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Published on: September 26, 2018
Long-term cardiovascular risk prediction in the emergency department: a mixed-methods study protocol
Charles Reynard1,2, Brian McMillan3, Anisa Jafar4
1Division of Cardiovascular Sciences, The University of Manchester, Manchester, UK charlie.reynard@manchester.ac.uk.
Insights
This study investigates using emergency department data to predict long-term cardiovascular disease (CVD) outcomes. Findings will inform a new care pathway to improve patient care and reduce healthcare inefficiencies.
Area of Science:
- Cardiology
- Public Health
- Health Services Research
Background:
- Cardiovascular disease (CVD) is a leading cause of preventable death in Europe.
- Emergency departments (EDs) collect CVD risk factors but often fail to act on them.
- Utilizing routinely collected ED data could improve patient care and system efficiency.
Purpose of the Study:
- To determine the prognostic value of routinely collected ED data for long-term CVD outcomes.
- To co-design a prototype care pathway for implementing this knowledge with stakeholders.
- To enhance holistic patient care and reduce health system inefficiencies.
Main Methods:
- Mixed-methods approach combining quantitative and qualitative research.
- Quantitative analysis of ~21,000 chest pain patient episodes with 7.3-year follow-up using Cox regression.
- Qualitative semi-structured interviews and thematic analysis to co-design a care pathway prototype.
Main Results:
- Investigating the prognostic characteristics of routinely collected ED data for long-term CVD outcomes.
- Developing a logic model based on thematic analysis to structure pathway development.
- Co-designing a prototype care pathway with stakeholders for improved CVD risk management.
Conclusions:
- Routinely collected ED data holds prognostic value for long-term CVD outcomes.
- A co-designed care pathway can effectively integrate this knowledge into clinical practice.
- This approach promises more efficient and holistic cardiovascular care.
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.
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