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Modeling ventilation of patients with interstitial lung disease at rest and exercise: a bench study

BMC Pulmonary Medicine volume 24, Article number: 566 (2024) Cite this article

Abstract

Background

The ventilatory physiopathology of patients with interstitial lung disease (ILD) remains poorly understood. We aimed to personalize a mechanical simulator to model healthy and ILD profiles ventilation, and to evaluate the effect of spontaneous breathing on respiratory mechanics at rest and during exercise.

Methods

In a 2-compartment lung simulator (ASL 5000®), we modeled 1 healthy and 3 ILD profiles, at rest and during exercise, based on physiological data from literature and patients. Measurements were: tidal volume, end-expiratory lung volume, driving pressure, transpulmonary driving pressure, dynamic alveolar strain, mechanical power, and time lag of inspiratory flow between compartments 1 and 2.

Results

Healthy and ILD models were validated: maximum differences between real and simulated tidal volume were 5% (96 ml) and 6% (54 ml) at rest and during exercise respectively, considered clinically negligible. When we simulated lung inhomogeneity (compliance in compartment 1 > compartment 2), tidal volume, end-expiratory lung volume, driving pressure and mechanical power increased in compartment 1 and decreased in compartment 2. Driving transpulmonary pressure and dynamic alveolar strain increased in compartment 2 and decreased in compartment 1. Time lag of inspiratory flow between compartments 1 and 2 was positively correlated with a difference of compliance between compartments (r = 0.98, CI95% (0.9106; 0.9962), p < 0.0001).

Conclusion

In this bench study, we personalized a mechanical simulator thatmodels the lung inhomogeneity and spontaneous breathing of healthy subjects and ILD patients at rest and during exercise. Our results suggest that lung inhomogeneity could increase lung vulnerability to volo-atelec-trauma mechanisms in ILD. Further physiological studies are needed to evaluate the impact of this vulnerability on acute or chronic ILD worsening.

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Background

Chronic interstitial lung diseases (ILDs) include different types of idiopathic or secondary parenchymal lung injuries related to various proportions of inflammatory lesions and fibrosis [1]. Pulmonary fibrosis and reduced lung compliance progressively lead to exertional dyspnea and exercise capacity impairment, resting dyspnea and ultimately, to chronic respiratory failure. The prognosis is variable and depends on the underlying disease responsible for the fibrotic process, ranging from a median survival of 2 to 3 years for idiopathic pulmonary fibrosis (IPF) [2], to 10 to 15 years for hypersensitivity pneumonitis [3]. Chronic ILD worsening could be prevented with appropriate management (elimination of disease promoting stimuli, antifibrosing or immunosuppressive drugs) but a subset of patients will continue to have worsening disease with a progressive fibrosing ILD phenotype [4]. It has been shown that the presence of a usual interstitial pneumonia (UIP) pattern or the extension of fibrosis lesions on computed tomography (CT) scan are factors favoring this phenotype, which may suggest an effect of lung compliance inhomogeneity on lung fibrosis worsening [4].Recent studies have highlighted the role of lung compliance inhomogeneity in the worsening of initial lung damage during acute respiratory distress syndrome (ARDS), with an increase in volutrauma and atelectrauma mechanisms during spontaneous breathing (SB) efforts [5,6,7]. However, the effect of lung inhomogeneity on fibrosis progression, and the risk of volutrauma and atelectrauma, in a chronic inflammatory lung like that of ILD have never been studied.

The aim of this study was to simulate on a mechanical lung model the ventilatory behavior of the fibrotic inhomogenous lung, and to evaluate the effect of spontaneous breathing on respiratory mechanics at rest and during peak exercise.

Methods

We configured a ventilation model using a mechanical lung simulator (ASL 5000®, IngMar Medical-Ltd, Pittsburgh, PA) to simulate healthy and ILD ventilatory profiles, at rest and during exercise. We used physiological data from the literature, pulmonary function tests (PFT) and cardiopulmonary exercise tests (CPET) obtained from patients in our department (Table E1). All included patients consented that their data were used on research purpose. This study was approved by the Rouen university hospital Ethics Committee (n° E2022-64).

ASL 5000® lung simulator

The ASL 5000® is an electronic lung simulator, previously described [8]. For this study, it was configured in two compartments (Ct1,Ct2) to simulate lung inhomogeneity using different lung compliances (Fig. 1). In our original configuration, compartments were set up either with the same (symmetrical compartments) or different (asymmetrical compartments) non-linear compliance curves.

