A physiology-based mathematical model for the selection of appropriate ventilator controls for lung and diaphragm protection

Abstract

Mechanical 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 CO 2 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.

The physiological underpinnings of life-saving respiratory support

“…As physiological understanding of the complex interaction between the risks of harm and benefits of mechanical ventilation and technical development of monitoring tools progress, the amount of data for decision-making becomes overwhelming. In this context, automated tools for analysis [58], decision support systems aided by artificial intelligence integrating physiological data [59,60], and automated modes of ventilation might help clinicians efficiently care for patients while minimizing harm. These will need to be rigorously developed, validated, and prospectively tested.…”

Lung- and diaphragm-protective strategies in acute respiratory failure: an in silico trial

“…We recently described [ 17 ] a mathematical model of control of breathing during mechanical ventilation based on known physiological relationships governing respiratory mechanics, control of breathing [ 19 , 20 ], acid–base homeostasis (using the Stewart approach) [ 21 ], ventilation, and pharmacokinetic and pharmacodynamic models of the effect of propofol on respiratory effort [ 22 , 23 ]. The model predicts ΔPes, ΔP L,dyn , and pH in response to varying inspiratory support or propofol infusion rate in two different modes of assisted ventilation, pressure support ventilation (PSV) and proportional assistance ventilation with load-adjustable gain factors (PAV+).…”

“…We recently described a physiology-based mathematical model [ 17 ] to simulate the effect of modifying ventilation and sedation on acid–base homeostasis, respiratory effort, and lung-distending pressure. In the present study, this model was deployed as a digital simulator to conduct an in silico clinical trial of a pre-defined algorithm, tested in a previously published clinical study, for titrating ventilator support and sedation to achieve LDP targets [ 18 ].…”

Pleural and transpulmonary pressures to tailor protective ventilation in children

“…The use by trained physicians of standardised protocols allowing bedside assessment of respiratory and lung mechanics has shown to be associated with an improvement in protective ventilation and oxygenation 88 89. The growing interest in machine learning over the last decades led to the development of computer-based clinical decision support systems (CDS) for protective ventilation 90 91. A CDS including P ES to maintain the effort of breathing within a safe window has been recently developed for critically ill children, and may shorten the duration of MV 33.…”