Transient Ischaemic Attack 999 Emergency Referral (TIER): a cluster randomised feasibility trial facilitated by data linkage IJPDS (2017) Issue 1, Vol 1:326 Proceedings of the IPDLN Conference (August 2016)

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Anne Seagrove
Jenna Bulger
Helen Snooks
Nigel Rees
Martin Heaven
Published online: Apr 19, 2017


Studies demonstrate TIA patients are at risk of further TIAs, stroke and death. TIA incidence is unknown, however estimated at 35 per 100,000 people annually in the UK, costing approximately £7 billion. Many TIA patients call 999, are assessed, stabilised and conveyed to Emergency Department (ED). Rapid assessment of TIA severity and risk and intervention is emerging as the new standard for TIA care leading to alternative pathways with direct referral to specialist services. However, uncertainties exist over this new model of care.

We will develop and assess feasibility of paramedic assessment and referral of low-risk TIA patients directly to TIA clinic for early review, thus providing timely specialist review without: adverse consequences; inconvenience of ED attendance; unnecessary cost to the NHS.

This feasibility trial is designed to test the methods of a pragmatic cluster randomised trial, utilising data linkage for capturing outcome data, but with a qualitative component. To develop the treatment protocol, training and referral processes, working with clinicians/stakeholders, we will conduct:

  • survey across UK ambulance services to find referral pathways for low-risk TIA patients

  • systematic review of TIA prehospital care

  • paramedic focus groups pre-implementation


  • randomise paramedics (intervention/control)

  • recruit patients

  • interview patients, key clinicians and service managers

  • collect routine data via data linkage using the SAIL databank

  • hold paramedic focus groups post-implementation

We will:

  • measure uptake and compliance with treatment protocols

  • validate TIA assessment tool

  • analyse qualitative data

  • pilot recruitment processes

  • test data collection methods

  • estimate key outcomes effect size to inform full trial sample size calculation

Will inform full trial development using criteria: intervention acceptability to practitioners and patients; trial design feasibility; outcome data completeness.


  • If indicated, full trial conducted

  • If not, but positive results - advise intervention development for immediate implementation

  • If not, but negative results – advise delivery of intervention should cease.


Data safe havens can bring together and combine a rich array of anonymised person-based data for research and policy evaluation within a secure setting. To date, the majority of available datasets have been structured micro-data derived from routine health-related records. Possibilities are opening up for the greater reuse of genomic data such as Genome Wide Association studies (GWAS) and Whole Exome/Genome Sequencing (WES or WGS). However, there are considerable challenges to be addressed if the benefits of using these data in combination with health-related data are to be realized safely.


We explore the benefits and challenges of using genomic datasets with health-related data, and using the Secure Anonymised Information Linkage (SAIL) system as a case study, the implications and way forward for Data Safe Havens in seeking to incorporate genomic data for use with health-related data.


The benefits of using GWAS, WES and WGS data in conjunction with health-related data include the potential to explore genetics at a population level and open up novel research areas. These include the ability to increasingly stratify and personalize how medical indications are detected and treated through precision medicine by understanding rare conditions and adding socioeconomic and environmental context to genomic data. Among the challenges are: data availability, computing capacity, technical solutions, legal and regulatory frameworks, public perceptions, individual privacy and organizational risk. Many of the challenges within these areas are common to person-based data in general, and often Data Safe Havens have been designed to address these. But there are also aspects of these challenges, and other challenges, specific to genomic data. These include issues due to the unknown clinical significance of genomic information now or in the future, with corresponding risks for privacy and impact on individuals.


Genomic data sets contain vast amounts of valuable information, some of which is currently undefined, but which may have direct bearing on individual health at some point. The use of these data in combination with health-related data has the potential to bring great benefits, better clinical trial stratification, epidemiology project design and clinical improvements. It is, therefore, essential that such data are surrounded by a properly-designed, robust governance framework including technical and procedural access controls that enable the data to be used safely.

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