Bookmarks

Investigating the impact of frailty, age and illness severity during COVID-19

Population Size

73,204

People

Population Size statistic card

Years

2020 - 2020

Years statistic card

Associated BioSamples

None/not available

Associated BioSamples statistic card

Geographic coverage

United Kingdom

England

Geographic coverage statistic card

Lead time

1-2 months

Lead time statistic card

Summary

Longitudinal data to investigate interactions of frailty, age and illness severity on outcomes in adults admitted during the COVID-19 pandemic. Clinicians’ frailty scoring versus electronic frailty scoring. Highly granular structured serial data.

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

Health and disease, Measurements/Tests

Dataset sub-type

Respiratory

Dataset population size

73204

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

Care

Source of data extraction

EPR

Collection source setting

Secondary care - Accident and Emergency, Secondary care - In-patients, Secondary care - Outpatients

Patient pathway description

Data is representative of the multi-ethnicity population within the West Midlands (42% non white). Data includes all patients admitted during this timeframe, with National data Opt Outs applied, and therefore is representative of admissions to secondary care. Data focuses on in-patient stay in hospital during the acute episode but can be supplemented on request to include previous and subsequent hospital contacts (including outpatient appointments) and ambulance, 111, 999 data.

Image contrast

Not stated

Biological sample availability

None/not available

Structural Metadata

Details

Publishing frequency

Quarterly

Version

1.0.0

Modified

08/10/2024

Distribution release date

25/05/2021

Citation Requirements

This publication uses data from PIONEER, an ethically approved database and analytical environment (East Midlands Derby Research Ethics 20/EM/0158)

Coverage

Start date

05/03/2020

End date

05/11/2020

Time lag

Other

Geographic coverage

United Kingdom, England, West Midlands

Minimum age range

18

Maximum age range

110

Follow-up

1 - 10 Years

Accessibility

Language

en

Alignment with standardised data models

LOCAL

Controlled vocabulary

SNOMED CT

Format

SQL

Data Access Request

Dataset pipeline status

Available

Time to dataset access

1-2 months

Access request cost

www.pioneerdatahub.co.uk/data/data-services-costs/

Access method category

TRE/SDE

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

GB-ENG

Data use limitation

General research use

Data use requirements

Project-specific restrictions

Data Controller

University Hospitals Birmingham NHS Foundation Trust

Dataset Types: Health and disease, Measurements/Tests

Dataset Sub-types: Respiratory


Collection Sources: Secondary care - Accident and Emergency, Secondary care - In-patients, Secondary care - Outpatients

end of page