Publications

Surface electromyography (sEMG) can be used to measure the electrical activity of the respiratory muscles. The possible applications of sEMG span from patients suffering from acute respiratory failure to patients receiving chronic home mechanical ventilation, to evaluate muscle function, titrate ventilatory support and guide treatment. However, sEMG is mainly used as a monitoring tool for research and its use in clinical practice is still limited-in part due to a lack of standardization and transparent reporting. During this round table meeting, recommendations on data acquisition, processing, interpretation, and potential clinical applications of respiratory sEMG were discussed. This paper informs the clinical researcher interested in respiratory muscle monitoring about the current state of the art on sEMG, knowledge gaps and potential future applications for patients with respiratory failure.

JTD Keywords: Acute respiratory failure, Artificial ventilation, Asthmatic-children, Breathing muscle, Clinical monitoring, Clinical practice, Clinical research, Consensus development, Data interpretation, Disease exacerbation, Drive, Electrode positioning, Electrode removal, Electromyography, Force, Home care, Human, Human diaphragm, Humans, Information processing, Inspiratory muscle training, Inspiratory muscles, Intensive care unit, Knowledge gap, Long term care, Mechanical ventilation, Medical procedures, Muscle contraction, Muscle fatigue, Muscle function, Muscle training, Muscle, skeletal, Muscle-activity, Noninvasive ventilation, Patient monitoring, Patient-ventilator asynchrony, Physiology, Prognosis, Quality of life, Reporting and data system, Respiratory failure, Respiratory muscles, Review, Severe exacerbations, Signal processing, Skeletal muscle, Standardization, Surface electromyography, Time factor

A fundamental task of the respiratory system is to operate as a mechanical gas pump ensuring that fresh air gets in close contact with the blood circulating through the lung capillaries to achieve O2 and CO2 exchange. To ventilate the lungs, the respiratory muscles provide the pressure required to overcome the viscoelastic mechanical load of the respiratory system. From a mechanical viewpoint, the most relevant respiratory system properties are the resistance of the airways (R aw), and the compliance of the lung tissue (C L) and chest wall (C CW). Both airflow and lung volume changes in spontaneous breathing and mechanical ventilation are determined by applying the fundamental mechanical laws to the relationships between the pressures inside the respiratory system (at the airway opening, alveolar, pleural, and muscular) and R aw, C L, and C CW. These relationships also are the basis of the different methods available to measure respiratory mechanics during spontaneous and artificial ventilation. Whereas a simple mechanical model (R aw, C L, and C CW) describes the basic understanding of ventilation mechanics, more complex concepts (nonlinearity, inhomogeneous ventilation, or viscoelasticity) should be employed to better describe and measure ventilation mechanics in patients.Thieme. All rights reserved.

JTD Keywords: airway-resistance, alveolar, compliance, dilution, elastance, flow, inhomogeneous ventilation, input impedance, lung-volume, mechanical ventilation, monitoring, pendelluft, pleural pressure, respiratory-distress-syndrome, viscoelasticity, Chest-wall mechanics, Resistance

The optimal extubating moment is still a challenge in clinical practice. Respiratory pattern variability analysis in patients assisted through mechanical ventilation to identify this optimal moment could contribute to this process. This work proposes the analysis of this variability using several time series obtained from the respiratory flow and electrocardiogram signals, applying techniques based on artificial intelligence. 154 patients undergoing the extubating process were classified in three groups: successful group, patients who failed during weaning process, and patients who after extubating failed before 48 hours and need to reintubated. Power Spectral Density and time-frequency domain analysis were applied, computing Discrete Wavelet Transform. A new Q index was proposed to determine the most relevant parameters and the best decomposition level to discriminate between groups. Forward selection and bidirectional techniques were implemented to reduce dimensionality. Linear Discriminant Analysis and Neural Networks methods were implemented to classify these patients. The best results in terms of accuracy were, 84.61 ± 3.1% for successful versus failure groups, 86.90 ± 1.0% for successful versus reintubated groups, and 91.62 ± 4.9% comparing the failure and reintubated groups. Parameters related to Q index and Neural Networks classification presented the best performance in the classification of these patients.

