Model Of Respiratory Mechanics Research Articles

Atelectrauma can be avoided if expiration is sufficiently brief: evidence from inverse modeling and oscillometry during airway pressure release ventilation

BackgroundAirway pressure release ventilation (APRV) has been shown to be protective against atelectrauma if expirations are brief. We hypothesize that this is protective because epithelial surfaces are not given enough time to come together and adhere during expiration, thereby avoiding their highly damaging forced separation during inspiration.MethodsWe investigated this hypothesis in a porcine model of ARDS induced by Tween lavage. Animals were ventilated with APRV in 4 groups based on whether inspiratory pressure was 28 or 40 cmH2O, and whether expiration was terminated when end-expiratory flow reached either 75% (a shorter expiration) or 25% (a longer expiration) of its initial peak value. A mathematical model of respiratory system mechanics that included a volume-dependent elastance term characterized by the parameter E2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{2}$$\\end{document} was fit to airway pressure-flow data obtained each hour for 6 h post-Tween injury during both expiration and inspiration. We also measured respiratory system impedance between 5 and 19 Hz continuously through inspiration at the same time points from which we derived a time-course for respiratory system resistance (Rrs\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{rs}$$\\end{document}).ResultsE2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{2}$$\\end{document}during both expiration and inspiration was significantly different between the two longer expiration versus the two shorter expiration groups (ANOVA, p < 0.001). We found that E2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{2}$$\\end{document} was most depressed during inspiration in the higher-pressure group receiving the longer expiration, suggesting that E2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${E}_{2}$$\\end{document} reflects a balance between strain stiffening of the lung parenchyma and ongoing recruitment as lung volume increases. We also found in this group that Rrs\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${R}_{rs}$$\\end{document} increased progressively during the first 0.5 s of inspiration and then began to decrease again as inspiration continued, which we interpret as corresponding to the point when continuing derecruitment was reversed by progressive lung inflation.ConclusionsThese findings support the hypothesis that sufficiently short expiratory durations protect against atelectrauma because they do not give derecruitment enough time to manifest. This suggests a means for the personalized adjustment of mechanical ventilation.

Evaluation and modeling of diaphragm displacement using ultrasound imaging for wearable respiratory assistive robot.

Assessing the influence of respiratory assistive devices on the diaphragm mobility is essential for advancing patient care and improving treatment outcomes. Existing respiratory assistive robots have not yet effectively assessed their impact on diaphragm mobility. In this study, we introduce for the first time a non-invasive, real-time clinically feasible ultrasound method to evaluate the impact of soft wearable robots on diaphragm displacement. We measured and compared diaphragm displacement and lung volume in eight participants during both spontaneous and robotic-assisted respiration. Building on these measurements, we proposed a human-robot coupled two-compartment respiratory mechanics model that elucidates the underlying mechanism by which our extracorporeal wearable robots augments respiration. Specifically, the soft robot applies external compression to the abdominal wall muscles, inducing their inward movement, which consequently pushes the diaphragm upward and enhances respiratory function. Finally, we investigated the level and shape of various robotic assistive forces on diaphragm motion. This robotic intervention leads to a significant increase in average diaphragm displacement by 1.95 times and in lung volume by 2.14 times compared to spontaneous respiration. Furthermore, the accuracy of the proposed respiratory mechanics model is confirmed by the experimental results, with less than 7% error in measurements of both diaphragm displacement and lung volume. Finally, the magnitude of robotic assistive forces positively correlates with diaphragm movement, while the shape of the forces shows no significant relationship with diaphragm activity. Our experimental findings validate the effective assistance mechanism of the proposed robot, which enhances diaphragm mobility and assists in ventilation through extracorporeal robotic intervention. This robotic system can assist with ventilation while increasing diaphragm mobility, potentially resolving the issue of diaphragm atrophy. Additionally, this work paves the way for improved robotic designs and personalized assistance, tailored to the dynamics of the diaphragm in respiratory rehabilitation.

An enhanced respiratory mechanics model based on double-exponential and fractional calculus.

