Bookmarks
Investigating the impact of frailty, age and illness severity during COVID-19
Population Size
73,204
People
Years
2020 - 2020
Associated BioSamples
None/not available
Geographic coverage
United Kingdom
England
Lead time
1-2 months
Summary
Documentation
Frailty is a syndrome of increased vulnerability to incomplete resolution of homeostasis (healing or return to baseline function) following a stressor event (such as an infection or fall) and it is associated with poor outcomes including increased mortality and reduced quality of life. Prevalence increases with age, but it should not be considered an inevitable consequence of ageing.
The pathophysiology of frailty is poorly understood but the immune and endocrine systems appear to be involved in its development or response. Age and frailty have been proven to be independently predictive of outcomes in patients admitted with an acute illness.
In COVID-19, routine frailty identification has been used to inform decision making about high level of treatment. This is because frailty usually moderates the effect of age on mortality. Anecdotally, this effect has not been recognised by clinicians looking after older COVID-19 patients. Four papers have been published so far on the effect of frailty on COVID-19 with differing results. However, all papers show the independent predictive value of age when controlling for frailty, which is not usually seen in studies of age and frailty in other acute illnesses.
PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.
EHR: UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.
Scope: All patients aged 18 years and above admitted for an acute illness in hospitals within University Hospitals Birmingham NHS trust during the COVID-19 pandemic. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, imaging reports, all prescribed & administered treatments (fluids, blood products, procedures), all outcomes.
Available supplementary data: Matched controls; ambulance, synthetic data.
Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.
Dataset type
Dataset sub-type
Dataset population size
Keywords
Observations
Observed Node | Disambiguating Description | Measured Value | Measured Property | Observation Date |
---|---|---|---|---|
Persons | 73,204 Acute Hospital Admissions between 05-March-2020 and 05-Nov-2020. | 73204 | Count | 25 May 2021 |
Provenance
Purpose of dataset collection
Source of data extraction
Collection source setting
Patient pathway description
Image contrast
Biological sample availability
Structural Metadata
Details
Publishing frequency
Version
Modified
08/10/2024
Distribution release date
25/05/2021
Citation Requirements
Coverage
Start date
05/03/2020
End date
05/11/2020
Time lag
Geographic coverage
Minimum age range
Maximum age range
Follow-up
Accessibility
Language
Alignment with standardised data models
Controlled vocabulary
Format
Data Access Request
Dataset pipeline status
Time to dataset access
Access request cost
Access method category
Access service description
Trusted Research Environments (TRE) are built using Microsoft Azure services and hosted in the UK to provide research teams a safe, secure and agile environment which allows users to quickly analyse, interpret and form an enriched view of primary care information through a range of integrated datasets.
Health data collated from multiple sources is ingested into a secure data lake which will then allow subsets of data to be made available to research teams on approval of a data request. Once approved a customer specific TRE is made available with a standard set of leading analytical tools from Microsoft including Azure Databricks, Azure Machine Learning, Azure SQL and Azure Synapse (for large-scale data warehouses). Specific tools can be provided at an additional cost over the standard platform data access charge and the PIONEER team will work with you to determine your exact needs.
Access to the TRE is managed using the latest virtual desktop technology to provide a safe and secure end-user experience. By utilising leading edge design PIONEER are able to create TREs rapidly to enable us to service any customer requirement.
Jurisdiction
Data use limitation
Data use requirements
Data Controller