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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.

Purpose of reviewTo summarize the key concepts, physiological rationale and clinical evidence for titrating positive end-expiratory pressure (PEEP) using transpulmonary pressure (PL) derived from esophageal manometry, and describe considerations to facilitate bedside implementation.Recent findingsThe goal of an esophageal pressure-based PEEP setting is to have sufficient PLat end-expiration to keep (part of) the lung open at the end of expiration. Although randomized studies (EPVent-1 and EPVent-2) have not yet proven a clinical benefit of this approach, a recent posthoc analysis of EPVent-2 revealed a potential benefit in patients with lower APACHE II score and when PEEP setting resulted in end-expiratory PLvalues close to 0 ± 2 cmH2O instead of higher or more negative values. Technological advances have made esophageal pressure monitoring easier to implement at the bedside, but challenges regarding obtaining reliable measurements should be acknowledged.SummaryEsophageal pressure monitoring has the potential to individualize the PEEP settings. Future studies are needed to evaluate the clinical benefit of such approach.

Rationale: Defining lung recruitability is needed for safe positive end-expiratory pressure (PEEP) selection in mechanically ventilated patients. However, there is no simple bedside method including both assessment of recruitability and risks of overdistension as well as personalized PEEP titration. Objectives: To describe the range of recruitability using electrical impedance tomography (EIT), effects of PEEP on recruitability, respiratory mechanics and gas exchange, and a method to select optimal EIT-based PEEP. Methods: This is the analysis of patients with coronavirus disease (COVID-19) from an ongoing multicenter prospective physiological study including patients with moderate-severe acute respiratory distress syndrome of different causes. EIT, ventilator data, hemodynamics, and arterial blood gases were obtained during PEEP titration maneuvers. EIT-based optimal PEEP was defined as the crossing point of the overdistension and collapse curves during a decremental PEEP trial. Recruitability was defined as the amount of modifiable collapse when increasing PEEP from 6 to 24 cm H2O (DCollapse24–6). Patients were classified as low, medium, or high recruiters on the basis of tertiles of DCollapse24–6. Measurements and Main Results: In 108 patients with COVID-19, recruitability varied from 0.3% to 66.9% and was unrelated to acute respiratory distress syndrome severity. Median EIT-based PEEP differed between groups: 10 versus 13.5 versus 15.5 cm H2O for low versus medium versus high recruitability (P, 0.05). This approach assigned a different PEEP level from the highest compliance approach in 81% of patients. The protocol was well tolerated; in four patients, the PEEP level did not reach 24 cm H2O because of hemodynamic instability. Conclusions: Recruitability varies widely among patients with COVID-19. EIT allows personalizing PEEP setting as a compromise between recruitability and overdistension.

There is a well-recognised importance for personalising mechanical ventilation settings to protect the lungs and the diaphragm for each individual patient. Measurement of oesophageal pressure (Poes) as an estimate of pleural pressure allows assessment of partitioned respiratory mechanics and quantification of lung stress, which helps our understanding of the patient’s respiratory physiology and could guide individualisation of ventilator settings. Oesophageal manometry also allows breathing effort quantification, which could contribute to improving settings during assisted ventilation and mechanical ventilation weaning. In parallel with technological improvements, Poes monitoring is now available for daily clinical practice. This review provides a fundamental understanding of the relevant physiological concepts that can be assessed using Poes measurements, both during spontaneous breathing and mechanical ventilation. We also present a practical approach for implementing oesophageal manometry at the bedside. While more clinical data are awaited to confirm the benefits of Poes-guided mechanical ventilation and to determine optimal targets under different conditions, we discuss potential practical approaches, including positive end-expiratory pressure setting in controlled ventilation and assessment of inspiratory effort during assisted modes.

In critically ill patients receiving mechanical ventilation, expiratory muscles are recruited with high respiratory loading and/or low inspiratory muscle capacity. In this case report, we describe a previously unrecognized patient-ventilator dyssynchrony characterized by ventilator triggering by expiratory muscle relaxation, an observation that we termed expiratory muscle relaxation-induced ventilator triggering (ERIT). ERIT can be recognized with in-depth respiratory muscle monitoring as (1) an increase in gastric pressure (Pga) during expiration, resulting from expiratory muscle recruitment; (2) a drop in Pga (and hence, esophageal pressure) at the time of ventilator triggering; and (3) diaphragm electrical activity onset occurring after ventilator triggering. Future studies should focus on the incidence of ERIT and the impact in the patient receiving mechanical ventilation.

The accuracy of diaphragm electromyogram (EMGdi) derived parameters, as used in critically ill intensive care unit (ICU) patients, can be compromised due to electrocardiographic (ECG) interference in the EMGdi signal. Removal of ECG contamination from the esophageal recordings of the EMGdi is challenging due to spectral overlapping of EMG and ECG signals and because of variability in ECG shape and amplitude. Therefore, we designed an Estimated ECG Subtraction (EES) method, based on three steps: (1) identification of the timing of the ECG artifact without an ECG reference channel, (2) estimation of the normalized ECG, considering the EMGdi as noise, and (3) subtraction of the denormalized ECG estimate from the EMGdi recordings. We evaluated the EES method against the use of a single wavelet-based adaptive filter. Using EMGdi signals of ten ICU patients and simulated contaminated EMG, we demonstrated that the EES method yields uncontaminated EMGdi, and showed that it is more effective than a wavelet-based adaptive filter only. Implementation of this technique may offer means to improve diaphragm activity monitoring and control in clinical practice., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Biomechatronics & Human-Machine Control

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