Search

Objective. Geometrical region of interest (ROI) selection in electrical impedance tomography (EIT) monitoring may lack sensitivity to subtle changes in ventilation distribution. Therefore, we demonstrate a new physiological method for ROI definition. This is relevant when using ROIs to compute subsequent EIT-parameters, such as the ventral-to-dorsal ratio during a positive end-expiratory pressure (PEEP) trial. Approach. Our physiological approach divides an EIT image to ensure exactly 50% tidal impedance variation in the ventral and dorsal region. To demonstrate the effects of our new method, EIT measurements during a decremental PEEP trial in 49 mechanically ventilated ICU-patients were used. We compared the center of ventilation (CoV), a robust parameter for changes in ventro-dorsal ventilation distribution, to our physiological ROI selection method and different commonly used ROI selection methods. Moreover, we determined the impact of different ROI selection methods on the PEEP level corresponding to a ventral-to-dorsal ratio closest to 1. Main results. The division line separating the ventral and dorsal ROI was closer to the CoV for our new physiological method for ROI selection compared to geometrical ROI definition. Moreover, the PEEP level corresponding to a ventral-to-dorsal ratio of 1 is strongly influenced by the chosen ROI selection method, which could have a profound clinical impact; the within-subject range of PEEP level was 6.2 cmH2O depending on the chosen ROI selection method. Significance. Our novel physiological method for ROI definition is sensitive to subtle ventilation-induced changes in regional impedance (i.e. due to (de)recruitment) during mechanical ventilation, similar to the CoV. [ABSTRACT FROM AUTHOR]

This article is one of ten reviews selected from the Annual Update in Intensive Care and Emergency Medicine 2020. Other selected articles can be found online at https://www.biomedcentral.com/collections/annualupdate2020. Further information about the Annual Update in Intensive Care and Emergency Medicine is available from http://www.springer.com/series/8901. [ABSTRACT FROM AUTHOR]

Hypoxemic respiratory failure is one of the leading causes of mortality in intensive care. Frequent assessment of individual physiological characteristics and delivery of personalized mechanical ventilation (MV) settings is a constant challenge for clinicians caring for these patients. Electrical impedance tomography (EIT) is a radiation-free bedside monitoring device that is able to assess regional lung ventilation and changes in aeration. With real-time tomographic functional images of the lungs obtained through a thoracic belt, clinicians can visualize and estimate the distribution of ventilation at different ventilation settings or following procedures such as prone positioning. Several studies have evaluated the performance of EIT to monitor the effects of different MV settings in patients with acute respiratory distress syndrome, allowing more personalized MV. For instance, EIT could help clinicians find the positive end-expiratory pressure that represents a compromise between recruitment and overdistension and assess the effect of prone positioning on ventilation distribution. The clinical impact of the personalization of MV remains to be explored. Despite inherent limitations such as limited spatial resolution, EIT also offers a unique noninvasive bedside assessment of regional ventilation changes in the ICU. This technology offers the possibility of a continuous, operator-free diagnosis and real-time detection of common problems during MV. This review provides an overview of the functioning of EIT, its main indices, and its performance in monitoring patients with acute respiratory failure. Future perspectives for use in intensive care are also addressed. [ABSTRACT FROM AUTHOR]

This article discusses a case study of an 18-year-old woman who was intubated due to severe lung injury caused by vaping. The patient was receiving pressure-controlled ventilation and venovenous extracorporeal membrane oxygenation (VV-ECMO) to provide lung rest for tissue regeneration. On the fifth day of VV-ECMO, the patient exhibited reverse triggering (RT), where the patient's efforts triggered the ventilator. The study found that increasing ventilator inspiratory pressure reduced the magnitude of RT efforts but increased the transpulmonary driving pressure. The authors suggest that lung/chest wall receptors, rather than CO2 levels, mediate the magnitude of RT efforts. [Extracted from the article]

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., (Creative Commons Attribution license.)