Real-time tracking of parameters of lung mechanics: emphasis on algorithm tuning

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.

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.

Oscillation mechanics of the respiratory system: applications to lung disease

Since its introduction in the 1950s, the forced oscillation technique (FOT) and the measurement of respiratory impedance have evolved into powerful tools for the assessment of various mechanical phenomena in the mammalian lung during health and disease. In this review, we highlight the most recent developments in instrumentation, signal processing, and modeling relevant to FOT measurements. We demonstrate how FOT provides unparalleled information on the mechanical status of the respiratory system compared to more widely used pulmonary function tests. The concept of mechanical impedance is reviewed, as well as the various measurement techniques used to acquire such data. Emphasis is placed on the analysis of lower, physiologic frequency ranges (typically less than 10 Hz) that are most sensitive to normal physical processes as well as pathologic structural alterations. Various inverse modeling approaches used to interpret alterations in impedance are also discussed, specifically in the context of three common respiratory diseases: asthma, chronic obstructive pulmonary disease, and acute lung injury. Finally, we speculate on the potential role for FOT in the clinical arena.

Respiratory impedance measurements for assessment of lung mechanics: focus on asthma

This review discusses the history and current state of the art of the forced oscillation technique (FOT) to measure respiratory impedance. We focus on how the FOT and its interaction with models have emerged as a powerful method to extract out not only clinically relevant information, but also to advance insight on the mechanisms and structures responsible for human lung diseases, especially asthma. We will first provide a short history of FOT for basic clinical assessment either directly from the data or in concert with lumped element models to extract out specific effective properties. We then spend several sections on the more exciting recent advances of FOT to probe the relative importance of tissue versus airway changes in disease, the impact of the disease on heterogeneous lung function, and the relative importance of small airways via synthesis of FOT with imaging. Most recently, the FOT approach has been able to directly probe airway caliber in humans and the distinct airway properties of asthmatics that seem to be required for airway hyperresponsiveness. We introduce and discuss the mechanism and clinical implications of this approach, which may be substantial for treatment assessment. Finally, we highlight important future directions for the FOT, particularly its use to probe specific lung components (e.g., isolated airways, isolated airway smooth muscle, etc.) and relate such data to the whole lung. The intent is to substantially advance an integrated understanding of structure-function relationships in the lung.

Selected contribution: airway caliber in healthy and asthmatic subjects: effects of bronchial challenge and deep inspirations

In 9 healthy and 14 asthmatic subjects before and after a standard bronchial challenge and a modified [deep inspiration (DI), inhibited] bronchial challenge and after albuterol, we tracked airway caliber by synthesizing a method to measure airway resistance (Raw; i.e., lung resistance at 8 Hz) in real time. We determined the minimum Raw achievable during a DI to total lung capacity and the subsequent dynamics of Raw after exhalation and resumption of tidal breathing. Results showed that even after a bronchial challenge healthy subjects can dilate airways maximally, and the dilation caused by a single DI takes several breaths to return to baseline. In contrast, at baseline, asthmatic subjects cannot maximally dilate their airways, and this worsens considerably postconstriction. Moreover, after a DI, the dilation that does occur in airway caliber in asthmatic subjects constricts back to baseline much faster (often after a single breath). After albuterol, asthmatic subjects could dilate airways much closer to levels of those of healthy subjects. These data suggest that the asthmatic smooth muscle resides in a stiffer biological state compared with the stimulated healthy smooth muscle, and inhibiting a DI in healthy subjects cannot mimic this.

Clinical relevance of monitoring respiratory mechanics in the ventilator-supported patient: an update (1995–2000)

The introduction of mechanical ventilation in the intensive care unit environment had the merit of putting a potent life-saving tool in the physicians' hands in a number of situations; however, like most sophisticated technologies, it can cause severe side effects and eventually increase mortality if improperly applied. Assessment of respiratory mechanics serves as an aid in understanding the patient-ventilator interactions with the aim to obtain a better performance of the existing ventilator modalities. It has also provided a better understanding of patients' pathophysiology. Thanks to it, new ventilatory strategies and modalities have been developed. Finally, on-line monitoring of respiratory mechanics parameters is going to be more than a future perspective.

