Parameters for Simulation of Adult Subjects During Mechanical Ventilation
=========================================================================

* Jean-Michel Arnal
* Aude Garnero
* Mathieu Saoli
* Robert L Chatburn

## Abstract

**BACKGROUND:** Simulation studies are often used to examine ventilator performance. However, there are no standards for selecting simulation parameters. This study collected data in passively-ventilated adult human subjects and summarized the results as a set of parameters that can be used for simulation studies of intubated, passive, adult subjects with normal lungs, COPD, or ARDS.

**METHODS:** Consecutive adult patients admitted to the ICU were included if they were deeply sedated and mechanically ventilated for <48 h without any spontaneous breathing activity. Subjects were classified as having normal lungs, COPD, or ARDS. Respiratory mechanics variables were collected once per subject. Static compliance was calculated as the ratio between tidal volume and driving pressure. Inspiratory resistance was measured by the least-squares fitting method. The expiratory time constant was estimated by the tidal volume/flow ratio.

**RESULTS:** Of the 359 subjects included, 138 were classified as having normal lungs, 181 as ARDS, and 40 as COPD. Median (interquartile range) static compliance was significantly lower in ARDS subjects as compared with normal lung and COPD subjects (39 [32–50] mL/cm H2O vs 54 [44–64] and 59 [43–75] mL/cm H2O, respectively, *P* < .001). Inspiratory resistance was significantly higher in COPD subjects as compared with normal lung and ARDS subjects (22 [16–33] cm H2O/L/s vs 13 [10–15] and 12 [9–14] cm H2O/L/s, respectively, *P* < .001). The expiratory time constant was significantly different for each lung condition (0.60 [0.51–0.71], 1.07 [0.68–2.14], and 0.46 [0.40–0.55] s for normal lung, COPD, and ARDS subjects, respectively, *P* < .001). In the subgroup of subjects with ARDS, there were no significant differences in respiratory mechanics variables among mild, moderate, and severe ARDS.

**CONCLUSIONS:** This study provides educators, researchers, and manufacturers with a standard set of practical parameters for simulating the respiratory system's mechanical properties in passive conditions.

*   medical simulation
*   critical care
*   artificial respiration
*   respiratory mechanics
*   ARDS
*   COPD
*   lung compliance
*   airway resistance

## Introduction

Simulation studies are often used to examine ventilator performance because models of the respiratory system are much easier to understand and experiment with than real respiratory systems (either human or animal). According to the Society for Simulation in Healthcare, “Simulation is the imitation or representation of one act or system by another. Healthcare simulations can be said to have four main purposes—education, assessment, research, and health system integration in facilitating patient safety.”1 Implicit in the concept of simulation is the understanding that the parameters of the simulation reflect realistic values of the system under study. If the model does not accurately represent the system being simulated, then any conclusions about the how the real system behaves are suspect.1 More importantly, models can be created that do not vary with time or with ventilator settings, so that the differences observed in measurements are presumed to be related only to performance differences among the ventilators in the study.

In the past, studies reporting ventilator performance2,3 often used some version of the Michigan Instruments Training and Test Lung simulator. This device has 1 or 2 spring-loaded bellows to model compliance (adjustable spring tension varies compliance values) connected in series with parabolic flow resistors. The Training and Test Lung is a passive device. To simulate inspiratory effort (muscle pressure [Pmus] in the equation of motion), researchers have improvised by linking the 2 bellows and using one to drive the other by connecting it to a separate ventilator.4 Thus, the larger the tidal volume and the higher the inspiratory flow of the drive ventilator, the higher the simulated inspiratory effort. Unfortunately, this procedure limits the shape of the Pmus(t) to the available flow shapes from the drive ventilator, which are quite limited. More recent studies5–15 have used more sophisticated devices that allow high-fidelity simulation of both lung model (ie, resistance and compliance) and effort model (Pmus waveform).

