A mathematical model approach quantifying patients' response to changes in mechanical ventilation: Evaluation in pressure support

Modeling Mechanical Ventilation In Silico-Potential and Pitfalls

Computer simulation offers a fresh approach to traditional medical research that is particularly well suited to investigating issues related to mechanical ventilation. Patients receiving mechanical ventilation are routinely monitored in great detail, providing extensive high-quality data-streams for model design and configuration. Models based on such data can incorporate very complex system dynamics that can be validated against patient responses for use as investigational surrogates. Crucially, simulation offers the potential to "look inside" the patient, allowing unimpeded access to all variables of interest. In contrast to trials on both animal models and human patients, in silico models are completely configurable and reproducible; for example, different ventilator settings can be applied to an identical virtual patient, or the same settings applied to different patients, to understand their mode of action and quantitatively compare their effectiveness. Here, we review progress on the mathematical modeling and computer simulation of human anatomy, physiology, and pathophysiology in the context of mechanical ventilation, with an emphasis on the clinical applications of this approach in various disease states. We present new results highlighting the link between model complexity and predictive capability, using data on the responses of individual patients with acute respiratory distress syndrome to changes in multiple ventilator settings. The current limitations and potential of in silico modeling are discussed from a clinical perspective, and future challenges and research directions highlighted.

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

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 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.

A Pilot Bench Study of Decision Support for Proportional Assist Ventilation

The purpose was to develop a bench setup for testing a decision support system (DSS) for proportional assist ventilation (PAV). The test setup was based on a patient simulator connected to a mechanical ventilator with the DSS measurement sensors connected to the respiratory circuit. A test case was developed with parameters of lung mechanics reflecting a patient with mild acute respiratory distress syndrome. Five experiments were performed starting at different levels of percentage support (%Supp) and continuing until the DSS advised to remain at current settings. Final advice ranged from %Supp of 50-70%, indicating some dependence of baseline level, but with resulting patient effort estimates indicating that this may not be clinically important. Further studies are required of test cases reflecting different patient types and in patients.

Determining the appropriate model complexity for patient-specific advice on mechanical ventilation

Mathematical physiological models can be applied in medical decision support systems. To do so requires consideration of the necessary model complexity. Models that simulate changes in the individual patient are required, meaning that models should have a complexity where parameters can be uniquely identified at the bedside from clinical data and where the models adequately represent the individual patient's (patho)physiology. This paper describes the models included in a system for providing decision support for mechanical ventilation. Models of pulmonary gas exchange, respiratory mechanics, acid-base, and respiratory control are described. The parameters of these models are presented along with the necessary clinical data required for their estimation and the parameter estimation process. In doing so, the paper highlights the need for simple, minimal models for application at the bedside, directed toward well-defined clinical problems.

Typical patterns of expiratory flow and carbon dioxide in mechanically ventilated patients with spontaneous breathing

Incomplete expiration of tidal volume can lead to dynamic hyperinflation and auto-PEEP. Methods are available for assessing these, but are not appropriate for patients with respiratory muscle activity, as occurs in pressure support. Information may exist in expiratory flow and carbon dioxide measurements, which, when taken together, may help characterize dynamic hyperinflation. This paper postulates such patterns and investigates whether these can be seen systematically in data. Two variables are proposed summarizing the number of incomplete expirations quantified as a lack of return to zero flow in expiration (IncExp), and the end tidal CO₂ variability (varETCO₂), over 20 breaths. Using these variables, three patterns of ventilation are postulated: (a) few incomplete expirations (IncExp < 2) and small varETCO₂; (b) a variable number of incomplete expirations (2 ≤ IncExp ≤ 18) and large varETCO₂; and (c) a large number of incomplete expirations (IncExp > 18) and small varETCO₂. IncExp and varETCO₂ were calculated from data describing respiratory flow and CO₂ signals in 11 patients mechanically ventilated at 5 levels of pressure support. Data analysis showed that the three patterns presented systematically in the data, with periods of IncExp < 2 or IncExp > 18 having significantly lower variability in end-tidal CO₂ than periods with 2 ≤ IncExp ≤ 18 (p < 0.05). It was also shown that sudden change in IncExp from either IncExp < 2 or IncExp > 18 to 2 ≤ IncExp ≤ 18 results in significant, rapid, change in the variability of end-tidal CO₂ p < 0.05. This study illustrates that systematic patterns of expiratory flow and end-tidal CO₂ are present in patients in supported mechanical ventilation, and that changes between these patterns can be identified. Further studies are required to see if these patterns characterize dynamic hyperinflation. If so, then their combination may provide a useful addition to understanding the patient at the bedside.