NDLI: A simple gas exchange model predicting arterial oxygen content for various FiO2 levels

Content Provider	IEEE Xplore Digital Library
Author	Kretschmer, J. Becher, T. Riedlinger, A. Schadler, D. Weiler, N. Moller, K.
Copyright Year	2013
Description	Author affiliation: Inst. of Tech. Med., Furtwangen Univ., Villingen-Schwenningen, Germany (Kretschmer, J.; Riedlinger, A.; Moller, K.) \|\| Univ. Med. Center Schleswig-Holstein, Kiel, Germany (Becher, T.; Schadler, D.; Weiler, N.)
Abstract	The application of mechanical ventilation is a life-saving routine therapy that allows the patient to overcome the physiological impact of surgeries, trauma or critical illness by ensuring vital oxygenation and carbon dioxide removal. Above a certain level of minute ventilation (usually set to ensure acceptable carbon dioxide removal and oxygenation) oxygenation is only marginally affected by a further increase in minute ventilation. Thus, oxygenation is predominantly influenced by inspiratory oxygen fraction $(FiO_{2})$ Usually, finding the appropriate setting is a trial-and-error procedure, as the clinician is unaware of the exact value that needs to be set in order to reach the desired arterial oxygen partial pressures $(PaO_{2})$ in the patient. Mathematical models of physiological processes in the human body may be used to predict patient reactions towards alterations in the therapy regime. These predictions can be exploited by Medical Decision Support Systems to find optimal therapy settings. A simple mathematical model is presented, that allows calculation of a patient's shunt fraction, i.e. the percentage of blood that is not participating in lung gas exchange. On this basis, it predicts $PaO_{2}$ at various $FiO_{2}-levels$ and thus allows reaching desired $PaO_{2}$ in just one step. Due to its simple design it does not require complicated - and possibly error-prone - parameter identification procedures, thus allowing its application at the bedside. Retrospective analysis of oxygenation data from a patient data management system showed that the presented model predicted $PaO_{2}$ with less than 10% deviation in 23 out of 29 measurements, proving the practical applicability of the presented model approach.
Starting Page	465
Ending Page	468
File Size	639014
Page Count	4
File Format	PDF
ISBN	9781457702167
ISSN	1557170X
DOI	10.1109/EMBC.2013.6609537
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-03
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Blood Mathematical model Ventilation Predictive models Carbon dioxide Medical treatment Data models
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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