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

• Main Authors: Shuen Ang, Christopher Yew, Wai Lee, Jay Wing, Chiew, Yeong Shiong, Wang, Xin, Tan, Chee Pin, Mat Nor, Mohd Basri, E Cove, Matthew, Zhou, Cong, Desaivee, Thomas, Chase, Geoffrey
• Format: Article
• Language: English
• Published: Elsevier Ltd. 2022
• Subjects: RC82 Medical Emergencies, Critical Care, Intensive Care, First Aid
• Online Access: http://irep.iium.edu.my/100472/1/100472_Virtual%20patient%20framework%20for%20the%20testing.pdf; http://irep.iium.edu.my/100472/; https://www.sciencedirect.com/science/article/abs/pii/S0169260722005272?via%3Dih
• Institution: Universiti Islam Antarabangsa Malaysia

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.