We address mathematical modelling of respiratory mechanics and put forward a model based on double-exponential and fractional calculus for parameter estimation, model simulation, and evaluation based on actual data. Our model has been implemented on a publicly available executable code with adjustable parameters, making it suitable for different applications. Our analysis represents the first application of fractional calculus and double-exponential modelling to respiratory mechanics, and allows us to propose a hybrid model fitting experimental data in different ventilation modes. Furthermore, our model can be used to study the mechanical features of the respiratory system, improve the safety of ventilation techniques, reduce ventilation damages, and provide strong support for fast and adaptive determination of ventilation parameters.

O039 A Novel Approach to Quantify Breathing effort in Obstructive Sleep Apnoea

Abstract Introduction Breathing effort is an important component of obstructed breathing and determinant of arousal in obstructive sleep apnoea (OSA). However, relative effects of hypopnoea versus apnoea on effort and work of breathing (WOB) are largely unknown. This study applied a novel modelling approach to quantify breathing effort, WOB and obstruction severity to evaluate differences between hypopnoea and apnoea events in OSA. Methods 1659 apnoea and hypopnea events from six obese males (BMI: 30-41 kg/m2; age: 34-56 years; AHI: 31-114 events/hr) with severe OSA were evaluated using conventional sleep measures and a 3-compartment (nasal, collapsible upper airway and lungs) respiratory mechanics model using mask airflow and pressure, and oesophageal and epiglottic pressures. Attempted airflow and WOB, and WOB loss to obstruction (attempted minus measured WOB), were estimated breath-by-breath via the model for comparisons between hypopnoea and apnoea events and sleep stages. Results The model explained most of the variance in attempted flow (Pearson r= 0.91 [95% CI 0.71 to 0.98]). Compared to hypopnoea events, WOB loss during obstructive apnoeas was 3.5 [95%CI 1.7 to 5.4] times higher. WOB losses were also higher during deep versus N2 sleep. Visualising breath-by-breath WOB loss revealed a significant number of flow- limited breaths not detected using traditional human scoring. Over 30% of breaths not classified as hypopnoeas or apnoeas exhibited WOB loss higher than for hypopnoea events. Conclusions Respiratory mechanics modelling is useful to assess breathing effort and upper airway obstruction severity and could provide novel mechanistic insights into sleep-related breathing problems and treatment outcomes.

An approach to study recruitment/derecruitment dynamics in a patient-specific computational model of an injured human lung.

We present a new approach for physics-based computational modeling of diseased human lungs. Our main object is the development of a model that takes the novel step of incorporating the dynamics of airway recruitment/derecruitment into an anatomically accurate, spatially resolved model of respiratory system mechanics, and the relation of these dynamics to airway dimensions and the biophysical properties of the lining fluid. The importance of our approach is that it potentially allows for more accurate predictions of where mechanical stress foci arise in the lungs, since it is at these locations that injury is thought to arise and propagate from. We match the model to data from a patient with acute respiratory distress syndrome (ARDS) to demonstrate the potential of the model for revealing the underlying derangements in ARDS in a patient-specific manner. To achieve this, the specific geometry of the lung and its heterogeneous pattern of injury are extracted from medical CT images. The mechanical behavior of the model is tailored to the patient's respiratory mechanics using measured ventilation data. In retrospective simulations of various clinically performed, pressure-driven ventilation profiles, the model adequately reproduces clinical quantities measured in the patient such as tidal volume and change in pleural pressure. The model also exhibits physiologically reasonable lung recruitment dynamics and has the spatial resolution to allow the study of local mechanical quantities such as alveolar strains. This modeling approach advances our ability to perform patient-specific studies in silico, opening the way to personalized therapies that will optimize patient outcomes.