JTD Keywords: Mechanical ventilation, Neural networks, Wavelet transform, Weaning

Assessing tidal volume during mechanical ventilation is critical to improving gas exchange while avoiding ventilator-induced lung injury. Conventional flow and volume measurements are usually carried out by built-in pneumotachographs in the ventilator or by stand-alone flowmeters. Such flow/volume measurement devices are expensive and thus usually unaffordable in low-resource settings. Here, we aimed to design and test low-cost and technically-simple calibration and assembly pneumotachographs. The proposed pneumotachographs are made by manual perforation of a plate with a domestic drill. Their pressure-volume relationship is characterized by a quadratic equation with parameters that can be tailored by the number and diameter of the perforations. We show that the calibration parameters of the pneumotachographs can be measured through two maneuvers with a conventional resuscitation bag and by assessing the maneuver volumes with a cheap and straightforward water displacement setting. We assessed the performance of the simplified low-cost pneumotachographs to measure flow/volume during mechanical ventilation as carried out under typical conditions in low-resource settings, i.e., lacking gold standard expensive devices. Under realistic mechanical ventilation settings (pressure- and volume-control; 200-600 mL), inspiratory tidal volume was accurately measured (errors of 2.1% on average and <4% in the worst case). In conclusion, a simple, low-cost procedure facilitates the construction of affordable and accurate pneumotachographs for monitoring mechanical ventilation in low- and middle-income countries.

JTD Keywords: calibration, flow measurement, low- and middle-income countries, mechanical ventilation, pneumotachograph, Calibration, Flow, Flow measurement, Low- and middle-income countries, Lung injury, Mechanical ventilation, Pneumotachograph, Pressure-drop, Resistance, Tidal volume

In clinical practice, when a patient is undergoing mechanical ventilation, it is important to identify the optimal moment for extubation, minimizing the risk of failure. However, this prediction remains a challenge in the clinical process. In this work, we propose a new protocol to study the extubation process, including the electromyographic diaphragm signal (diaEMG) recorded through 5-channels with surface electrodes around the diaphragm muscle. First channel corresponds to the electrode on the right. A total of 40 patients in process of withdrawal of mechanical ventilation, undergoing spontaneous breathing tests (SBT), were studied. According to the outcome of the SBT, the patients were classified into two groups: successful (SG: 19 patients) and failure (FG: 21 patients) groups. Parameters extracted from the envelope of each channel of diaEMG in time and frequency domain were studied. After analyzing all channels, the second presented maximum differences when comparing the two groups of patients, with parameters related to root mean square (p = 0.005), moving average (p = 0.001), and upward slope (p = 0.017). The third channel also presented maximum differences in parameters as the time between maximum peak (p = 0.004), and the skewness (p = 0.027). These results suggest that diaphragm EMG signal could contribute to increase the knowledge of the behaviour of respiratory system in these patients and improve the extubation process.Clinical Relevance - This establishes the characterization of success and failure patients in the extubation process. © 2021 IEEE.

JTD Keywords: classification, recognition, Airway extubation, Artificial ventilation, Clinical practices, Clinical process, Diaphragm, Diaphragm muscle, Diaphragms, Electrodes, Electromyographic, Extubation, Frequency domain analysis, Human, Humans, Maximum differences, Mechanical ventilation, New protocol, Respiration, artificial, Respiratory system, Risk of failure, Spontaneous breathing, Surface electrode, Surface emg signals, Thorax, Ventilation, Ventilator weaning

To optimize long-term nocturnal non-invasive ventilation in patients with chronic obstructive pulmonary disease, surface diaphragm electromyography (EMGdi) might be helpful to detect patient-ventilator asynchrony. However, visual analysis is labor-intensive and EMGdi is heavily corrupted by electrocardiographic (ECG) activity. Therefore, we developed an automatic method to detect inspiratory onset from EMGdi envelope using fixed sample entropy (fSE) and a dynamic threshold based on kernel density estimation (KDE). Moreover, we combined fSE with adaptive filtering techniques to reduce ECG interference and improve onset detection. The performance of EMGdi envelopes extracted by applying fSE and fSE with adaptive filtering was compared to the root mean square (RMS)-based envelope provided by the EMG acquisition device. Automatic onset detection accuracy, using these three envelopes, was evaluated through the root mean square error (RMSE) between the automatic and mean visual onsets (made by two observers). The fSE-based method provided lower RMSE, which was reduced from 298 ms to 264 ms when combined with adaptive filtering, compared to 301 ms provided by the RMS-based method. The RMSE was negatively correlated with the proposed EMGdi quality indices. Following further validation, fSE with KDE, combined with adaptive filtering when dealing with low quality EMGdi, indicates promise for detecting the neural onset of respiratory drive.

