Division of Cardiovascular Sciences, The University of Manchester, Manchester, UK
Emergency Department, Manchester University NHS Foundation Trust, Manchester, UK.
BMJ Open. 2022 Apr 8;12(4):e054311. doi: 10.1136/bmjopen-2021-054311.
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.
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.
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.
ISRCTN41008456.
V.1.0-7 June 2021.
心血管疾病(CVD)仍然是欧洲可预防死亡的主要原因之一,因此应尽可能利用一切机会进行干预并改善护理。在急诊科(ED)中,通常会常规收集已知的 CVD 危险因素,但这些危险因素往往没有得到处理。如果这些危险因素具有预测价值并且可以创建一个途径,那么这将为患者提供更全面的护理,并减少卫生系统的效率低下。
在这项混合方法研究中,我们将使用定量方法研究常规收集数据对长期 CVD 结局的预测特征,并用定性方法研究如何使用和实施这些知识。定量部分将使用一个大约 21000 例胸痛患者病例的数据库,平均随访 7.3 年。我们将使用 Cox 回归来评估常规收集 ED 数据对长期 CVD 结局的预测特征。我们还将通过主题分析与利益相关者一起使用一系列半结构化访谈来共同设计原型护理途径。为了能够开发原型,主题将被构造成一个逻辑模型,包括情况、投入、产出和机制。
这项工作已经得到了研究伦理委员会(威尔士 REC7)和人类研究管理局的批准,参考号为 19/WA/0312 和 19/WA/0311。它还得到了保密性咨询小组的批准,参考号为 19/CAG/0209。NHS 数字将在数据集上应用 NHS 退出计划中记录的异议。这项工作将通过同行评审出版物、会议演讲和公众传播策略进行传播。
ISRCTN41008456。
V.1.0-2021 年 6 月 7 日。