Model iterative airway pressure reconstruction during mechanical ventilation asynchrony: Shapes and sizes of reconstruction

Yeong Shiong Chiew1[0000-0003-3222-2006], Chee Pin Tan1, J. Geoffrey Chase2, Yeong Woei Chiew3, Christopher Pretty2, Thomas Desaive4, Azrina Md Ralib5 and Mohd Basri Mat5

1 Monash University Malaysia, 47500 Subang Jaya, Selangor, Malaysia

2 University of Canterbury, Christchurch 8041, New Zealand

3 Lam Wah Ee Hospital, 11600 George Town, Pulau Pinang, Malaysia

4 University of Liege, 4000 Liege, Belgium

5 International Islamic University Malaysia, 25200 Kuantan, Pahang, Malaysia
chiew.yeong.shiong@monash.edu

Abstract. Model-based methods estimating patient-specific respiratory mechanics may help intensive care clinicians in setting optimal ventilation parameters. However, these methods rely heavily on the quality of measured airway pressure and flow profiles for reliable respiratory mechanics estimation. Thus, asynchronous and/ or spontaneous breathing cycles that do not follow a typical passive airway profile affect the performance and reliability of model-based methods. In this study, a model iterative airway pressure reconstruction method is presented. It aims to reconstruct a measured airway pressure affected by asynchronous breathing iteratively, trying to match the profile of passive breaths with no asynchrony or spontaneous breathing effort. Thus, reducing the variability of identified respiratory mechanics over short time periods where changes would be due only to asynchrony or spontaneous artefacts. A total of 2000 breathing cycles from mechanically ventilated patients with known asynchronous breathing were analyzed. It was found that this method is capable of reconstructing an airway pressure free from asynchronous or spontaneous breathing effort. This work focuses on several cases, detailing how iterative pressure reconstruction method performs under different cases, as well as its limitation.

Keywords: Mechanical Ventilation, Spontaneous Breathing, Asynchrony, Airway Pressure Reconstruction.

Introduction

Application of model-based methods in the intensive care environment has become increasingly popular [1, 2]. It aims to provide personalized care based on patient-specific condition and patient-specific response to treatment [3-5]. Similar trends have also emerged in mechanical ventilation (MV) treatment for respiratory failure patients. These model-based methods use mathematical models to describe and estimate patient-specific respiratory conditions in real-time, as identified from clinical data, and are able to provide useful new insight to attending clinicians in order to devise suitable, more patient-specific treatment.

In particular, these model-based methods are able to estimate respiratory system parameters such as respiratory system elastance (1/compliance), airway resistance, impedance, reactance, time constant decay, alveoli threshold opening pressures, viscoelasticity, alveoli recruitment, perfusion etc; these information can then be used by clinicians to optimize MV settings. However, currently, accurate estimation of patient-specific respiratory system parameters relies on specialized clinical protocols that require patients to be sedated and paralyzed during data acquisition. Some patients are required to undergo protocols such as staircase recruitment maneuvers [6], super syringe method [7], and/ or low flow maneuver [8], all of which are added invasive, care interrupting protocols that also consume nursing time and effort. They are thus not ideal.

Sedation and paralysis during MV is given partly to reduce patient’s discomfort, as well as to reduce asynchronous events [9]. When the patient is synchronized with the ventilator, data acquisition is more consistent, the airway pressure and flow are uninterrupted, and, the respiratory system parameters can be accurately estimated [10, 11]. Without patient-ventilator synchronization, the data are altered by the patient’s spontaneous and unpredictable breathing effort, leading to inaccurate parameter identification [12]. In addition, frequent use of sedation and paralysis drugs may also affect patient recovery time and increase ventilator dependency, worsening patient outcomes [13]. Thus, there is an increasing need for research to model and/or to account for the effects of asynchronous breathing or spontaneous breathing (SB) to enhance model-based respiratory mechanics estimation [10, 14, 15].

