Influence of flow pattern on the parameter estimates of a simple breathing mechanics model

Evaluation of Ventilation-Induced Lung Inflammation Through Multi-Scale Simulations

Ventilation-induced lung injury is a common problem faced by patients with respiratory problems who require mechanical ventilation (MV). This injury may lead to a greater chance of developing or exacerbating the acute respiratory distress syndrome which further complicates the therapeutic use of MV. The chain of events begins with the MV initiating an immune response that leads to inflammation induced tissue material alteration (stiffening) and eventually the loss of lung resistance. It is clear from this sequence of events that the phenomenon of ventilation induced injury is multi-scale by nature and, hence, requires holistic analysis involving simulations and informatics. An effective approach to this problem is to break it down into several major physical models. Each physical model is developed separately and can be seen as a component in a larger system that comprises the scale of the problem being investigated. In this paper, a multi-scale system consisting of breathing mechanics, tissue deformation, and cellular mechanics models is developed to assess the immune response. To demonstrate the potential of the model, a fluid–solid model is employed for breathing mechanics, a plane-strain elasticity model is applied to assess tissue deformation, and a cellular automata (CA) model is developed to account for immune response. A case study of three lower airways is presented. The CA model shows that this increased the immune response by five times, which correlates with alteration in the tissue microstructure. This alteration in turn is reflected in the material constant value obtained in the tissue mechanics model. However, the changes in strain rates in the airways after inflammation (and hence, lung compliance) were not as significant as the rates of change in immune response. Finally, results from the fluid–solid model demonstrate its potential for airflow characterization caused by tissue deformation that could lead to disease identification.

Real-time noninvasive estimation of intrapleural pressure in mechanically ventilated patients: a feasibility study

A method for real-time noninvasive estimation of intrapleural pressure in mechanically ventilated patients is proposed. The method employs a simple first-order lung mechanics model that is fitted in real-time to flow and pressure signals acquired non-invasively at the opening of the patient airways, in order to estimate lung resistance (RL), lung compliance (CL) and intrapleural pressure (Ppl) continuously in time. Estimation is achieved by minimizing the sum of squared residuals between measured and model predicted airway pressure using a modified Recursive Least Squares (RLS) approach. Particularly, two different RLS algorithms, namely the conventional RLS with Exponential Forgetting (EF-RLS) and the RLS with Vector-type Forgetting Factor (VFF-RLS), are considered in this study and their performances are first evaluated using simulated data. Simulations suggest that the conventional EF-RLS algorithm is not suitable for our purposes, whereas the VFF-RLS method provides satisfactory results. The potential of the VFF-RLS based method is then proved on experimental data collected from a mechanically ventilated pig. Results show that the method provides continuous estimated lung resistance and compliance in normal physiological ranges and pleural pressure in good agreement with invasive esophageal pressure measurements.

Nonlinear model for mechanical ventilation of human lungs

A complex nonlinear model for mechanical ventilation, its computer implementation and validation are presented. The model includes the morphometry-based symmetrical structure of the 23 airway generations, dynamic properties of the respiratory system, as well as the description of a ventilator. Distributed character of airway mechanical properties is taken into account when determining airway inertance, resistance and compliance, including turbulence of flow, airway collapsing and the wave speed theory. In effect, the airway parameters vary within the ventilatory cycle and their values are nonlinear functions of control signals. Results of simulations corresponding to normal conditions and airway narrowing are consistent with the published experimental data. The model enables investigations on how specific pathological changes influence the signals and physiological variables during mechanical ventilation, as well as testing known and developing new algorithms tracking time-variability of the respiratory parameters.

A Closed-Loop Mechanical Ventilation Controller With Explicit Objective Functions

A closed-loop lung ventilation controller was designed, aiming to: 1) track a desired end-tidal CO2 pressure (Pet CO2), 2) find the positive end-expiratory pressure (PEEP) of minimum estimated respiratory system elastance (Ers,e), and 3) follow objective functions conjectured to reduce lung injury. After numerical simulations, tests were performed in six paralyzed piglets. Respiratory mechanics parameters were estimated by the recursive least squares (RLS) method. The controller incorporated a modified PI controller for Pet CO2 and a gradient descent method for PEEP. In each animal, three automated PEEP control runs were performed, as well as a manual PEEP titration of Ers,e and a multiple PetCO2 step change trial. Overall performance indexes were obtained from PEEP control, such as minimum Ers,e (37.0 +/- 4.5 cmH2O x L(-1)), time to reach the minimum Ers,e (235 +/- 182 s) and associated PEEP (6.5 +/- 1.0 cmH2O), and from Pet CO2 control, such as rise time (53 +/- 22 s), absolute overshoot/undershoot of PetCO2 (3 +/- 1 mmHg), and settling time (145 +/- 72 s). The resulting CO2 controller dynamics approximate physiological responses, and results from PEEP control were similar to those obtained by manual titration. Multiple dependencies linking the involved variables are discussed. The present controller can help to implement and evaluate objective functions that meet clinical goals.