Fig. 1
figure 1

Bench test settings. A. Non linear pressure/volume curve designed from patient cohort data FVC: forced vital capacity, IC: inspiratory capacity, TV: tidal volume, RV: residual volume, P: pressure, Pmax: maximal pressure generated with ASL 5000®, C: compliance. B. Muscular pressure (Pmus) according to the following settings: P0.1 of 3 cmH2O with a respiratory rate of 20 cpm, Ti/Ttot ratio of 0.34. The muscular contraction corresponds to the inspiratory phase, and the relaxation to passive expiration. C. Measurement of the parameters from the ASL 5000 respiratory curves. Example of a symmetrical simulation (C1 = C2 in the flow panel), at the exception of the zoom-in which shows curves from an asymmetrical condition to illustrate the time difference between C1 and C2 inspiratory flow beginning (Δt(Q2−Q1)). Qres: residual flow. From the pressure panel (alveolar pressure is identical for C1 and C2 compartments for the example): inspiration and expiration are defined by the muscular pressure Pmus, PEEPtot: total end-expiratory positive pressure, ΔP: surrogate driving pressure, ΔPtp: transcompartmental pressure. From the volume panel (C1 and C2 volumes are identical and superposed): Vteleinsp: teleinspiratory volume, TV: tidal volume, Vteleexp: teleexpiratory volume

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In this model, global lung compliance was expressed as the sum of the compliance of each compartment and global airway resistance was calculated with the following formula:

Rglob = Rtrach + R1*R2 / (R1 + R2), with Rglob: global airway resistance ; Rtrach: tracheal resistance ; R1 and R2 the resistance of each compartment.

The bench also simulated spontaneous breathing (SB) cycles through various respiratory rates (RR), inspiratory times (Ti), total breathing cycle times (Ttot) and the airway pressure drop observed during the first 100 ms of an inspiratory effort made against the occluded airway opening (P0.1), a well-established index of inspiratory drive [9;10].

Modeling of clinical situations

Disease severity profiles were based on forced vital capacity (FVC) following gender age physiology (GAP)-ILD score criteria (mild: FVC > 75%, moderate: 75%< FVC < 50% and severe: FVC < 50%) [11].

Four distinct patient profiles were simulated: one healthy control profile, and three fibrosing ILD profiles according to severity (mild, moderate and severe). All profiles were evaluated in two simulated clinical situations: rest and exercise. Respiratory conditions (resistance and lung compliance) were adjusted according to literature data to simulate the four patient profiles in SB. Global airway resistance was set at 5 cmH2O/L.s for all profiles [12]. Global compliance was defined as 200 ml/cmH2O for the healthy control profile, and 150 ml/cmH2O, 100 ml/cmH2O and 50 ml/cmH2O for the mild, moderate and severe ILD profiles, respectively [12,13,14,15,16].

In our two-compartment model, compliance for the healthy control profile was the same in the two compartments, i.e., C1 = C2 = 50%*Cglob=100 ml/cmH2O (C1: compartment 1 compliance, C2: compartment 2 compliance and Cglob: global lung compliance). For ILD profiles, we simulated 1 symmetrical and 2 asymmetrical scenarios for each profile, condition 1: C1 = C2 = 50%*Cglob, condition 2: C1 = 70%*Cglob and C2 = 30%*Cglob, and condition 3: C1 = 90%*Cglob and C2 = 10%*Cglob.

In two-compartments model, resistance was expressed as a tracheal resistance (Rtrach) of 3 cmH2O/L.s and a resistance of each compartment, R1 and R2, calculated as follows: Rglob - Rtrach= R1*R2 / (R1 + R2), that is R1 = R2 = 4 cmH2O/L.s.

Breathing pattern parameters (respiratory rates (RR), inspiratory times (Ti), tidal volume (TV), FVC, inspiratory capacity (IC), minute ventilation (VE)), and residual volume (RV) settings of ASL 5000 at rest and exercise for each of the four patient profiles were set up based on literature data [12,13,14,15,16] and clinical data retrospectively collected from a cohort of healthy controls and ILD patients investigated with PFT and CPET (rest and peak exercise) (Tables E2). All volumes were normalized on weight (60 kg) and height (1.70 m) and patients requiring oxygen therapy during CPET were excluded (Table E1). We have arbitrarily chosen the Ti, the Ti/Ttot, and the RR values to fit with the values of the literature and of our cohort and kept the Ti/Ttot constant between all profiles at rest and at exercise.

