Literature DB >> 22532227

A model-based decision support system for critiquing mechanical ventilation treatments.

Fleur T Tehrani1, Soraya Abbasi.   

Abstract

A computerized system for critiquing mechanical ventilation treatments is presented that can be used as an aide to the intensivist. The presented system is based on the physiological model of the subject's respiratory system. It uses modified versions of previously developed models of adult and neonatal respiratory systems to simulate the effects of different ventilator treatments on the patient's blood gases. The physiological models that have been used for research and teaching purposes by many researchers in the field include lungs, body tissue, and the brain tissue. The lung volume is continuously time-varying and the effects of shunt in the lung, changes in cardiac output and cerebral blood flow, and the arterial transport delays are included in the system. Evaluation tests were done on adult and neonate patients with different diagnoses. In both groups combined, the differences between the arterial partial pressures of CO(2) predicted by the system and the experimental values were 1.86 ± 1.6 mmHg (mean ± SD), and the differences between the predicted arterial hemoglobin oxygen saturation values, S(aO2), and the experimental values measured by using pulse oximetry, S(pO2), were 0.032 ± 0.02 (mean ± SD). The proposed system has the potential to be used alone or in combination with other decision support systems to set ventilation parameters and optimize treatment for patients on mechanical ventilation.

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Year:  2012        PMID: 22532227     DOI: 10.1007/s10877-012-9362-0

Source DB:  PubMed          Journal:  J Clin Monit Comput        ISSN: 1387-1307            Impact factor:   2.502


  28 in total

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Journal:  J Biomed Eng       Date:  1983-01

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5.  The accuracy of pulse oximetry in emergency department patients with severe sepsis and septic shock: a retrospective cohort study.

Authors:  Ben J Wilson; Hamish J Cowan; Jason A Lord; Dan J Zuege; David A Zygun
Journal:  BMC Emerg Med       Date:  2010-05-05

Review 6.  Intelligent decision support systems for mechanical ventilation.

Authors:  Fleur T Tehrani; James H Roum
Journal:  Artif Intell Med       Date:  2008-09-02       Impact factor: 5.326

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Journal:  Int J Clin Monit Comput       Date:  1992-12

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Authors:  Fleur T Tehrani; James H Roum
Journal:  J Clin Monit Comput       Date:  2008-03-07       Impact factor: 2.502

Review 10.  Automatic control of mechanical ventilation. Part 2: the existing techniques and future trends.

Authors:  Fleur T Tehrani
Journal:  J Clin Monit Comput       Date:  2008-11-20       Impact factor: 2.502

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Journal:  Healthc Technol Lett       Date:  2019-04-03

2.  An Improved Dynamic Model for the Respiratory Response to Exercise.

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3.  A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection.

Authors:  Binghao Zhang; Damian Ratano; Laurent J Brochard; Dimitrios Georgopoulos; James Duffin; Michael Long; Tom Schepens; Irene Telias; Arthur S Slutsky; Ewan C Goligher; Timothy C Y Chan
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  3 in total

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