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

Continuous estimation of respiratory system compliance and airway resistance during pressure-controlled ventilation without end-inspiration occlusion

BACKGROUND

Assessing mechanical properties of the respiratory system (Cst) during mechanical ventilation necessitates an end-inspiration flow of zero, which requires an end-inspiratory occlusion maneuver. This lung model study aimed to observe the effect of airflow obstruction on the accuracy of respiratory mechanical properties during pressure-controlled ventilation (PCV) by analyzing dynamic signals.

METHODS

A Hamilton C3 ventilator was attached to a lung simulator that mimics lung mechanics in healthy, acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD) models. PCV and volume-controlled ventilation (VCV) were applied with tidal volume (VT) values of 5.0, 7.0, and 10.0 ml/kg. Performance characteristics and respiratory mechanics were assessed and were calibrated by virtual extrapolation using expiratory time constant (RCexp).

RESULTS

During PCV ventilation, drive pressure (DP) was significantly increased in the ARDS model. Peak inspiratory flow (PIF) and peak expiratory flow (PEF) gradually declined with increasing severity of airflow obstruction, while DP, end-inspiration flow (EIF), and inspiratory cycling ratio (EIF/PIF%) increased. Similar estimated values of Crs and airway resistance (Raw) during PCV and VCV ventilation were obtained in healthy adult and mild obstructive models, and the calculated errors did not exceed 5%. An underestimation of Crs and an overestimation of Raw were observed in the severe obstruction model.

CONCLUSION

Using the modified dynamic signal analysis approach, respiratory system properties (Crs and Raw) could be accurately estimated in patients with non-severe airflow obstruction in the PCV mode.

Hypothesis-driven modeling of the human lung-ventilator system: A characterization tool for Acute Respiratory Distress Syndrome research

Mechanical ventilation is an essential tool in the management of Acute Respiratory Distress Syndrome (ARDS), but it exposes patients to the risk of ventilator-induced lung injury (VILI). The human lung-ventilator system (LVS) involves the interaction of complex anatomy with a mechanical apparatus, which limits the ability of process-based models to provide individualized clinical support. This work proposes a hypothesis-driven strategy for LVS modeling in which robust personalization is achieved using a pre-defined parameter basis in a non-physiological model. Model inversion, here via windowed data assimilation, forges observed waveforms into interpretable parameter values that characterize the data rather than quantifying physiological processes. Accurate, model-based inference on human-ventilator data indicates model flexibility and utility over a variety of breath types, including those from dyssynchronous LVSs. Estimated parameters generate static characterizations of the data that are 50%-70% more accurate than breath-wise single-compartment model estimates. They also retain sufficient information to distinguish between the types of breath they represent. However, the fidelity and interpretability of model characterizations are tied to parameter definitions and model resolution. These additional factors must be considered in conjunction with the objectives of specific applications, such as identifying and tracking the development of human VILI.

Comparison of a micro-electro-mechanical system airflow sensor with the pneumotach in the forced oscillation technique

Purpose

This study supports the use of thin-film micro-electro-mechanical system (MEMS) airflow sensors in the forced oscillation technique.

Materials and methods

The study employed static testing using air flow standards and computer-controlled sound attenuations at 8 Hz. Human feasibility studies were conducted with a testing apparatus consisting of a pneumotach and thin-film MEMS air flow sensors in series. Short-time Fourier transform spectra were obtained using SIGVIEW software.

Results

Three tests were performed, and excellent correlations were observed between the probes. The thin-film MEMS probe showed superior sensitivity to higher frequencies up to 200 Hz.

Conclusion

The results suggest that lower-cost thin-film MEMS can be used for forced oscillation technique applications (including home care devices) that will benefit patients suffering from pulmonary diseases such as asthma, COPD, and cystic fibrosis.

Numerical simulation of volume-controlled mechanical ventilated respiratory system with 2 different lungs

Mechanical ventilation is a key therapy for patients who cannot breathe adequately by themselves, and dynamics of mechanical ventilation system is of great significance for life support of patients. Recently, models of mechanical ventilated respiratory system with 1 lung are used to simulate the respiratory system of patients. However, humans have 2 lungs. When the respiratory characteristics of 2 lungs are different, a single-lung model cannot reflect real respiratory system. In this paper, to illustrate dynamic characteristics of mechanical ventilated respiratory system with 2 different lungs, we propose a mathematical model of mechanical ventilated respiratory system with 2 different lungs and conduct experiments to verify the model. Furthermore, we study the dynamics of mechanical ventilated respiratory system with 2 different lungs. This research study can be used for improving the efficiency and safety of volume-controlled mechanical ventilation system.

