P4345Supporting big data research in cardiovascular medicine using routinely-collected data
Authors: Kaura A , Davies J , Panoulas V , Glampson B , Mulla A , Woods K , Omigie J , Shah A D , Melikian N , Francis D P , Kharbanda R , Perera D , Shah A M , Patel R S , Mayet J 
Published: October 2019

Background

Many of the data points required to support translational research are collected as a matter of routine, and should be available within electronic patient records. Variations in clinical and data recording practice can mean that the extraction and standardisation of this data, with the aim of producing a large-scale, research-ready dataset, presents a number of challenges.

Purpose

We set out to create a large-scale, research-ready dataset to support translational research in cardiovascular medicine, using routinely-collected data from five large university-hospital partnerships. As an initial focus, we selected those data points that would support an investigation of the relationship between test results and outcomes in acute coronary syndrome (ACS).

Methods

The National Institute of Health Research (NIHR) Health Informatics Collaborative (HIC) is a programme of infrastructure development aimed at increasing the quality and availability of routinely-collected data for collaborative, translational research. Eighteen university-hospital partnerships signed the data sharing agreement, and are working to facilitate the sharing and re-use of data across centres, for approved research purposes.

With support from the Directors of the NIHR Biomedical Research Centres (BRCs) within five of the largest partnerships, we established a clinical data collaboration, specifying a dataset and selecting an initial research question (Figure 1). The NIHR HIC team worked to extract data against this specification. With approval from an ethics committee, and from the information governance teams at each contributing centre, data was processed by one of the centres for standardisation and analysis.

Results

The specified dataset represented a longitudinal record for patients presenting with a suspected ACS, characterised by a request for a troponin test (Figure 1). The dataset included 156 data points, grouped into demographics, cardiovascular risk factor profile, emergency department attendance and inpatient episodes, blood tests, echocardiography and mortality. Data was extracted from the records of patients for whom a troponin test was requested between 2010 and 2017. A total of 257,948 records were standardised and analysed.

The collaboration has been successful, and an initial version of the combined dataset has been created. The size of the dataset has yielded new insights into the relationship between test results and outcomes, and publications are in preparation. An expanded dataset of over 800 data points has been agreed for the next phase of the collaboration, and three other centres have joined.

Figure 1. NIHR HIC dataset generation

Conclusion

It is perfectly feasible – in terms of governance and technology – to re-use routinely-collected data for collaborative, translational research in cardiovascular medicine. The resulting dataset will be large and complex enough to require big data tools and techniques, and will yield the kind of insights afforded only by big data in medicine.

Acknowledgement/Funding

Funded by NIHR Imperial Biomedical Research Centre (BRC) using NIHR Health Informatics Collaborative data service, supported by OUH, GSTT & UCLH BRCs