In this study, the authors investigate an airway pressure reconstruction method for airway pressure profiles altered by asynchronous or SB. This reconstruction method, model iterative pressure reconstruction (MIPR), attempts to model the asynchronous breathing cycle to a non-asynchronous cycle using a single compartment lung model. In particular, this study presents the airway pressure reconstruction algorithm, demonstrating how pressure reconstruction can be performed on several irregular airway pressures and analyses their performance and effect on respiratory mechanics estimation.

Methods

Patient Data

Retrospective data from respiratory failure patients from the CURE pilot study were used [16]. Patients were ventilated using Puritan Bennet 840 ventilator, using synchronous intermittent mandatory ventilation (SIMV) volume controlled mode (tidal volume = 6-8 ml/kg). Patients with clinically observed ventilation asynchrony were selected. As patients do not experience asynchronous breathing deliberately, these datasets provide a wide range of asynchronous breaths in shape and size of asynchrony to test the performance of the pressure reconstruction method. All data were sampled at 50 Hz and processed using MATLAB (R2014b, The Mathworks, Natick, MA, USA).

Model Iterative Pressure Reconstruction (MIPR)

A 3-step iterative pressure reconstruction (MIPR) method is developed, incorporating methods proposed by Damanhuri et al. [17] and Newberry et al. [18], where the maximum original airway pressure and model fit pressure are used to improve airway pressure reconstruction. Fig. 1 shows the sequence of how airway pressure of an asynchronous breathing cycle is reconstructed iteratively to create an unaffected ‘non-asynchronous’ cycle using MIPR.

Step 1: Model fit to original inspiratory airway pressure:

The inspiratory airway pressure of an asynchronous breathing cycle fitted using a single compartment linear lung model [19, 20]. The model is defined as an elastic compartment with a connecting airway as shown in Equation (1):

Paw(t) = ErsV(t) + RrsQ(t) + P0	(1)

Paw is the airway pressure, t is time, Ers is the respiratory system elastance, V ishe air volume, Rrs is the respiratory system airway resistance, Q is the flow and P0 is the offset pressure. Fig. 1a shows the model fit with asynchronous data.

Step 2: New maximum airway pressure:

The reconstruction of the new airway pressure is performed using the intersection between the original airway pressure and model fitted pressure. This reconstructed airway pressure begins to ‘fill’ the empty space caused by asynchronous breathing as shown in Fig. 1b. This reconstructed airway pressure is then used as a new airway pressure for subsequent model fitting as shown in Fig. 1c.

Step 3: Iterative pressure reconstruction:

Pressure reconstruction is performed iteratively following Figs. 1d to 1g, where Fig. 1g shows all iterations together, illustrating how the pressure profile becomes ‘normal’ and unaffected by asynchrony. The pressure reconstruction process stops after 20 iterations. Fig. 1g shows all 20 iterations with the final reconstructed airway pressure located at the top-most of the pressure reconstruction curves. Fig. 1h shows a typical normal airway pressure not affected by asynchrony.

In this study, several different shapes and magnitude of asynchronous airway pressure profiles are reconstructed to highlight the details and performance of the MIPR.

a.	b.	c.	d.
e.	f.	g.	h.

Fig. 1. A figure caption is always placed below the illustration. Short captions are centered, while long ones are justified. The macro button chooses the correct format automatically.

Data Analysis

Respiratory Mechanics

The airway pressure generated by MIPR and the original airway pressure are used for respiratory mechanics estimation, yielding parameters Ers and Rrs. Estimated parameters are compared between the original and reconstructed profiles. A student t-test is used to test the difference of Ers distribution, with p-value < 0.05 considered significant. This analysis determines if the MIPR method provides a measurable change in the identified respiratory properties.

Coefficient of Variation and Spread Analysis

The MIPR performance in estimating respiratory system parameters is assessed using variation and spread analysis. The coefficient of variation (CV) and Range90 are calculated for the estimated parameters using the reconstructed and the original pressure profiles. CV is calculated as the mean absolute difference (MAD) of the parameters divided by the mean. Range90 is calculated as the difference between the 95th percentile and the 5th percentile of the parameter distribution [21]. Higher CV and Range90 values indicate higher variability, and lower values indicate less variability which is desirable over short periods given no other changes in care of patient condition. Thus, lower or reduced values indicate the success of the method in accounting for asynchrony.

