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Virtual patient with temporal evolution for mechanical ventilation trial studies: a stochastic model approach

Background and objective: Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic data generation in hospital critical care setting...

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Main Authors: Shuen Ang, Christopher Yew, Chiew, Yeong Shiong, Wang, Xin, Ooi, Ean Hin, Mat Nor, Mohd Basri, Cove, Matthew E., Chase, J. Geoffrey
Format: Article
Language:English
English
Published: Elsevier 2023
Subjects:
QA75 Electronic computers. Computer science
RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
Online Access:http://irep.iium.edu.my/105906/7/105906_Virtual%20patient%20with%20temporal%20evolution.pdf
http://irep.iium.edu.my/105906/13/105906_Virtual%20patient%20with%20temporal%20evolution%20for%20mechanical%20ventilation%20trial%20studies_Scopus.pdf
http://irep.iium.edu.my/105906/
https://www.sciencedirect.com/science/article/abs/pii/S0169260723003942?via%3Dihub
https://doi.org/10.1016/j.cmpb.2023.107728
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spelling my.iium.irep.1059062024-01-08T09:01:25Z http://irep.iium.edu.my/105906/ Virtual patient with temporal evolution for mechanical ventilation trial studies: a stochastic model approach Shuen Ang, Christopher Yew Chiew, Yeong Shiong Wang, Xin Ooi, Ean Hin Mat Nor, Mohd Basri Cove, Matthew E. Chase, J. Geoffrey QA75 Electronic computers. Computer science RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid Background and objective: Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic data generation in hospital critical care settings. This research presents a realistic, timevarying mechanically ventilated respiratory failure VP profile synthesised using a stochastic model. Methods: A stochastic model was developed using respiratory elastance (Ers) data from two clinical cohorts and averaged over 30-minute time intervals. The stochastic model was used to generate future Ers data based on current Ers values with added normally distributed random noise. Self-validation of the VPs was performed via Monte Carlo simulation and retrospective Ers profile fitting. A stochastic VP cohort of temporal Ers evolution was synthesised and then compared to an independent retrospective patient cohort data in a virtual trial across several measured patient responses, where similarity of profiles validates the realism of stochastic model generated VP profiles. Results: A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5–10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of Ers profiles. Results of self-validation show the retrospective Ers profiles were able to be recreated accurately with a mean squared error of only 0.099 [0.009–0.790]% for the PC cohort and 0.051 [0.030–0.126]% for the VC cohort. A virtual trial demonstrates the ability of the stochastic VP cohort to capture Ers trends within and beyond the retrospective patient cohort providing cohort-level validation. Conclusion: VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation. Elsevier 2023-07-21 Article PeerReviewed application/pdf en http://irep.iium.edu.my/105906/7/105906_Virtual%20patient%20with%20temporal%20evolution.pdf application/pdf en http://irep.iium.edu.my/105906/13/105906_Virtual%20patient%20with%20temporal%20evolution%20for%20mechanical%20ventilation%20trial%20studies_Scopus.pdf Shuen Ang, Christopher Yew and Chiew, Yeong Shiong and Wang, Xin and Ooi, Ean Hin and Mat Nor, Mohd Basri and Cove, Matthew E. and Chase, J. Geoffrey (2023) Virtual patient with temporal evolution for mechanical ventilation trial studies: a stochastic model approach. Computer Methods and Programs in Biomedicine, 240. pp. 1-24. ISSN 0169-2607 E-ISSN 1872-7565 https://www.sciencedirect.com/science/article/abs/pii/S0169260723003942?via%3Dihub https://doi.org/10.1016/j.cmpb.2023.107728
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 QA75 Electronic computers. Computer science
RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
spellingShingle QA75 Electronic computers. Computer science
RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
Shuen Ang, Christopher Yew
Chiew, Yeong Shiong
Wang, Xin
Ooi, Ean Hin
Mat Nor, Mohd Basri
Cove, Matthew E.
Chase, J. Geoffrey
Virtual patient with temporal evolution for mechanical ventilation trial studies: a stochastic model approach
description Background and objective: Healthcare datasets are plagued by issues of data scarcity and class imbalance. Clinically validated virtual patient (VP) models can provide accurate in-silico representations of real patients and thus a means for synthetic data generation in hospital critical care settings. This research presents a realistic, timevarying mechanically ventilated respiratory failure VP profile synthesised using a stochastic model. Methods: A stochastic model was developed using respiratory elastance (Ers) data from two clinical cohorts and averaged over 30-minute time intervals. The stochastic model was used to generate future Ers data based on current Ers values with added normally distributed random noise. Self-validation of the VPs was performed via Monte Carlo simulation and retrospective Ers profile fitting. A stochastic VP cohort of temporal Ers evolution was synthesised and then compared to an independent retrospective patient cohort data in a virtual trial across several measured patient responses, where similarity of profiles validates the realism of stochastic model generated VP profiles. Results: A total of 120,000 3-hour VPs for pressure control (PC) and volume control (VC) ventilation modes are generated using stochastic simulation. Optimisation of the stochastic simulation process yields an ideal noise percentage of 5–10% and simulation iteration of 200,000 iterations, allowing the simulation of a realistic and diverse set of Ers profiles. Results of self-validation show the retrospective Ers profiles were able to be recreated accurately with a mean squared error of only 0.099 [0.009–0.790]% for the PC cohort and 0.051 [0.030–0.126]% for the VC cohort. A virtual trial demonstrates the ability of the stochastic VP cohort to capture Ers trends within and beyond the retrospective patient cohort providing cohort-level validation. Conclusion: VPs capable of temporal evolution demonstrate feasibility for use in designing, developing, and optimising bedside MV guidance protocols through in-silico simulation and validation. Overall, the temporal VPs developed using stochastic simulation alleviate the need for lengthy, resource intensive, high cost clinical trials, while facilitating statistically robust virtual trials, ultimately leading to improved patient care and outcomes in mechanical ventilation.
format Article
author Shuen Ang, Christopher Yew
Chiew, Yeong Shiong
Wang, Xin
Ooi, Ean Hin
Mat Nor, Mohd Basri
Cove, Matthew E.
Chase, J. Geoffrey
author_facet Shuen Ang, Christopher Yew
Chiew, Yeong Shiong
Wang, Xin
Ooi, Ean Hin
Mat Nor, Mohd Basri
Cove, Matthew E.
Chase, J. Geoffrey
author_sort Shuen Ang, Christopher Yew
title Virtual patient with temporal evolution for mechanical ventilation trial studies: a stochastic model approach
title_short Virtual patient with temporal evolution for mechanical ventilation trial studies: a stochastic model approach
title_full Virtual patient with temporal evolution for mechanical ventilation trial studies: a stochastic model approach
title_fullStr Virtual patient with temporal evolution for mechanical ventilation trial studies: a stochastic model approach
title_full_unstemmed Virtual patient with temporal evolution for mechanical ventilation trial studies: a stochastic model approach
title_sort virtual patient with temporal evolution for mechanical ventilation trial studies: a stochastic model approach
publisher Elsevier
publishDate 2023
url http://irep.iium.edu.my/105906/7/105906_Virtual%20patient%20with%20temporal%20evolution.pdf
http://irep.iium.edu.my/105906/13/105906_Virtual%20patient%20with%20temporal%20evolution%20for%20mechanical%20ventilation%20trial%20studies_Scopus.pdf
http://irep.iium.edu.my/105906/
https://www.sciencedirect.com/science/article/abs/pii/S0169260723003942?via%3Dihub
https://doi.org/10.1016/j.cmpb.2023.107728
_version_ 1787520326126534656
score 13.211869

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