As ASL 5000 allows to set non-linear compliance curves, we modelled realistically a sigmoidal (S-shaped) curve (Fig. 1A). The RV cannot be set on the mechanical lung, and the volume curve starts at a relative zero corresponding to the RV. The maximum value therefore corresponds to the FVC. We assumed that the compliance of our model was constant for volumes between zero and inspiratory capacity (IC), corresponding to the linear portion of the pressure/volume curve. We were then able to draw a curve from our 4 points: point 1 (volume = 0 ml and pressure = 0 cmH2O), point 2 (volume = TV and pressure = TV/C), point 3 (volume = IC and pressure = IC/C), and point 4 (volume = FVC and pressure = 45 cmH2O, pressure range required for total inflation from the literature [17]). C represents the compliance of the linear portion of the curve, and was designed specifically for each compartment as ratio of the global compliance (for each profile). The first intercept of the slope was included in the linear portion of the compliance curve (close to the tidal) and therefore did not affect the shape of the curve. The second intercept was adjusted to smooth the curve in the upper part, at 90% of the inspiratory capacity. By using data from PFT to set those inflections points, we assume that the design of the curve fits the ILD population.

Inspiratory efforts were adjusted by setting the P0.1 as previously described (Fig. 1B) and to achieve VE of each profile at rest and exercise (Table E1) according to the VE of our cohort of patients (Tables E2 and E3) [18]. In the 2 asymmetrical compartments conditions, P0.1 were adjusted to keep the VE constant for all profiles (Table E3).

Modeling of clinical situations

Experimental protocol

From our four simulated profiles, we developed eight scripts with two symmetrical compartments (4 at rest and 4 at exercise, bench test settings are reported in Table E2) and six scripts with asymmetrical compartments (2 for each ILD profiles (70/30 and 90/10), at rest only, bench test settings are reported in Table E3) on the ASL 5000®.

Data were recorded from ASL 5000 Software 3.6® at a sampling frequency of 512 Hz: respiratory flow in global model and in each compartment, airway, alveolar and muscular pressure (Pmus), global volume and in each compartment. The following indicators were then computed and averaged over ten breathing cycles after stabilization during three breathing cycles (Fig. 1):

  • tidal volume (TV),

  • tele-expiratory volume (Vteleexp),

  • end-expiratory lung volume (EELV) at rest, as the sum of Vteleexp and mean RV of each patient profiles. For asymmetrical scenarios at rest, we applied a corrective proportion to the RV corresponding to the proportion of compliance in each compartment to calculate EELV. At peak exercise, EELV was calculated as the ratio of (TV + Vteleexp) on strain.

  • minute ventilation (VE),

  • surrogate driving pressure (∆P, absolute difference between maximum inspiratory and expiratory compartmental pressure),

  • transcompartmental driving pressure as a surrogate of transpulmonary driving pressure (∆Ptp, difference between differences of compartment and muscular pressure at the end of inspiration and expiration) [19],

  • total positive end-expiratory pressure (PEEP total), i.e. intrinsic PEEP in spontaneous breathing,

  • a surrogate of the alveolar strain (Strain), as the ratio of TV on EELV at rest in symmetrical scenarios [20], and as the ratio of end-inspiratory transcompartmental pressure on k, the model specific elastance at rest in asymmetrical scenarios and at peak exercise [20],

  • dynamic mechanical power (MP = 0.098 * RR * TV * (∆Ptp + PEEP)) (21–22),

  • inspiratory work of breathing (WOB) [23],

  • time lag of inspiratory flow between Ct1 and Ct2 (∆t (Q2-Q1)) as the time between the moment when the respiratory flow became positive in each compartment,

  • Model specific elastance (κ) was calculated for global lung at rest as the ratio of end-inspiratory transcompartmental pressure on strain for each profile and was considered as constant at peak exercise [24].

