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Background: Neurally Adjusted Ventilatory Assist (NAVA) is a proportional ventilatory mode that uses the electrical activity of the diaphragm (EAdi) to offer ventilatory assistance in proportion to patient effort. NAVA has been increasingly used for critically ill patients, but it has not been evaluated during spontaneous breathing trials (SBT). We designed a pilot trial to assess the feasibility of using NAVA during SBTs, and to compare the breathing pattern and patient-ventilator asynchrony of NAVA with Pressure Support (PSV) during SBTs.Methods: We conducted a crossover trial in the ICU of a university hospital in Brazil and included mechanically ventilated patients considered ready to undergo an SBT on the day of the study. Patients underwent two SBTs in randomized order: 30 min in PSV of 5 cmH2O or NAVA titrated to generate equivalent peak airway pressure (Paw), with a positive end-expiratory pressure of 5 cmH2O. The ICU team, blinded to ventilatory mode, evaluated whether patients passed each SBT. We captured flow, Paw and electrical activity of the diaphragm (EAdi) from the ventilator and used it to calculate respiratory rate (RR), tidal volume (VT), and EAdi. Detection of asynchrony events used waveform analysis and we calculated the asynchrony index as the number of asynchrony events divided by the number of neural cycles.Results: We included 20 patients in the study. All patients passed the SBT in PSV, and three failed the SBT in NAVA. Five patients were reintubated and the extubation failure rate was 25% (95% CI 9-49%). Respiratory parameters were similar in the two modes: VT = 6.1 (5.5-6.5) mL/Kg in NAVA vs. 5.5 (4.8-6.1) mL/Kg in PSV (p = 0.076) and RR = 27 (17-30) rpm in NAVA vs. 26 (20-30) rpm in PSV, p = 0.55. NAVA reduced AI, with a median of 11.5% (4.2-19.7) compared to 24.3% (6.3-34.3) in PSV (p = 0.033).Conclusions: NAVA reduces patient-ventilator asynchrony index and generates a respiratory pattern similar to PSV during SBTs. Patients considered ready for mechanical ventilation liberation may be submitted to an SBT in NAVA using the same objective criteria used for SBTs in PSV.Trial Registration: ClinicalTrials.gov ( NCT01337271 ), registered April 12, 2011. [ABSTRACT FROM AUTHOR]

Background: In patients with post-extubation respiratory distress, delayed reintubation may worsen clinical outcomes. Objective measures of extubation failure at the bedside are lacking, therefore clinical parameters are currently used to guide the need of reintubation. Electrical activity of the diaphragm (EAdi) provides clinicians with valuable, objective information about respiratory drive and could be used to monitor respiratory effort.Case Presentation: We describe the case of a patient with Chronic Obstructive Pulmonary Disease (COPD), from whom we recorded EAdi during four different ventilatory conditions: 1) invasive mechanical ventilation, 2) spontaneous breathing trial (SBT), 3) unassisted spontaneous breathing, and 4) Noninvasive Positive Pressure Ventilation (NPPV). The patient had been intubated due to an exacerbation of COPD, and after four days of mechanical ventilation, she passed the SBT and was extubated. Clinical signs of respiratory distress were present immediately after extubation, and EAdi increased compared to values obtained during mechanical ventilation. As we started NPPV, EAdi decreased substantially, indicating muscle unloading promoted by NPPV, and we used the EAdi signal to monitor respiratory effort during NPPV. Over the next three days, she was on NPPV for most of the time, with short periods of spontaneous breathing. EAdi remained considerably lower during NPPV than during spontaneous breathing, until the third day, when the difference was no longer clinically significant. She was then weaned from NPPV and discharged from the ICU a few days later.Conclusion: EAdi monitoring during NPPV provides an objective parameter of respiratory drive and respiratory muscle unloading and may be a useful tool to guide post-extubation ventilatory support. Clinical studies with continuous EAdi monitoring are necessary to clarify the meaning of its absolute values and changes over time. [ABSTRACT FROM AUTHOR]

The mean linear intercept (Lm) can be used to estimate the surface area for gas exchange in the lung. However, in recent years, it is most commonly used as an index for characterizing the enlargement of airspaces in emphysema and the associated severity of structural destruction in the lung. Specifically, an increase in Lm is thought to result from an increase in airspace sizes. In this paper, we examined how accurately Lm measures the linear dimensions of airspaces from histological sections and a variety of computer-generated test images. To this end, we developed an automated method for measuring linear intercepts from digitized images of tissue sections and calculate Lm as their mean. We examined how the shape of airspaces and the variability of their sizes influence Lm as well as the distribution of linear intercepts. We found that, for a relatively homogeneous enlargement of airspaces, Lm was a reliable index for detecting emphysema. However, in the presence of spatial heterogeneities with a large variability of airspace sizes, Lm did not significantly increase and sometimes even decreased compared with its value in normal tissue. We also developed an automated method for measuring the area and computed an equivalent diameter of each individual airspace that is independent of shape. Finally, we introduced new indexes based on the moments of diameter that we found to be more reliable than Lm Io characterize airspace enlargement in the presence of heterogeneities. [ABSTRACT FROM AUTHOR]

The structure and the functioning of cardio-pulmonary system is complex and statistical physics appear to be suitable for their characterization. In this review, we examine scaling in cardio-pulmonary physiology. The focus will be on the interpretation of scaling behaviors and their relation to structure-function in the normal and diseased cardio-pulmonary system. First, we overview fluctuations and scaling in respiratory rate variability in terms of a neural network model. Next, we analyze fluctuations in human heartbeat dynamics under healthy and pathologic conditions using wavelets and multifractal approaches. We then discuss avalanche behavior of airway openings as well as scaling behavior of crackling sound generated during the process of airway openings. We also examine the relationship between the observed scaling properties and the design features of the pulmonary vascular tree. Finally, we show how the network failure of lung tissue structure leads to emphysema, a leading cause of respiratory disability and death worldwide. [ABSTRACT FROM AUTHOR]

Abstract: The spectral analysis of crackles sounds has been carried out based on the assumption that they are stationary signals, and the majority of the work on the crackles was accomplished before the publication of the Computerized Respiratory Sound Analysis (CORSA) guidelines. This works characterizes crackles acquired from patients with fibrosis, heart failure, and pneumonia, breathing at a constant rate, with a system developed according to the CORSA guidelines. Their maximum frequency was obtained by applying discrete pseudo Wigner-Ville distribution, suitable for non-stationary signals, and an objective method to estimate the maximum frequency, the modified geometric method. The effects of the breathing rate and the tidal volume on the spectra of the crackles were also investigated. The role of the high-pass filter cutoff frequency of the acquisition system on the characteristics of the acquired crackles was also assessed in this present study. Higher high-pass filter cutoff frequency allows for higher amplification which modifies the maximum frequency and the 2CD index. It is shown that the crackles acquired according to the CORSA guidelines have higher frequencies and shorter 2CD indexes than those previously reported, highlighting the need for the standardization and detailed report of the acquisition setup when quantifying lung sounds. The results pointed out that the maximum frequency and the 2CD indexes may allow crackles generated by fibrosis to be distinguished from the ones generated by the heart failure and pneumonia. It is not possible, however, by means of these two indexes, to differentiate between pneumonia and heart failure crackles. [Copyright &y& Elsevier]