Nonlinear N – Compartments model of respiratory mechanics considering viscoelasticity, inertia and surface tension properties

Respiratory biomechanics constitutes an important topic in clinical practice. Different strategies like mathematical models have been implemented to understand and replicate scenarios allowing deeper analysis. In this paper, a nonlinear N - compartments model is presented, allowing to represent the lung in a heterogeneous way. It considers the resistance of each generation of the airway and each alveolar compartment characterized independently. Includes properties of nonlinear elastance, viscoelasticity, inertia, and surface tension. In this work, to show the functionality of the model, a simulation of four alveolar units coupled to the airway model is presented using pressure as input signal simulating mechanical ventilation. However, the model can be used to simulate any desired number of alveolar units. Values at airway output were compared to the linear model, obtaining a correlation close to 1. Also, was compared to a physical test lung using Hamilton - S1 mechanical ventilator obtaining a positive correlation. The model makes it possible to evaluate the effects of different properties during spontaneous respiration or mechanical ventilation, both at the airway opening and alveolar. These properties include viscoelasticity, surface tension, inertia, among others.

Virtual patient framework for the testing of mechanical ventilation airway pressure and flow settings protocol

Background and ObjectiveModel-based and personalised decision support systems are emerging to guide mechanical ventilation (MV) treatment for respiratory failure patients. However, model-based treatments require resource-intensive clinical trials prior to implementation. This research presents a framework for generating virtual patients for testing model-based decision support, and direct use in MV treatment. MethodsThe virtual MV patient framework consists of 3 stages: 1) Virtual patient generation, 2) Patient-level validation, and 3) Virtual clinical trials. The virtual patients are generated from retrospective MV patient data using a clinically validated respiratory mechanics model whose respiratory parameters (respiratory elastance and resistance) capture patient-specific pulmonary conditions and responses to MV care over time. Patient-level validation compares the predicted responses from the virtual patient to their retrospective results for clinically implemented MV settings and changes to care. Patient-level validated virtual patients create a platform to conduct virtual trials, where the safety of closed-loop model-based protocols can be evaluated. ResultsThis research creates and presents a virtual patient platform of 100 virtual patients generated from retrospective data. Patient-level validation reported median errors of 3.26% for volume-control and 6.80% for pressure-control ventilation mode. A virtual trial on a model-based protocol demonstrates the potential efficacy of using virtual patients for prospective evaluation and testing of the protocol. ConclusionThe virtual patient framework shows the potential to safely and rapidly design, develop, and optimise new model-based MV decision support systems and protocols using clinically validated models and computer simulation, which could ultimately improve patient care and outcomes in MV.

Computational modeling of capillary perfusion and gas exchange in alveolar tissue

Gas exchange is an essential function of the respiratory system that couples fundamentally with perfusion in respiratory alveoli. Current mathematical formulations and computational models of these two phenomena rely on one-dimensional approximations that neglect the intricate volumetric microstructure of alveolar structures. In this work, we introduce a coupled three-dimensional computational model of pulmonary capillary perfusion and gas exchange that conforms to alveolar morphology. To this end, we derive non-linear partial differential equations and boundary conditions from physical principles that govern the behavior of blood and gases in arbitrary alveolar domains. We numerically solve the resulting formulation by proposing and implementing a non-linear finite-element scheme. Further, we carry out several numerical experiments to validate our model against one-dimensional simulations and demonstrate its applicability to morphologically-inspired geometries. Numerical simulations show that our model predicts blood pressure drops and blood velocities expected in the pulmonary capillaries. Moreover, we replicate partial pressure dynamics of oxygen and carbon dioxide reported in previous studies. This overall behavior is also observed in three-dimensional alveolar geometries, providing more detail associated with the spatial distribution of fields of interest and the influence of the shape of the domain. We envision that this model opens the door for enhanced in silico studies of gas exchange and perfusion on realistic geometries, coupled models of respiratory mechanics and gas exchange, and multi-scale analysis of lung function; furthering our understanding of lung physiology and pathology. Codes are available at https://github.com/comp-medicine-uc/num-model-alv-perfusion-gas-exchange.

Analysis of respiratory mechanics models with different kernels

Abstract In this article, we investigate the mechanics of breathing performed by a ventilator with different kernels by an effective integral transform. We mainly obtain the solutions of the fractional respiratory mechanics model. Our goal is to give the underlying model flexibly by making use of the advantages of the non-integer order operators. The big advantage of fractional derivatives is that we can formulate models describing much better the systems with memory effects. Fractional operators with different memories are related to different types of relaxation process of the non-local dynamical systems. Additionally, since we consider the utilisation of different kinds of fractional derivatives, most often having benefit in the implementation, the similarities and differences can be obviously seen between these derivatives.