Statistical analysis

Patients’ characteristics on PFT, CPET and all data recorded in our bench study are expressed as mean ± standard deviation. The normality of variables distributions was confirmed with Shapiro-Wilk test. To validate our model, we have titrated the inspiratory efforts to generate volumes close to our patient cohort, and checked that these efforts generated inspiratory muscle amplitudes and lung specific elastance which were consistent with literature data [12, 14, 24, 25]. We compared simulated TV with TV from our clinical cohort of patients and we defined a tolerance margin of 12% or 200 mL [26,27,28]. Patients’ physiological data were compared with the ANOVA test. As there was no variability in the ASL 5000® measurements, there was no need for hypothesis tests. The correlation between normal variables (global compliance and ∆t (Q2−Q1)) was evaluated using the Pearson correlation test. A p value < 0.05 was considered statistically significant. All analyses were performed using GraphPad PRISM 8.4.2 (GraphPad Software, San Diego, CA).

Results

Modeling validation

Real TV and simulated TV were compared between a clinical cohort of patients during CPET and four modeled patient profiles at rest and during exercise (Fig. 2). Maximum differences between real and simulated TV were 96 ml (5%) in the healthy control profile and 54 ml (6%) in the severe ILD profile during exercise (Fig. 2). Pmus generated during our simulations to reach the targeted VE were respectively at rest and exercise: -6.7 and − 29.7cmH2O in healthy control profile, -8.6 and − 20.4 cmH2O in mild ILD profile, -9.1 and − 22.7 cmH2O in moderate ILD profile, and − 13.0 and − 28.67 cmH2O in severe ILD profile.

Fig. 2
figure 2

Comparison between real and simulated tidal volumes at rest and peak exercise ∆: difference between real and simulated tidal volumes in ml, % of diff: percentage of difference between real tidal volumes of a patient cohort (n = 14 healthy patients; n = 16 mild ILD, n = 13 moderate ILD; n = 4 severe ILD and simulated tidal volumes of modeled patient profiles

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We calculated the values of k in each profile at rest and found 13.7, 13.5, 14.4, and 23.5 cmH2O in healthy control, mild, moderate and severe ILD profiles respectively.

Ventilatory behavior of the model with symmetrical compartments at rest and during exercise

Simulation results at rest are shown in Table 1 and Figure E1. The lower the compliance, the more TV and EELV decreased, and the more ∆P, ∆Ptp, Strain, MP and WOB increased .

We also studied respiratory flow behavior in each compartment at rest (Figure E1).

During exercise, we observed an increase of TV, PEEP, ∆P, ∆Ptp, Strain, MP and WOB, compared to rest, in all profiles. EELV increased in all profiles except in severe ILD (Table 1 and Figure E2). TV, PEEP, ∆P, Strain, MP and WOB were higher in the healthy control profile than in all ILD profiles. In ILD profiles, the lower the compliance, the more EELV and PEEP decreased and the more ∆P, ∆Ptp, Strain, MP and WOB increased.

As we showed at rest, flow at inspiration and expiration in each compartment was also synchronized during exercise (∆t(Q2−Q1) = 0 ms) (Figure E2).

Table 1 Respiratory mechanics parameters of the model with two symmetrical compartments, for the 4 profiles and the 2 clinical situations
Full size table

Ventilatory behavior of the model with asymmetrical compartments at rest

Results for the moderate ILD profile are presented in Fig. 3, and Table E4, and exhaustively in online supplement for the mild and severe ILD profiles (Tables E5, E6 and Figures E3 to E4). Regardless of ILD profile, TV was distributed mainly in Ct1, which had the highest compliance. The greater the difference in compliance between the two compartments, the more the overall EELV increased. EELV decrease in the compartment with lower compliance (Ct2), tending towards 0, whereas EELV increased significantly in the compartment with higher compliance (Ct1). The total PEEP between Ct1 and Ct2 followed the same pattern for all ILD profiles: increasing in global lung, exclusively in Ct1 and decreasing in Ct2, as the difference of compliance between the 2 compartments increased. Global ∆P was stable despite the increased inhomogeneity of compartment compliance but ∆P increased in Ct1 and decreased in Ct2. As the difference of compliance between the 2 compartments increased, the ∆Ptp increased in the global lung and in Ct2 while it decreased or was stable in Ct1. Strain and WOB slightly increased in global lung. MP increased in global lung and in Ct1 and decreased in Ct2. When the compliance in the two compartments was different, we observed a time lag between the flow curves of the 2 compartments (Fig. 3, E7 and E9). Indeed, the flow was positive earlier in Ct2, the compartment with lower compliance, compared to the overall flow or in Ct1, suggesting a gas transfer from Ct1 to Ct2 before the start of inspiration. This phenomenon was observed at rest in mild, moderate and severe ILD profiles (Tables E5-E6 and Fig. 3, E3 and E4). A time lag of inspiratory flow between Ct1 and Ct2 was correlated with a difference of compliance between compartments (r = 0.98, CI95% (0.9106; 0.9962), p < 0.0001).