JTD Keywords: Fixed sample entropy, Adaptive filtering, Root mean square, Diaphragm electromyography, Non-invasive mechanical ventilation, Chronic obstructive pulmonary disease

Measuring diaphragmatic electromyography (EMGdi) provides an indirect quantification of neural respiratory drive and allows the delimitation of diaphragm neural activation and deactivation during inspiration. EMGdi recordings have been incorporated in novel modes of assisted mechanical ventilation, such as neurally adjusted ventilatory assist (NAVA), to trigger and cycle-off the ventilator. The EMGdi signal improves the assistance delivered by more conventional ventilatory modes, in which the ventilator is synchronized with the patient employing a pneumatic triggering. In this work, we evaluate the time delay between the onset and offset of inspiratory activity estimated from EMGdi and three respiratory mechanical signals: the respiratory flow (FL), the transdiaphragmatic pressure (Pdi) and the diaphragm length (Ldi) signals. To this purpose, these signals were acquired in three mongrel dogs surgically instrumented under general anesthesia. Onsets and offsets were estimated manually and by automatic algorithms on these signals. The highest delays were obtained between EMGdi and FL (100 ms) while the lowest delays were obtained between EMGdi and Pdi (8 ms). Moreover, differences between manual and automatic estimations showed a mean absolute error lower than 45 ms. In conclusion, our study points out that both EMGdi and Pdi signals detect the onset and offset of inspiratory activity earlier than the FL signal, and would therefore be better for the improvement of patient-ventilator synchrony.

JTD Keywords: Estimation, Ventilation, Anesthesia, Dogs, Manuals, Power harmonic filters

In this work, the cardiorespiratory pattern of patients undergoing extubation process is studied. First, the respiratory and cardiac signals were resampled, next the Symbolic Dynamics (SD) technique was implemented, followed of a dimensionality reduction applying Forward Selection (FS) and Moving Window with Variance Analysis (MWVA) methods. Finally, the Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM) classifiers were used. The study analyzed 153 patients undergoing weaning process, classified into 3 groups: Successful Group (SG: 94 patients), Failed Group (FG: 39 patients), and patients who had been successful during the extubation and had to be reintubated before 48 hours, Reintubated Group (RG: 21 patients). According to the results, the best classification present an accuracy higher than 88.98 ± 0.013% in all proposed combinations.

JTD Keywords: Support vector machines, Standards, Time series analysis, Resonant frequency, Nonlinear dynamical systems, Ventilation

Lung bioengineering using decellularized organ scaffolds is a potential alternative for lung transplantation. Clinical application will require donor scaffold sterilization. As gamma-irradiation is a conventional method for sterilizing tissue preparations for clinical application, the aim of this study was to evaluate the effects of lung scaffold sterilization by gamma irradiation on the mechanical properties of the acellular lung when subjected to the artificial ventilation maneuvers typical within bioreactors. Twenty-six mouse lungs were decellularized by a sodium dodecyl sulfate detergent protocol. Eight lungs were used as controls and 18 of them were submitted to a 31kGy gamma irradiation sterilization process (9 kept frozen in dry ice and 9 at room temperature). Mechanical properties of acellular lungs were measured before and after irradiation. Lung resistance (RL) and elastance (EL) were computed by linear regression fitting of recorded signals during mechanical ventilation (tracheal pressure, flow and volume). Static (Est) and dynamic (Edyn) elastances were obtained by the end-inspiratory occlusion method. After irradiation lungs presented higher values of resistance and elastance than before irradiation: RL increased by 41.1% (room temperature irradiation) and 32.8% (frozen irradiation) and EL increased by 41.8% (room temperature irradiation) and 31.8% (frozen irradiation). Similar increases were induced by irradiation in Est and Edyn. Scanning electron microscopy showed slight structural changes after irradiation, particularly those kept frozen. Sterilization by gamma irradiation at a conventional dose to ensure sterilization modifies acellular lung mechanics, with potential implications for lung bioengineering.