Mechanical power in AVM-2 versus conventional ventilation modes in a normal lung model: A bench study

Introduction Recent studies suggested that the energy delivered by the mechanical ventilator to the lungs termed the mechanical power can induce and increase the risks of ventilator induced lung injury. The components of the mechanical power include the variables delivered by the ventilator: tidal volume, respiratory rate, inspiratory flow, airway pressure. Adaptive Ventilator Mode-2 (AVM-2) is a pressure-controlled mode with an optimal targeting scheme based on the inspiratory power equation that adjusts the respiratory rate and tidal volume to achieve a target minute ventilation. This mode conceptually should reduce the mechanical power delivered to the patients and thus reduce the incidence of ventilator induced lung injury. Methodology A bench study using a lung simulator (TTL, Michigan Instruments, Michigan, USA) was conducted. We constructed a passive single compartment normal respiratory mechanics model with compliance of 50 ml/cmH2O, and resistance of 10 cmH2O/L/s, with IBW 70 kg. We compared three different ventilator modes: Adaptive Ventilation Mode-2 (AVM-2), Pressure Regulated Volume Control (PRVC), and Volume Controlled Ventilation (VCV) in four different scenarios: 2 levels of minute ventilation 7 and 10.5 Lit/min (Experiment 1 and 2 respectively), each with 2 different PEEP levels 5 and 10 cmH2O (Experiment A and B respectively) termed Experiments 1A, 1B, 2A, and 2B respectively. The AVM-2 mode automatically selects the optimal tidal volume, and respiratory rate per the dialed percent minute ventilation with an I:E ratio of 1:1. In the PRVC, VCV we selected target tidal volume 6ml/kg/IBW (420 ml), and respiratory rate adjusted to match the minute ventilation for the AVM-2 mode. I:E ratio was kept 1:2 to avoid intrinsic PEEP. The study was conducted using a bellavista™ 1000 e Ventilator (Vyaire Medical, Illinois, USA). The mechanical power delivered by the ventilator for each mode was computed and compared between the three modes in each experiment. Statistical analysis was done using Kruskal-Wallis test to analyze the difference between the three modes, post HOC Tukey test was used to analyze the difference between each mode with the confidence intervals, P &lt; 0.05 was considered statistically significant. Results There were statistically significant differences between all the three modes regarding the ventilator delivered mechanical power. The AVM-2 mode delivered significantly less mechanical power than VCV which in turn was less than PRVC. Experiment 1A: AVM-2 8.76 土 0.05, VCV 9.78 土 0.04, PRVC 10.82 土 0.08, P &lt; 0.001 Experiment 1B: AVM-2 11.27 ± 0.09 VCV 12.81 ± 0.05, PRVC 13.88 ± 0.06, P &lt; 0.001. Experiment 2A: AVM-2 14.76 ± 0.05, VCV 15.79 ± 0.05, PRVC 18.29 ± 0.07, P &lt; 0.001, Experiment 2B: AVM-2 18.76 ± 0.04, VCV 20.56 ± 0.04, PRVC 21.17 土 0.03, P &lt; 0.001. Discussion AVM2 mode delivered less mechanical power compared to two conventional modes using low tidal volume in a normal lung model. This might reduce the incidence of ventilator induced lung injury. Results need to be validated in more clinical studies.

Respiratory mechanics models in the frame of non-local fractional operators

This study is intended to provide an exhaustive comparison of various non-local fractional operators when analyzing the mechanics of breathing performed by a ventilator. We present the solutions of the fractional respiratory mechanics model mainly for allowing the health care team to monitor patients' conditions in detail. The aim is to introduce the underlying model flexibly by making use of the advantages of the non-integer order operators with one, two, and three parameters. On the other hand, since we concern the usage of different types of fractional operators, most often having virtue in the application, it can be clearly observed the similarities and differences between these operators. Moreover, we observe the mobility of the solutions curves of the above-stated fractional model for different values of the parameters $\alpha$, $\rho$, and $\gamma$ with the help of many graphs.

