Single Compartment Lung Model Research Articles

Estimating patient spontaneous breathing effort in mechanical ventilation using a b-splines function approach

Background:Patient work of breathing is a key clinical metric strongly to guide patient care and weaning from mechanical ventilation (MV). Measurement requires added equipment, well-trained clinicians, or/and extra interventions. This study combines a spontaneous breathing effort model using b-spline functions with a nonlinear, predictive MV digital-twin model to monitor patient effort in real-time. Methods:Data from 22 patients for two assisted spontaneous breathing MV modes, NAVA (neurally adjusted ventilatory assist) and PSV (pressure support ventilation), are employed. The patient effort function estimates a pleural pressure Pˆp surrogate of muscular work of breathing induced pressure. To ensure identifiability Pˆp is identified with a negative constraint level of 75%. Estimated patient effort is compared to electrical activity of the diaphragm (EAdi) signals from the NAVA naso-gastric tude, airway pressure, and tidal volume (VT) as well as physiological and clinical expectations. Results:Pˆp generalizes well across the digital twin model and MV modes in comparison to the original single compartment lung model. Strong neuro-muscular correlations are identified with Pˆp compared to EAdi, VT, and airway pressure in NAVA. They are lower in PSV, as expected, as pressure delivery is not a function of EAdi in this MV mode, while the uncontrolled variable VT shows a stronger association with Pˆp than EAdi. Conclusion:The digital twin model relates patient-specific induced breathing effort, modeled as Pˆp, as well as or better than EAdi in both assisted breathing MV modes. Results differ between NAVA and PSV modes due to the poorer patient–ventilator interaction typical in PSV. The ability to estimate patient work of breathing allows non-invasive, real-time quantification of ventilator unloading, heretofore not possible without extra sensors or maneuvers, to help guide weaning or changes in MV settings for assisted spontaneous breathing (ASB) MV modes.

Model-based patient matching for in-parallel pressure-controlled ventilation

BackgroundSurges of COVID-19 infections have led to insufficient supply of mechanical ventilators (MV), resulting in rationing of MV care. In-parallel, co-mechanical ventilation (Co-MV) of multiple patients is a potential solution. However, due to lack of testing, there is currently no means to match ventilation requirements or patients, with no guidelines to date. In this research, we have developed a model-based method for patient matching for pressure control mode MV.MethodsThe model-based method uses a single-compartment lung model (SCM) to simulate the resultant tidal volume of patient pairs at a set ventilation setting. If both patients meet specified safe ventilation criteria under similar ventilation settings, the actual mechanical ventilator settings for Co-MV are determined via simulation using a double-compartment lung model (DCM). This method allows clinicians to analyse Co-MV in silico, before clinical implementation.ResultsThe proposed method demonstrates successful patient matching and MV setting in a model-based simulation as well as good discrimination to avoid mismatched patient pairs. The pairing process is based on model-based, patient-specific respiratory mechanics identified from measured data to provide useful information for guiding care. Specifically, the matching is performed via estimation of MV delivered tidal volume (mL/kg) based on patient-specific respiratory mechanics. This information can provide insights for the clinicians to evaluate the subsequent effects of Co-MV. In addition, it was also found that Co-MV patients with highly restrictive respiratory mechanics and obese patients must be performed with extra care.ConclusionThis approach allows clinicians to analyse patient matching in a virtual environment without patient risk. The approach is tested in simulation, but the results justify the necessary clinical validation in human trials.

A Damaged-Informed Lung Ventilator Model for Ventilator Waveforms.

Motivated by a desire to understand pulmonary physiology, scientists have developed physiological lung models of varying complexity. However, pathophysiology and interactions between human lungs and ventilators, e.g., ventilator-induced lung injury (VILI), present challenges for modeling efforts. This is because the real-world pressure and volume signals may be too complex for simple models to capture, and while complex models tend not to be estimable with clinical data, limiting clinical utility. To address this gap, in this manuscript we developed a new damaged-informed lung ventilator (DILV) model. This approach relies on mathematizing ventilator pressure and volume waveforms, including lung physiology, mechanical ventilation, and their interaction. The model begins with nominal waveforms and adds limited, clinically relevant, hypothesis-driven features to the waveform corresponding to pulmonary pathophysiology, patient-ventilator interaction, and ventilator settings. The DILV model parameters uniquely and reliably recapitulate these features while having enough flexibility to reproduce commonly observed variability in clinical (human) and laboratory (mouse) waveform data. We evaluate the proof-in-principle capabilities of our modeling approach by estimating 399 breaths collected for differently damaged lungs for tightly controlled measurements in mice and uncontrolled human intensive care unit data in the absence and presence of ventilator dyssynchrony. The cumulative value of mean squares error for the DILV model is, on average, ≈12 times less than the single compartment lung model for all the waveforms considered. Moreover, changes in the estimated parameters correctly correlate with known measures of lung physiology, including lung compliance as a baseline evaluation. Our long-term goal is to use the DILV model for clinical monitoring and research studies by providing high fidelity estimates of lung state and sources of VILI with an end goal of improving management of VILI and acute respiratory distress syndrome.