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.

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.

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

The first-order model of breathing mechanics is widely used in clinical practice to assess the viscoelastic properties of the respiratory system. Although simple, this model takes the predominant features of the pressure-flow relationship into account but gives highly systematic residuals between measured and model-predicted variables. To achieve a better fit of the entire data set, an approach hypothesizing deterministic time-variations of model parameters, summarized by information-weighted histograms was recently proposed by Bates and Lauzon. The present study uses flow and pressure data measured in intensive care patients to evaluate the real potential of this approach in clinical practice. Information-weighted histograms of the model parameters, estimated by an on-line identification algorithm, were first constructed by taking into account the parameter percentage standard deviations. Then, the influence of the respiratory flow pattern on the calculated histograms was evaluated by the Kolmogorov-Smirnov statistical test. The results show that the method gives good reproducibility under stable experimental conditions. In addition, for a given airflow waveform, an increase in respiratory frequency shifts the histograms representing time-varying viscous properties strongly versus lower values, whereas it shifts the histograms representing time-varying elastic properties slightly versus higher values. On the other hand, the same histograms were highly dependent on the airflow waveform, especially for the viscous properties. Even in a limited experimental work, in all the conditions considered, the method provides results which agree well with the physiological knowledge of nonlinear and multicompartment behavior of respiratory mechanics.

Assessment of time-domain analyses for estimation of low-frequency respiratory mechanical properties and impedance spectra

Time-domain estimation has been invoked for tracking of respiratory mechanical properties using primarily a simple single-compartment model containing a series resistance (Rrs) and elastance (Ers). However, owing to the viscoelastic properties of respiratory tissues, Rrs and Ers exhibit frequency dependence below 2 Hz. The goal of this study was to investigate the bias and statistical accuracy of various time-domain approaches with respect to model properties, as well as the estimated impedance spectra. Particular emphasis was placed on establishing the tracking capability using a standard step ventilation. A simulation study compared continuous-time versus discrete-time approaches for both the single-compartment and two-compartment models. Data were acquired in four healthy humans and two dogs before and after induced severe pulmonary edema while applying sinusoidal and standard ventilator forcing. Rrs and Ers were estimated either by the standard Fast Fourier Transform (FFT) approach or by a time-domain least square estimation. Results show that the continuous-time model form produced the least bias and smallest parameter uncertainty for a single-compartment analysis and is quite amenable for reliable on-line tracking. The discrete-time approach exhibits large uncertainty and bias, particularly with increasing noise in the flow data. In humans, the time-domain approach produced smooth estimates of Rrs and Ers spectra, but they were statistically unreliable at the lower frequencies. In dogs, both the FFT and time-domain analysis produced reliable and stable estimates for Rrs or Ers spectra for frequencies out to 2 Hz in all conditions. Nevertheless, obtaining stable on-line parameter estimates for the two-compartment viscoelastic models remained difficult. We conclude that time-domain analysis of respiratory mechanics should invoke a continuous-time model form.

A nonstatistical approach to estimating confidence intervals about model parameters: application to respiratory mechanics

Estimates of parameters obtained by fitting models to physiologic data are of little use unless accompanied by confidence intervals. The standard methods for estimating confidence intervals are statistical, and make the assumption that the fitted model accounts for all the deterministic variation in the data while the residuals between the fitted model and the data reflect only stochastic noise. In practice, this is frequently not the case, as one often finds the residuals to be systematically distributed about zero. In this paper, we develop an approach for assessing confidence in a parameter estimate when the order of the model is clearly less than that of the system being modeled. Our approach does not require a parameter to have a single value located within a region of confidence. Instead, we let the parameter value vary over the data set in such a way as to provide a good fit to the entire data set. We apply our approach to the estimation of the resistance of the respiratory system in which a simple model is fitted to measurements of tracheal pressure and flow by recursive multiple linear regression. The values of resistance required to achieve a good fit are represented as a modified histogram in which the contribution of a particular resistance value to the histogram is weighted by the amount of information used in its determination. Our approach provides parameter frequency distribution functions that convey the degree of confidence one may have in the parameter, while not being based on erroneous statistical assumptions.