Monitoring of total positive end-expiratory pressure during mechanical ventilation by artificial neural networks

Ventilation treatment of acute lung injury (ALI) requires the application of positive airway pressure at the end of expiration (PEEPapp) to avoid lung collapse. However, the total pressure exerted on the alveolar walls (PEEPtot) is the sum of PEEPapp and intrinsic PEEP (PEEPi), a hidden component. To measure PEEPtot, ventilation must be discontinued with an end-expiratory hold maneuver (EEHM). We hypothesized that artificial neural networks (ANN) could estimate the PEEPtot from flow and pressure tracings during ongoing mechanical ventilation. Ten pigs were mechanically ventilated, and the time constant of their respiratory system (τRS) was measured. We shortened their expiratory time (TE) according to multiples of τRS, obtaining different respiratory patterns (Rpat). Pressure (PAW) and flow (V′AW) at the airway opening during ongoing mechanical ventilation were simultaneously recorded, with and without the addition of external resistance. The last breath of each Rpat included an EEHM, which was used to compute the reference PEEPtot. The entire protocol was repeated after the induction of ALI with i.v. injection of oleic acid, and 382 tracings were obtained. The ANN had to extract the PEEPtot, from the tracings without an EEHM. ANN agreement with reference PEEPtot was assessed with the Bland–Altman method. Bland Altman analysis of estimation error by ANN showed −0.40 ± 2.84 (expressed as bias ± precision) and ±5.58 as limits of agreement (data expressed as cmH2O). The ANNs estimated the PEEPtot well at different levels of PEEPapp under dynamic conditions, opening up new possibilities in monitoring PEEPi in critically ill patients who require ventilator treatment

Expiratory model-based method to monitor ARDS disease state

INTRODUCTION: Model-based methods can be used to characterise patient-specific condition and response to mechanical ventilation (MV) during treatment for acute respiratory distress syndrome (ARDS). Conventional metrics of respiratory mechanics are based on inspiration only, neglecting data from the expiration cycle. However, it is hypothesised that expiratory data can be used to determine an alternative metric, offering another means to track patient condition and guide positive end expiratory pressure (PEEP) selection. METHODS: Three fully sedated, oleic acid induced ARDS piglets underwent three experimental phases. Phase 1 was a healthy state recruitment manoeuvre. Phase 2 was a progression from a healthy state to an oleic acid induced ARDS state. Phase 3 was an ARDS state recruitment manoeuvre. The expiratory time-constant model parameter was determined for every breathing cycle for each subject. Trends were compared to estimates of lung elastance determined by means of an end-inspiratory pause method and an integral-based method. All experimental procedures, protocols and the use of data in this study were reviewed and approved by the Ethics Committee of the University of Liege Medical Faculty. RESULTS: The overall median absolute percentage fitting error for the expiratory time-constant model across all three phases was less than 10 %; for each subject, indicating the capability of the model to capture the mechanics of breathing during expiration. Provided the respiratory resistance was constant, the model was able to adequately identify trends and fundamental changes in respiratory mechanics. CONCLUSION: Overall, this is a proof of concept study that shows the potential of continuous monitoring of respiratory mechanics in clinical practice. Respiratory system mechanics vary with disease state development and in response to MV settings. Therefore, titrating PEEP to minimal elastance theoretically results in optimal PEEP selection. Trends matched clinical expectation demonstrating robustness and potential for guiding MV therapy. However, further research is required to confirm the use of such real-time methods in actual ARDS patients, both sedated and spontaneously breathing.Peer reviewe

Automated Detection of Incomplete Exhalation as an Indirect Detection of Auto-PEEP on Mechanically Ventilated Adults