Effect of small airways and viscoelasticity on lung mechanics from expiratory occlusion

Monitoring the decay rate of airflow in spirometry may be clinically useful. The decay rate is expected to represent a combination of lung elastance and airway resistance. However, the decay rate calculated using the single compartment lung model is not expected to account for slower lung mechanics, such as small airways resistance and tissue viscoelasticity. This study assesses whether the decay rate is affected by these lung mechanics. An exponentially decaying flow was created using a shutter to occlude airflow during passive expiration for 15 healthy subjects. To approximate small airways resistance and viscoelasticity, the gradient of pressure increase (relaxation gradient) during shutter closure was measured. The occlusion resistance, elastance, and decay rate were also calculated for these breaths. None of these mechanics were found to be correlated with the relaxation gradient. The relaxation gradient was also found to be independent of driving pressure. Conversely, the relaxation gradient was found to depend on lung volume. The results of this study suggest using lung mechanics and decay rate to monitor changes in lung condition over time may miss information about changes in the small airways and viscoelastic lung tissue. Thus, it is useful for monitoring large airways disease, but may be ineffective for small airways disease such as ARDS.

Virtual Mechanical Ventilation Protocol – A Model-based Method To determine MV Settings

Intensive care mechanical ventilation (MV) therapy is a lifesaving intervention for a patient with respiratory failure. MV supports patients breathing by maintaining positive airway pressure and airflow to the lung. However, there is currently little clinical consensus protocol to set the best MV setting. Hence, it is important to provide an objective and patient-specific MV settings to support patient recovery. This study presents a model-based method to find optimal MV settings using clinical bedside data. A mathematical model of the respiratory system is first used to estimate patient-specific respiratory mechanics. These mechanics are then incorporated with significant clinical findings from the literature to simulate a series of MV settings. The simulation of MV settings is performed using the single compartment lung model using the MATLAB software. From this series of simulated MV settings, optimal MV settings can be determined objectively by the clinician. This model-based method potentially provides decision support for the clinician to set optimal MV settings.

Non-invasive measurement of tidal breathing lung mechanics using expiratory occlusion

A great amount of research looks at whether information about lung mechanics can be obtained using spirometry, as these mechanics give clinically useful information about lung condition and disease progression. This study uses a time-varying elastance, single compartment lung model to calculate lung mechanics of 15 tidally breathing healthy subjects. A plethysmograph with a built-in shutter was used to induce an exponentially decaying airflow. Lung elastance and respiratory system resistance were separated from the decay rate of flow caused by the shutter. Occlusion resistance was calculated at shutter closure. To simulate upper airway obstruction, progressively larger resistances were added to the plethysmograph mouthpiece.Decay rates measured ranged from 5-42, with large intra-subject variation associated with muscular breathing effort. Measured lung elastance ranged from 3.9-21.2 cmH2O/L and often remained constant as resistance was increased. Resistance calculated from the decay rate was very small, ranging from 0.15-1.95 cmH2Os/L. The low resistance is due to the airflow measured originating from low resistance areas in the centre of airways. Occlusion resistance measurements were as expected for healthy subjects, and followed the expected resistance trend as resistance was increased.