JTD Keywords: Gamma irradiation, Lung bioengineering, Lung decellularization, Organ scaffold, Pulmonary mechanics, Decellularization, Gamma irradiation, Mouse lung, Pulmonary mechanics, dodecyl sulfate sodium, animal tissue, Article, artificial ventilation, bioengineering, bioreactor, compliance (physical), controlled study, freezing, gamma irradiation, lung, lung mechanics, lung resistance, male, mouse, nonhuman, room temperature, scanning electron microscopy, tissue scaffold, trachea pressure

Lung bioengineering based on decellularized organ scaffolds is a potential alternative for transplantation. Freezing/thawing, a usual procedure in organ decellularization and storage could modify the mechanical properties of the lung scaffold and reduce the performance of the bioengineered lung when subjected to the physiological inflation-deflation breathing cycles. The aim of this study was to determine the effects of repeated freezing/thawing on the mechanical properties of decellularized lungs in the physiological pressure-volume regime associated with normal ventilation. Fifteen mice lungs (C57BL/6) were decellularized using a conventional protocol not involving organ freezing and based on sodium dodecyl sulfate detergent. Subsequently, the mechanical properties of the acellular lungs were measured before and after subjecting them to three consecutive cycles of freezing/thawing. The resistance (RL) and elastance (EL) of the decellularized lungs were computed by linear regression fitting of the recorded signals (tracheal pressure, flow, and volume) during mechanical ventilation. RL was not significantly modified by freezing-thawing: from 0.88 ± 0.37 to 0.90 ± 0.38 cmH2O·s·mL-1 (mean ± SE). EL slightly increased from 64.4 ± 11.1 to 73.0 ± 16.3 cmH2O·mL-1 after the three freeze-thaw cycles (p = 0.0013). In conclusion, the freezing/thawing process that is commonly used for both organ decellularization and storage induces only minor changes in the ventilation mechanical properties of the organ scaffold.

JTD Keywords: Elastance, Freezing/thawing, Lung bioengineering, Lung decellularization, Mechanical ventilation, Organ scaffold

Lung decellularization is based on the use of physical, chemical, or enzymatic methods to break down the integrity of the cells followed by a treatment to extract the cellular material from the lung scaffold. The aim of this study was to characterize the mechanical changes throughout the different steps of lung decellularization process. Four lungs from mice (C57BL/6) were decellularized by using a conventional protocol based on sodium dodecyl sulfate. Lungs resistance (RL) and elastance (EL) were measured along decellularization steps and were computed by linear regression fitting of tracheal pressure, flow, and volume during mechanical ventilation. Transients differences found were more distinct in an intermediate step after the lungs were rinsed with deionized water and treated with 1% SDS, whereupon the percentage of variation reached approximately 80% for resistance values and 30% for elastance values. In conclusion, although a variation in extracellular matrix stiffness was observed during the decellularization process, this variation can be considered negligible overall because the resistance and elastance returned to basal values at the final decellularization step.

JTD Keywords: Lung bioengineering, Lung decellularization, Organ scaffold, dodecyl sulfate sodium, animal tissue, article, artificial ventilation, compliance (physical), controlled study, enzyme chemistry, extracellular matrix, female, flow, lung, lung decellularization, lung pressure, lung resistance, mouse, nonhuman, positive end expiratory pressure, priority journal, rigidity, tissue engineering, trachea pressure

In this work, the flow signal Expiratory Pause (EP) temporal analysis is used in 18 patients with obstructive lung diseases going through spontaneous breathing trial at weaning process. The main objective was to identify the patients who were successfully disconnected (success group: 9 patients), and those who were not (failure and reintubated group: 9 patients). A variable selection stage was done by mean group comparison and step wise variable inclusion, leading to a 3 parameters set: EP time median; cycle time mean; and median absolute deviation of the EP maxima local number. Next, this set was used in a classifier based on linear discriminant analysis, which results in 17 patients (94.4%) correctly classified, with 88.9% of specificity (Sp) and 100% of sensitivity (Se). Finally, applying the leave-one-out cross validation method, results were 88.9% of correctly classified patients (Sp=77.8% and Se=100%). In conclusion, the proposed parameters showed a good performance and could be used to help therapists to wean patients with obstructive diseases.