Model-based Patient Matching for in-parallel Multiplexing Mechanical Ventilation Support.

Surges of COVID-19 infections could lead to insufficient supply of mechanical ventilators, and rationing of needed care. Multiplexing mechanical ventilators (co-MV) to serve multiple patients is a potential temporary solution. However, if patients are ventilated in parallel ventilation, there is currently no means to match ventilation requirements or patients, with no guidelines to date for co-MV. This research uses patient-specific clinically validated respiratory mechanics models to propose a method for patient matching and mechanical ventilator settings for two-patient co-MV under pressure control mode. The proposed method can simulate and estimate the resultant tidal volume of different combinations of co-ventilated patients. With both patients fulfilling the specified constraint under similar ventilation settings, the actual mechanical ventilator settings for co-MV are determined. This method allows clinicians to analyze in silico co-MV before clinical implementation.

A Respiratory Mechanics Model of the Human Lung for Positive Pressure Breathing at High Altitude

Abstract A respiratory mechanics model of the human lung is developed for studying pressure-compensated breathing through a respiration mask in low-ambient pressure environments encountered during high-altitude flight. The model formulation is described using a bond graph (BG) approach to convey the key elements used to capture critical effects in lung airways and effects of dissipative and energy storing processes. Specific extensions to constitutive relations are described, along with derivation and solution of system state equations via simulation. Gas exchange effects are not incorporated in the model, with emphasis placed on developing and assessing a respiratory mechanics model for integration with breathing support systems. Results from several case studies with variations in the lung characteristics and operational conditions are presented to demonstrate the effectiveness of the model in predicting key physiological measures, reported in the form of flow-volume loops and work of breathing (WoB). Favorable comparisons with the past results reported in the literature confirm the suitability of this model as part of a system-level model capable of guiding modifications and explaining anomalous behavior in these critical systems.

Comparison of Inspiratory Effort, Workload and Cycling Synchronization Between Non-Invasive Proportional-Assist Ventilation and Pressure-Support Ventilation Using Different Models of Respiratory Mechanics.

BackgroundThis study assessed lung models for the influence of respiratory mechanics and inspiratory effort on breathing pattern and simulator-ventilator cycling synchronization in non-invasive ventilation.Material/MethodsA Respironics V60 ventilator was connected to an active lung simulator modeling mildly restrictive, severely restrictive, obstructive and mixed obstructive/restrictive profiles. Pressure-support ventilation (PSV) and proportional-assist ventilation (PAV) were set to obtain similar tidal volume (VT). PAV was applied at flow assist (FA) 40–90% of resistance (Rrs) and volume assist (VA) 40–90% of elastance (Ers). Measurements were performed with system air leak of 25–28 L/minute. Ventilator performance and simulator-ventilator asynchrony were evaluated.ResultsAt comparable VT, PAV had slightly lower peak inspiratory flow and higher driving pressure compared with PSV. Premature cycling occurred in the obstructive, severely restrictive and mildly restrictive models. During PAV, time for airway pressure to achieve 90% of maximum during inspiration (T90) in the severely restrictive model was shorter than those of the obstructive and mixed obstructive/restrictive models and close to that measured in the PSV mode. Increasing FA level reduced inspiratory trigger workload (PTP300) in obstructive and mixed obstructive/restrictive models. Increasing FA level decreased inspiratory time (TI) and tended to aggravate premature cycling, whereas increasing VA level attenuated this effect.ConclusionsPAV with an appropriate combination of FA and VA decreases work of breathing during the inspiratory phase and improves simulator-ventilator cycling synchrony. FA has greater impact than VA in the adaptation to inspiratory effort demand. High VA level might help improve cycling synchrony.

Simulation of spontaneous breathing for healthy adults using a nonlinear airway-segmented model of respiratory mechanics

One-compartment lumped-parameter models of respiratory mechanics, representing the airflow resistance of the tracheobronchial tree with a linear or nonlinear resistor, are not able to describe the mechanical property of airways in different generations. Therefore, based on the anatomic structure of tracheobronchial tree and the mechanical property of airways in each generation, this study classified the human airways into three segments: the upper airway segment, the collapsible airway segment, and the small airway segment. Finally, a nonlinear, multi-compartment lumped-parameter model of respiratory mechanics with three airway segments was established. With the respiratory muscle effort as driving pressure, the model was used to simulate the tidal breathing of healthy adults. The results were consistent with the physiological data and the previously published results, suggesting that this model could be used for pathophysiological research of respiratory system.