Dynamic Characteristics of Mechanical Ventilation System of Double Lungs with Bi-Level Positive Airway Pressure Model

In recent studies on the dynamic characteristics of ventilation system, it was considered that human had only one lung, and the coupling effect of double lungs on the air flow can not be illustrated, which has been in regard to be vital to life support of patients. In this article, to illustrate coupling effect of double lungs on flow dynamics of mechanical ventilation system, a mathematical model of a mechanical ventilation system, which consists of double lungs and a bi-level positive airway pressure (BIPAP) controlled ventilator, was proposed. To verify the mathematical model, a prototype of BIPAP system with a double-lung simulators and a BIPAP ventilator was set up for experimental study. Lastly, the study on the influences of key parameters of BIPAP system on dynamic characteristics was carried out. The study can be referred to in the development of research on BIPAP ventilation treatment and real respiratory diagnostics.

Time-varying respiratory system elastance: a physiological model for patients who are spontaneously breathing

BACKGROUND

Respiratory mechanics models can aid in optimising patient-specific mechanical ventilation (MV), but the applications are limited to fully sedated MV patients who have little or no spontaneously breathing efforts. This research presents a time-varying elastance (E(drs)) model that can be used in spontaneously breathing patients to determine their respiratory mechanics.

METHODS

A time-varying respiratory elastance model is developed with a negative elastic component (E(demand)), to describe the driving pressure generated during a patient initiated breathing cycle. Data from 22 patients who are partially mechanically ventilated using Pressure Support (PS) and Neurally Adjusted Ventilatory Assist (NAVA) are used to investigate the physiology relevance of the time-varying elastance model and its clinical potential. E(drs) of every breathing cycle for each patient at different ventilation modes are presented for comparison.

RESULTS

At the start of every breathing cycle initiated by patient, E(drs) is < 0. This negativity is attributed from the E(demand) due to a positive lung volume intake at through negative pressure in the lung compartment. The mapping of E(drs) trajectories was able to give unique information to patients' breathing variability under different ventilation modes. The area under the curve of E(drs) (AUCE(drs)) for most patients is > 25 cmH2Os/l and thus can be used as an acute respiratory distress syndrome (ARDS) severity indicator.

CONCLUSION

The E(drs) model captures unique dynamic respiratory mechanics for spontaneously breathing patients with respiratory failure. The model is fully general and is applicable to both fully controlled and partially assisted MV modes.

Pressure dynamic characteristics of pressure controlled ventilation system of a lung simulator

Mechanical ventilation is an important life support treatment of critically ill patients, and air pressure dynamics of human lung affect ventilation treatment effects. In this paper, in order to obtain the influences of seven key parameters of mechanical ventilation system on the pressure dynamics of human lung, firstly, mechanical ventilation system was considered as a pure pneumatic system, and then its mathematical model was set up. Furthermore, to verify the mathematical model, a prototype mechanical ventilation system of a lung simulator was proposed for experimental study. Last, simulation and experimental studies on the air flow dynamic of the mechanical ventilation system were done, and then the pressure dynamic characteristics of the mechanical system were obtained. The study can be referred to in the pulmonary diagnostics, treatment, and design of various medical devices or diagnostic systems.

Comparing surrogates of oxygenation and ventilation between airway pressure release ventilation and biphasic airway pressure in a mechanical model of adult respiratory distress syndrome

BACKGROUND

No objective data directly comparing the 2 modes are available. Based on a simple mathematical model, APRV and BIPAP can presumably be set to achieve the same mean airway pressure (mPaw), end expiratory pressure, and tidal volume (V(T)). Herein, we tested this hypothesis when using a real ventilator and clinically relevant settings based on expiratory time constants.

METHODS

A spontaneously breathing acute respiratory distress syndrome patient was modeled with a lung simulator. Mode settings: P high and the number of releases were the same in both modes; T low=1 time constant in APRV (expected auto-positive end-expiratory pressure [PEEP], ≈9 cmH(2)O) and 5 time constants in BIPAP; P low, 0 cmH(2)O in APRV and 9 cmH(2)O in BIPAP (equal to the expected auto-PEEP in APRV). The mean mandatory release volumes, minute ventilation [V(E)], mPaw, and total PEEP were compared with t-tests using a P value of 0.05 to reject the null hypothesis.

RESULTS

APRV yielded significantly higher mPaw than did BIPAP. Minute ventilation was significantly higher in BIPAP. The total PEEP was significantly higher in APRV; the total PEEP was significantly higher than expected.

CONCLUSION

We found that neither mode was superior to the other, and that a real ventilator does not behave like a mathematical model. Extreme prolongation of T high generated a higher mPaw at the expense of V(E), and vice versa. The lower V(T) with APRV was due to the higher total PEEP, which was higher than expected. Setting the T low according to the respiratory system time constant for either mode resulted in an unpredictable total PEEP.