As suggested above, studies simulating the mechanical properties of the respiratory system are common. Unfortunately, although international standards for ventilator performance testing (for manufacturers) are available ([https://www.iso.org/standard/51141.html](https://www.iso.org/standard/51141.html), *Accessed July 29, 2017*), there are no commonly accepted, clinically relevant standards for selecting simulation parameters that are evidence-based and linked to particular lung disease states (eg, normal, COPD, ARDS) or patient populations (eg, adult vs child vs infant). As a result, researchers typically pick values that seem reasonable to them, generally without regard to actual human data. This makes comparison of results among studies of ventilator performance difficult, and it makes meta-analysis impossible. Therefore, the purpose of this paper was to gather data on a large number of ventilated adult human subjects and to summarize the results as a set of parameters (grouped by lung disease) that can be used to construct standardized patients for simulation studies of intubated, passive patients (ie, all breaths are machine-triggered). In particular, we provide standardized values for respiratory system resistance and compliance that can be used in the equation of motion (either in physical or mathematical models). These values are grouped by lung condition: normal; COPD; and mild, moderate or severe ARDS.

### QUICK LOOK

#### Current knowledge

Although international standards for ventilator performance testing are available, there are no commonly accepted, clinically relevant standards for selecting simulation parameters that are evidence-based and linked to particular lung disease states.

#### What this paper contributes to our knowledge

This study provides educators, researchers, and manufacturers with standardized values for respiratory system resistance and compliance that can be used in the equation of motion (either in physical or mathematical models) for simulating the respiratory system's mechanical properties according to lung condition: normal; COPD; and mild, moderate, or severe ARDS.

## Methods

This prospective, observational, comparative study was conducted from June 2015 to November 2016 in the 16-bed general adult ICU of Sainte Musse Hospital in Toulon, France. The institutional review board approved the protocol, which was also declared at the Commission Nationale Informatique et Liberté. According to French regulation, signed consent was waived, and each patient's next of kin was informed by a document explaining the study and could refuse the patient's participation.

### Subjects

Consecutive adult patients admitted in the ICU were included if they were deeply sedated (Richmond Agitation-Sedation Scale −4 or −5), were mechanically ventilated for <48 h without any spontaneous breathing activity, and met criteria for a single lung condition (normal lungs, COPD, or ARDS). Patients were excluded in case of pregnancy, body mass index >30 kg/m2, severe hemodynamic impairment, bronchopleural fistula, brain death, or a mix of lung conditions (eg, COPD with ARDS). Sedation and myorelaxant were prescribed by the physician in charge of the subject, combining either midazolam or propofol with sufentanil, and cisatracurium were used. Subjects were positioned semirecumbently (30–45°) and mechanically ventilated using a Hamilton-S1 ventilator (Hamilton Medical, Rhäzüns, Switzerland).A heat-and-moisture exchanger or a heated humidifier was used for gas conditioning.

Subjects were classified according to their lung condition as having normal lungs, COPD, or ARDS. Normal lungs was selected for subjects with no underlying respiratory disease, normal chest radiography on the inclusion day, and PaO2/FIO2 ratio of ≥300 mm Hg; ARDS was defined according to the Berlin definition with an arterial blood gas measured at a minimum of 5 cm H2O of PEEP;16 COPD was defined using Global Initiative for Chronic Obstructive Lung Disease criteria.17

### Ventilator Settings

All subjects were ventilated with the mode called INTELLiVENT-ASV.18 Tidal volume (VT) was automatically controlled to be in the range of 5–8 mL/kg predicted body weight for ARDS subjects, 6–9 mL/kg predicted body weight for normal lung subjects, and 7–10 mL/kg predicted body weight for COPD subjects. PEEP was automatically set in the range of 5–8 cm H2O for normal lung subjects and 5–10 cm H2O for COPD subjects, according to the PEEP-FIO2 table implemented in INTELLiVENT-ASV, which is derived from the ARDSnet tables.19 Plateau pressure was limited at 30 cm H2O for all subjects.

