Cover Image

Stochastic modelling of respiratory system elastance for mechanically ventilated respiratory failure patients

While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure sa...

Full description

Saved in:
Bibliographic Details
Main Authors: Wai Lee, Jay Wing, Chiew, Yeong Shiong, Wang, Xin, Tan, Chee Pin, Mat Nor, Mohd Basri, Damanhuri, Nor Salwa, Chase, Geoffrey, ,
Format: Article
Language:English
English
Published: Kluwer Academic Publishers 2021
Subjects:
RC731 Specialties of Internal Medicine-Diseases of The Respiratory System
RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
TJ Mechanical engineering and machinery
Online Access:http://irep.iium.edu.my/92724/7/92724_Stochastic%20modelling%20of%20respiratory%20system%20elastance_SCOPUS.pdf
http://irep.iium.edu.my/92724/8/92724_Stochastic%20modelling%20of%20respiratory%20system%20elastance.pdf
http://irep.iium.edu.my/92724/
https://link.springer.com/article/10.1007%2Fs10439-021-02854-4
https://doi.org/10.1007/s10439-021-02854-4
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.iium.irep.92724
record_format dspace
spelling my.iium.irep.927242021-10-28T03:36:54Z http://irep.iium.edu.my/92724/ Stochastic modelling of respiratory system elastance for mechanically ventilated respiratory failure patients Wai Lee, Jay Wing Chiew, Yeong Shiong Wang, Xin Tan, Chee Pin Mat Nor, Mohd Basri Damanhuri, Nor Salwa Chase, Geoffrey , , RC731 Specialties of Internal Medicine-Diseases of The Respiratory System RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid TJ Mechanical engineering and machinery While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, Ers, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future Ers values using retrospective patient data to develop and validate a model indicating future intra-patient variability of Ers. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5–95% and the 25–75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future Ers values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility. Kluwer Academic Publishers 2021-08-25 Article PeerReviewed application/pdf en http://irep.iium.edu.my/92724/7/92724_Stochastic%20modelling%20of%20respiratory%20system%20elastance_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/92724/8/92724_Stochastic%20modelling%20of%20respiratory%20system%20elastance.pdf Wai Lee, Jay Wing and Chiew, Yeong Shiong and Wang, Xin and Tan, Chee Pin and Mat Nor, Mohd Basri and Damanhuri, Nor Salwa and Chase, Geoffrey and UNSPECIFIED and UNSPECIFIED (2021) Stochastic modelling of respiratory system elastance for mechanically ventilated respiratory failure patients. Annals of Biomedical Engineering. pp. 1-16. ISSN 00906964 E-ISSN 15216047 https://link.springer.com/article/10.1007%2Fs10439-021-02854-4 https://doi.org/10.1007/s10439-021-02854-4
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
topic RC731 Specialties of Internal Medicine-Diseases of The Respiratory System
RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
TJ Mechanical engineering and machinery
spellingShingle RC731 Specialties of Internal Medicine-Diseases of The Respiratory System
RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
TJ Mechanical engineering and machinery
Wai Lee, Jay Wing
Chiew, Yeong Shiong
Wang, Xin
Tan, Chee Pin
Mat Nor, Mohd Basri
Damanhuri, Nor Salwa
Chase, Geoffrey
,
,
Stochastic modelling of respiratory system elastance for mechanically ventilated respiratory failure patients
description While lung protective mechanical ventilation (MV) guidelines have been developed to avoid ventilator induced lung injury (VILI), a one-size-fits-all approach cannot benefit every individual patient. Hence, there is significant need for the ability to provide patient-specific MV settings to ensure safety, and optimise patient care. Model based approaches enable patient-specific care by identifying time-varying patient-specific parameters, such as respiratory elastance, Ers, to capture inter- and intra-patient variability. However, patient-specific parameters evolve with time, as a function of disease progression and patient condition, making predicting their future values crucial for recommending patient-specific MV settings. This study employs stochastic modelling to predict future Ers values using retrospective patient data to develop and validate a model indicating future intra-patient variability of Ers. Cross validation results show stochastic modelling can predict future elastance ranges with 92.59 and 68.56% of predicted values within the 5–95% and the 25–75% range, respectively. This range can be used to ensure patients receive adequate minute ventilation should elastance rise and minimise the risk of VILI should elastance fall. The results show the potential for model-based protocols using stochastic model prediction of future Ers values to provide safe and patient-specific MV. These results warrant further investigation to validate its clinical utility.
format Article
author Wai Lee, Jay Wing
Chiew, Yeong Shiong
Wang, Xin
Tan, Chee Pin
Mat Nor, Mohd Basri
Damanhuri, Nor Salwa
Chase, Geoffrey
,
,
author_facet Wai Lee, Jay Wing
Chiew, Yeong Shiong
Wang, Xin
Tan, Chee Pin
Mat Nor, Mohd Basri
Damanhuri, Nor Salwa
Chase, Geoffrey
,
,
author_sort Wai Lee, Jay Wing
title Stochastic modelling of respiratory system elastance for mechanically ventilated respiratory failure patients
title_short Stochastic modelling of respiratory system elastance for mechanically ventilated respiratory failure patients
title_full Stochastic modelling of respiratory system elastance for mechanically ventilated respiratory failure patients
title_fullStr Stochastic modelling of respiratory system elastance for mechanically ventilated respiratory failure patients
title_full_unstemmed Stochastic modelling of respiratory system elastance for mechanically ventilated respiratory failure patients
title_sort stochastic modelling of respiratory system elastance for mechanically ventilated respiratory failure patients
publisher Kluwer Academic Publishers
publishDate 2021
url http://irep.iium.edu.my/92724/7/92724_Stochastic%20modelling%20of%20respiratory%20system%20elastance_SCOPUS.pdf
http://irep.iium.edu.my/92724/8/92724_Stochastic%20modelling%20of%20respiratory%20system%20elastance.pdf
http://irep.iium.edu.my/92724/
https://link.springer.com/article/10.1007%2Fs10439-021-02854-4
https://doi.org/10.1007/s10439-021-02854-4
_version_ 1715189390859304960
score 13.211869

Similar Items