JTD Keywords: Chronic Obstructive Pulmonary Disease (COPD), Weaning, Mechanical ventilation, Expiratory pause

Disconnection from mechanical ventilation, called the weaning process, is an additional difficulty in the management of patients in intensive care units (ICU). Unnecessary delays in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we propose an extubation index based on the power of the respiratory flow signal (Pi). A total of 132 patients on weaning trials were studied: 94 patients with successful trials (group S) and 38 patients who failed to maintain spontaneous breathing and were reconnected (group F). The respiratory flow signals were processed considering the following three stages: a) zero crossing detection of the inspiratory phase, b) inflection point detection of the flow curve during the inspiratory phase, and c) calculation of the signal power on the time instant indicated by the inflection point. The zero crossing detection was performed using an algorithm based on thresholds. The inflection points were marked considering the zero crossing of the second derivative. Finally, the inspiratory power was calculated from the energy contained over the finite time interval (between the instant of zero crossing and the inflection point). The performance of this parameter was evaluated using the following classifiers: logistic regression, linear discriminant analysis, the classification and regression tree, Naive Bayes, and the support vector machine. The best results were obtained using the Bayesian classifier, which had an accuracy, sensitivity and specificity of 87%, 90% and 81% respectively.

JTD Keywords: Bayes methods, Bayesian classifier, Indexes, Logistics, Niobium, Regression tree analysis, Support vector machines, Ventilation

One of the most challenging problems in intensive care is still the process of discontinuing mechanical ventilation, called weaning process. Both an unnecessary delay in the discontinuation process and a weaning trial that is undertaken too early are undesirable. In this study, we analyzed respiratory pattern variability using the respiratory volume signal of patients submitted to two different levels of pressure support ventilation (PSV), prior to withdrawal of the mechanical ventilation. In order to characterize the respiratory pattern, we analyzed the following time series: inspiratory time, expiratory time, breath duration, tidal volume, fractional inspiratory time, mean inspiratory flow and rapid shallow breathing. Several autoregressive modeling techniques were considered: autoregressive models (AR), autoregressive moving average models (ARMA), and autoregressive models with exogenous input (ARX). The following classification methods were used: logistic regression (LR), linear discriminant analysis (LDA) and support vector machines (SVM). 20 patients on weaning trials from mechanical ventilation were analyzed. The patients, submitted to two different levels of PSV, were classified as low PSV and high PSV. The variability of the respiratory patterns of these patients were analyzed. The most relevant parameters were extracted using the classifiers methods. The best results were obtained with the interquartile range and the final prediction errors of AR, ARMA and ARX models. An accuracy of 95% (93% sensitivity and 90% specificity) was obtained when the interquartile range of the expiratory time and the breath duration time series were used a LDA model. All classifiers showed a good compromise between sensitivity and specificity.

JTD Keywords: autoregressive moving average processes, feature extraction, medical signal processing, patient care, pneumodynamics, signal classification, support vector machines, time series, ARX, autoregressive modeling techniques, autoregressive models with exogenous input, autoregressive moving average model, breath duration time series, classification method, classifier method, discontinuing mechanical ventilation, expiratory time, feature extraction, final prediction errors, fractional inspiratory time, intensive care, interquartile range, linear discriminant analysis, logistic regression analysis, mean inspiratory flow, patient respiratory volume signal, pressure support level, pressure support ventilation, rapid shallow breathing, respiratory pattern variability characterization, support vector machines, tidal volume, weaning trial, Analytical models, Autoregressive processes, Biological system modeling, Estimation, Support vector machines, Time series analysis, Ventilation

The process of weaning from mechanical ventilation is one of the challenges in intensive care units. 66 patients under extubation process (T-tube test) were studied: 33 patients with successful trials and 33 patients who failed to maintain spontaneous breathing and were reconnected. Each patient was characterized using 7 time series from respiratory signals, and for each serie was evaluated the discrete wavelet transform. It trains a neural network for discriminating between patients from the two groups.