### Measurements, Data Collection, and Calculations

Airway pressure and flow were measured using the ventilator's proximal pneumotachograph (single-use flow sensor, PN 279331, Hamilton Medical, linear between −120 and 120 L/min with a ±5% error) inserted between the endotracheal tube and the Y-piece. When a heat-and-moisture exchanger was used, it was positioned distally to the sensor, so that measurements took into account the resistance of the heat-and-moisture exchanger. Volume was obtained by integration of the flow signal.

Respiratory mechanics variables and ventilator settings were collected once per subject at the exact time of an arterial blood gas measurement. The time of data collection was chosen to be apart from nursing care and medical procedures.

Static compliance was calculated as the ratio between VT and driving pressure. Driving pressure was calculated as the difference between plateau pressure and total PEEP measured by a 5-s end-inspiratory and end-expiratory occlusion, respectively.20 Because subjects were ventilated in pressure control mode (with an exponential decay of inspiratory flow waveform), inspiratory resistance was measured by the least squares fitting method over the full respiratory cycle.21 The expiratory time constant was estimated by the VT/flow ratio at 75% of the expiratory VT.22

### Statistical Methods

To perform subgroup analysis, the study was planned to stop after the inclusion of at least 100 normal lung subjects, 30 ARDS subjects for each severity subgroup, and 40 COPD subjects. Values are expressed as medians (interquartile range). Kruskal-Wallis analysis of variance was used to compare values between each type of lung conditions. Statistical significance was assumed for *P* ≤ .05. Statistical analysis was performed using SigmaStat software 3.5 (SPSS, Chicago, Illinois).

## Results

Among the 359 subjects included, 138 subjects were classified as normal lungs, 181 as ARDS, and 40 as COPD. Subjects' flow diagram and characteristics at inclusion are presented in Figure 1 and Table 1, respectively. Ventilator settings and arterial blood gas results are presented in Table 2.

![Fig. 1.](http://rc.rcjournal.com/https://rc.rcjournal.com/content/respcare/63/2/158/F1.medium.gif)

[Fig. 1.](http://rc.rcjournal.com/content/63/2/158/F1)

Fig. 1. 
Flow chart. NIV = noninvasive ventilation, BMI = body mass index.

View this table:
[Table 1](http://rc.rcjournal.com/content/63/2/158/T1)

Table 1 
Subject Characteristics at Inclusion

View this table:
[Table 2](http://rc.rcjournal.com/content/63/2/158/T2)

Table 2 
Ventilator Settings and Arterial Blood Gas Results

Respiratory mechanics results are presented in Table 3 and Figure 2. Static compliance was significantly lower in ARDS subjects, as compared with normal lung and COPD subjects (39 [32–50] mL/cm H2O vs 54 [44–64] and 59 [43–75] mL/cm H2O, respectively, *P* < .001). Inspiratory resistance was significantly higher in COPD subjects as compared with normal lung and ARDS subjects (22 [16–33] cm H2O/L/s vs 13 [10–15] and 12 [9–14] cm H2O/L/s, respectively, *P* < .001). Inspiratory resistance according to the size of endotracheal tube and humidification system are presented in Table 4. Expiratory time constant was significantly different for each lung condition (0.60 [0.51–0.71], 1.07 [0.68–2.14], and 0.46 [0.40–0.55] s for normal lung, COPD, and ARDS subjects, respectively, *P* < .001).

View this table:
[Table 3](http://rc.rcjournal.com/content/63/2/158/T3)

Table 3 
Respiratory Mechanics Results for All Subjects

![Fig. 2.](http://rc.rcjournal.com/https://rc.rcjournal.com/content/respcare/63/2/158/F2.medium.gif)

[Fig. 2.](http://rc.rcjournal.com/content/63/2/158/F2)

Fig. 2. 
Respiratory mechanics for all subjects and according to lung condition. Box plots represent median and interquartile range. * *P* < .001 for pairwise comparison using Dunn's method.

