Common causes of cerebrovascular events include arrhythmias such as atrial fibrillation, damage to the small vessels of the brain termed ‘small vessel disease’, large vessel disease and haemorrhage.

Anecdotally, clinicians have described an increased prevalence of newly diagnosed cancers in people presenting with cerebrovascular disease. However, there is limited information on how common cancer is associated with stroke, what types of cancers are most commonly diagnosed, and how this effects prognosis both in relation to the stroke and the cancer.

Furthermore, it is unclear how people with cancer-related strokes should be treated; including if standard treatments are still beneficial or whether a more tailored approach is required.

This is a highly granular dataset of >16,000 patients with a confirmed cerebrovascular event including hospital presentation, serial physiology, every treatment prescribed and administered, and outcomes for the subsequent 12 months. It differentiates patients into those with a known or newly diagnosed malignancy and those without, and cancer types can be linked to pathology staining information, if needed.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million and includes a diverse ethnic and socio-economic mix.

UHB is one of the largest NHS Trusts in England, providing direct acute services and specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary and secondary care record (Your Care Connected) and a patient portal “My Health”.

Scope: Investigating the relationship between cancer and stroke and whether a cancer related stroke is associated with a worse clinical outcome compared with patients with non-cancer related stroke. Longitudinal and individually linked, so that the preceding and subsequent health journey can be mapped and healthcare utilisation prior to and after admission understood. The dataset includes highly granular patient demographics, co-morbidities taken from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to process of care (timings and admissions), presenting complaint, procedures, physiology readings (blood pressure, respiratory rate, heart rate, oxygen saturations, swallow screening), Lab analysis results (blood sodium level, estimated Glomerular filtration rate (GFR), urea, albumin, cholesterol, full blood counts and others), drug administered and all outcomes.

Available supplementary data: Matched controls; ambulance, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation and refinement; A.I.; Data partner support for ETL (extract, transform and load) process, Clinical expertise, Patient and end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.