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.

Leakage estimation using Kalman filtering in noninvasive mechanical ventilation

Noninvasive mechanical ventilation is today often used to assist patient with chronic respiratory failure. One of the main reasons evoked to explain asynchrony events, discomfort, unwillingness to be treated, etc., is the occurrence of nonintentional leaks in the ventilation circuit, which are difficult to account for because they are not measured. This paper describes a solution to the problem of variable leakage estimation based on a Kalman filter driven by airflow and the pressure signals, both of which are available in the ventilation circuit. The filter was validated by showing that based on the attained leakage estimates, practically all the untriggered cycles can be explained.

Online estimation of respiratory mechanics in non-invasive pressure support ventilation: a bench model study

An online algorithm for determining respiratory mechanics in patients using non-invasive ventilation (NIV) in pressure support mode was developed and embedded in a ventilator system. Based on multiple linear regression (MLR) of respiratory data, the algorithm was tested on a patient bench model under conditions with and without leak and simulating a variety of mechanics. Bland-Altman analysis indicates reliable measures of compliance across the clinical range of interest (± 11-18% limits of agreement). Resistance measures showed large quantitative errors (30-50%), however, it was still possible to qualitatively distinguish between normal and obstructive resistances. This outcome provides clinically significant information for ventilator titration and patient management.

Estimating intratidal nonlinearity of respiratory system mechanics: a model study using the enhanced gliding-SLICE method

In the clinical situation and in most research work, the analysis of respiratory system mechanics is limited to the estimation of single-value compliances during static or quasi-static conditions. In contrast, our SLICE method analyses intratidal nonlinearity under the dynamic conditions of mechanical ventilation by calculating compliance and resistance for six conjoined volume portions (slices) of the pressure-volume loop by multiple linear regression analysis. With the gliding-SLICE method we present a new approach to determine continuous intratidal nonlinear compliance. The performance of the gliding-SLICE method was tested both in computer simulations and in a physical model of the lung, both simulating different intratidal compliance profiles. Compared to the original SLICE method, the gliding-SLICE method resulted in smaller errors when calculating the compliance or pressure course (all p < 0.001) and in a significant reduction of the discontinuity error for compliance determination which was reduced from 12.7 +/- 7.2 cmH(2)O s L(-1) to 0.8 +/- 0.3 cmH(2)O s L(-1) (mathematical model) and from 7.2 +/- 3.9 cmH(2)O s L(-1) to 0.4 +/- 0.2 cmH(2)O s L(-1) (physical model) (all p < 0.001). We conclude that the new gliding-SLICE method allows detailed assessment of intratidal nonlinear respiratory system mechanics without discontinuity error.

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.

On-line monitoring of intrinsic PEEP in ventilator-dependent patients

Measurement of the intrinsic positive end-expiratory pressure (PEEP(i)) is important in planning the management of ventilated patients. Here, a new recursive least squares method for on-line monitoring of PEEP(i) is proposed for mechanically ventilated patients. The procedure is based on the first-order model of respiratory mechanics applied to experimental measurements obtained from eight ventilator-dependent patients ventilated with four different ventilatory modes. The model PEEP(i) (PEEP(i,mod)) was recursively constructed on an inspiration-by-inspiration basis. The results were compared with two well-established techniques to assess PEEP(i): end-expiratory occlusion to measure static PEEP(i) (PEEP(i, st)) and change in airway pressure preceding the onset of inspiratory airflow to measure dynamic PEEP(i) (PEEP(i,dyn)). PEEP(i, mod) was significantly correlated with both PEEP(i,dyn) (r = 0.77) and PEEP(i,st) (r = 0.90). PEEP(i,mod) (5.6 +/- 3.4 cmH(2)O) was systematically >PEEP(i,dyn) and PEEP(i,st) (2.7 +/- 1.9 and 8.1 +/- 5.5 cmH(2)O, respectively), in all the models without external PEEP. Focusing on the five patients with chronic obstructive pulmonary disease, PEEP(i,mod) was significantly correlated with PEEP(i,st) (r = 0.71), whereas PEEP(i,dyn) (r = 0.22) was not. When PEEP was set 5 cmH(2)O above PEEP(i,st), all the methods correctly estimated total PEEP, i.e., 11.8 +/- 5.3, 12.5 +/- 5.0, and 12.0 +/- 4.7 cmH(2)O for PEEP(i,mod), PEEP(i,st), and PEEP(i,dyn), respectively, and were highly correlated (0.97-0.99). We interpreted PEEP(i,mod) as the lower bound of PEEP(i,st) and concluded that our method is suitable for on-line monitoring of PEEP(i) in mechanically ventilated patients.