Parameter estimation of an artificial respiratory system under mechanical ventilation following a noisy regime

Esophageal balloon catheter system identification to improve respiratory effort time features and amplitude determination

Objective. Understanding a patient's respiratory effort and mechanics is essential for the provision of individualized care during mechanical ventilation. However, measurement of transpulmonary pressure (the difference between airway and pleural pressures) is not easily performed in practice. While airway pressures are available on most mechanical ventilators, pleural pressures are measured indirectly by an esophageal balloon catheter. In many cases, esophageal pressure readings take other phenomena into account and are not a reliable measure of pleural pressure.Approach.A system identification approach was applied to provide accurate pleural measures from esophageal pressure readings. First, we used a closed pressurized chamber to stimulate an esophageal balloon and model its dynamics. Second, we created a simplified version of an artificial lung and tried the model with different ventilation configurations. For validation, data from 11 patients (five male and six female) were used to estimate respiratory effort profile and patient mechanics.Main results.After correcting the dynamic response of the balloon catheter, the estimates of resistance and compliance and the corresponding respiratory effort waveform were improved when compared with the adjusted quantities in the test bench. The performance of the estimated model was evaluated using the respiratory pause/occlusion maneuver, demonstrating improved agreement between the airway and esophageal pressure waveforms when using the normalized mean squared error metric. Using the corrected muscle pressure waveform, we detected start and peak times 130 ± 50 ms earlier and a peak amplitude 2.04 ± 1.46 cmH2O higher than the corresponding estimates from esophageal catheter readings.Significance.Compensating the acquired measurements with system identification techniques makes the readings more accurate, possibly better portraying the patient's situation for individualization of ventilation therapy.

Alveolar Tissue Fiber and Surfactant Effects on Lung Mechanics—Model Development and Validation on ARDS and IPF Patients

Goal: Alveolar compliance is a main determinant of lung airflow. The compliance of the alveoli is a function of their tissue fiber elasticity, fiber volume, and surface tension. The compliance varies during respiration because of the nonlinear nature of fiber elasticity and the time-varying surface tension coating the alveoli. Respiratory conditions, like acute respiratory distress syndrome (ARDS) and idiopathic pulmonary fibrosis (IPF) affect fiber elasticity, fiber volume and surface tension. In this paper, we study the alveolar tissue fibers and surface tension effects on lung mechanics. Methods: To better understand the lungs, we developed a physiology-based mathematical model to 1) describe the effect of tissue fiber elasticity, fiber volume and surface tension on alveolar compliance, and 2) the effect of time-varying alveolar compliance on lung mechanics for healthy, ARDS and IPF conditions. Results: We first present the model sensitivity analysis to show the effects of model parameters on the lung mechanics variables. Then, we perform model simulation and validate on healthy non-ventilated subjects and ventilated ARDS or IPF patients. Finally, we assess the robustness and stability of this dynamic system. Conclusions: We developed a mathematical model of the lung mechanics comprising alveolar tissue and surfactant properties that generates reasonable lung pressures and volumes compared to healthy, ARDS, and IPF patient data.

Advanced mechanical ventilation modes: design and computer simulations

In this paper, three different advanced mechanical ventilation modes, pressure regulated volume control ventilation (PRVC), proportional assist ventilation (PAV), and adaptive support ventilation (ASV) are designed and simulated on the computer via MATLAB/Simulink. In the algorithms of advanced ventilation modes, a closed-loop control structure is used and recursive least squares method is considered for the estimation of respiratory mechanics. The designed algorithms are then applied to the human respiratory system model for the active and/or passive patient cases. Simulation results show that such algorithms can be designed and simulated on the computer successfully. In addition, the simulation environment helps us to understand the working principles of the advanced modes and to see the results such as ventilator waveforms, the effect of the parameter changes. Moreover, it also allows us to improve and test the algorithms and strategies quickly and efficiently.

Modelling approach to obtain regional respiratory mechanics using electrical impedance tomography and volume-dependent elastance model

OBJECTIVE

This paper presents a method for breath-by-breath estimation of regional respiratory mechanics without the need for special manoeuvres (such as inspiratory pause or low-flow inflation) using electrical impedance tomography (EIT) associated with pressure/airflow waveforms.

APPROACH

We developed a method to estimate regional parameters using the regional impedance fraction, by multiplying it by global flow and volume waveforms. A volume-dependent elastance model was used to obtain compliance, resistance, volume-independent (E ₁), and volume-dependent (E ₂) components. Three swine under invasive mechanical ventilation were used to assess internal consistency and illustrate potential applications of our method. One animal (case 1) was ventilated with a broad range of tidal volumes to compare the consistency between regional and global resistances and compliances. Two other animals (cases 2 and 3) had respiratory compliance decreased, respectively, by overdistension and collapse as quantified by x-ray computed tomography.

MAIN RESULTS

In case 1, derived global estimates obtained from the independent regional estimates were strongly associated with direct measurements of global mechanics (correlation coefficients of 0.9976 and 0.9981 for compliances and resistances, respectively), suggesting consistency of our modelling. In cases 2 and 3, the development of lung overdistension and collapse over time was captured by regional estimates.

CONCLUSIONS

Using EIT and pressure/airflow waveforms, regional respiratory parameters can be obtained cycle-by-cycle, refining lung function monitoring.

SIGNIFICANCE

The method allows real-time monitoring of regional parameters and their trends over time, which might be helpful to differentiate deterioration in lung compliance due to overdistension or collapse.