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Demographic risk factors, biomarkers, physiology for Acute Compartment Syndrome

Dataset
Version: 1.0.0
Demography, physiology, injury status, muscle and blood biomarkers for Acute compartment syndrome patients, including intra-compartmental pressure and tissue oxygenation data alongside clinical observations and treatments.

Summary

Citation:
Demographic risk factors, biomarkers, physiology for Acute Compartment Syndrome

Documentation

Description:
Acute compartment syndrome (ACS) is an emergency orthopaedic condition wherein a rapid rise in compartmental pressure compromises blood perfusion to the tissues leading to ischaemia and muscle necrosis. This serious condition is often misdiagnosed or associated with significant diagnostic delay, and can lead to limb amputations and death. The most common causes of ACS are high impact trauma, especially fractures of the lower limbs which account for 40% of ACS cases. ACS is a challenge to diagnose and treat effectively, with differing clinical thresholds being utilised which can result in unnecessary osteotomy. The highly granular data for over 800 patients with ACS provide the following key parameters to support critical research into this condition: 1. Patient data (injury type, location, age, sex, pain levels, pre-injury status and comorbidities) 2. Physiological parameters (intracompartmental pressure, pH, tissue oxygenation, compartment hardness) 3. Muscle biomarkers (creatine kinase, myoglobin, lactate dehydrogenase) 4. Blood vessel damage biomarkers (glycocalyx shedding markers, endothelial permeability markers) PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. 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: Enabling data-driven research and machine learning models towards improving the diagnosis of Acute compartment syndrome. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, physiological parameters, muscle biomarkers, blood biomarkers and co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings and admissions), presenting complaint, lab analysis results (creatinine, eGFR, troponin, CRP, INR, ABG glucose), systolic and diastolic blood pressures, procedures and surgery details. Available supplementary data: Matched controls; ambulance, OMOP data, 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.

Coverage

Spatial:
United Kingdom, England, West Midlands
Typical Age Range:
10-110
Follow Up:
1 - 10 YEARS
Physical Sample Availability:
NOT AVAILABLE
Pathway:
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.

Provenance

Origin

Purposes:
CARE
Sources:
EPR
Collection Situations:
  • ACCIDENT AND EMERGENCY
  • IN-PATIENTS
  • OUTPATIENTS

Temporal

Accrual Periodicity:
QUARTERLY
Distribution Release Date:
2022-01-14
Start Date:
2004-07-08
End Date:
2021-07-27
Time Lag:
OTHER

Accessibility

Access

Access Service:
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.
Access Request Cost:
www.pioneerdatahub.co.uk/data/data-services-costs/
Delivery Lead Time:
1-2 MONTHS
Jurisdictions:
GB-ENG
Data Controller:
University Hospitals Birmingham NHS Foundation Trust
Data Processor:
NOT APPLICABLE

Usage

Data Use Limitations:
GENERAL RESEARCH USE
Data Use Requirements:
PROJECT SPECIFIC RESTRICTIONS
Resource Creators:
  • This publication uses data from PIONEER
  • an ethically approved database and analytical environment (East Midlands Derby Research Ethics 20/EM/0158)

Format and Standards

Vocabulary Encoding Schemes:
  • ICD10
  • OPCS4
  • SNOMED CT
Conforms To:
LOCAL
Languages:
en
Formats:
SQL

Enrichment and Linkage

Derivations:
Not Available

Observations

Statistical Population
Population Description
Population Size
Measured Property
Observation Date
Events
812 spells for patients with Acute compartment syndrome between 08-07-2004 and 27-07-2021
812
Count
2022-01-14