Intracranial pressure responsiveness to positive end-expiratory pressure in different respiratory mechanics: a preliminary experimental study in pigs

BackgroundRespiratory mechanics affects the effect of positive end-expiratory pressure (PEEP) on intracranial pressure (ICP). Respiratory mechanics of the lung and the chest wall was not differentiated in previous studies. In the present study, we investigated the influence of the following possible determinants of ICP responsiveness to PEEP: chest wall elastance (ECW), lung elastance (EL), and baseline ICP.MethodsEight healthy Bama miniature pigs were studied. The increase of EL was induced by instillation of hydrochloride, and the increase of ECW was induced by strapping the animals’ chest wall and abdomen. A balloon-tipped catheter was placed intracranially for inducing intracranial hypertension. Six experimental conditions were investigated in sequence: 1) Normal; 2) Stiff Chest Wall; 3) Lung Injury; 4) Lung Injury + Stiff Chest Wall; 5) Lung Injury + Stiff Chest Wall + Intracranial Hypertension and 6) Lung Injury + Intracranial Hypertension. PEEP was gradually increased in a 5 cm H2O interval from 5 to 25 cm H2O in each condition. Blood pressure, central venous pressure, ICP, airway pressure and esophageal pressure were measured.ResultsHydrochloride instillation significantly increased EL in conditions with lung injury. ECW significantly increased in the conditions with chest wall and abdomen strapping (all p < 0.05). ICP significantly increased with increments of PEEP in all non-intracranial hypertension conditions (p < 0.001). The greatest cumulative increase in ICP was observed in the Stiff Chest Wall condition (6 [5.3, 6.8] mm Hg), while the lowest cumulative increase in ICP was observed in the Lung Injury condition (2 [1.3, 3.8] mm Hg). ICP significantly decreased when PEEP was increased in the intracranial hypertension conditions (p < 0.001). There was no significant difference in cumulative ICP change between the two intracranial hypertension conditions (p = 0.924).ConclusionsDifferent respiratory mechanics models can be established via hydrochloride induced lung injury and chest wall and abdominal strapping. The effect of PEEP on ICP is determined by respiratory mechanics in pigs with normal ICP. However, the responsiveness of ICP to PEEP is independent of respiratory mechanics when there is intracranial hypertension.

Comparison of pressure, volume and gas washout characteristics between PCV and HFPV in healthy and formalin fixed ex vivo porcine lungs

Objective: This study employs a recently developed experimental technique for comparison of the flow characteristics and the effectiveness of gas washout between pressure control ventilation (PCV) and high-frequency percussive ventilation (HFPV) in high-compliance and low-compliance ex vivo porcine respiratory tracts. Approach: The ex vivo porcine lungs are filled with nitrogen prior to ventilating with atmospheric gas using either PCV or HFPV to investigate the flow characteristics and gas washout characteristics. The study considered freshly removed lungs from porcine carcasses that were humanely harvested for human consumption. Subsequently, the porcine lungs were exposed externally to formalin to simulate low-compliance conditions. The first order models of respiratory mechanics were employed to predict the lung compliance and resistance in normal and formalin exposed lungs. HFPV was operated in two different modes based upon the set pressures, namely HFPV-Low and HFPV-High. The peak pressures of HFPV and PCV were matched in HFPV-Low and the peak pressures are increased to about 20–30% in the HFPV-High mode. Main results: Both HFPV-Low and HFPV-High mode deliver smaller tidal volume (VT) as compared to PCV in high and compliance states (about 70% and 40% for healthy and formalin treated lungs, repsectively). Although the tidal volume delivered by HFPV-High and HFPV-Low are comparable, they reveal a substantial difference in washout time as well as total ventilation volumes. In a high compliant lung (healthy lung), HFPV-High washes out the nitrogen within the lung more rapidly, whereas HFPV-Low washes out the inert gas more slowly as compared to PCV. In a low-compliance lung, HFPV-Low delivers similar washout rates as PCV at a much smaller VT and lower mean airway pressure. Significance: The ex vivo study supports the hypothesis that in low compliant lungs HFPV provides effective washout with a protective ventilation.

