[en] 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 (respiratory 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.
Disciplines :
Anesthesia & intensive care
Author, co-author :
Ang, Christopher Yew Shuen; School of Engineering, Monash University Malaysia, Selangor, Malaysia. Electronic address: Christopher.Ang@monash.edu.
Lee, Jay Wing Wai; School of Engineering, Monash University Malaysia, Selangor, Malaysia.
Chiew, Yeong Shiong; School of Engineering, Monash University Malaysia, Selangor, Malaysia. Electronic address: chiew.yeong.shiong@monash.edu.
Wang, Xin; School of Engineering, Monash University Malaysia, Selangor, Malaysia.
Tan, Chee Pin; School of Engineering, Monash University Malaysia, Selangor, Malaysia.
Cove, Matthew E; Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Health System, Singapore.
Nor, Mohd Basri Mat; Kulliyah of Medicine, International Islamic University Malaysia, Kuantan, 25200, Malaysia.
Zhou, Cong; Center of Bioengineering, University of Canterbury, Christchurch, New Zealand.
Desaive, Thomas ; Université de Liège - ULiège > Département d'astrophysique, géophysique et océanographie (AGO) > Thermodynamique des phénomènes irréversibles
Chase, J Geoffrey; Center of Bioengineering, University of Canterbury, Christchurch, New Zealand.
Language :
English
Title :
Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol.
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