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PhenoAge as a predictor of outcome in acute admissions with Covid-19

Population Size

Not reported
Population Size statistic card

Years

2020 - 2021

Years statistic card

Associated BioSamples

None/not available

Associated BioSamples statistic card

Geographic coverage

West Midlands

Geographic coverage statistic card

Lead time

2-6 months

Lead time statistic card

Summary

To describe the PhenoAgeAccel (difference between biological age and chronological age – a marker of accelerated ageing) of adults admitted to hospital with Covid-19. To compare the outcomes (mortality, length of stay, ITU admission, change in residence on discharge) of Covid-19 patients with an increased PhenoAgeAccel (accelerated ageing) with Covid-19 patients with a balanced or decreased PhenoAgeAccel.

Documentation

Covid-19 has had an unprecedented effect on life over the last 3 years. Despite numerous advances in vaccines and treatments for Covid-19 it continues to significantly contribute towards death and poor health in the UK. 5.3% of deaths in the week ending 21st January 2023 were certified as deaths involving Covid-19. Age, frailty, sex, and certain co-morbidities have been identified as predictors of death, critical care admission and changes in residence (new care home placements) on discharge. Additionally, several laboratory markers have been identified as predictors of poor prognosis on admission to hospital.

However, despite these advances in the understanding of the risks to prognosis in patients with Covid-19 there has been no universally accepted prognostication model. PhenoAgeAccel is an epigenetic biomarker of ageing for lifespan and healthspan calculated using readily available laboratory markers. It is a DNA methylation-based biomarker that has been tested in several populations and been found to be highly predictive of death and poor health.

PhenoAgeAccel measured in UK biobank participants predicted the severity of outcomes of those participants when they were diagnosed with Covid-19 more than 10 years later. Several of the components (CRP, white cell count, red cell distribution width) of PhenoAgeAccel have already been identified as predictors of poor prognosis in Covid-19.

PhenoAgeAccel is simple to calculate for inpatients with Covid-19 from routinely collected parameters and could provide a simple method of prognostication that could be automatically calculated at admission.

Dataset type

Health and disease

Keywords

Dataset and BioSample Aliases

Provenance

Purpose of dataset collection

Study

Source of data extraction

Machine generated

Collection source setting

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

Patient pathway description

Acute admissions (medical, surgical and critical care) with Covid-19.

Image contrast

Not stated

Biological sample availability

None/not available

Structural Metadata

Details

Publishing frequency

Static

Version

1.0.0

Modified

09/05/2025

Distribution release date

20/06/2024

Citation Requirements

This publication uses data from the PATHWAY, an ethically approved Research Data Hub (NRES Reference 22/EE/0161)

Coverage

Start date

01/02/2020

End date

28/02/2021

Time lag

More than 6 months

Geographic coverage

West Midlands

Follow-up

1 - 10 Years

Accessibility

Language

en

Alignment with standardised data models

LOCAL

Controlled vocabulary

ICD10, SNOMED CT, OPCS4

Format

SQL

Data Access Request

Dataset pipeline status

Not available

Access rights

Information Governance and Ethics - West Midlands Secure Data Environment (https://westmidlandssde.nhs.uk/information-governance-and-ethics)

Time to dataset access

2-6 months

Access request cost

Please email wmsde@uhb.nhs.uk

Access method category

TRE/SDE

Access service description

Data Request Process - West Midlands Secure Data Environment (https://westmidlandssde.nhs.uk/research/data-request)

Jurisdiction

GB

Data use limitation

General research use

Data use requirements

Ethics approval required,Project-specific restrictions

Data Controller

University Hospitals Birmingham NHS Foundation Trust

Data Processor

University Hospitals Birmingham NHS Foundation Trust

Dataset Types: Health and disease


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