A virtual patient model for mechanical ventilation

Background and ObjectivesMechanical ventilation (MV) is a primary therapy for patients with acute respiratory failure. However, poorly selected ventilator settings can cause further lung damage due to heterogeneity of healthy and damaged alveoli. Varying positive-end-expiratory-pressure (PEEP) to a point of minimum elastance is a lung protective ventilator strategy. However, even low levels of PEEP can lead to ventilator induced lung injury for individuals with highly inflamed pulmonary tissue. Hence, models that could accurately predict peak inspiratory pressures after changes to PEEP could improve clinician confidence in attempting potentially beneficial treatment strategies. MethodsThis study develops and validates a physiologically relevant respiratory model that captures elastance and resistance via basis functions within a well-validated single compartment lung model. The model can be personalised using information available at a low PEEP to predict lung mechanics at a higher PEEP. Proof of concept validation is undertaken with data from four patients and eight recruitment manoeuvre arms. ResultsResults show low error when predicting upwards over the clinically relevant pressure range, with the model able to predict peak inspiratory pressure with less than 10% error over 90% of the range of PEEP changes up to 12 cmH2O. ConclusionsThe results provide an in-silico model-based means of predicting clinically relevant responses to changes in MV therapy, which is the foundation of a first virtual patient for MV.

Lung Mechanics in Premature infants: Modelling and clinical validation

Abstract Mechanical ventilation (MV) is widely used in Neonatal Intensive Care Unit (NICU) to support or fully control the breathing of patients of patients with respiratory distress syndrome (RDS). Current clinical practice involves clinician intuition or a generalized, “one size fits all” approach as inter-patient variability and lung heterogeneity can make selection of optimum ventilator settings difficult. Model-based methods can be used to identify patient-specific lung mechanics. This study aims to apply the single compartment lung model with an added endotracheal tube compensation term (ΔPett) to fit neonatal clinical data to describe neonatal lung mechanics. Airway flow and pressure data was collected from 10 mechanically ventilated infants in the Christchurch women’s hospital NICU as part of an observational trial under standard care conditions. 205.9 hours of conventional ventilation (across 9 patients), and 53 hours of HFOV (across 3 patients) were recorded. The model fit was very good with median [interquartile range] of percentage MARD of 5.7 [5.2-6.3]% across all conventionally ventilated patients. Subgroup analyses was also performed based on weight, RDS and surgical cohort, and comparison between patients who were treated and not treated with surfactant therapy. Overall, the single compartment model fit was able to capture lung mechanics in premature infants, but showed large variability in mechanics across and within patients. Future work will use these results and models to better understand and monitor changes in patient condition for the improvement of delivered MV.

Analysis of Neonatal Pulmonary Mechanics

Mechanical ventilation (MV) is widely used in the Neonatal Intensive Care Unit (NICU) for patients suffering from respiratory distress syndrome (RDS). MV treatment is difficult due to intra-patient and inter-patient differences in lung mechanics over time, highlighting the need for patient-specific methods. Model-based methods allow identification of patient-specific lung mechanics which can be used to guide care. The aim of this study is to determine if the single compartment lung model can be used with neonatal MV data to provide more insight into their lung mechanics. Neonatal patient data was collected from published literature, and results were compared to data obtained from previously conducted clinical trials in the adult ICU. The single compartment lung model was found to fit the data very well (model fit error range: 2.2 - 6.6%) giving patient-specific elastance and resistance values for each breath. Lung elastance was compared for adults and infants and it was found that infants have ~30x stiffer lungs than adults (elastance: 1 — 1.75 cmH2O/mL vs. 0.017 — 0.059 cmH2O/mL) for similar driving pressures. The ventilated neonatal lung has different lung mechanics to an adult’s, suggesting that the lung of a neonate should not be treated as a small adult lung. Further work will validate these results using patient data collected from the NICU. Ultimately, this research will provide more knowledge into neonatal pulmonary mechanics and can be used as the first step towards optimised patient-specific care in the NICU.

Predicting the Effects of Changing PEEP Using a Basis Function Method

Current methods to optimise mechanical ventilation involve increasing positive end expiratory pressure (PEEP) in steps to maximize recruitment. If PEEP is too high, overdistension and damage occur. There is thus an inherent risk involved when increasing PEEP. This study predicts dynamic elastance and lung mechanics for higher PEEP using clinically relevant elastance basis functions, capturing distension, recruitment and constant stiffness, in a first order model of lung mechanics. The clinically relevant basis functions were used to fit elastance using a single compartment lung model for 10 patients undergoing recruitment maneuvers, where 2-4 PEEP levels were analysed, and then used to predict the elastance and pressure waveforms for PEEP level increases of 5 and 10 cmH2O. The mean error for the pressure fits from the clinically relevant basis functions was 2.06%. Mean error for pressure predictions with a PEEP level increase of 5 cmH2O was 3.8-5.5%. Mean error for PEEP level increases of 10 cmH2O was slightly higher, between 5.0 and 6.6%. Good pressure fits and predictions show these basis functions accurately fit and predict elastance and thus lung behavior at increased PEEP levels. Each clinically relevant basis function behaved as expected, however improvements to the identifiability of distension would further improve the overall accuracy.