Auto-PEEP is auto positive end-expiratory pressure due to excessive amounts of alveolar gas produced by sustained recurrent incomplete exhalation. Incomplete exhalation occurs when the exhaled breath never reaches a flow rate of 0 L/min. The objective of this dissertation is to develop an automated detection system of auto-PEEP through incomplete exhalation as revealed by ventilator graphics for mechanically ventilated adults. Auto-PEEP can cause adverse effects if allowed to linger and if not quickly identified. An automated detection system will be instrumental in helping to quickly identify auto-PEEP. A computerized algorithm was developed to detect incomplete exhalation based on the following three parameters:1) Foi, was used to represent the value of the flow at the onset of inhalation, 2) ∆T, was used to represent the value of time difference between onset inhalation to the 0 L/min mark, and 3) slope threshold, a value set for the slope of change of flow over ∆T. Optimum parameters of the algorithm were achieved for Foi = -3 L/min, ∆T = 0.2 s, and slope threshold = 90 L-s/min. A novel data set was introduced to validate the algorithm, yielding no significant difference in true positive rates (t = 1.5, df = 12.402, p-value = 0.1408) and false positive rates (t = 1.9, df = 16.765, p-value = 0.0725) as outcomes for two-tailed t-tests comparing the novel and old data set. To determine the relationship between auto-PEEP and detection of sustained incomplete exhalation, a correlation of a linear model of the novel data set between auto-PEEP and the percentage of incomplete exhalation detection out of the existing breaths (index) was investigated. A linear model should interpret the index value that corresponds to significant auto-PEEP presence; unfortunately, no significant linear model was found between incomplete exhalation index and auto-PEEP (F1,62 = 1.67, p-value = 0.2010). However, there was a relationship between the intrinsic PEEP values and the incomplete exhalation index as functions of time. The automated detection algorithm produced by this work provides a non-invasive method of automatically detecting auto-PEEP

Adaptive Closed-Loop Neuromorphic Controller for Use in Respiratory Pacing

Respiratory pacing can treat ventilatory insufficiency through electrical stimulation of the respiratory muscles, or the respective innervating nerves, to induce ventilation. It avoids some of the adverse effects associated with mechanical ventilation such as risk of diaphragm atrophy and lung damage. However, current respiratory pacing systems provide stimulation in an open-loop manner. This often requires users to undergo frequent tuning sessions with trained clinicians if the specified stimulation parameters are unable to induce sufficient ventilation in the presence of time-varying changes in muscle properties, chest biomechanics, and metabolic demand. Lack of adaptation to these changes may lead to complications arising from hyperventilation or hypoventilation. A novel adaptive closed-loop neuromorphic controller for respiratory pacing has been developed to address the need for closed-loop control respiratory pacing capable of responding to changes in metabolic production of CO2, diaphragm muscle health, and biomechanics. A 3-stage processes was utilized to develop the controller. First, an adaptive controller that could follow a preset within-breath volume profile was developed in silico and evaluated in vivo in anesthetized rats with an intact spinal cord or with diaphragm hemiparesis induced by spinal cord hemisection. Second, a neuromorphic computational model was developed to generate a desired trajectory that reflects changes in breath volume and respiratory rate in response to arterial CO2 levels. An enhanced controller capable of generating and matching this model-based desired trajectory was evaluated in silico and in vivo on rats with depressed ventilation and diaphragm hemiparesis. Finally, the enhanced adaptive controller was modified for human-related biomechanics and CO2 dynamics and evaluated in silico under changes of metabolic demand, presence of muscle fatigue, and after randomization of model parameters to reproduce expected between-subject differences. Results showed that the adaptive controller could adapt and modulate stimulation parameters and respiratory rate to follow a desired model-generated breath volume trajectory in response to dynamic arterial CO2 levels. In silico studies aimed at assessing potential for clinical translation showed that an enhanced controller modified for human use could successfully control ventilation to achieve and maintain normocapnic arterial CO2 levels. Overall, these results suggest that use of an adaptive closed-loop controller could lead to improved ventilatory outcomes and quality of life for users of adaptive respiratory pacing

Indoor air quality in California homes with code-required mechanical ventilation.

