Patient asynchrony modelling during controlled mechanical ventilation therapy

Background and objective: Mechanical ventilation therapy of respiratory failure patients can be guided by monitoring patient-specific respiratory mechanics. However, the patient’s spontaneous breathing effort during controlled ventilation changes airway pressure waveform and thus affects the model...

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Main Authors:	Arunachalam, Ganesaramachandran, Chiew, Yeong Shiong, Tan, Chee Pin, Md Ralib, Azrina, Mat Nor, Mohd Basri
Format:	Article
Language:	English English English
Published:	Elsevier Ireland Ltd 2020
Subjects:	R Medicine (General) RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
Online Access:	http://irep.iium.edu.my/76234/ http://irep.iium.edu.my/76234/ http://irep.iium.edu.my/76234/ http://irep.iium.edu.my/76234/1/Computer%20methods%20and%20programme%20in%20biomedicine_asynchrony_2019.pdf http://irep.iium.edu.my/76234/8/76234_Patient%20asynchrony%20modelling%20during%20controlled_scopus.pdf http://irep.iium.edu.my/76234/7/76234_Patient%20asynchrony%20modelling%20during%20controlled_wos.pdf

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spelling	iium-762342019-12-26T00:59:22Z http://irep.iium.edu.my/76234/ Patient asynchrony modelling during controlled mechanical ventilation therapy Arunachalam, Ganesaramachandran Chiew, Yeong Shiong Tan, Chee Pin Md Ralib, Azrina Mat Nor, Mohd Basri R Medicine (General) RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid Background and objective: Mechanical ventilation therapy of respiratory failure patients can be guided by monitoring patient-specific respiratory mechanics. However, the patient’s spontaneous breathing effort during controlled ventilation changes airway pressure waveform and thus affects the model-based identification of patient-specific respiratory mechanics parameters. This study develops a model to estimate respiratory mechanics in the presence of patient effort. Methods: Gaussian effort model (GEM) is a derivative of the single-compartment model with basis function. GEM model uses a linear combination of basis functions to model the nonlinear pressure waveform of spontaneous breathing patients. The GEM model estimates respiratory mechanics such as Elastance and Resistance along with the magnitudes of basis functions, which accounts for patient inspiratory effort. Results and discussion: The GEM model was tested using both simulated data and a retrospective observational clinical trial patient data. GEM model fitting to the original airway pressure waveform is better than any existing models when reverse triggering asynchrony is present. The fitting error of GEM model was less than 10% for both simulated data and clinical trial patient data. Conclusion: GEM can capture the respiratory mechanics in the presence of patient effect in volume control ventilation mode and also can be used to assess patient-ventilator interaction. This model determines basis functions magnitudes, which can be used to simulate any waveform of patient effort pressure for future studies. The estimation of parameter identification GEM model can further be improved by constraining the parameters within a physiologically plausible range during least-square nonlinear regression. Elsevier Ireland Ltd 2020-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/76234/1/Computer%20methods%20and%20programme%20in%20biomedicine_asynchrony_2019.pdf application/pdf en http://irep.iium.edu.my/76234/8/76234_Patient%20asynchrony%20modelling%20during%20controlled_scopus.pdf application/pdf en http://irep.iium.edu.my/76234/7/76234_Patient%20asynchrony%20modelling%20during%20controlled_wos.pdf Arunachalam, Ganesaramachandran and Chiew, Yeong Shiong and Tan, Chee Pin and Md Ralib, Azrina and Mat Nor, Mohd Basri (2020) Patient asynchrony modelling during controlled mechanical ventilation therapy. Computer Methods and Programs in Biomedicine, 183 (January 2020). ISSN 0169-2607 E-ISSN 1872-7565 https://reader.elsevier.com/reader/sd/pii/S016926071931051X?token=E05C9C010D445F7408D58793D962B376090F2AB9B8243D20F500242A6EBCBCF666AE0A3576D5791A6118C5873323266E 10.1016/j.cmpb.2019.105103
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	R Medicine (General) RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
spellingShingle	R Medicine (General) RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid Arunachalam, Ganesaramachandran Chiew, Yeong Shiong Tan, Chee Pin Md Ralib, Azrina Mat Nor, Mohd Basri Patient asynchrony modelling during controlled mechanical ventilation therapy
description	Background and objective: Mechanical ventilation therapy of respiratory failure patients can be guided by monitoring patient-specific respiratory mechanics. However, the patient’s spontaneous breathing effort during controlled ventilation changes airway pressure waveform and thus affects the model-based identification of patient-specific respiratory mechanics parameters. This study develops a model to estimate respiratory mechanics in the presence of patient effort. Methods: Gaussian effort model (GEM) is a derivative of the single-compartment model with basis function. GEM model uses a linear combination of basis functions to model the nonlinear pressure waveform of spontaneous breathing patients. The GEM model estimates respiratory mechanics such as Elastance and Resistance along with the magnitudes of basis functions, which accounts for patient inspiratory effort. Results and discussion: The GEM model was tested using both simulated data and a retrospective observational clinical trial patient data. GEM model fitting to the original airway pressure waveform is better than any existing models when reverse triggering asynchrony is present. The fitting error of GEM model was less than 10% for both simulated data and clinical trial patient data. Conclusion: GEM can capture the respiratory mechanics in the presence of patient effect in volume control ventilation mode and also can be used to assess patient-ventilator interaction. This model determines basis functions magnitudes, which can be used to simulate any waveform of patient effort pressure for future studies. The estimation of parameter identification GEM model can further be improved by constraining the parameters within a physiologically plausible range during least-square nonlinear regression.
format	Article
author	Arunachalam, Ganesaramachandran Chiew, Yeong Shiong Tan, Chee Pin Md Ralib, Azrina Mat Nor, Mohd Basri
author_facet	Arunachalam, Ganesaramachandran Chiew, Yeong Shiong Tan, Chee Pin Md Ralib, Azrina Mat Nor, Mohd Basri
author_sort	Arunachalam, Ganesaramachandran
title	Patient asynchrony modelling during controlled mechanical ventilation therapy
title_short	Patient asynchrony modelling during controlled mechanical ventilation therapy
title_full	Patient asynchrony modelling during controlled mechanical ventilation therapy
title_fullStr	Patient asynchrony modelling during controlled mechanical ventilation therapy
title_full_unstemmed	Patient asynchrony modelling during controlled mechanical ventilation therapy
title_sort	patient asynchrony modelling during controlled mechanical ventilation therapy
publisher	Elsevier Ireland Ltd
publishDate	2020
url	http://irep.iium.edu.my/76234/ http://irep.iium.edu.my/76234/ http://irep.iium.edu.my/76234/ http://irep.iium.edu.my/76234/1/Computer%20methods%20and%20programme%20in%20biomedicine_asynchrony_2019.pdf http://irep.iium.edu.my/76234/8/76234_Patient%20asynchrony%20modelling%20during%20controlled_scopus.pdf http://irep.iium.edu.my/76234/7/76234_Patient%20asynchrony%20modelling%20during%20controlled_wos.pdf
first_indexed	2023-09-18T21:47:45Z
last_indexed	2023-09-18T21:47:45Z
_version_	1777413563718041600

Patient asynchrony modelling during controlled mechanical ventilation therapy

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