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

Autor: Ang CYS; School of Engineering, Monash University Malaysia, Selangor, Malaysia. Electronic address: Christopher.Ang@monash.edu., Lee JWW; School of Engineering, Monash University Malaysia, Selangor, Malaysia., Chiew YS; School of Engineering, Monash University Malaysia, Selangor, Malaysia. Electronic address: chiew.yeong.shiong@monash.edu., Wang X; School of Engineering, Monash University Malaysia, Selangor, Malaysia., Tan CP; School of Engineering, Monash University Malaysia, Selangor, Malaysia., Cove ME; Division of Respiratory & Critical Care Medicine, Department of Medicine, National University Health System, Singapore., Nor MBM; Kulliyah of Medicine, International Islamic University Malaysia, Kuantan, 25200, Malaysia., Zhou C; Center of Bioengineering, University of Canterbury, Christchurch, New Zealand., Desaive T; GIGA In-Silico Medicine, University of Liege, Liege, Belgium., Chase JG; Center of Bioengineering, University of Canterbury, Christchurch, New Zealand.
Jazyk: angličtina
Zdroj: Computer methods and programs in biomedicine [Comput Methods Programs Biomed] 2022 Nov; Vol. 226, pp. 107146. Date of Electronic Publication: 2022 Sep 18.
DOI: 10.1016/j.cmpb.2022.107146
Abstrakt: 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.
Competing Interests: Conflict of Interest None declared.
(Copyright © 2022. Published by Elsevier B.V.)
Databáze: MEDLINE
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