Fig. 3
figure 3

Ventilatory (A) and respiratory flow (B) behavior of the model with two compartments symmetrical or asymmetrical, moderate ILD profile at rest TV: tidal volume, EELV: end-expiratory lung volume, PEEP tot: total positive end-expiratory pressure, ΔP: driving pressure, ΔPtp: driving transcompartmental pressure, Strain: compartmental strain, MP: mechanical power, WOB: work of breathing, ILD: interstitial lung disease. 50/50: mild ILD profile with C1 = C2 = 50%*Cglob, 70/30: mild ILD profile with C1 = 70%Cglob and C2 = 30%Cglob, 90/10 mild ILD profile with C1 = 90%Cglob and C2 = 10%Cglob. In the 50/50 condition, the flow curves of compartments 1 and 2 are superimposed because the compliances and resistances are equal. Values are presented as mean. These simulated data have zero variance

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Discussion

In this bench study, we modeled ventilatory behavior in patients with ILD, using a lung simulator to reproduce cycle-to-cycle breathing at rest and during peak exercise. We assessed that the model generated tidal volumes consistent with those of a clinical cohort of ILD patients and healthy subjects and the simulated inspiratory efforts of modeled patient profiles were close to muscular pressures reported in the literature. Our results suggest that fibrosing lung inhomogeneity increases volutrauma and atelectrauma vulnerability during spontaneous breathing at rest.

A simulation study using a similar methodology was conducted to model respiratory behavior of healthy pediatric patients [26]. The authors showed a maximum difference of 1.6% between TV observed at rest in the literature and simulated TV, a difference that was not considered as clinically significant. In our study, the maximum difference between real and simulated TV during peak exercise was 6%, or 54 ml, which seems acceptable in view of the standard deviation of measures for the same patient during repeated PFT which led the experts to retain reproducible volume variations of less than 5 to 10% or 100 to 200 mL (27–28). Our model also appears realistic when analyzing applied muscle pressure values. Indeed, at rest, the P0.1 values of our model (1.3 cmH2O in the healthy model and 4.8 cmH2O in the severe ILD model) were close to those observed in the literature (1.9 ± 0.67 cmH2O in healthy subjects and 5.26 ± 1.4 cmH2O in patients with ILD) [14]. Moreover, during exercise, the muscle pressures applied in our model were 29.7 cmH2O in the healthy model and from 20.4 to 28.6 cmH2O in the ILD models, which was consistent with the literature (transdiaphragmatic pressures of 24.2 ± 6.6 cmH2O in healthy subjects and 27.4 ± 9.3 cmH2O in patients with ILD) [12]. In addition, the lung specific elastance, defined as the ratio of stress to compartmental strain and calculated at rest from the surrogate driving transpulmonary pressures, TV and EELV simulated for the control profile, was 13.7 ± 0 cmH2O, which was very close to that usually reported in the literature (13.5 cmH2O) [24]. In our ILD profiles, elastance increased inversely with compliance, up to 23.5 ± 0 cmH2O, which was also expected and consistent with the literature [25].

In this study, we used the equation of respiratory motion to informatically simulate the breathing of healthy and ILD subjects at rest and during exercise. Interestingly, our model provides reliable physiological data extrapolated from ARDS physiology, in order to better understand the vulnerabilities of the fibrotic lung in ILD. Indeed, compared to the healthy lung at rest, our ILD model exhibited an increase in ∆P, ∆Ptp, Strain and MP proportional to a decrease in compliance. Our results confirm those of Marchioni et al. [25] regarding the fibrotic lung acting as a “squishy ball”. Lung inhomogeneity, responsible for a concentration of alveolar stress in the ventilated alveolar regions, has also been suggested to be a worsening factor of alveolar lesions in ARF patients [6]. As ILD lung is inhomogeneous at baseline, we aimed to reproduce this feature in our experimental model by creating two asymmetrical compartments based on two different levels of compliance. Interestingly, we observed an increase in EELV in the global lung and in the compartment with higher compliance and a decrease in the compartment with lower compliance compared to the reference simulation with two symmetrical compartments. This feature, associated with lung inhomogeneity, clearly illustrates the risk of alveolar collapse in lung regions of low compliance and of recruitment or overdistention in regions of normal compliance.