View this table:
[Table 4](http://rc.rcjournal.com/content/63/2/158/T4)

Table 4 
Inspiratory Resistances According to Endotracheal Tube Size and Humidification

In the group of ARDS subjects, there were no significant differences in respiratory mechanics variables among mild, moderate, and severe ARDS subjects (Table 5 and Fig. 3).

View this table:
[Table 5](http://rc.rcjournal.com/content/63/2/158/T5)

Table 5 
Respiratory Mechanics Results for the Subgroup of ARDS Subjects

![Fig. 3.](http://rc.rcjournal.com/https://rc.rcjournal.com/content/respcare/63/2/158/F3.medium.gif)

[Fig. 3.](http://rc.rcjournal.com/content/63/2/158/F3)

Fig. 3. 
Respiratory mechanics for ARDS subjects and according to severity: Box plots represent median and interquartile range.

## Discussion

This study represents the largest single study describing respiratory system mechanical properties for all 3 populations commonly requiring mechanical ventilation in ICUs (ie, normal, COPD, and ARDS). As expected, all parameters show a large variation, yet median values permit distinction among patient populations and subpopulations (for ARDS) as shown in Tables 3 and 5. These median values are within the ranges of data reported in other studies for subjects with normal lungs,23–33 subjects with COPD,23–25,30,33,34 and subjects with ARDS (Tables 6⇓–8).23–28,30,35–61

View this table:
[Table 6](http://rc.rcjournal.com/content/63/2/158/T6)

Table 6 
Respiratory Mechanics Results From Other Studies for Normal Subjects

View this table:
[Table 7](http://rc.rcjournal.com/content/63/2/158/T7)

Table 7 
Respiratory Mechanics Results From Other Studies for Subjects With COPD

View this table:
[Table 8](http://rc.rcjournal.com/content/63/2/158/T8)

Table 8 
Respiratory Mechanics Results From Other Studies for Subjects With ARDS

The question, of course, is what to do with all of this information. One obvious application, and the primary purpose of this study, is to provide the basis of simulation-based medical education and research, particularly in the art and science of mechanical ventilation.62 Simulation-based medical education and simulation-based mastery learning have become well-established in undergraduate and graduate medical, nursing, and allied health-care training programs.63 Much like a standardized patient,64 a high-fidelity lung simulator can provide the framework for teaching virtually any aspect of the physical interaction of a patient and a mechanical ventilator. Furthermore, a standard set of simulation parameters would provide researchers with a common basis for conducting ventilator performance studies. Manufacturers of ventilators could also use this information for conducting realistic new product verification and validation studies.

### Standard Simulation Parameters

A convenient theoretical basis for standardizing simulation parameters is a mathematical model of respiratory system mechanics. The simplest model of the respiratory system used for ventilator studies is the single-compartment model, composed of a single flow resistance and a single elastic compartment, represented by the equation of motion for the respiratory system,65,66 ![Formula][1]</img>(1) where the following are true. Pvent (t) = the change in transrespiratory pressure difference (ie, airway opening pressure minus body surface pressure) as a function of time (t), measured relative to end-expiratory airway pressure. This is the pressure generated by a ventilator during an assisted breath. Pmus(t) = ventilatory muscle pressure difference as a function of time (t); the theoretical chest wall transmural pressure difference that would produce movements identical to those produced by the ventilatory muscles during breathing maneuvers (positive during inspiratory effort, negative during expiratory effort). V(t) = volume change relative to end-expiratory volume as a function of time (t). V̇(t) = flow as a function of time (t), the first derivative of volume with respect to time. E = elastance (inverse of compliance; E = 1/C). R = resistance. auto-PEEP = end-expiratory alveolar pressure above end-expiratory airway pressure.