Theoretical open-loop model of respiratory mechanics in the extremely preterm infant

Non-invasive ventilation is increasingly used for respiratory support in preterm infants, and is associated with a lower risk of chronic lung disease. However, this mode is often not successful in the extremely preterm infant in part due to their markedly increased chest wall compliance that does not provide enough structure against which the forces of inhalation can generate sufficient pressure. To address the continued challenge of studying treatments in this fragile population, we developed a nonlinear lumped-parameter respiratory system mechanics model of the extremely preterm infant that incorporates nonlinear lung and chest wall compliances and lung volume parameters tuned to this population. In particular we developed a novel empirical representation of progressive volume loss based on compensatory alveolar pressure increase resulting from collapsed alveoli. The model demonstrates increased rate of volume loss related to high chest wall compliance, and simulates laryngeal braking for elevation of end-expiratory lung volume and constant positive airway pressure (CPAP). The model predicts that low chest wall compliance (chest stiffening) in addition to laryngeal braking and CPAP enhance breathing and delay lung volume loss. These results motivate future data collection strategies and investigation into treatments for chest wall stiffening.

Modulation–demodulation hypothesis of periodic breathing in human respiration

Periodic breathing (PB) is a diseased condition of the cardiorespiratory system, and mathematically it is modelled as an oscillation. Modeling approaches replicate periodic oscillation in the minute ventilation due to a higher than normal gain of the feedback signals from the chemoreceptors coupled with a longer than normal latency in feedback, and do not consider the waxing–waning pattern of the oronasal airflow. In this work, a noted regulation model is extended by integrating respiratory mechanics and respiratory central pattern generator (rCPG) model, using modulation–demodulation1 hypothesis. This is a top-down modeling approach, and it is assumed that the sensory feedback signal from the chemoreceptors modulates the output of the rCPG model. It is also assumed that the brainstem network is responsible for the demodulation process. The respiratory mechanics is modeled as a multi-input multi-output (MIMO) system, where modulated and demodulated neural signals are applied as input and the minute ventilation and the oronasal airflow are specified as output. The minute ventilation signal drives the regulation model, completing the feedback loop. The proposed model is validated by comparing the model output with the clinical data. Using the modulation–demodulation hypothesis, a respiratory mechanics model is formulated in the form of a linear state-space model, which can be useful for providing assisted ventilation in clinical conditions.

Estimation of Inspiratory Respiratory Elastance Using Expiratory Data

Abstract Models of respiratory mechanics can be used to titrate patient-specific mechanical ventilation (MV) settings in critical care, but often perform poorly in the presence of patient breathing effort. Respiratory mechanics are conventionally calculated using only inspiratory data. Muscle activity is normally assumed relatively minimal or absent during passive expiration regardless of the presence of inspiratory spontaneous breathing (SB) efforts. Hence, this study assesses whether expiratory lung elastance can be used to estimate inspiratory lung elastance for spontaneously breathing, reverse triggered patients. Clinical data from recruitment manoeuvres in fully sedated patients were used to determine a relationship between inspiratory and expiratory modeled lung elastance. The validity of this relationship was assessed using data recorded pre- and post- sedation from different patients. A strong, linear relationship was found between inspiratory and expiratory elastance in fully sedated patients, with gradient 1.04 [95% CI: 1.03-1.07] and intercept 1.66 [1.06-2.08] with R2 = 0.94. After adjustment according to the linear relationship, expiratory elastance produced stable estimations post sedation, with similar median and variance as inspiratory elastance. However, variation in estimates pre-sedation, although significantly improved, may be larger than clinically acceptable in some cases. The results of this study show that the typically ignored expiratory data may be able to provide insight into patient condition when conventional methods fail. Clinically, these methods could have an impact in guiding MV therapy by providing clinicians with information about lung mechanics under the effect of patient SB effort.