Data were collected in 70 detached houses built in 2011-2017 in compliance with the mechanical ventilation requirements of California's building energy efficiency standards. Each home was monitored for a 1-week period with windows closed and the central mechanical ventilation system operating. Pollutant measurements included time-resolved fine particulate matter (PM2.5 ) indoors and outdoors and formaldehyde and carbon dioxide (CO2 ) indoors. Time-integrated measurements were made for formaldehyde, NO2 , and nitrogen oxides (NOX ) indoors and outdoors. Operation of the cooktop, range hood, and other exhaust fans was continuously recorded during the monitoring period. Onetime diagnostic measurements included mechanical airflows and envelope and duct system air leakage. All homes met or were very close to meeting the ventilation requirements. On average, the dwelling unit ventilation fan moved 50% more airflow than the minimum requirement. Pollutant concentrations were similar to or lower than those reported in a 2006-2007 study of California new homes built in 2002-2005. Mean and median indoor concentrations were lower by 44% and 38% for formaldehyde and 44% and 54% for PM2.5 . Ventilation fans were operating in only 26% of homes when first visited, and the control switches in many homes did not have informative labels as required by building standards

Comparative study of four sigmoid models of pressure-volume curve in acute lung injury

BACKGROUND: The pressure-volume curve of the respiratory system is a tool to monitor and set mechanical ventilation in acute lung injury. Mathematical models of the static pressure-volume curve of the respiratory system have been proposed to overcome the inter- and intra-observer variability derived from eye-fitting. However, different models have not been compared. METHODS: The goodness-of-fit and the values of derived parameters (upper asymptote, maximum compliance and points of maximum curvature) in four sigmoid models were compared, using pressure-volume data from 30 mechanically ventilated patients during the early phase of acute lung injury. RESULTS: All models showed an excellent goodness-of-fit (R(2 )always above 0.92). There were significant differences between the models in the parameters derived from the inspiratory limb, but not in those derived from the expiratory limb of the curve. The within-case standard deviations of the pressures at the points of maximum curvature ranged from 2.33 to 6.08 cmH(2)O. CONCLUSION: There are substantial variabilities in relevant parameters obtained from the four different models of the static pressure-volume curve of the respiratory system

New Techniques and Optimizations of Short Echo-time 1H MRI with Applications in Murine Lung

Although x-ray computed tomography (CT) is a gold standard for pulmonary imaging, it has high ionizing radiation, which puts patients at greater risk of cancer, particularly in a longitudinal study with cumulative doses. Magnetic resonance imaging (MRI) doesn\u27t involve exposure to ionizing radiation and is especially useful for visualizing soft tissues and organs such as ligaments, cartilage, brain, and heart. Many efforts have been made to apply MRI to study lung function and structure of both humans and animals. However, lung is a unique organ and is very different from other solid organs like the heart and brain due to its complex air-tissue interleaved structure. The magnetic susceptibility differences at the air-tissue interfaces result in very short T2* (~ 1 ms) of lung parenchyma, which is even shorter in small-animal MRI (often at higher field) than in human MRI. Both low proton density and short T2* of lung parenchyma are challenges for pulmonary imaging via MRI because they lead to low signal-to-noise ratio (SNR) in images with traditional Cartesian methods that necessitate longer echo times (≥ 1 ms). This dissertation reports the work of optimizing pulmonary MRI techniques by minimizing the negative effects of low proton density and short T2* of murine lung parenchyma, and the application of these techniques to imaging murine lung. Specifically, echo time (TE) in the Cartesian sequence is minimized, by simultaneous slice select rephasing, phase encoding and read dephasing gradients, in addition to partial Fourier imaging, to reduce signal loss due to T2* relaxation. Radial imaging techniques, often called ultra-short echo-time MRI or UTE MRI, with much shorter time between excitation and data acquisition, were also developed and optimized for pulmonary imaging. Offline reconstruction for UTE data was developed on a Linux system to regrid the non-Cartesian (radial in this dissertation) k-space data for fast Fourier transform. Slabselected UTE was created to fit the field-of-view (FOV) to the imaged lung without fold-in aliasing, which increases TE slightly compared to non-slab-selected UTE. To further reduce TE as well as fit the FOV to the lung without aliasing, UTE with ellipsoidal k-space coverage was developed, which increases resolution and decreases acquisition time. Taking into account T2* effects, point spread function (PSF) analysis was performed to determine the optimal acquisition time for maximal single-voxel SNR. Retrospective self-gating UTE was developed to avoid the use of a ventilator (which may cause lung injury) and to avoid possible prospective gating errors caused by abrupt body motion. Cartesian gradient-recalled-echo imaging (GRE) was first applied to monitor acute cellular rejection in lung transplantation. By repeated imaging in the same animals, both parenchymal signal and lung compliance were measured and were able to detect rejection in the allograft lung. GRE was also used to monitor chronic cellular rejection in a transgenic mouse model after lung transplantation. In addition to parenchymal signal and lung compliance, the percentage of highdensity lung parenchyma was defined and measured to detect chronic rejection. This represents one of the first times quantitative pulmonary MRI has been performed. For 3D radial UTE MRI, 2D golden means (1) were used to determine the direction of radial spokes in k-space, resulting in pseudo-random angular sampling of spherical k-space coverage. Ellipsoidal k-space coverage was generated by expanding spherical coverage to create an ellipsoid in k-space. UTE MRI with ellipsoidal k-space coverage was performed to image healthy mice and phantoms, showing reduced FOV and enhanced in-plane resolution compared to regular UTE. With this modified UTE, T2* of lung parenchyma was measured by an interleaved multi-TE strategy, and T1 of lung parenchyma was measured by a limited flip angle method (2). Retrospective self-gating UTE with ellipsoidal k-space coverage was utilized to monitor the progression of pulmonary fibrosis in a transforming growth factor (TGF)-α transgenic mouse model and compared with histology and pulmonary mechanics. Lung fibrosis progression was not only visualized by MRI images, but also quantified and tracked by the MRIderived lung function parameters like mean lung parenchyma signal, high-density lung volume percentage, and tidal volume. MRI-derived lung function parameters were strongly correlated with the findings of pulmonary mechanics and histology in measuring fibrotic burden. This dissertation demonstrates new techniques and optimizations in GRE and UTE MRI that are employed to minimize TE and image murine lungs to assess lung function and structure and monitor the time course of lung diseases. Importantly, the ability to longitudinally image individual animals by these MRI techniques minimizes the number of animals required in preclinical studies and increases the statistical power of future experiments as each animal can serve at its own control

