JOURNAL ARTICLE

Chronic obstructive pulmonary disease: reducing the risk of winter exacerbations.

  • Published In: Primary Health Care, 2024, v. 34, n. 1. P. 34 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Jones-Barry, Sian; Vennard, Karen 3 of 3

Abstract

Why you should read this article: • To understand why exacerbations of chronic obstructive pulmonary disease occur • To familiarise yourself with the year-round patient management of chronic obstructive pulmonary disease • To contribute towards revalidation as part of your 35 hours of CPD (UK readers) • To contribute towards your professional development and local registration renewal requirements (non-UK readers). In winter, exacerbations of chronic obstructive pulmonary disease (COPD) are frequent. These exacerbations are associated with increased hospital admissions, morbidity and mortality. Reducing the risk of winter exacerbations of COPD is crucial for alleviating pressures on health services and can be achieved by providing optimal year-round patient management. Identifying, reviewing and assessing patients at risk of COPD exacerbations well ahead of the winter season helps put in place preventive interventions such as checking inhaler technique, educating patients to recognise exacerbations and promoting self-management. This article highlights risk factors for COPD exacerbations, describes how to undertake a comprehensive review of a patient with COPD, and discusses interventions that community and primary care nurses can deliver to reduce the risk of winter exacerbations. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Primary Health Care. 2024/02, Vol. 34, Issue 1, p34
  • Document Type:Article
  • Subject Area:Health and Medicine
  • Publication Date:2024
  • ISSN:0264-5033
  • DOI:10.7748/phc.2023.e1798
  • Accession Number:175069074
  • Copyright Statement:Copyright of Primary Health Care is the property of Royal College of Nursing of the United Kingdom (The) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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