Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol

Shuen Ang, Christopher Yew and Wai Lee, Jay Wing and Chiew, Yeong Shiong and Wang, Xin and Tan, Chee Pin and Mat Nor, Mohd Basri and E Cove, Matthew and Zhou, Cong and Desaivee, Thomas and Chase, Geoffrey (2022) Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol. Computer Methods and Programs in Biomedicine, 12 (226). pp. 1-14. ISSN 0169-2607 E-ISSN 0169-2607

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Abstract

Background and Objective: Model-based and personalised decision support systems are emerging to guide mechanical ventilation (MV) treatment for respiratory failure patients. However, model-based treatments require resource-intensive clinical trials prior to implementation. This research presents a framework for generating virtual patients for testing model-based decision support, and direct use in MV treatment. Methods: The virtual MV patient framework consists of 3 stages: 1) Virtual patient generation, 2) Patient- level validation, and 3) Virtual clinical trials. The virtual patients are generated from retrospective MV patient data using a clinically validated respiratory mechanics model whose respiratory parameters (res- piratory elastance and resistance) capture patient-specific pulmonary conditions and responses to MV care over time. Patient-level validation compares the predicted responses from the virtual patient to their retrospective results for clinically implemented MV settings and changes to care. Patient-level validated virtual patients create a platform to conduct virtual trials, where the safety of closed-loop model-based protocols can be evaluated. Results: This research creates and presents a virtual patient platform of 100 virtual patients generated from retrospective data. Patient-level validation reported median errors of 3.26% for volume-control and 6.80% for pressure-control ventilation mode. A virtual trial on a model-based protocol demonstrates the potential efficacy of using virtual patients for prospective evaluation and testing of the protocol. Conclusion: The virtual patient framework shows the potential to safely and rapidly design, develop, and optimise new model-based MV decision support systems and protocols using clinically validated models and computer simulation, which could ultimately improve patient care and outcomes in MV.

Item Type:	Article (Journal)
Uncontrolled Keywords:	Mechanical ventilation Respiratory mechanics Patient-specific Virtual patient Digital twin Respiratory elastance
Subjects:	R Medicine > RC Internal medicine > RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Medicine > Department of Anaesthesiology & Intensive Care
Depositing User:	Dr. Mohd Basri Mat Nor
Date Deposited:	11 Oct 2022 09:26
Last Modified:	11 Oct 2022 09:26
URI:	http://irep.iium.edu.my/id/eprint/100472

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