• Binghao Zhang, Damian Ratano, Laurent J Brochard, Dimitrios Georgopoulos, James Duffin, Michael Long, Tom Schepens, Irene Telias, Arthur S Slutsky, Ewan C Goligher, and ChanTimothy C YTCYDepartment of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Rd, Toronto, ON, M5S 3G8, Canada..
• Department of Mechanical and Industrial Engineering, University of Toronto, 5 King's College Rd, Toronto, ON, M5S 3G8, Canada. binghao.zhang@mail.utoronto.ca.
• J Clin Monit Comput. 2021 Apr 1; 35 (2): 363-378.

AbstractMechanical ventilation is used to sustain respiratory function in patients with acute respiratory failure. To aid clinicians in consistently selecting lung- and diaphragm-protective ventilation settings, a physiology-based decision support system is needed. To form the foundation of such a system, a comprehensive physiological model which captures the dynamics of ventilation has been developed. The Lung and Diaphragm Protective Ventilation (LDPV) model centers around respiratory drive and incorporates respiratory system mechanics, ventilator mechanics, and blood acid-base balance. The model uses patient-specific parameters as inputs and outputs predictions of a patient's transpulmonary and esophageal driving pressures (outputs most clinically relevant to lung and diaphragm safety), as well as their blood pH, under various ventilator and sedation conditions. Model simulations and global optimization techniques were used to evaluate and characterize the model. The LDPV model is demonstrated to describe a CO2 respiratory response that is comparable to what is found in literature. Sensitivity analysis of the model indicate that the ventilator and sedation settings incorporated in the model have a significant impact on the target output parameters. Finally, the model is seen to be able to provide robust predictions of esophageal pressure, transpulmonary pressure and blood pH for patient parameters with realistic variability. The LDPV model is a robust physiological model which produces outputs which directly target and reflect the risk of ventilator-induced lung and diaphragm injury. Ventilation and sedation parameters are seen to modulate the model outputs in accordance with what is currently known in literature.

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