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Long-term cardiovascular risk prediction in the emergency department: a mixed-methods study protocol
INTRODUCTION: Cardiovascular disease (CVD) remains one of the leading causes of preventable death in Europe, therefore any opportunity to intervene and improve care should be maximised. Known CVD risk factors are routinely collected in the emergency department (ED), yet they are often not acted on....
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8996042/ https://www.ncbi.nlm.nih.gov/pubmed/35396287 http://dx.doi.org/10.1136/bmjopen-2021-054311 |
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author | Reynard, Charles McMillan, Brian Jafar, Anisa Heagerty, Anthony Martin, Glen Philip Kontopantelis, Evangelos Body, Richard |
author_facet | Reynard, Charles McMillan, Brian Jafar, Anisa Heagerty, Anthony Martin, Glen Philip Kontopantelis, Evangelos Body, Richard |
author_sort | Reynard, Charles |
collection | PubMed |
description | INTRODUCTION: Cardiovascular disease (CVD) remains one of the leading causes of preventable death in Europe, therefore any opportunity to intervene and improve care should be maximised. Known CVD risk factors are routinely collected in the emergency department (ED), yet they are often not acted on. If the risk factors have prognostic value and a pathway can be created, then this would provide more holistic care for patients and reduce health system inefficiency. METHODS AND ANALYSIS: In this mixed-methods study, we will use quantitative methods to investigate the prognostic characteristics of routinely collected data for long-term CVD outcomes, and qualitative methods to investigate how to use and implement this knowledge. The quantitative arm will use a database of approximately 21 000 chest pain patient episodes with a mean follow-up of 7.3 years. We will use Cox regression to evaluate the prognostic characteristics of routinely collected ED data for long-term CVD outcomes. We will also use a series of semi-structured interviews to co-design a prototype care pathway with stakeholders via thematic analysis. To enable the development of prototypes, themes will be structured into a logic model consisting of situation, inputs, outputs and mechanism. ETHICS AND DISSEMINATION: This work has been approved by Research Ethics Committee (Wales REC7) and the Human Research Authority under reference 19/WA/0312 and 19/WA/0311. It has also been approved by the Confidentiality Advisory Group reference 19/CAG/0209. Dissent recorded in the NHS’ opt-out scheme will be applied to the dataset by NHS Digital. This work will be disseminated through peer-review publication, conference presentation and a public dissemination strategy. TRIAL REGISTRATION NUMBER: ISRCTN41008456. PROTOCOL VERSION: V.1.0—7 June 2021. |
format | Online Article Text |
id | pubmed-8996042 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-89960422022-04-27 Long-term cardiovascular risk prediction in the emergency department: a mixed-methods study protocol Reynard, Charles McMillan, Brian Jafar, Anisa Heagerty, Anthony Martin, Glen Philip Kontopantelis, Evangelos Body, Richard BMJ Open Public Health INTRODUCTION: Cardiovascular disease (CVD) remains one of the leading causes of preventable death in Europe, therefore any opportunity to intervene and improve care should be maximised. Known CVD risk factors are routinely collected in the emergency department (ED), yet they are often not acted on. If the risk factors have prognostic value and a pathway can be created, then this would provide more holistic care for patients and reduce health system inefficiency. METHODS AND ANALYSIS: In this mixed-methods study, we will use quantitative methods to investigate the prognostic characteristics of routinely collected data for long-term CVD outcomes, and qualitative methods to investigate how to use and implement this knowledge. The quantitative arm will use a database of approximately 21 000 chest pain patient episodes with a mean follow-up of 7.3 years. We will use Cox regression to evaluate the prognostic characteristics of routinely collected ED data for long-term CVD outcomes. We will also use a series of semi-structured interviews to co-design a prototype care pathway with stakeholders via thematic analysis. To enable the development of prototypes, themes will be structured into a logic model consisting of situation, inputs, outputs and mechanism. ETHICS AND DISSEMINATION: This work has been approved by Research Ethics Committee (Wales REC7) and the Human Research Authority under reference 19/WA/0312 and 19/WA/0311. It has also been approved by the Confidentiality Advisory Group reference 19/CAG/0209. Dissent recorded in the NHS’ opt-out scheme will be applied to the dataset by NHS Digital. This work will be disseminated through peer-review publication, conference presentation and a public dissemination strategy. TRIAL REGISTRATION NUMBER: ISRCTN41008456. PROTOCOL VERSION: V.1.0—7 June 2021. BMJ Publishing Group 2022-04-08 /pmc/articles/PMC8996042/ /pubmed/35396287 http://dx.doi.org/10.1136/bmjopen-2021-054311 Text en © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Public Health Reynard, Charles McMillan, Brian Jafar, Anisa Heagerty, Anthony Martin, Glen Philip Kontopantelis, Evangelos Body, Richard Long-term cardiovascular risk prediction in the emergency department: a mixed-methods study protocol |
title | Long-term cardiovascular risk prediction in the emergency department: a mixed-methods study protocol |
title_full | Long-term cardiovascular risk prediction in the emergency department: a mixed-methods study protocol |
title_fullStr | Long-term cardiovascular risk prediction in the emergency department: a mixed-methods study protocol |
title_full_unstemmed | Long-term cardiovascular risk prediction in the emergency department: a mixed-methods study protocol |
title_short | Long-term cardiovascular risk prediction in the emergency department: a mixed-methods study protocol |
title_sort | long-term cardiovascular risk prediction in the emergency department: a mixed-methods study protocol |
topic | Public Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8996042/ https://www.ncbi.nlm.nih.gov/pubmed/35396287 http://dx.doi.org/10.1136/bmjopen-2021-054311 |
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