Linked Life Data

19864039

Source:http://linkedlifedata.com/resource/pubmed/id/19864039

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2010-7-8
pubmed:abstractText
Arterial blood gas (ABG) analyses are essential for assessing the acid-base status and guiding the adjustment of mechanical ventilation in critically ill patients. Conventional ABG sampling requires repeated arterial punctures or the insertion of an arterial catheter causing pain, haemorrhage and thrombosis to the patients. Less invasive and non-invasive blood gas analysers, with a technology still in transition, have offered some promise in the recent years. SOPAVent (Simulation of Patients under Artificial Ventilation) is a five compartment blood gas model which captures the basic features of respiratory physiology and gas exchange in the human lungs. It uses ventilator settings and routinely monitored physiological parameters as inputs to produce steady-state estimates of the patient's ABG. This paper overviews the original SOPAVent model and presents an improved data-driven hybrid model that is patient-specific and gives continuous and totally non-invasive ABG predictions. The model has been comprehensively tested in simulations and validated using recorded measurements of ABG and ventilator parameters from ICU patients.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1872-7565
pubmed:author
pubmed:copyrightInfo
2009 Elsevier Ireland Ltd. All rights reserved.
pubmed:issnType
Electronic
pubmed:volume
99
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
195-207
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Intelligent model-based advisory system for the management of ventilated intensive care patients: Hybrid blood gas patient model.
pubmed:affiliation
Process Automation, ABB Limited, Eaton Socon, Cambridgeshire, UK. ang.wang@gb.abb.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't