Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol. (November 2022) - Ang, Christopher Yew Shuen; Lee, Jay Wing Wai; Chiew, Yeong Shiong; Wang, Xin; Tan, Chee Pin; Cove, Matthew E; Nor, Mohd Basri Mat; Zhou, Cong; Desaive, Thomas; Chase, J. Geoffrey
Highlights: Model-based mechanical ventilation (MV) treatments require clinical trials prior to implementation. A virtual patient (VP) framework is developed for testing model-based decision support. A VP platform of 100 virtual patients were generated from retrospective data. A virtual trial demonstrates efficacy of VPs for prospective protocol testing. Potential to safely and rapidly design, develop and optimise new MV protocols. 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.