Shaoxi, Chen, Yahong, Chen, Yan, Li, Manxiang, Zhang, Zhiqiang, Kang, Jian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9462440/
https://www.ncbi.nlm.nih.gov/pubmed/36092968
http://dx.doi.org/10.2147/COPD.S363935
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author Yin, Yan
Xu, Jinfu
Cai, Shaoxi
Chen, Yahong
Chen, Yan
Li, Manxiang
Zhang, Zhiqiang
Kang, Jian
author_facet Yin, Yan
Xu, Jinfu
Cai, Shaoxi
Chen, Yahong
Chen, Yan
Li, Manxiang
Zhang, Zhiqiang
Kang, Jian
author_sort Yin, Yan
collection PubMed
description PURPOSE: There is an unmet clinical need for an accurate and objective diagnostic tool for early detection of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). DETECT (NCT03556475) was a multicenter, observational, cross-sectional study aiming to develop and validate multivariable prediction models for AECOPD occurrence and severity in patients with chronic obstructive pulmonary disease (COPD) in China. PATIENTS AND METHODS: Patients aged ≥40 years with moderate/severe COPD, AECOPD, or no COPD were consecutively enrolled between April 22, 2020, and January 18, 2021, across seven study sites in China. Multivariable prediction models were constructed to identify AECOPD occurrence (primary outcome) and AECOPD severity (secondary outcome). Candidate variables were selected using a stepwise procedure, and the bootstrap method was used for internal model validation. RESULTS: Among 299 patients enrolled, 246 were included in the final analysis, of whom 30.1%, 40.7%, and 29.3% had COPD, AECOPD, or no COPD, respectively. Mean age was 64.1 years. Variables significantly associated with AECOPD occurrence (P<0.05) and severity (P<0.05) in the final models included COPD disease-related characteristics, as well as signs and symptoms. Based on cut-off values of 0.374 and 0.405 for primary and secondary models, respectively, the performance of the primary model constructed to identify AECOPD occurrence (AUC: 0.86; sensitivity: 0.84; specificity: 0.77), and of the secondary model for AECOPD severity (AUC: 0.81; sensitivity: 0.90; specificity: 0.73) indicated high diagnostic accuracy and clinical applicability. CONCLUSION: By leveraging easy-to-collect patient and disease data, we developed identification tools that can be used for timely detection of AECOPD and its severity. These tools may help physicians diagnose AECOPD in a timely manner, before further disease progression and possible hospitalizations.
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spelling pubmed-94624402022-09-10 Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study Yin, Yan Xu, Jinfu Cai, Shaoxi Chen, Yahong Chen, Yan Li, Manxiang Zhang, Zhiqiang Kang, Jian Int J Chron Obstruct Pulmon Dis Original Research PURPOSE: There is an unmet clinical need for an accurate and objective diagnostic tool for early detection of acute exacerbation of chronic obstructive pulmonary disease (AECOPD). DETECT (NCT03556475) was a multicenter, observational, cross-sectional study aiming to develop and validate multivariable prediction models for AECOPD occurrence and severity in patients with chronic obstructive pulmonary disease (COPD) in China. PATIENTS AND METHODS: Patients aged ≥40 years with moderate/severe COPD, AECOPD, or no COPD were consecutively enrolled between April 22, 2020, and January 18, 2021, across seven study sites in China. Multivariable prediction models were constructed to identify AECOPD occurrence (primary outcome) and AECOPD severity (secondary outcome). Candidate variables were selected using a stepwise procedure, and the bootstrap method was used for internal model validation. RESULTS: Among 299 patients enrolled, 246 were included in the final analysis, of whom 30.1%, 40.7%, and 29.3% had COPD, AECOPD, or no COPD, respectively. Mean age was 64.1 years. Variables significantly associated with AECOPD occurrence (P<0.05) and severity (P<0.05) in the final models included COPD disease-related characteristics, as well as signs and symptoms. Based on cut-off values of 0.374 and 0.405 for primary and secondary models, respectively, the performance of the primary model constructed to identify AECOPD occurrence (AUC: 0.86; sensitivity: 0.84; specificity: 0.77), and of the secondary model for AECOPD severity (AUC: 0.81; sensitivity: 0.90; specificity: 0.73) indicated high diagnostic accuracy and clinical applicability. CONCLUSION: By leveraging easy-to-collect patient and disease data, we developed identification tools that can be used for timely detection of AECOPD and its severity. These tools may help physicians diagnose AECOPD in a timely manner, before further disease progression and possible hospitalizations. Dove 2022-09-05 /pmc/articles/PMC9462440/ /pubmed/36092968 http://dx.doi.org/10.2147/COPD.S363935 Text en © 2022 Yin et al. https://creativecommons.org/licenses/by-nc/3.0/This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License (http://creativecommons.org/licenses/by-nc/3.0/ (https://creativecommons.org/licenses/by-nc/3.0/) ). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms (https://www.dovepress.com/terms.php).
spellingShingle Original Research
Yin, Yan
Xu, Jinfu
Cai, Shaoxi
Chen, Yahong
Chen, Yan
Li, Manxiang
Zhang, Zhiqiang
Kang, Jian
Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study
title Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study
title_full Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study
title_fullStr Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study
title_full_unstemmed Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study
title_short Development and Validation of a Multivariable Prediction Model to Identify Acute Exacerbation of COPD and Its Severity for COPD Management in China (DETECT Study): A Multicenter, Observational, Cross-Sectional Study
title_sort development and validation of a multivariable prediction model to identify acute exacerbation of copd and its severity for copd management in china (detect study): a multicenter, observational, cross-sectional study
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9462440/
https://www.ncbi.nlm.nih.gov/pubmed/36092968
http://dx.doi.org/10.2147/COPD.S363935
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