Visualisation of time-varying respiratory system elastance in experimental ARDS animal models.

BackgroundPatients with acute respiratory distress syndrome (ARDS) risk lung collapse, severely altering the breath-to-breath respiratory mechanics. Model-based estimation of respiratory mechanics characterising patient-specific condition and response to treatment may be used to guide mechanical ventilation (MV). This study presents a model-based approach to monitor time-varying patient-ventilator interaction to guide positive end expiratory pressure (PEEP) selection.MethodsThe single compartment lung model was extended to monitor dynamic time-varying respiratory system elastance, Edrs, within each breathing cycle. Two separate animal models were considered, each consisting of three fully sedated pure pietrain piglets (oleic acid ARDS and lavage ARDS). A staircase recruitment manoeuvre was performed on all six subjects after ARDS was induced. The Edrs was mapped across each breathing cycle for each subject.ResultsSix time-varying, breath-specific Edrs maps were generated, one for each subject. Each Edrs map shows the subject-specific response to mechanical ventilation (MV), indicating the need for a model-based approach to guide MV. This method of visualisation provides high resolution insight into the time-varying respiratory mechanics to aid clinical decision making. Using the Edrs maps, minimal time-varying elastance was identified, which can be used to select optimal PEEP.ConclusionsReal-time continuous monitoring of in-breath mechanics provides further insight into lung physiology. Therefore, there is potential for this new monitoring method to aid clinicians in guiding MV treatment. These are the first such maps generated and they thus show unique results in high resolution. The model is limited to a constant respiratory resistance throughout inspiration which may not be valid in some cases. However, trends match clinical expectation and the results highlight both the subject-specificity of the model, as well as significant inter-subject variability.

Time-Varying Respiratory Elastance for Spontaneously Breathing Patients

Respiratory mechanics models can be used to optimise patient-specific mechanical ventilation (MV), but are limited to fully sedated MV patients who are not spontaneously breathing. This research presents a non-invasive model-based method to determine respiratory mechanics of spontaneously breathing MV patients. Patient-specific respiratory mechanics of 22 spontaneously breathing patients are described using a single compartment lung model with time-varying elastance (Edrs). The normalised Edrs trajectories and the area under the curves (AUCEdrs) are calculated using clinical data from 22 patients ventilated using pressure support (PS) and neurally adjusted ventilatory assist (NAVA). Edrs trajectories are also compared between ventilation modes. Edrs for PS ventilation were significantly higher compared to NAVA (p < 0.05). Edrs trajectories were more variable during NAVA than PS (p < 0.05). 20 of 22 patients had AUCEdrs > 25 cmH2Os/l. The AUCEdrs is a surrogate of elastance, and thus can be used as a respiratory failure severity indicator. This non-invasive model-based approach captures unique dynamic respiratory mechanics for spontaneously breathing patients during PS and NAVA. The model is fully general and is applicable to both fully controlled and partially assisted MV modes, with the resulting potential to standardise treatment for all MV patients.

Analysis of different model-based approaches for estimating dFRC for real-time application

BackgroundAcute Respiratory Distress Syndrome (ARDS) is characterized by inflammation, filling of the lung with fluid and the collapse of lung units. Mechanical ventilation (MV) is used to treat ARDS using positive end expiratory pressure (PEEP) to recruit and retain lung units, thus increasing pulmonary volume and dynamic functional residual capacity (dFRC) at the end of expiration. However, simple, non-invasive methods to estimate dFRC do not exist.MethodsFour model-based methods for estimating dFRC are compared based on their performance on two separate clinical data cohorts. The methods are derived from either stress-strain theory or a single compartment lung model, and use commonly controlled or measured parameters (lung compliance, plateau airway pressure, pressure-volume (PV) data). Population constants are determined for the stress-strain approach, which is implemented using data at both single and multiple PEEP levels. Estimated values are compared to clinically measured values to assess the reliability of each method for each cohort individually and combined.ResultsThe stress-strain multiple breath (at multiple PEEP levels) method produced an overall correlation coefficient R2 = 0.966. The stress-strain single breath method produced R2 = 0.530. The single compartment single breath method produced R2 = 0.415. A combined method at single and multiple PEEP levels produced R2 = 0.963.ConclusionsThe results suggest that model-based, single breath and non-invasive approaches to estimating dFRC may be viable in a clinical scenario, ensuring no interruption to MV. The models provide a means of estimating dFRC at any PEEP level. However, model limitations and large estimation errors limit the use of the methods at very low PEEP.