JTD Keywords: discrete wavelet transforms, neural nets, patient treatment, pneumodynamics, time series, ventilation, T-tube test, discrete wavelet transform, extubation process, intensive care units, mechanical ventilation, moment of disconnection, neural network, patients, respiratory signals, spontaneous breathing, time series, weaning, Mechanical Ventilation, Neural Networks, Time series from respiratory signals, Wavelet Transform

Even if the performance of a given ventilator has been evaluated in the laboratory under very well controlled conditions, inappropriate maintenance and lack of long-term stability and accuracy of the ventilator sensors may lead to ventilation errors in actual clinical practice. The aim of this study was to evaluate the actual performances of ventilators during clinical routines. A resistance (7.69 cmH(2)O/L/s) - elastance (100 mL/cmH(2)O) test lung equipped with pressure, flow, and oxygen concentration sensors was connected to the Y-piece of all the mechanical ventilators available for patients in four intensive care units (ICUs; n = 66). Ventilators were set to volume-controlled ventilation with tidal volume = 600 mL, respiratory rate = 20 breaths/minute, positive end-expiratory pressure (PEEP) = 8 cmH(2)O, and oxygen fraction = 0.5. The signals from the sensors were recorded to compute the ventilation parameters. The average standard deviation and range (min-max) of the ventilatory parameters were the following: inspired tidal volume = 607 36 (530-723) mL, expired tidal volume = 608 36 (530-728) mL, peak pressure = 20.8 2.3 (17.2-25.9) cmH(2)O, respiratory rate = 20.09 0.35 (19.5-21.6) breaths/minute, PEEP = 8.43 0.57 (7.26-10.8) cmH(2)O, oxygen fraction = 0.49 0.014 (0.41-0.53). The more error-prone parameters were the ones related to the measure of flow. In several cases, the actual delivered mechanical ventilation was considerably different from the set one, suggesting the need for improving quality control procedures for these machines.

JTD Keywords: Equipment and supplies, Medical devices, Intravenous, Quality assurance, Health care quality assessment, Ventilator accuracy, Ventilation error

Bone marrow-derived mesenchymal stem cells (MSCs) reduce acute lung injury in animals challenged by bleomycin or bacterial lipopolysaccaride. It is not known, however, whether MSCs protect from ventilator-induced lung injury (VILI). This study investigated whether MSCs have a potential role in preventing or modulating VILI in healthy rats subjected to high-volume ventilation. 24 Sprague-Dawley rats (250-300 g) were subjected to high-volume mechanical ventilation (25 mL.kg(-1)). MSCs (5 x 10(6)) were intravenously or intratracheally administered (n=8 each) 30 min before starting over-ventilation and eight rats were MSC-untreated. Spontaneously breathing anesthetised rats (n=8) served as controls. After 3 h of over-ventilation or control the animals were sacrificed and lung tissue and bronchoalveolar lavage fluid (BALF) were sampled for further analysis. When compared with controls, MSC-untreated over-ventilated rats exhibited typical VILI features. Lung oedema, histological lung injury index, concentrations of total protein, interleukin-1 beta, macrophage inflammatory protein-2 and number of neutrophils in BALF and vascular cell adhesion protein-1 in lung tissue significantly increased in over-ventilated rats. All these indices of VILI moved significantly towards normalisation in the rats treated with MSCs, whether intravenously or intratracheally. Both local and systemic pre-treatment with MSCs reduced VILI in a rat model.

JTD Keywords: Acute lung injury, Cell therapy, Injurious ventilation, Lung inflammation, Lung oedema, Mechanical ventilation

One objective of mechanical ventilation is the recovery of spontaneous breathing as soon as possible. Remove the mechanical ventilation is sometimes more difficult that maintain it. This paper proposes the study of respiratory flow signal of patients on weaning trials process by autoregressive moving average model (ARMA), through the location of poles and zeros of the model. A total of 151 patients under extubation process (T-tube test) were analyzed: 91 patients with successful weaning (GS), 39 patients that failed to maintain spontaneous breathing and were reconnected (GF), and 21 patients extubated after the test but before 48 hours were reintubated (GR). The optimal model was obtained with order 8, and statistical significant differences were obtained considering the values of angles of the first four poles and the first zero. The best classification was obtained between GF and GR, with an accuracy of 75.3% on the mean value of the angle of the first pole.