To our knowledge, there are no data regarding mechanical power in ILD except during acute exacerbation of interstitial lung diseases (AE-ILD). MP, reflecting the energy applied to the lung by mechanical ventilation, was involved in VILI [6]. During AE-ILD, Tonelli et al. [22] reported a MP of 71 J/min in patients with SB and high flow oxygen therapy. We observed an increase in MP that correlated at rest with ILD severity. During peak exercise, we found very high MP levels, up to 160 J/min for the control profile and 95 J/min for severe ILD. Noteworthy, this was a maximum effort, a situation that can only be maintained for a few seconds in patients in stable condition. Interestingly, our asymmetrical model at rest, allowed us to observe an increase in MP, while lung inhomogeneity increased, with possibly an increased risk of volutrauma and atelectrauma.

We also analyzed the synchronization of flow between the two compartments of our model. When compliance was different between the two compartments, there was a time lag between the start of inspiration in both compartments, correlated with the compliance difference between compartments: the greater the difference, the greater the lag. We hypothesized that this shift could be related to the Pendelluft phenomenon, previously reported to be involved in volutrauma and atelectrauma mechanisms [29]. This asynchronous alveolar ventilation, caused by different regional time constants or dynamic pleural pressures, has already been described in patients with SB, using electrical impedance tomography to assess the regional dynamic distribution of ventilation [30]. To our knowledge, the Pendelluft phenomenon has never been demonstrated in ILD and this hypothesis still has to be confirmed in a clinical study.

Our study has some limitations. We have chosen lung compliance values for each profile from literature data which did not take into account sex and height variability. In order not to multiply the different parameters, we chose to keep the same airways resistance between healthy lung and diseased lung in our model to focus on the effect of compliance variations, this model being first and foremost a restrictive model. However, at rest and during exercise, Faisal et al. [12] reported slightly but not significantly lower resistances in ILD compared to healthy lungs. But they showed a lower resistance during exercise compared to rest in controls and ILD patients. We used previously described tuned Pmus curves which were validated for healthy patients mechanically ventilated, with the result that different muscle pressure cannot be applied to each compartment. To improve the model, it would be interesting to compare the Pmus curves used in our study to those of real ILD patients. We also used the lung simulator as a two-compartment model, but the ASL 5000® physically consists of one mechanical compartment. The data for each compartment are derived from a global signal through mathematical post-processing analysis based on the equation of motion performed by the ASL 5000 software. We also arbitrarily have chosen a maximal pressure of 45 cmH2O associated with the FVC to model the non linear part of our pressure/volume curve and this choice may impact the results of some parameters notably the mechanical power during exercise. Nevertheless, as this work is the first step in validating a model for testing the consequences in terms of volo and atelectrauma of oxygenation and ventilation strategies, the simulator part of the ASL will be used at a much later phase.

The strengths of our study were the original configuration of a mathematical lung model to explore the pathophysiological consequences of lung inhomogeneity and increased respiratory drive in fibrotic lung. The results of these simulations will need to be confirmed in physiological studies on patients with ILD.

Conclusion

In this study, we customized a lung simulator to model ventilation of healthy and ILD patients, taking pulmonary inhomogeneity into account, thanks to the two compartments of the model. We described the effects of SB at rest and during exercise and hypothesized that lung compliance inhomogeneity could contribute to the fibrosing lung and a vulnerability to volo and atelectrauma in some patients with ILD. Physiological studies are needed to confirm these preclinical results and to evaluate the impact of this vulnerability on acute or chronic worsening of ILD.

Data availability

The data that support the findings of this study are available from Elise Artaud-Macari but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Elise Artaud-Macari.