In this equation, pressure, volume, and flow are variables, whereas elastance and resistance are parameters (assumed to be constants). This happens to be the same model used by ventilators that calculate and display resistance and compliance values of patients. Indeed, the graphic waveforms for pressure, volume, and flow displayed by such ventilators are nothing other than the graphical representation of the equation of motion. The compliance (C) of the model is generally assumed to be linear for ventilator performance studies, in the form of C = Δvolume/Δpressure. The resistance (R) is modeled as either linear (R = Δpressure/Δflow) or non-linear (eg, parabolic). A parabolic resistor is one for which pressure is proportional to the square of flow. Note that for a parabolic resistor, the resistance is often defined as Δpressure/Δflow at a particular flow value. This means that the equivalent linear resistance is different for each flow at which it is evaluated. In other words, using a parabolic resistor, the resistive load of the lung model changes as flow changes. Hence, to maintain consistent testing parameters across different flows, it is usually better to select linear rather than parabolic resistors for ventilator performance evaluation studies.

To our knowledge, this study is the first attempt to provide standardized, evidence-based simulation parameters for the mechanical properties of the adult human respiratory system linked to realistic, general mechanical ventilator settings and patient demographics. In creating our recommendations, we necessarily must summarize and simplify the data at hand. Hence, we have condensed the information from the previous tables in Table 9. We have also simplified it by rounding values to whole numbers for convenience while maintaining the general magnitude of the parameter within realistic ranges, as indicated by our study and those of others. Obviously, we could have selected other values, but these seem to be reasonable, within the specifications of available mechanical simulators, and perhaps also fairly easily manufactured from scratch (eg, rubber test lungs with variable resistance and compliance settings or rigid walled lung models).67

View this table:
[Table 9](http://rc.rcjournal.com/content/63/2/158/T9)

Table 9 
Standardized Parameters for Simulation of Mechanical Ventilation in the Adult

Our study is limited by the fact that it was conducted at a single institution with a single mode of ventilation. To the extent that a mode of ventilation and a particular style of clinical management (including selection of optimum PEEP levels) can influence the mechanical properties of various populations of patients, our data are biased. However, the bias may be trivial, given the favorable comparison of our median values with those of a vast number of previous studies. Furthermore, our data are not intended to be optimal or even benchmark values for actual patient care. Another limitation is that all measurements were performed in passively ventilated subjects. Respiratory mechanics variables (ie, resistance and compliance) are the same in passively and actively breathing patients during assisted ventilation, other factors being essentially equal (eg, VT and flow). The main problem with simulation of the spontaneously breathing patient is the effort model parameters (ie, Pmus waveform characteristics).

## Conclusions

Effort model parameters are difficult to evaluate during mechanical ventilation with the current technology, and they have a lot of variability. However, having reasonable values for passive mechanics (such as the results of our study), future researchers may then impose any effort model parameters they can imagine for the intended simulation study. In conclusion, this study provides educators, researchers, and manufacturers with a standard set of practical, evidence-based parameters for simulating the respiratory system's mechanical properties under passive conditions.

## Footnotes

*   Correspondence: Jean-Michel Arnal MD, Service de Réanimation Polyvalente, Hôpital Sainte Musse, 54 Avenue Henri Sainte Claire Deville, 3056 Toulon, France. E-mail: jean-michel{at}arnal.org.
*   Dr Arnal has disclosed a relationship with Hamilton Medical AG. Mr Chatburn has disclosed relationships with CareFusion, Covidien, Dräger, Hamilton, IngMar Medical, Inogen, Invacare, Philips, ResMed, Neotech, and Breathe Technologies. The other authors have disclosed no conflicts of interest.

*   Mr Saoli presented a version of this work at the Congrès Réanimation, held January 11–13, 2017, in Paris, France.

*   Copyright © 2018 by Daedalus Enterprises

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