Results and Discussion

Case 1: A Typical Pressure Reconstruction

Fig. 2 shows a typical successful pressure reconstruction. The observed shape and magnitude of asynchronous breathing is higher compared to Fig. 1, and the MIPR method is capable of reconstructing the airway pressure to an airway pressure free of asynchronous breathing. In each iteration, the intersection and overlaying of model fit and the original airway pressure (solid line) provides a new airway pressure profile for model identification. As it reconstructs iteratively, the error between the model fit and reconstructed pressure profile decreases, resulting in a non-asynchronous airway pressure profile.

a.	b.	c.	d.

Fig. 2. Pressure reconstruction performed iteratively, resulting in a non-asynchronous airway pressure

Case 2: Late Asynchronies

In some cases, patient induced asynchronous breathing occurs at the end of the breath, which cannot be reconstructed using the typical pressure reconstruction method. In particular, the MIPR method fails due to the amount of ‘missing data’ at the end of inspiration. Figs. 3a-d shows a sample of failed pressure reconstruction due to late asynchronies. The reconstruction process depends on the quality of model fitting using linear regression where it attempts to best fit a passive model to data. However, if most data are ‘incorrect’, the first step of pressure reconstruction will not provide a ‘correct trend’ for the iterative reconstruction process.

In these cases of late asynchronies, modification of the MIPR algorithm is required. The MIPR method is thus modified to include a pressure filling method as proposed by Damanhuri, Chiew [17], where the end of inspiratory pressure should at least be similar to the maximum observed airway pressure. Similarly, any airway pressure points that is in between the end of inspiratory pressure and the observed peak will be ‘filled’ with an estimated maximum airway pressure as shown in Figs. 3e-h. From this modified starting point, the MIPR method is used to obtain an unaffected final airway pressure.

a.	b.	c.	d.
e.	f.	g.	h.

Fig. 3. a-d) Failed pressure reconstruction due to missing of end of inspiratory pressure data. e-h) Modified pressure reconstruction with pressure filling.

Case 3: Early Asynchrony

Another potential anomaly in inspiratory airway pressure is the early patient breathing asynchrony or spontaneous breath triggering. This condition occurs when the initial pressure step increase due to the step or ramp airway flow increase is not observed. The increase in airway pressure is an important feature during parameter identification. If it is missing, model fitting fails. During model fitting of the original airway pressure, this lack of the pressure increase results in non-physiological respiratory mechanics parameters, such as negative elastance or resistance, which are impossible.

In some instances, this negative value captures patient triggering effort as an active external source, resulting in negative elastance or resistance [22]. However, that is not the case in this paper, where the airway pressure reconstruction has to assume a positive value. Hence, the reconstruction algorithm will force a positive respiratory elastance and airway resistance for model fit. The minimum parameter used in this study were Ers = 5cmH2O/l and Rrs = 5cmH2Os/l. These minimal respiratory parameter values give a baseline feature of the modelled airway pressure, resulting in better reconstruction. Fig. 4 shows an example of early asynchrony, and how the iterative pressure reconstruction caters for this form of asynchrony or spontaneous breath.

a.	b.	c.	d.

Fig. 4. Early asynchrony pressure reconstruction. a) The airway pressure does not show pressure increase as start of breathing cycle due to early asynchrony. b) Pressure reconstruction for the first iteration. c) New model fit using reconstructed pressure. d) Complete pressure reconstruction.

Respiratory Mechanics and Variation Analysis

In this study, a total of 2000 breathing cycles were analyzed. The values of Ers and Rrs estimated from the original airway pressure are 24.0 cmH2O/l [interquartile range (IQR): 20.5-28.5] and 10.9 cmH2Os/l [IQR: 10.1-11.8]. The estimated Ers and Rrs after pressure reconstruction are 28.6 cmH2O/l [IQR: 26.6-32.1] and 10.9 cmH2Os/l [IQR: 10.4-11.5]. Notice that the Ers estimated from reconstructed airway pressure is significantly higher than the original (p<0.05); this result is expected, where, the MIPR reconstruct the asynchronous airway pressure to a higher value, thus, resulting in higher Ers given the same tidal volume. No significant difference was found in the estimated Rrs.