Abbreviations

AE-ILD:

Acute exacerbation of interstitial lung diseases

ARDS:

Acute respiratory distress syndrome

ARF:

Acute respiratory failure

ASL:

Active servo lung

C:

Compliance

C1 :

Compartment 1 compliance

C2 :

Compartment 2 compliance

Cglob :

Global lung compliance

CPET:

Cardiopulmonary exercise test

COVID-19:

Coronarovirus infectious disease 2019

CT:

Computed tomography

Ct:

Compartment

Cttot :

Total compartment (Ct1 + Ct2)

Ct1 :

Compartment 1

Ct2 :

Compartment 2

∆P:

Driving pressure

∆Ptp :

Driving transpulmonary pressure

∆t(Q2-Q1):

Time lag of inspiratory flow between Ct1 and Ct2

EELV:

End-expiratory lung volume

FVC:

Forced vital capacity

GAP ILD:

Gender age physiology interstitial lung disease

ILD:

Interstitial lung disease

IPF:

Idiopathic pulmonary fibrosis

K:

Lung specific elastance

MP:

Mechanical power

PEEP:

Positive end-expiratory pressure

PFT:

Pulmonary function test

P0.1:

Airway pressure drop observed during the first 100 ms of an inspiratory effort made against the occluded airway opening

RR:

Respiratory rate

RV:

Residual volume

SB:

Spontaneous breathing

Strainalv :

Dynamic alveolar strain

Ti:

Inspiratory time

TLC:

Total lung capacity

TV:

Tidal volume

Ttot:

Total breathing cycle time

UIP:

Usual interstitial pneumonia

VE:

Minute ventilation

WOB:

Work of breathing

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Acknowledgements

The authors are grateful to Nikki Sabourin-Gibbs, CHU Rouen, for her help in editing the manuscript.

Funding

This study received grants from ADIR Association, Rouen, France.

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Authors and Affiliations

  1. Univ Rouen Normandie, GRHVN UR 3830, CHU Rouen, Department of Pulmonary, Thoracic Oncology and Respiratory Intensive Care, F-76000, Rouen, France

    Elise Artaud-Macari & Antoine Cuvelier

  2. Kernel Biomedical, Rouen, France

    Emeline Fresnel & Adrien Kerfourn

  3. Department of Pulmonary, Thoracic Oncology and Respiratory Intensive Care Department, CHU Rouen, Rouen, 76000, France

    Clémence Roussel & Marie-Anne Melone

  4. Department of Respiratory and Exercise Physiology, CHU Rouen, Rouen, 76000, France

    David Debeaumont

  5. Univ Rouen Normandie, GRHVN UR 3830, ADIR Association, CHU Rouen, Institute for Research and Innovation in Biomedicine (IRIB), F-76000 , Rouen, France

    Francis-Edouard Gravier & Tristan Bonnevie

  6. Univ Rouen Normandie, UR4108-LITIS Lab QuantIF, CHU Rouen, Department of Pulmonary, Thoracic Oncology and Respiratory Intensive Care, F-76000, Rouen, France

    Mathieu Salaün

  7. Univ Rouen Normandie, GRHVN UR 3830, CHU Rouen, Department of Medical Intensive Care, F-76000 , Rouen, France

    Christophe Girault

Authors
  1. Elise Artaud-Macari
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  2. Emeline Fresnel
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  10. Antoine Cuvelier
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Contributions

Substantial contributions to the conception or design of the work, E. AM., E.F., A.K, A.C. and C.G. Acquisition, analysis, or interpretation of data for the work, E. AM., E.F., A.K, A.C. and C.G. Drafting the work or revising it critically for important intellectual content, E. AM., E.F., A.K, A.C. and C.G. Final approval of the version submitted for publication, all authors. Accountability for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved, E.AM., A.C. and C.G.

Corresponding author

Correspondence to Elise Artaud-Macari.

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All included patients consented that their data were used.

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Not available.

Competing interests

The authors declare no competing interests.

on research purpose. This study was approved by the Rouen university hospital.

Ethics Committee (n° E2022-64). Informed consent was obtained from all the patients in this study.

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This study has been presented at the SRLF meeting in Paris on 14-06-2023, at the ERS meeting in Milan on 10-09-2023 and accepted at the CPLF meeting in Lille on 28-09-2024 but have not been previously published.

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Artaud-Macari, E., Fresnel, E., Kerfourn, A. et al. Modeling ventilation of patients with interstitial lung disease at rest and exercise: a bench study. BMC Pulm Med 24, 566 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12890-024-03383-1

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Keywords

BMC Pulmonary Medicine

ISSN: 1471-2466

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