JTD Keywords: Analytical models, Biological system modeling, Computational modeling, Estimation, Hospitals, Poles and zeros, Ventilation, Autoregressive moving average processes, Patient care, Patient monitoring, Pneumodynamics, Poles and zeros, Ventilation, ARMA model, T-tube test, Autoregressive moving average model, Extubation process, Mechanical ventilation, Optimal model, Patient classification, Respiratory flow signal, Roots, Spontaneous breathing, Weaning trials

High altitude periodic breathing (PB) shares some common pathophysiologic aspects with sleep apnea, Cheyne-Stokes respiration and PB in heart failure patients. Methods that allow quantifying instabilities of respiratory control provide valuable insights in physiologic mechanisms and help to identify therapeutic targets. Under the hypothesis that high altitude PB appears even during physical activity and can be identified in comparison to visual analysis in conditions of low SNR, this study aims to identify PB by characterizing the respiratory pattern through the respiratory volume signal. A number of spectral parameters are extracted from the power spectral density (PSD) of the volume signal, derived from respiratory inductive plethysmography and evaluated through a linear discriminant analysis. A dataset of 34 healthy mountaineers ascending to Mt. Muztagh Ata, China (7,546 m) visually labeled as PB and non periodic breathing (nPB) is analyzed. All climbing periods within all the ascents are considered (total climbing periods: 371 nPB and 40 PB). The best crossvalidated result classifying PB and nPB is obtained with Pm (power of the modulation frequency band) and R (ratio between modulation and respiration power) with an accuracy of 80.3% and area under the receiver operating characteristic curve of 84.5%. Comparing the subjects from 1_st and 2_nd ascents (at the same altitudes but the latter more acclimatized) the effect of acclimatization is evaluated. SaO₂ and periodic breathing cycles significantly increased with acclimatization (p-value <; 0.05). Higher Pm and higher respiratory frequencies are observed at lower SaO₂, through a significant negative correlation (p-value <; 0.01). Higher Pm is observed at climbing periods visually labeled as PB with >; 5 periodic breathing cycles through a significant positive correlation (p-value <; 0.01). Our data demonstrate that quantification of the respiratory volum- signal using spectral analysis is suitable to identify effects of hypobaric hypoxia on control of breathing.

JTD Keywords: Frequency domain analysis, Frequency modulation, Heart, Sleep apnea, Ventilation, Visualization, Cardiology, Medical disorders, Medical signal processing, Plethysmography, Pneumodynamics, Sensitivity analysis, Sleep, Spectral analysis, Cheyne-Stokes respiration, Climbing periods, Dataset, Heart failure patients, High altitude PB, High altitude periodic breathing, Hypobaric hypoxia, Linear discriminant analysis, Pathophysiologic aspects, Physical activity, Physiologic mechanisms, Power spectral density, Receiver operating characteristic curve, Respiratory control, Respiratory frequency, Respiratory inductive plethysmography, Respiratory pattern, Respiratory volume signal, Sleep apnea, Spectral analysis, Spectral parameters

During alternating ventilation (AV) one lung is inflating while the other is deflating. Considering the possible respiratory and hemodynamic advantages of AV, we investigated its effects during increased intra-abdominal pressure (IAP = 10 mmHg). In Sprague-Dawley rats (n = 6, 270–375 g) the main bronchi were independently cannulated, and respiratory mechanics determined while animals underwent different ventilatory patterns: synchronic ventilation without increased IAP (SV-0), elevated IAP during SV (SV-10), and AV with elevated IAP (AV-10). Thirty-three other animals (SV-0, n = 10; SV-10, n = 11 and AV-10, n = 12) were ventilated during 3 h. Mean arterial pressure (MAP), and lung histology were assessed. Increased IAP resulted in significantly higher elastances (p < 0.001), being AV-10 lower than SV-10 (p < 0.020). SV-10 showed higher central venous pressure (p < 0.003) than S-0; no change was observed in AV-10. Wet/dry lung weight ratio was lower in AV-10 than SV-10 (p = 0.009). Application of AV reduced hemodynamic and lung impairments induced by increased IAP during SV.

