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

Christopher Yew Shuen Ang, Jay Wing Wai Lee, Yeong Shiong Chiew, Xin Wang, Chee Pin Tan, Matthew E Cove, Mohd Basri Mat Nor, Cong Zhou, Thomas Desaive, J. Geoffrey Chase

Research output: Contribution to journalArticleResearchpeer-review

5 Citations (Scopus)

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 (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.

Original languageEnglish
Article number107146
Number of pages14
JournalComputer Methods and Programs in Biomedicine
Volume226
DOIs
Publication statusPublished - Nov 2022

Keywords

  • Digital twin
  • Mechanical ventilation
  • Patient-specific
  • Respiratory elastance
  • Respiratory mechanics
  • Virtual patient

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