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Prediction models for the development of COPD: a systematic review
Early identification of people at risk of developing COPD is crucial for implementing preventive strategies. We aimed to systematically review and assess the performance of all published models that predicted development of COPD. A search was conducted to identify studies that developed a prediction...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove Medical Press
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6005295/ https://www.ncbi.nlm.nih.gov/pubmed/29942125 http://dx.doi.org/10.2147/COPD.S155675 |
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author | Matheson, Melanie C Bowatte, Gayan Perret, Jennifer L Lowe, Adrian J Senaratna, Chamara V Hall, Graham L de Klerk, Nick Keogh, Louise A McDonald, Christine F Waidyatillake, Nilakshi T Sly, Peter D Jarvis, Deborah Abramson, Michael J Lodge, Caroline J Dharmage, Shyamali C |
author_facet | Matheson, Melanie C Bowatte, Gayan Perret, Jennifer L Lowe, Adrian J Senaratna, Chamara V Hall, Graham L de Klerk, Nick Keogh, Louise A McDonald, Christine F Waidyatillake, Nilakshi T Sly, Peter D Jarvis, Deborah Abramson, Michael J Lodge, Caroline J Dharmage, Shyamali C |
author_sort | Matheson, Melanie C |
collection | PubMed |
description | Early identification of people at risk of developing COPD is crucial for implementing preventive strategies. We aimed to systematically review and assess the performance of all published models that predicted development of COPD. A search was conducted to identify studies that developed a prediction model for COPD development. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was followed when extracting data and appraising the selected studies. Of the 4,481 records identified, 30 articles were selected for full-text review, and only four of these were eligible to be included in the review. The only consistent predictor across all four models was a measure of smoking. Sex and age were used in most models; however, other factors varied widely. Two of the models had good ability to discriminate between people who were correctly or incorrectly classified as at risk of developing COPD. Overall none of the models were particularly useful in accurately predicting future risk of COPD, nor were they good at ruling out future risk of COPD. Further studies are needed to develop new prediction models and robustly validate them in external cohorts. |
format | Online Article Text |
id | pubmed-6005295 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Dove Medical Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-60052952018-06-25 Prediction models for the development of COPD: a systematic review Matheson, Melanie C Bowatte, Gayan Perret, Jennifer L Lowe, Adrian J Senaratna, Chamara V Hall, Graham L de Klerk, Nick Keogh, Louise A McDonald, Christine F Waidyatillake, Nilakshi T Sly, Peter D Jarvis, Deborah Abramson, Michael J Lodge, Caroline J Dharmage, Shyamali C Int J Chron Obstruct Pulmon Dis Original Research Early identification of people at risk of developing COPD is crucial for implementing preventive strategies. We aimed to systematically review and assess the performance of all published models that predicted development of COPD. A search was conducted to identify studies that developed a prediction model for COPD development. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies was followed when extracting data and appraising the selected studies. Of the 4,481 records identified, 30 articles were selected for full-text review, and only four of these were eligible to be included in the review. The only consistent predictor across all four models was a measure of smoking. Sex and age were used in most models; however, other factors varied widely. Two of the models had good ability to discriminate between people who were correctly or incorrectly classified as at risk of developing COPD. Overall none of the models were particularly useful in accurately predicting future risk of COPD, nor were they good at ruling out future risk of COPD. Further studies are needed to develop new prediction models and robustly validate them in external cohorts. Dove Medical Press 2018-06-14 /pmc/articles/PMC6005295/ /pubmed/29942125 http://dx.doi.org/10.2147/COPD.S155675 Text en © 2018 Matheson et al. 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/). 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. |
spellingShingle | Original Research Matheson, Melanie C Bowatte, Gayan Perret, Jennifer L Lowe, Adrian J Senaratna, Chamara V Hall, Graham L de Klerk, Nick Keogh, Louise A McDonald, Christine F Waidyatillake, Nilakshi T Sly, Peter D Jarvis, Deborah Abramson, Michael J Lodge, Caroline J Dharmage, Shyamali C Prediction models for the development of COPD: a systematic review |
title | Prediction models for the development of COPD: a systematic review |
title_full | Prediction models for the development of COPD: a systematic review |
title_fullStr | Prediction models for the development of COPD: a systematic review |
title_full_unstemmed | Prediction models for the development of COPD: a systematic review |
title_short | Prediction models for the development of COPD: a systematic review |
title_sort | prediction models for the development of copd: a systematic review |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6005295/ https://www.ncbi.nlm.nih.gov/pubmed/29942125 http://dx.doi.org/10.2147/COPD.S155675 |
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