A program based on a 'selective' least-squares method for respiratory mechanics monitoring in ventilated patients

This paper proposes a program for continuous estimation of respiratory mechanics parameters in ventilated patients. This program can be used with any ventilator providing airway pressure and flow signals without additional equipment. Overall breathing resistance, dynamic elastance (E) and positive end expiratory pressure (P(0)) are periodically estimated by multiple linear regression on selected parts of breathing cycles. Experimental validation together with justification of the selection procedure are based on signals obtained while ventilating a lung mechanical analogue with various intensive care ventilators. Clinical validity has been tested on 12 ventilated patients. The quality of estimation has been assessed by mean square difference between measured and reconstituted pressure (MSE), coefficient of determination (R(2)) and the condition number (a confidence index), and by comparison of E and P(0) with corresponding static values. The high R(2) and the low MSE obtained on most clinical cycles indicate that selected parts of cycles obey closely the model underlying parameter estimation. Agreement between static and dynamic parameters demonstrates the clinical validity of our program.

Effect of compliant intermediate airways on total respiratory resistance and elastance in mechanical ventilation

Total respiratory resistance and elastance were estimated off-line in a sample of 60 patients undergoing mechanical ventilation by means of two regression models in order to analyse and understand a possible physiological mechanism determining differences in inspiration and expiration. The first model considered a single value for resistance and elastance over a whole breathing cycle, whereas the second model considered separate values for inspiratory and expiratory resistance and a single value for elastance. Inspiratory resistance was found to be lower than expiratory resistance, and intermediate values were obtained for resistance estimated over the whole breathing cycle. Student's t-test showed a highly significant difference between these resistance estimates, and principal components analysis demonstrated a significant increase in information when both inspiratory and expiratory resistances were used. Minor differences were found between values of elastance calculated with the two approaches. In an attempt to interpret these experimental results, a lung model incorporating the non-linear viscoelastic properties of the intermediate airways was considered. This model suggested that changes in intermediate airway volume play a significant role in breathing mechanics during artificial ventilation and indicated that inspiratory and expiratory resistance could be useful parameters for locating airway obstruction.

Estimating respiratory mechanics in the presence of flow limitation

Dynamic collapse of the pulmonary airways, leading to flow limitation, is a significant event in a number of respiratory pathologies, including obstructive sleep apnea syndrome and chronic obstructive pulmonary disease. Quantitative evaluation of the mechanical status of the respiratory system in these conditions provides useful insights into airway caliber and tissue stiffness, which are hallmarks of such abnormalities. However, assessing respiratory mechanics in the presence of flow limitation is problematic because the single-compartment linear model on which most assessment methods are based is not valid over the entire breath. Indeed, even deciding which parts of a breath are flow limited from measurement of mouth flow and pleural pressure often proves to be difficult. In this study, we investigated the use of two approaches to assessing the overall mechanical properties of the respiratory system in the presence of inspiratory flow limitation. The first method is an adaptation of the classic Mead-Whittenberger method, and the second method is based on information-weighted histograms obtained from recursively estimated signals of respiratory resistance and elastance. We tested the methods on data simulated by using a computer model of the respiratory system and on data collected from obese sleeping pigs. We found that the information-weighted histograms provided the more robust overall estimates of respiratory mechanics.

A new approach for tracking respiratory mechanical parameters in real-time

A new recursive least-squares procedure for on-line tracking of changes in viscoelastic properties of respiratory mechanics is proposed and applied to artificially ventilated patients. Classical least-squares methods based on simple first-order linear models with time-constant parameters generally provide systematic residuals that hardly satisfy standard statistical tests for model validation in terms of residuals. On the other hand, high order and/or nonlinear models introduce parameters whose estimates are of difficult interpretation in a clinical context. The present procedure overcomes these limitations by using the well-known first-order model of respiratory mechanics, wherein variability of resistance and elastance during the breathing cycle is allowed to take into account nonlinear and high-order behavior. Mean and standard deviation of resistance and elastance estimates, relative to a respiratory cycle, are then determined recursively. Feasibility of the method is evaluated by applying it both to experimental and simulated pressure-airflow signals measured in an intensive care unit during mechanical ventilation of patients recovering from heart surgery. Results demonstrate that the proposed procedure provides data description satisfying statistical tests, such as residual whiteness, and reliable estimates of viscoelastic lung parameters even during substantial and fast variations in the respiratory status. In addition, unlike classical methods, the new technique provides the means for on-line evaluation of parameter variability during each respiratory cycle, by the estimate of their standard deviations. This is important in clinical practice, because only the knowledge of reliable parameter values and standard deviations enables significant changes in the respiratory viscoelastic characteristics, and thus in patient status, to be assessed.