Unmatched ventilation and perfusion measured by electrical impedance tomography predicts the outcome of ARDS

Background In acute respiratory distress syndrome (ARDS), non-ventilated perfused regions coexist with non-perfused ventilated regions within lungs. The number of unmatched regions might reflect ARDS severity and affect the risk of ventilation-induced lung injury. Despite pathophysiological relevance, unmatched ventilation and perfusion are not routinely assessed at the bedside. The aims of this study were to quantify unmatched ventilation and perfusion at the bedside by electrical impedance tomography (EIT) investigating their association with mortality in patients with ARDS and to explore the effects of positive end-expiratory pressure (PEEP) on unmatched ventilation and perfusion in subgroups of patients with different ARDS severity based on PaO2/FiO2 and compliance. Methods Prospective observational study in 50 patients with mild (36%), moderate (46%), and severe (18%) ARDS under clinical ventilation settings. EIT was applied to measure the regional distribution of ventilation and perfusion using central venous bolus of saline 5% during end-inspiratory pause. We defined unmatched units as the percentage of only ventilated units plus the percentage of only perfused units. Results Percentage of unmatched units was significantly higher in non-survivors compared to survivors (32[27–47]% vs. 21[17–27]%, p < 0.001). Percentage of unmatched units was an independent predictor of mortality (OR 1.22, 95% CI 1.07–1.39, p = 0.004) with an area under the ROC curve of 0.88 (95% CI 0.79–0.97, p < 0.001). The percentage of ventilation to the ventral region of the lung was higher than the percentage of ventilation to the dorsal region (32 [27–38]% vs. 18 [13–21]%, p < 0.001), while the opposite was true for perfusion (28 [22–38]% vs. 36 [32–44]%, p < 0.001). Higher percentage of only perfused units was correlated with lower dorsal ventilation (r =  − 0.486, p < 0.001) and with lower PaO2/FiO2 ratio (r =  − 0.293, p = 0.039). Conclusions EIT allows bedside assessment of unmatched ventilation and perfusion in mechanically ventilated patients with ARDS. Measurement of unmatched units could identify patients at higher risk of death and could guide personalized treatment