JTD Keywords: Alternating ventilation, Respiratory mechanics, Intra-abdominal pressure, Hemodynamic, Mechanical ventilation, Animal model

The process of discontinuing mechanical ventilation is called weaning and is one of the most challenging problems in intensive care. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This study aims to characterize the respiratory pattern through features that permit the identification of patients' conditions in weaning trials. Three groups of patients have been considered: 94 patients with successful weaning trials, who could maintain spontaneous breathing after 48 h ( GSucc ); 39 patients who failed the weaning trial ( GFail ) and 21 patients who had successful weaning trials, but required reintubation in less than 48 h ( GRein ). Patients are characterized by their cardiorespiratory interactions, which are described by joint symbolic dynamics (JSD) applied to the cardiac interbeat and breath durations. The most discriminating features in the classification of the different groups of patients ( GSucc , GFail and GRein ) are identified by support vector machines (SVMs). The SVM-based feature selection algorithm has an accuracy of 81% in classifying GSucc versus the rest of the patients, 83% in classifying GRein versus GSucc patients and 81% in classifying GRein versus the rest of the patients. Moreover, a good balance between sensitivity and specificity is achieved in all classifications.

JTD Keywords: Mechanical ventilation, Weaning, Support vector machines, Joint symbolic dynamics

Worsening of respiratory mechanics during a spontaneous breathing trial (SBT) has been traditionally associated with weaning failure, although this finding is based on studies with chronic obstructive pulmonary disease patients only. The aim of our study was to assess the course of respiratory impedance non-invasively measured by forced oscillation technique (FOT) during a successful and failed SBT in a mixed population. Thirty-four weaning trials were reported in 29 consecutive mechanically ventilated patients with different causes of initiation of ventilation. During the SBT, the patient was breathing through a conventional T-piece connected to the tracheal tube. FOT (5 Hz, +/- 1 cm H2O, 30 s) was applied at 5, 10, 15, 20, 25, and 30 min. Respiratory resistance (Rrs) and reactance (Xrs) were computed from pressure and flow measurements. The frequency to tidal volume ratio f/V-t was obtained from the flow signal. At the end of the trial, patients were divided into two groups: SBT success and failure. Mixed model analysis showed no significant differences in Rrs and Xrs over the course of the SBT, or between the success (n=16) and the failure (n=18) groups. In contrast, f/V-t was significantly (P < 0.001) higher in the failure group. Worsening of respiratory impedance measured by FOT is not a common finding during a failed SBT in a typically heterogeneous intensive care unit population of mechanically ventilated patients.

JTD Keywords: Ventilation, High frequency oscillation, Ventilation, Mechanical, Ventilation, Respiratory impedance

The dissemination of the available telemedicine systems for the optimization of home mechanical ventilation (HMV) is prevented by the need of complex infrastructures. We developed a device which, once connected to Internet through the mobile phone network, allows an authorized physician connected to Internet to monitor the ventilator signals and modify the settings in real-time without the need of external data servers. The system was evaluated during experiments performed by tele-controlling a mechanical ventilator in Barcelona from Milano. A bench study verified the reliability and robustness of the system while an in-vivo test showed that it was possible to monitor and tele-control the ventilator to maintain the oxygen saturation of a rat ventilated in Barcelona subjected to interventions. Given that the system avoids the need for any complex telemedicine architecture and allows an individual and independent ventilator tele-control, it can be a new helpful tool to optimize HMV.

JTD Keywords: Home mechanical ventilation, Non-invasive mechanical ventilation, Telemedicine

The aim was to assess whether induction of ventilator-induced lung injury (VILI) in one lung triggers a concomitant inflammatory response in the normally ventilated contralateral lung. To this end, a differential ventilator was used in 6 rats. One lung was normally ventilated (3.5 ml/kg b.w.) and the contralateral lung was overstretched (15 ml/kg b.w.). Six control rats were normally ventilated (3.5 ml/kg b.w. each lung). After 3h, edema and gene expression of MIP-2 in the lung, and plasma and liver TNF-alpha were assessed. Overexpression of MIP-2 and edema were found in the overventilated lung but not in the normally ventilated contralateral lung. No detectable levels of circulating and liver TNF-alpha were detected. These data do not support the hypothesis of an early positive feedback in the lung inflammation during the mechanical ventilation.

JTD Keywords: Mechanical ventilation, Lung injury, Lung edema, Lung over stretch, High volume ventilation, Differential ventilation