Advanced mechanical ventilation modes: design and computer simulations

An enhanced respiratory mechanics model based on double-exponential and fractional calculus

We address mathematical modelling of respiratory mechanics and put forward a model based on double-exponential and fractional calculus for parameter estimation, model simulation, and evaluation based on actual data. Our model has been implemented on a publicly available executable code with adjustable parameters, making it suitable for different applications. Our analysis represents the first application of fractional calculus and double-exponential modelling to respiratory mechanics, and allows us to propose a hybrid model fitting experimental data in different ventilation modes. Furthermore, our model can be used to study the mechanical features of the respiratory system, improve the safety of ventilation techniques, reduce ventilation damages, and provide strong support for fast and adaptive determination of ventilation parameters.

Numerical modeling and development of a dual lung simulator using partitioned fluid-structure interaction approach for ventilator testing

New designs of mechanical ventilators require extensive testing before utilizing the ventilator on a patient. Test lungs are commonly used to understand the behavior of new designs of ventilators and the lung mechanics. The current study aims to develop a numerical model of dual test lungs utilizing the partitioned fluid-structure interaction (FSI) approach and test it against the available experimental data of volume-controlled ventilation. Two breathing rates of 12 and 18 bpm were studied at two different tidal volumes of 500 and 600 ml for spontaneous breathing. It is found that with an increase in the compliance (tidal volume/pressure rise) of the lung, the peak pressure rise inside the test lung decreases irrespective of the breathing rate. The maximum average pressure of 44.73, 27.45, and 14 cm H₂ O is observed for static lung compliances of 10, 21 , and 39 ml/cm H₂ O, respectively at a tidal volume of 600 ml. Similarly, the maximum von-misses stress was higher (498 kPa) for the lung with lower compliance (10 ml/cm H₂ O) as compared to the lung with higher compliance (39 ml/cm H₂ O) at the end of inspiration. This study forms a basis for using computational methods to model simple lung simulators that can effectively investigate the lung mechanics for both spontaneous and ventilated breathing. Thus, it can be utilized as a reference to test novel designs of mechanical ventilators.

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.