Electrical Impedance Tomography as a monitoring tool during weaning from mechanical ventilation: an observational study during the spontaneous breathing trial.

BACKGROUND: Prolonged weaning from mechanical ventilation is associated with poor clinical outcome. Therefore, choosing the right moment for weaning and extubation is essential. Electrical Impedance Tomography (EIT) is a promising innovative lung monitoring technique, but its role in supporting weaning decisions is yet uncertain. We aimed to evaluate physiological trends during a T-piece spontaneous breathing trail (SBT) as measured with EIT and the relation between EIT parameters and SBT success or failure. METHODS: This is an observational study in which twenty-four adult patients receiving mechanical ventilation performed an SBT. EIT monitoring was performed around the SBT. Multiple EIT parameters including the end-expiratory lung impedance (EELI), delta Tidal Impedance (ΔZ), Global Inhomogeneity index (GI), Rapid Shallow Breathing Index (RSBIEIT), Respiratory Rate (RREIT) and Minute Ventilation (MVEIT) were computed on a breath-by-breath basis from stable tidal breathing periods. RESULTS: EELI values dropped after the start of the SBT (p < 0.001) and did not recover to baseline after restarting mechanical ventilation. The ΔZ dropped (p < 0.001) but restored to baseline within seconds after restarting mechanical ventilation. Five patients failed the SBT, the GI (p = 0.01) and transcutaneous CO2 (p < 0.001) values significantly increased during the SBT in patients who failed the SBT compared to patients with a successful SBT. CONCLUSION: EIT has the potential to assess changes in ventilation distribution and quantify the inhomogeneity of the lungs during the SBT. High lung inhomogeneity was found during SBT failure. Insight into physiological trends for the individual patient can be obtained with EIT during weaning from mechanical ventilation, but its role in predicting weaning failure requires further study.

Deventilation Syndrome in Patients with Chronic Obstructive Pulmonary Disease Using Nocturnal Noninvasive Ventilation: What Are the Underlying Mechanisms?

INTRODUCTION: Patients with chronic obstructive pulmonary disease (COPD) commonly experience severe dyspnea after discontinuation of nocturnal noninvasive ventilation (NIV), known as deventilation syndrome (DVS), which negatively affects quality of life. Despite various hypotheses, the precise mechanisms of DVS remain unknown. METHODS: An observational pilot study was performed monitoring 16 stable COPD patients before, during, and after an afternoon nap on NIV. Seven patients experienced DVS (Borg Dyspnea Scale ≥5), while nine served as controls (Borg Dyspnea Scale ≤2). Hyperinflation was evaluated through inspiratory capacity (IC) measurements and end-expiratory lung impedance (EELI) via electrical impedance tomography. Respiratory muscle activity was assessed by diaphragmatic surface electromyography (sEMG). RESULTS: Post-NIV dyspnea scores were significantly higher in the DVS group (5 [3-7] vs. 0 [0-1.5], p < 0.001). IC values were lower in the DVS group compared to controls, both pre-NIV (54 [41-63] vs. 88 [72-94] %pred., p = 0.006) and post-NIV (45 [40-59] vs. 76 [65-82] %pred., p = 0.005), while no intergroup difference was seen in IC changes pre- and post-NIV. EELI values after NIV indicated a tendency towards lower values in controls and higher values in DVS patients. sEMG amplitudes were higher in the DVS group within the first 5-min post-NIV (221 [112-294] vs. 100 [58-177]% of baseline, p = 0.030). CONCLUSION: This study suggests that it is unlikely that DVS originates from the inability to create diaphragmatic muscle activity after NIV. Instead, NIV-induced hyperinflation in individuals with static hyperinflation may play a significant role. Addressing hyperinflation holds promise in preventing DVS symptoms in COPD patients.

Improved filtering methods to suppress cardiovascular contamination in electrical impedance tomography recordings.

Objective.Electrical impedance tomography (EIT) produces clinical useful visualization of the distribution of ventilation inside the lungs. The accuracy of EIT-derived parameters can be compromised by the cardiovascular signal. Removal of these artefacts is challenging due to spectral overlapping of the ventilatory and cardiovascular signal components and their time-varying frequencies. We designed and evaluated advanced filtering techniques and hypothesized that these would outperform traditional low-pass filters.Approach.Three filter techniques were developed and compared against traditional low-pass filtering: multiple digital notch filtering (MDN), empirical mode decomposition (EMD) and the maximal overlap discrete wavelet transform (MODWT). The performance of the filtering techniques was evaluated (1) in the time domain (2) in the frequency domain (3) by visual inspection. We evaluated the performance using simulated contaminated EIT data and data from 15 adult and neonatal intensive care unit patients.Main result.Each filter technique exhibited varying degrees of effectiveness and limitations. Quality measures in the time domain showed the best performance for MDN filtering. The signal to noise ratio was best for DLP, but at the cost of a high relative and removal error. MDN outbalanced the performance resulting in a good SNR with a low relative and removal error. MDN, EMD and MODWT performed similar in the frequency domain and were successful in removing the high frequency components of the data.Significance.Advanced filtering techniques have benefits compared to traditional filters but are not always better. MDN filtering outperformed EMD and MODWT regarding quality measures in the time domain. This study emphasizes the need for careful consideration when choosing a filtering approach, depending on the dataset and the clinical/research question.