CV and Range90 for Ers after reconstruction fell from 0.3022 and 41.5cmH2O/l to 0.1698 and 23.5 cmH2O/l. Similarly, the CV and Range90 for Rrs, fell from 0.1567 and 8.7 cmH2Os/l to 0.0949 and 4.8 cmH2Os/l. These results show MIPR is able to provide a more consistent respiratory mechanics estimation, reducing the variability caused by spontaneous and asynchronous breathing, matching expectations of minimal change over the periods analyzed.

Discussion

The MIPR iteratively generates a maximal simulated airway pressure that is free from the patient-specific breathing effort. This simulated airway pressure follows the single compartment linear lung model, and is able to model the likely, underlying passive patient pressure profile, resulting in the identification of respiratory mechanics as if there is no asynchronous and/ or spontaneous breathing effort. However, it is important to note that this method may not necessarily reconstruct the airway pressure profile to its original, unaffected state; this limitation is due to the fact that the asynchronous affected pressure may have its underlying pressure profile altered. As seen in Fig. 1g and h, the reconstructed pressure may have lower peak airway pressure compared to other normal breathing cycles, and thus may not necessarily be perfect, although there is no way to be certain if the peak pressures that results are within natural breath-to-breath variability, as seen here.

In addition, there is no means to obtain a direct measurement to compare the reconstructed pressure. Thus, there is no way of knowing the exact airway pressure profile without a time machine. Invasive measuring tools or specific clinical protocols that requires sedation and paralysis may provide an insight to the original airway pressure. However, as noted in Redmond et al. [23] and Bibiano et al. [24] patient-specific respiratory mechanics are protocol dependent, affecting the absolute values of the respiratory mechanics. Thus, only a general trend can be derived from these values and there may be no ‘absolute value’ to be compared with.

In this study, the MIPR estimated airway pressure is compared with a normal breathing cycle. This normal unaffected breathing cycle, selected when there is no apparent anomaly in the pressure/ flow profile. It occurs within a short time frame before or after the MIPR breathing cycle. It is assumed that within a short time frame, if there is no significant changes in clinical protocol or MV settings, the patient-specific respiratory mechanics should be within a specific variation [25]. It thus provides a good comparison between the respiratory mechanics of MIPR to a normal breathing cycle, as long as this assumption holds.

Comparing the results with and without MIPR, the coefficient of variation for MIPR is lower than without. A higher variability during volume controlled ventilation indicates asynchronous breathing, resulting in misidentification of respiratory mechanics. This result also suggests that MIPR is also able to estimate the expected, consistent respiratory mechanics in the presence of asynchrony. The CV and Range90 results reflect this outcome

In this study, the MIPR is stopped after 20 iterations of pressure reconstruction; this number is chosen arbitrarily based on experience with the algorithm on this dataset. In future, a convergence criteria could be determined using the sum of square error (SSE) between the reconstructed airway pressures between each successive iterations of model fitted airway pressure. If the SSE between the model fitting and the reconstructed pressure, does not vary by more than a threshold between successive iterations for 5 consecutive times, convergence is deemed to be met.

Conclusion

The iterative pressure reconstruction (MIPR) method is shown to be effective and robust for the wide variety of cases seen in this study. It was found that there is significant changes in the model-estimated Ers, and the estimated parameters are more consistent with lower variability. Thus, this algorithm offers potential improvements for real-time respiratory mechanics estimation. It is however, with limitations. Thus, pressure reconstruction should be monitored carefully, and the application and its performance warrants further investigations.

Acknowledgement

The authors would like to thank the Health Research Council of New Zealand (HRC) (Grant Number: 13/213) and the Ministry of Higher Education Malaysia (MOHE) Fundamental research grant scheme (FRGS) (Ref: FRGS/1/2016/TK03/MUSM/03/2) for funding this research

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