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Computational modelling in critical illness

Population Size

100

People

Years

2019

Associated BioSamples

None/not available

Geographic coverage

Not reported

Lead time

Not applicable

Summary

High-fidelity computational model has been used to that capture key interactions between the cardiovascular and pulmonary systems to investigate whether current CPR protocols could potentially be improved - 3 datasets contain virtual subjects.

Documentation

Cardiac arrest (CA) is a leading cause of death in many countries – despite years of research, survival rates remain consistently low. Cardiopulmonary resuscitation (CPR) is an emergency procedure consisting of chest compressions combined with positive pressure ventilation, intended to restore flow of oxygenated blood to the brain and heart.

Dataset 1: 10 virtual subjects under cardiac arrest receiving cardiopulmonary resuscitation

  • We aimed to use a high-fidelity computational model that captures key interactions between the cardiovascular and pulmonary systems to investigate whether current CPR protocols could potentially be improved.

Dataset 2: 10 virtual pregnant women (with normal and high body mass index)

  • Hypoxaemia during general anaesthesia can cause harm. Apnoeic oxygenation extends safe apnoea time, reducing risk during airway management. We hypothesised that low-flow nasal oxygenation (LFNO) would extend safe apnoea time similarly to high-flow nasal oxygenation (HFNO), whilst allowing face-mask preoxygenation and rescue.

Dataset 3: 100 virtual healthy subjects

  • During induction of general anaesthesia a ‘cannot intubate, cannot oxygenate’ (CICO) situation can arise, leading to severe hypoxaemia. Evidence is scarce to guide ventilation strategies for small-bore emergency front of neck airways that ensure effective oxygenation without risking lung damage and cardiovascular depression.
Dataset type
Health and disease
Dataset sub-type
Not applicable
Dataset population size
100

Keywords

computational simulation, mathematical modelling, Critical Illness, pulmonary systems, cardiovascular systems, apnoea, airway obstruction, airway rescue, cannot intubate, cannot oxygenate, Oxygenation, mechanical ventilation, high-flow nasal oxygenation, low-flow nasal oxygenation, Obstetrics, obesity in pregnancy, cardiac arrest, cardiopulmonary resuscitation, chest compressions, Trauma, digital twins, digital twin

Observations

Observed Node
Disambiguating Description
Measured Value
Measured Property
Observation Date

Persons

100

Count

02 Sep 2019

Provenance

Purpose of dataset collection
Other
Source of data extraction
Machine generated
Collection source setting
Other
Image contrast
Not stated
Biological sample availability
None/not available

Details

Publishing frequency
Static
Version
1.0.0
Modified

08/10/2024

Citation Requirements
University of Nottingham

Coverage

Start date

02/09/2019

Time lag
Not applicable
Follow-up
Other

Accessibility

Language
en
Controlled vocabulary
LOCAL
Format
excel

Data Access Request

Dataset pipeline status
Not available
Access rights
In Progress
Time to dataset access
Not applicable
Jurisdiction
GB-GBN
Data Controller
mszml1@exmail.nottingham.ac.uk

Dataset Types: Health and disease


Collection Sources: No collection sources listed