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Chronic obstructive pulmonary disease risk assessment tools: is one better than the others?
Risk assessment tools are essential in COPD care to help clinicians identify patients at higher risk of accelerated lung function decline, respiratory exacerbations, hospitalizations, and death. RECENT FINDINGS: Conventional methods of assessing risk have focused on spirometry, patient-reported symp...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799486/ https://www.ncbi.nlm.nih.gov/pubmed/34652295 http://dx.doi.org/10.1097/MCP.0000000000000833 |
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author | Wang, Jennifer M. Han, MeiLan K. Labaki, Wassim W. |
author_facet | Wang, Jennifer M. Han, MeiLan K. Labaki, Wassim W. |
author_sort | Wang, Jennifer M. |
collection | PubMed |
description | Risk assessment tools are essential in COPD care to help clinicians identify patients at higher risk of accelerated lung function decline, respiratory exacerbations, hospitalizations, and death. RECENT FINDINGS: Conventional methods of assessing risk have focused on spirometry, patient-reported symptoms, functional status, and a combination of these tools in composite indices. More recently, qualitatively and quantitatively assessed chest imaging findings, such as emphysema, large and small airways disease, and pulmonary vascular abnormalities have been associated with poor long-term outcomes in COPD patients. Although several blood and sputum biomarkers have been investigated for risk assessment in COPD, most still warrant further validation. Finally, novel remote digital monitoring technologies may be valuable to predict exacerbations but their large-scale performance, ease of implementation, and cost effectiveness remain to be determined. SUMMARY: Given the complex heterogeneity of COPD, any single metric is unlikely to fully capture the risk of poor long-term outcomes. Therefore, clinicians should review all available clinical data, including spirometry, symptom severity, functional status, chest imaging, and bloodwork, to guide personalized preventive care of COPD patients. The potential of machine learning tools and remote monitoring technologies to refine COPD risk assessment is promising but remains largely untapped pending further investigation. |
format | Online Article Text |
id | pubmed-8799486 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-87994862022-02-04 Chronic obstructive pulmonary disease risk assessment tools: is one better than the others? Wang, Jennifer M. Han, MeiLan K. Labaki, Wassim W. Curr Opin Pulm Med OBSTRUCTIVE, OCCUPATIONAL AND ENVIRONMENTAL DISEASES: Edited by Manish Joshi and Basil Varkey Risk assessment tools are essential in COPD care to help clinicians identify patients at higher risk of accelerated lung function decline, respiratory exacerbations, hospitalizations, and death. RECENT FINDINGS: Conventional methods of assessing risk have focused on spirometry, patient-reported symptoms, functional status, and a combination of these tools in composite indices. More recently, qualitatively and quantitatively assessed chest imaging findings, such as emphysema, large and small airways disease, and pulmonary vascular abnormalities have been associated with poor long-term outcomes in COPD patients. Although several blood and sputum biomarkers have been investigated for risk assessment in COPD, most still warrant further validation. Finally, novel remote digital monitoring technologies may be valuable to predict exacerbations but their large-scale performance, ease of implementation, and cost effectiveness remain to be determined. SUMMARY: Given the complex heterogeneity of COPD, any single metric is unlikely to fully capture the risk of poor long-term outcomes. Therefore, clinicians should review all available clinical data, including spirometry, symptom severity, functional status, chest imaging, and bloodwork, to guide personalized preventive care of COPD patients. The potential of machine learning tools and remote monitoring technologies to refine COPD risk assessment is promising but remains largely untapped pending further investigation. Lippincott Williams & Wilkins 2022-03 2021-10-13 /pmc/articles/PMC8799486/ /pubmed/34652295 http://dx.doi.org/10.1097/MCP.0000000000000833 Text en Copyright © 2021 Wolters Kluwer Health, Inc. All rights reserved. This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections. |
spellingShingle | OBSTRUCTIVE, OCCUPATIONAL AND ENVIRONMENTAL DISEASES: Edited by Manish Joshi and Basil Varkey Wang, Jennifer M. Han, MeiLan K. Labaki, Wassim W. Chronic obstructive pulmonary disease risk assessment tools: is one better than the others? |
title | Chronic obstructive pulmonary disease risk assessment tools: is one better than the others? |
title_full | Chronic obstructive pulmonary disease risk assessment tools: is one better than the others? |
title_fullStr | Chronic obstructive pulmonary disease risk assessment tools: is one better than the others? |
title_full_unstemmed | Chronic obstructive pulmonary disease risk assessment tools: is one better than the others? |
title_short | Chronic obstructive pulmonary disease risk assessment tools: is one better than the others? |
title_sort | chronic obstructive pulmonary disease risk assessment tools: is one better than the others? |
topic | OBSTRUCTIVE, OCCUPATIONAL AND ENVIRONMENTAL DISEASES: Edited by Manish Joshi and Basil Varkey |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799486/ https://www.ncbi.nlm.nih.gov/pubmed/34652295 http://dx.doi.org/10.1097/MCP.0000000000000833 |
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