Tracheal gas insufflation-pressure control versus volume control ventilation. A lung model study.

Tracheal gas insufflation (TGI) has been recommended as an adjunct to mechanical ventilation in the presence of elevated Pa CO2. Based on our initial clinical experience with continuous flow TGI and pressure control ventilation (PCV), we were concerned about elevation in peak airway pressure as TGI was applied. In a lung model, we evaluated the effects of continuous flow TGI during both PCV and volume control ventilation (VCV). A single compartment lung model was configured with an artificial trachea into which an 8-mm endotracheal tube was positioned. TGI was established with a 16-G catheter positioned 2 cm beyond the tip of the endotracheal tube. Ventilation was provided by a Puritan-Bennett 7200ae ventilator with PCV 20 cm H2O or VCV with a tidal volume (VTt) similar to that with PCV. A rate of 15 breaths/min and PEEP of 10 cm H2O were used throughout. Inspiratory times (TI) of 1.0, 1.5, 2.0, and 2.5 s were used with TGI of 0, 4, 8, and 12 L/min. Lung model compliance (ml/cm H2O) and resistance (cm H2O/L/s) combinations of 20/20, 20/5, and 50/20 were used. Auto-PEEP, VT, and peak alveolar and airway opening pressures increased as TGI and Ti increased, regardless of lung mechanics settings (p<0.01). All increases were greater with VCV than PCV (p<0.05). Continuous flow TGI with both PCV and VT-uncorrected VCV may result in marked increases in Vt and system pressures, especially at long TI.

Measurement of alveolar ventilation and changes in deadspace by indirect calorimetry during mechanical ventilation

To validate the assessment of changes in alveolar ventilation and deadspace by indirect calorimetry. An open comparison of two methods using a criterion standard. Simultaneous measurement of minute ventilation with a metabolic monitor and a pneumotachometer during controlled and synchronized intermittent mandatory ventilation in intensive care patients (n = 14). Measurement of a change in alveolar ventilation with three different tidal volume values in a single-compartment lung model using an added external deadspace. Alveolar ventilation and deadspace/tidal volume were calculated from Bohr's equation using end-tidal PCO2 for the alveolar PCO2 value. The mean differences between minute ventilation measured by a metabolic monitor and minute ventilation measured by a pneumotachometer during controlled and synchronized intermittent mandatory ventilation were -0.04 +/- 0.61 (SD) L and 0.01 +/- 0.85 L, respectively. No significant difference was observed between measurements at the endotracheal tube and the expiratory port of the ventilator. In studies using the lung model, the external deadspace represented 6% to 19% of the three tidal volume measurements. The mean difference between the actual and measured deadspace was 3 +/- 9 mL (8.2 +/- 4.7%), with a slightly, but not significantly, lower precision at the high tidal volume. Changes in alveolar ventilation and deadspace can be accurately measured by combined use of indirect calorimetry and end-tidal CO2 analysis.

A nonlinear model of respiratory mechanics in emphysematous lungs.

Existing mechanical models of chronic obstructive lung disease have failed to explain a number of experimental findings of airway obstruction, e.g., the varying manners of frequency dependence of resistance (FDR). Departing from the parallel-unit concept and attempting to account for the "check-valve" mechanism in the emphysematous lung, we proposed a single-compartment lung model with a nonlinear pressure-flow relationship: P + P* = LV + K1V + K3(V +/- V*)3 + V/c, where P* = K3V*, V* is a constant. The plus and minus signs in the cubic term indicate the expiratory and inspiratory check valves, respectively. The choice of an asymmetric P - V relation reflects several properties of emphysematous lungs such as airflow limitation and higher expiratory resistance. Implementation of the above equation using sine wave, white noise, and step inputs resulted in various forms of FDR at frequencies between 0 and 40 Hz depending on the type of input used. Resistance was most sensitive to changes in input pressure amplitude. The model's results suggest that the P - V nonlinearity can have a significant influence on the impedance construct in obstructed lung disease.